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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Qinyuan Wang Bruce Gu He Zhang Yunfeng Li School of Music University of Sydney NSW Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China CNPIEC KEXIN Ltd Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions.

关键词： Adaptation models Ethics Instruments Data science Rhythm Data models User experience Resonance Cultural differences Artificial intelligence

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Anomaly-Free Prior Guided Knowledge Distillation for Industrial Anomaly Detection

Anomaly-Free Prior Guided Knowledge Distillation for Industr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Gang Li Tianjiao Chen Min Li Delong Han MingLe Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In industrial manufacturing, visual anomaly detection is critical for maintaining product quality by detecting and preventing production anomalies. Anomaly detection methods based on knowledge distillation demonstrate promising performance in addressing the unpredictability and diversity of anomalies. However, they suffer from a lack of effective guidance from anomaly-free priors when handling anomalous features and underutilize multi-scale features during the segmentation scoring stage, yielding suboptimal detection results. To alleviate these issues, we propose an Anomaly-free Prior Guided knowledge distillation (APG) for industrial anomaly detection. Firstly, it filters the abnormal features by training the de-noising target network with knowledge distillation structure. Concurrently, we propose the Prior Perception Propagation Module (P 3 M), which extracts more efficient anomaly-free features by imposing constraints on anomalous features. Secondly, we propose the Multi-scale Prior Guided Fusion Module (MPGFM) to improve anomaly detection accuracy by utilizing anomaly-free features from the target network as priors to guide the generation and fusion of cross-scale differential features. Finally, the Global Perception Enhancement Module (GPEM) is proposed to construct an anomaly scoring network, leveraging comprehensive scene features to enhance the detection and localization performance of numerous small-target anomalies in industrial manufacturing. Extensive experiments on the MVTecAD and BTAD datasets show that the proposed method demonstrates a consistent and significant outperformance against competing methods.

关键词： Training Location awareness Visualization Noise reduction Production Feature extraction Product design Manufacturing Quality assessment Anomaly detection

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A SUPERVISED information ENHANCED MULTI-GRANULARITY CONTRASTIVE LEARNING FRAMEWORK FOR EEG BASED EMOTION RECOGNITION

arXiv

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arXiv 2024年

作者： Li, Xiang Song, Jian Zhao, Zhigang Wang, Chunxiao Song, Dawei Hu, Bin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for EEG based Emotion Recognition (SI-CLEER). SI-CLEER employs multi-granularity contrastive learning to create robust EEG contextual representations, potentially improving emotion recognition effectiveness. Unlike existing methods solely guided by classification loss, we propose a joint learning model combining self-supervised contrastive learning loss and supervised classification loss. This model optimizes both loss functions, capturing subtle EEG signal differences specific to emotion detection. Extensive experiments demonstrate SI-CLEER's robustness and superior accuracy on the SEED dataset compared to state-of-the-art methods. Furthermore, we analyze electrode performance, highlighting the significance of central frontal and temporal brain region EEGs in emotion detection. This study offers an universally applicable approach with potential benefits for diverse EEG classification tasks. © 2024, CC0.

关键词： Emotion Recognition

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STUI-NET: Semi-Supervised Transformer for Underwater information Enhancement

STUI-NET: Semi-Supervised Transformer for Underwater Informa...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhenqiang Zhang Chuantao Li Jian Song Jialiang Lv Chunxiao Wang Zhigang Zhao Jidong Huo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China University of Petroleum (East China) Qingdao China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Underwater Image Restoration Technology (UIRT) constitutes a pivotal base for subsequent tasks yet confronts obstacles like data scarcity and image distortion in practical deployments. To optimize the use of scarce annotated and copious unlabeled data, we introduce an avant-garde semi-supervised method for underwater image restoration, named STUI-Net. Concurrently, we develop a novel teacher-student architecture utilizing Transformer technology, named GFT-Net. Initially, GFT-Net employs a tripartite branch network—comprising Gradient, Feature Extraction, and Transformer modules to thoroughly extract features from underwater images. This method then amalgamates multi-source features, encompassing edge, gradient, local, and global data, addressing underwater image deterioration in multifaceted environments. Additionally, we engineer a Supervisor role to rectify potential misdirection by the teacher network in the semi-supervised model, thereby enhancing training stability on unlabeled datasets. Empirical studies affirm the robust generalization competence of STUI-Net in authentic underwater environments.

