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IEEE Transactions on artificial intelligence 2024年第9期5卷 4428-4442页

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multilabel learning is an emergent topic that addresses the challenge of associating multiple labels with a single instance simultaneously. Multilabel datasets often exhibit high dimensionality with noisy, irrelevant, and redundant features. In recent years, multilabel feature selection (MLFS) has gained prominence as a crucial and emerging machine learning task due to its ability to handle such data effectively. However, existing approaches for MLFS often prioritize top-ranked features based on intrinsic data criteria, disregarding relationships within the feature subset. Additionally, compared with conventional feature selection, multiobjective evolutionary algorithms (MOEAs) have not been widely explored in the context of MLFS. This study aims to address these gaps by proposing a multimodal multiobjective evolutionary algorithm (MMOEA) called MMDE_SICD which incorporates a preelimination scheme, an improved initialization scheme, an exploration scheme inspired by genetic operations and a statistically inspired crowding distance scheme. The results show that the proposed MMDE_SICD algorithm can outperform a variety of MOEAs and MMOEAs as well as conventional MLFS algorithms. Notably, this study is the first of its kind to consider MLFS as a multimodal multiobjective problem. © 2020 IEEE.

关键词： Multiobjective optimization

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Multi-Domain Graph-Enhanced Joint Intent and Slot Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第2期52卷 374-382页

作者： Shi, Wu Yuan, Weihao Wang, Yang Dong, Yindong Xu, Gaojian School of Information and Artificial Intelligence Anhui Agricultural University Hefei230036 China School of Computer Science and Technology Xidian University Xi’an710126 China School of Engineering Anhui Agricultural University Hefei230036 China

Comprehension of spoken language is essential in dialog systems, as it supports two fundamental tasks: intent classification and slot filling. At present, federated modeling methodologies prevail in the domain of spoken language comprehension. Nevertheless, current models encounter constraints in accurately representing the interrelations among activities and utilizing cross-domain semantic data. This study introduces the Multi-Domain Graph-Enhanced Joint Intent and Slot Learning (MDG-JISL) paradigm to tackle these difficulties. MDG-JISL amalgamates a pre-trained BERT model with a self-trained FastText model to create superior sentence-level and word-level representations. A syntactic dependency tree is employed to create graph structures among words, which are subsequently refined by the implementation of a Graph Convolutional Network (GCN) to more effectively capture relationship properties. A Conditional Random Field (CRF) model is utilized for decoding, enhancing the model’s efficacy in natural language processing tasks. Experimental findings indicate that MDG-JISL attains remarkable performance on cross-domain datasets, with Slot(F1), Intent (Acc), and Overall (Acc) metrics of 98.7%, 98.4%, and 92.4%, respectively. The results validate the model’s efficacy in intent classification and slot filling. © (2025), (International Association of Engineers). All rights reserved.

关键词： Syntactics

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Outliers are Real: Detecting VLM-Generated Images via One-Class Classification 24th

Outliers are Real: Detecting VLM-Generated Images via One-C...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Liu, Baoping Liu, Bo Ding, Ming Australian Artificial Intelligence Institute School of Computer Science University of Technology Sydney Ultimo Australia Data61 CSIRO Eveleigh Australia

ISBN: (纸本)9789819615247

Recent generative artificial intelligence (AI) advancements have enabled high-quality images with cross-modal controllability, e.g., text-to-image generation. Vision-language models (VLMs) play an indispensable role in such cross-modal generation tasks by aligning the representations of conceptions in different modalities, which inspires us to detect VLM-generated images with traces and fingerprints of VLMs. Therefore, in this paper, we propose a one-class classification (OOC) framework, namely Outliers are Real (OaReal) to recognize VLM-generated images. In sight of the rarity of VLMs due to the high cost of training, we regard VLM-generated images as normal samples and explore their distributions in CLIP latent space. During the testing phase, samples far from the explored distribution (outliers) are regarded as real images, while samples in the explored distribution are classified as VLM-generated samples. Compared with binary classifiers, the OOC design of our OaReal significantly relieves training from learning complex patterns with diverse real and fake images. Furthermore, we propose a hard sample aware contrastive loss (Harsacol) that takes edited samples into consideration and improves the inclusiveness of the explored space for both VLM-synthetic and VLM-edited samples. We conducted comprehensive experiments to test the performance of our OaReal. The results suggested the superiority of our methods in terms of both effectiveness and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Visual languages

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DBFIA: Diffusion-Based Face Image Anonymization 24th