关键词： Measurement Training Image quality Transformers Feature extraction Stability analysis Robustness

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Real Underwater Image Restoration From a Unified Perspective

Real Underwater Image Restoration From a Unified Perspective

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International Joint Conference on Neural networks (IJCNN)

作者： Zhenqiang Zhang Jidong Huo Chuantao Li Zhigang Zhao Chunxiao Wang Jialiang Lv Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China University of Petroleum (East China) Qingdao China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The fidelity of underwater visual data is significantly affected by various factors, encompassing light refraction and color distortion in water. These factors often give rise to noise and distortion within underwater images. In response to the widespread nature of this issue, we propose a novel approach to restore authentic underwater images through a comprehensive perspective. To address the inherent scarcity of real underwater image datasets, we introduce an innovative underwater image generation model that leverages Generative Adversarial networks (GAN) with integrated physical constraints, aiming to produce realistic and stable underwater image datasets. Within the underwater image restoration framework, we incorporate a multi-channel feature extractor (MCFE) module, which is designed to enhance the model’s capability in image feature extraction. Additionally, we introduce object edge information as a novel loss function to perform different repairs on the target and background. Experimental evaluations demonstrate that our method exhibits superior performance in both qualitative and quantitative assessments compared to state-of-the-art approaches. Restoration results on real underwater images further showcase its exceptional performance in practical applications. The source code and sample dataset are publicly available at here.

关键词： Training Visualization Image synthesis Image edge detection Source coding Feature extraction Generative adversarial networks

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FGSNet: A Finer-Grained Siamese network for Industrial Few-Shot Anomaly Detection

FGSNet: A Finer-Grained Siamese Network for Industrial Few-S...

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IEEE International Conference on Systems, Man and Cybernetics

作者： DeLong Han Luo Xu MingLe Zhou Gang Li Min Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image anomaly detection usually relies on a large set of training samples and abnormal samples are relatively rare and challenging to obtain in daily industrial scenarios. In the scenario of Few-Shot Anomaly Detection (FSAD), how to better utilize the fine-grained features of the few images is a key issue. To solve these problems, a Finer-Grained Siamese network (FGSNet) is proposed in this paper. FGSNet considers the setting of Few-Shot Anomaly Detection (FSAD) and consists of two stages. The first stage is responsible for extracting finer-grained features and roughly aligning the image features, while incorporating an efficient Fine-grained Feature Fusion Module $(\mathbf{F}^{3}\mathbf{M})$ to enhance feature representation. Meanwhile, we design a Deep-supervised Loss to improve the level of fine-grained information extraction. The second is primarily used to refine and denoise the fused features, followed by detailed alignment operations for subsequent feature distribution modeling. Compared to most existing FASD models that use single-class training and single-model evaluation, FGSNet is more suitable for existing industrial scenarios. Extensive experiments are conducted on the publicly available MVTec AD and MPDD datasets in this paper. The results show that, for 2, 4 and 8-shot cases, FGSNet can increase the average Image-AUROC on MVTec AD by 0.87%, 1.27% and 1.33% respectively, compared to RegAD.

关键词： Training Feature extraction information retrieval Anomaly detection Cybernetics

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Contrastive Learning for Chest X-ray Classification: A Fusion of Topological Data Analysis and ResNet

Contrastive Learning for Chest X-ray Classification: A Fusio...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Hao Ren Zeyu Luo Fengshi Jing Xinyue Zhang Han He Yonghao Yu Dawei Zhao Faculty of Data Science City University of Macau China Minzu University of China Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Lung diseases such as pneumonia and tuberculosis present significant global health challenges, leading to millions of deaths annually. Chest X-ray imaging is crucial for diagnosing these conditions, but interpretation often relies on specialized radiologists, creating barriers in resource-limited areas. This study introduces a novel framework integrating topological data analysis (TDA), deep learning with ResNet-50, and contrastive learning to enhance the accuracy and computational efficiency of chest X-ray analysis. TDA captures global topological structures, while ResNet-50 extracts detailed local features. Contrastive learning then optimizes the feature space, enhancing the model’s discriminative power and generalization capabilities. Comprehensive evaluations on multiple datasets demonstrate the framework’s robustness and effectiveness, showing substantial improvements in diagnostic performance and computational efficiency. These findings suggest that the proposed approach is well-suited for real-time clinical applications, especially in resource-limited settings. Future work will focus on further optimizing the model and exploring its application to other medical imaging modalities, broadening its clinical utility.