DBFIA: Diffusion-Based Face Image Anonymization

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xue, Hanyu Yuan, Xin Liu, Bo Ding, Ming Australian Artificial Intelligence Institute School of Computer Science University of Technology Sydney Ultimo Australia Data61 CSIRO Eveleigh Australia

ISBN: (纸本)9789819615247

This paper presents a novel approach to address the issue of identity protection in facial image datasets. Our goal is to prevent any violation of privacy for the individuals depicted in the dataset while ensuring that the dataset is still useful for downstream training tasks and maintains the quality of the images. Previous methods have predominantly used Generative Adversarial Network (GAN)-based models to anonymize facial datasets. However, such models suffer from distortion and loss of detail when processing real-life images. To overcome these challenges, we propose a method for anonymizing faces using a diffusion model. Our approach retains the original facial attributes and produces anonymized images with high-quality image details. These attributes are essential when using anonymized images for downstream tasks. Our proposed framework optimizes the latent space vectors of the conditional Denoising diffusion probabilistic model (DDPM) to maintain a certain distance between the identity features (in the feature space of ArcFace) of the anonymized images and the original identity features while preserving the facial attributes. Through qualitative and quantitative experiments, we demonstrate that our method can effectively anonymize the identity of facial images while preserving image details and facial attributes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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Distributionally Robust Chance Constrained Trajectory Optimization for Mobile Robots within Uncertain Safe Corridor

Distributionally Robust Chance Constrained Trajectory Optimi...

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2024 IEEE International Conference on Robotics and Automation, ICRA 2024

作者： Xu, Shaohang Ruan, Haolin Zhang, Wentao Wang, Yian Zhu, Lijun Ho, Chin Pang Huazhong University of Science and Technology School of Artificial Intelligence and Automation China City University of Hong Kong School of Data Science Hong Kong

ISBN: (纸本)9798350384574

Safe corridor-based Trajectory Optimization (TO) presents an appealing approach for collision-free path planning of autonomous robots, because its convex formulation can guarantee global optimality. The safe corridor is constructed based on the obstacle map, however, the non-ideal perception induces uncertainty, which is rarely considered in the context of trajectory generation. In this paper, we propose Distributionally Robust Safe Corridor Constraints (DRSCCs) to consider the uncertainty of the safe corridor. Then, we integrate DRSCCs into the trajectory optimization framework using Bernstein basis polynomials. Theoretically, we rigorously prove that the proposed trajectory optimization problem is equivalent to a convex quadratic program, which is computationally efficient to deploy onto real robots. The simulation results show that our method enhances navigation safety by significantly reducing the infeasible motions compared to the baseline. Moreover, the proposed approach is validated through two robotic applications, a micro Unmanned Aerial Vehicle (UAV) and a quadruped robot Unitree A1. © 2024 IEEE.

关键词： Microrobots

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Enhancing Security in Distributed Drone-Based Litchi Fruit Recognition and Localization Systems

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computers, Materials & Continua 2025年第2期82卷 1985-1999页

作者： Liang Mao Yue Li Linlin Wang Jie Li Jiajun Tan Yang Meng Cheng Xiong Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute Shenzhen Polytechnic UniversityShenzhen518055China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable lighting and obstructive foliage. To reinforce security, the tasks of recognition and localization are distributed among multiple drones, enhancing resilience against tampering and data manipulation. This distribution also optimizes resource allocation through collaborative processing. The model remains lightweight and is optimized for rapid and accurate detection, which is essential for real-time applications. Our proposed system, validated with a D435 depth camera, achieves a mean Average Precision (mAP) of 0.943 and a frame rate of 169 FPS, which represents a significant improvement over the baseline by 0.039 percentage points and 25 FPS, respectively. Additionally, the average localization error is reduced to 0.82 cm, highlighting the model’s high precision. These enhancements render our system highly effective for secure, autonomous fruit-picking operations, effectively addressing significant performance and cybersecurity challenges in agriculture. This approach establishes a foundation for reliable, efficient, and secure distributed fruit-picking applications, facilitating the advancement of autonomous systems in contemporary agricultural practices.

关键词： Objective detection deep learning machine learning

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A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance

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Journal of Bionic Engineering 2025年第1期22卷 383-397页

作者： Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu610031China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031China

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization *** integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization *** effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective ***,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy *** research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关键词： Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation

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Semantic segmentation via pixel-to-center similarity calculation

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CAAI Transactions on intelligence technology 2024年第1期9卷 87-100页

作者： Dongyue Wu Zilin Guo Aoyan Li Changqian Yu Nong Sang Changxin Gao National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Meituan Inc. BeijingChina

Since the fully convolutional network has achieved great success in semantic segmentation,lots of works have been proposed to extract discriminative pixel ***,the authors observe that existing methods still suffer from two typical challenges:(i)The intra-class feature variation between different scenes may be large,leading to the difficulty in maintaining the consistency between same-class pixels from different scenes;(ii)The inter-class feature distinction in the same scene could be small,resulting in the limited performance to distinguish different classes in each *** authors first rethink se-mantic segmentation from a perspective of similarity between pixels and class *** weight vector of the segmentation head represents its corresponding semantic class in the whole dataset,which can be regarded as the embedding of the class ***,the pixel-wise classification amounts to computing similarity in the final feature space between pixels and the class *** this novel view,the authors propose a Class Center Similarity(CCS)layer to address the above-mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class *** CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene,which adapt the large intra-class variation between different *** designed Class Distance Loss(CD Loss)is introduced to control both inter-class and intra-class distances based on the predicted center-to-center and pixel-to-center ***,the CCS layer outputs the processed pixel-to-center similarity as the segmentation *** experiments demonstrate that our model performs favourably against the state-of-the-art methods.

关键词： computer vision deep neural networks image segmentation scene understanding

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Remote Sensing Object Detection Based on Fusion of Spatial and Channel Attention 14

Remote Sensing Object Detection Based on Fusion of Spatial a...

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14th International Conference on Information science and technology, ICIST 2024

作者： Sun, Wenyun Ji, Long School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350353334

Remote sensing object detection faces unique challenges due to objects' varied scales and orientations. To address these challenges, we propose the Spatial Channel Attention Fusion Module (SCAF-Module), designed to enhance detection accuracy by integrating multi-scale convolutions, adaptive rotated convolutions, and parallel spatial channel attention mechanisms. The experiments, conducted using the DOTA-vl.0 and HRSC2016 datasets, demonstrate the efficacy of the SCAF-Module. We achieved mean Average Precision (mAP) scores of 80.94% and 98.23% on these datasets, respectively. Comparative experiments reveal that the SCAF -Module surpasses several advanced models, including the baseline Oriented R-CNN. Additionally, ablation studies highlight the significance of the spatial and channel attention mechanisms and the impact of rotated convolutions on detection performance. The SCAF-Module presents a robust and adaptable framework for remote sensing object detection, offering significant improvements over existing methods. This work paves the way for further optimization and application of the module in other challenging remote sensing tasks. © 2024 IEEE.

关键词： Optical remote sensing

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Lay CO:Achieving Least Lossy Accuracy for Most Efficient RRAM-Based Deep Neural Network Accelerator via Layer-Centric Co-Optimization

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计算机科学技术学报（英文版） 2023年第2期38卷 328-347页

作者： Shao-Feng Zhao Fang Wang Bo Liu Dan Feng Yang Liu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cloud Computing and Big Data Institute Henan University of Economics and LawZhengzhou 450001China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen 518057China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cloud Computing and Big Data Institute Henan University of Economics and LawZhengzhou 450001China

Resistive random access memory(RRAM)enables the functionality of operating massively parallel dot prod-ucts and ***-based accelerator is such an effective approach to bridging the gap between Internet of Things devices'constrained resources and deep neural networks'tremendous *** to the huge overhead of Analog to Digital(A/D)and digital accumulations,analog RRAM buffer is introduced to extend the processing in analog and in *** analog RRAM buffer offers potential solutions to A/D conversion issues,the energy consumption is still challenging in resource-constrained environments,especially with enormous intermediate data ***,criti-cal concerns over endurance must also be resolved before the RRAM buffer could be frequently used in reality for DNN in-ference *** we propose LayCO,a layer-centric co-optimizing scheme to address the energy and endurance con-cerns altogether while strictly providing an inference accuracy *** relies on two key ideas:1)co-optimizing with reduced supply voltage and reduced bit-width of accelerator architectures to increase the DNN's error tolerance and achieve the accelerator's energy efficiency,and 2)efficiently mapping and swapping individual DNN data to a correspond-ing RRAM partition in a way that meets the endurance *** evaluation with representative DNN models demonstrates that LayCO outperforms the baseline RRAM buffer based accelerator by 27x improvement in energy effi-ciency(over TIMELY-like configuration),308x in lifetime prolongation and 6x in area reduction(over RAQ)while main-taining the DNN accuracy loss less than 1％.

关键词： deep learning accelerator resistive random access memory (RRAM) energy efficiency error tolerance

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