关键词： Deep learning Accuracy Computational modeling Contrastive learning Feature extraction Robustness Real-time systems Computational efficiency X-ray imaging Biomedical imaging

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Enhancing Small Object Detection in Aerial Imagery Based on Strong Feature Extraction and Batch Dimension

Enhancing Small Object Detection in Aerial Imagery Based on ...

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International Joint Conference on Neural networks (IJCNN)

作者： Bingnan Cao Di Wang Ying Guo Hu Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China The Institute of Data Science City University of Macau Macau China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Object detection is a fundamental component of computer vision, playing a pivotal role in various applications. However, the accurate detection of small and densely distributed objects remains a challenging problem in this field. This challenge is particularly exacerbated in the context of aerial imagery, characterized by its distinctive bird’s-eye view, intricate backgrounds, and the variability in object appearances. This paper addresses these persistent challenges in object detection, with a focus on the specific difficulties posed by aerial images. We propose a deformable end-to-end object detection with transformers (DETR)-based framework to enhance small object detection accuracy, ultimately contributing to improved computer vision capabilities in domains like remote sensing, surveillance, and autonomous aerial systems. Firstly, in order to aggregate the entire input sequence information in the backbone network and improve the detection accuracy of small objects, we propose DMCA based on deformable features and attention mechanisms. Secondly, in order to capture and model the relationships between samples for dense pixel-level representations in small objects and improve the detection accuracy of small objects, we try to introduce BatchEncoder by implementing an encoder in the batch dimension. Experimental results show that, compared to the baseline, our method significantly improves the accuracy of small object detection in aerial images. The processing and analysis of a large amount of remote sensing data require powerful computing power. The computing power Internet can integrate the scattered computing resources to provide flexible and scalable computing power to meet the computing needs of different sizes and types.

关键词： Computer vision Accuracy Surveillance Object detection Detectors Transformers Feature extraction

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LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks

arXiv

引用

arXiv 2024年

作者： Wang, Tianyi Huang, Mengxiao Cheng, Harry Zhang, Xiao Shen, Zhiqi Nanyang Technological University Singapore Singapore Qilu University of Technology Jinan China Shandong University Qingdao China Joint NTU-WeBank Research Centre on Fintech Nanyang Technological University Singapore Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted with hyper-realistic synthetic facial images. To tackle the problem, this paper proposes a proactive Deepfake detection approach by introducing a novel training-free landmark perceptual watermark, LampMark for short. We first analyze the structure-sensitive characteristics of Deepfake manipulations and devise a secure and confidential transformation pipeline from the structural representations, i.e. facial landmarks, to binary landmark perceptual watermarks. Subsequently, we present an end-to-end watermarking framework that imperceptibly and robustly embeds and extracts watermarks concerning the images to be protected. Relying on promising watermark recovery accuracies, Deepfake detection is accomplished by assessing the consistency between the content-matched landmark perceptual watermark and the robustly recovered watermark of the suspect image. Experimental results demonstrate the superior performance of our approach in watermark recovery and Deepfake detection compared to state-of-the-art methods across in-dataset, cross-dataset, and cross-manipulation scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Watermarking

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Optimization Strategy for Remote Differential Upgrade of IoT Firmware

Optimization Strategy for Remote Differential Upgrade of IoT...

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IEEE Joint International information Technology and Artificial Intelligence Conference (ITAIC)

作者： Wenkang Ding Ruirui Sun Qinglong Meng Zhong Wan Guanghe Cheng Huijuan Hao Jinqiang Bai Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

With the continuous increase of IoT devices, the management and optimization of devices become particularly important. The update cycle of device functions is short, and in order to facilitate management and optimize device performance, a fast and convenient upgrade method is needed to solve this problem. Remote upgrade technology is mainly divided into full package upgrade and differential upgrade. Compared to full package upgrade, differential upgrade has obvious advantages in remote upgrade of large files. The main problem with current differential upgrade is the problem of slow generation speed while ensuring a certain size range of differential packages. This article proposes an improved Bsdiff algorithm to solve the problem of slow generation speed of differential packages. Experimental verification shows that the efficiency of remote upgrade has been improved, Improved the speed of differential packet generation and upgrade efficiency while ensuring the size of differential packets.

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