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JOINT-SEMANTICS MULTI-SIMILARITY HASHING FOR CROSS-MODAL RETRIEVAL 49

JOINT-SEMANTICS MULTI-SIMILARITY HASHING FOR CROSS-MODAL RET...

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2024 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2024

作者： Wang, Weigang Guo, Zhongwen Yang, Chao Wang, Jinxin Jiang, Sining Zhang, Tianao Faculty of Information Science and Engineering Ocean Nuiversity of China Qingdao China School of Computer Science University of Technology Sydney Sydney Australia Qingdao High-Tech Zone Investment and Development Group Co. LTD Qingdao China

ISBN: (纸本)9798350344851

Recently, cross-modal hashing has attracted much attention in large-scale image retrieval scenarios. However, most existing methods ignore the potential higher-order relationships and label semantic information between heterogeneous modality data. Besides, the imbalanced training samples could bias the learning process in most classes and affect the retrieval performance. To solve the above problems, we proposed a Joint-semantics Multi-Similarity Hashing method for cross-modal retrieval (JMSH). We first construct a joint semantic similarity matrix, which supervises hash learning by integrating multi-modal features and semantic labels. This method generates higher-order semantic features that maintain semantic correlation effectively. Then, we propose a multi-similarity loss based on adaptive margin, which can collect and weight informative pairs efficiently and accurately, thus producing more discriminative hashing code and improving retrieval performance. Extensive experiments on two benchmark datasets show the superiority of JMSH in cross-modal retrieval tasks. © 2024 IEEE.

关键词： cross-modal hashing cross-modal retrieval multi-similarity loss supervised learning

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EEG Pattern Comparison and Classification performance of Motor Imagery Between Supernumerary and Inherent Limbs 46

EEG Pattern Comparison and Classification Performance of Mot...

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

作者： Wang, Zhuang Liu, Yuan Wu, Wenlai Huang, Shuaifei An, Xingwei Ming, Dong Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China

ISBN: (纸本)9798350371499

Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, whether neural patterns that are distinct from the traditional inherent limbs motor imagery (MI) paradigm can be extracted, which is essential for the high-dimensional control of external equipment. In this study, a novel type of MI paradigm based on SRLs was proposed, consisting of "the sixth-finger", "the third-arm"and "the third-leg", and validated the distinctness of EEG response patterns between the novel and the traditional (hand, arm and leg) MI paradigm. The results showed that imagining extra limbs induced more obvious event-related desynchronization (ERD) phenomenon in sensorimotor areas compared to imagining inherent limbs. Classification results indicate well separable performance among different mental tasks (all above 86%, with a maximum of 90.5%). This work proposed a novel type of MI paradigm, and offered new way for widening the control bandwidth of the BCI system. © 2024 IEEE.

关键词： Brain computer interface

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Optimization Algorithm of Multi-Attribute Group Decision-Making Based on Information Entropy Theory 4

Optimization Algorithm of Multi-Attribute Group Decision-Mak...

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4th International conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Feng, Xuan Cai, Youbao Institute of Computer Science Sichuan Province Chengdu China

ISBN: (纸本)9798350380323

The problems of complex mathematical models, high computational complexity, and repeated parameter adjustments in the subjective and objective weighting method is studied. Such problems are generally defined as multi-attribute group decision-making models. Multi-attribute group decision-making models have been widely applied in fields such as achievement evaluation, scientific research performance assessment, and project review, and are a popular research direction in the field of artificial intelligence. How to balance the subjectivity among experts and the objectivity of attribute weights is a challenging problem in the research of multi-attribute group decision-making models. The entropy weighting method is firstly used to obtain the objective weights of attributes. Then, the grey correlation degree method is applied to adjust the subjective weight. Finally, based on the minimum Kullback-Leibler divergence method criterion reflecting the distance between different weights, an optimization model is established to solve the integrated weight. It is provided a feasible weight integration method based on information entropy theory for multi-attribute group decision-making. A numerical experiment shows the feasibility and practicability of the method. It is provided a new solution to the problems of prioritizing quantity over quality in the performance evaluation of scientific research in universities and research institutions, which can effectively promote the enthusiasm of scientific and technological workers for scientific research innovation. © 2024 IEEE.

关键词： Optimization algorithms

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Graph based Consistency Learning for Contrastive Multi-View Clustering 24

Graph based Consistency Learning for Contrastive Multi-View ...

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32nd ACM International conference on Multimedia, MM 2024

作者： Xu, Binbin Yin, Jun Zhang, Nan College of Information Engineering Shanghai Maritime University Shanghai China College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China

ISBN: (纸本)9798400706868

Multi-View Clustering (MVC) aims to mine complementary information across different views to partition multi-view data more effectively and has attracted considerable interest. However, existing deep multi-view clustering methods frequently neglect the exploration of structural information within individual view and lack the learning of structural consistency among views, which results in limitations in the clustering performance. In this paper, we introduce a novel multi-view clustering framework based on graph consistency learning to address this issue. Specifically, we design intra-view graph contrastive learning to uncover structural information within each view and achieve structural conscistency objectives through cross-view graph consistency learning. Additionally, to address the conflict between different learning objectives when trained in the same space, we introduce two new feature spaces, one for cluster-levcel contrastive learning and the other for instance-level contrastive learning. Subsequently, to make the most of discriminative information from all views, we concatenate high-level features from all views to form global features and employ self-supervision to promote clustering consistency across different views. Experimental results on several challenging datasets demonstrate the outstanding performance of our proposed method. © 2024 ACM.

关键词： Contrastive Learning

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A 0.75mm2407µW Real-Time Speech Audio Denoiser with Quantized Cascaded Redundant Convolutional Encoder-Decoder for Wearable IoT Devices 38

A 0.75mm2407µW Real-Time Speech Audio Denoiser with Quantiz...

引用

38th International conference on VLSI Design, VLSID 2025

作者： Kochar, Dimple Vijay Ashok, Maitreyi Chandrakasan, Anantha P. Massachusetts Institute of Technology Electrical Engineering and Computer Science CambridgeMA United States

ISBN: (纸本)9798331522445

Audio denoising is crucial for delivering high-quality sound in applications ranging from communication devices to entertainment systems. On-device denoising is critical for en-suring consistent performance across various host platforms. Machine learning (ML) models exhibit strong audio processing performance in the frequency domain but require efficient hardware design. This paper focuses on enhancing audio quality using convolutional encoder-decoder ML models with low power consumption while meeting real-time processing constraints. We achieve this by developing a quantized network that optimally reduces computational costs without compromising enhancement quality. Furthermore, our hardware quantization scheme reduces memory usage by up to 75% while maintaining accuracy. Next, we design a complementary processing element activation routing scheme tailored to our algorithm, significantly reducing the on-chip memory accesses by 5-9×. Fabricated in 28nm CMOS process, our chip demonstrates real-time audio denoising, processing each frame within 8ms while consuming only 407µ or 3.24 µJ/frame at 0.65V, 18.5 MHz, making it ideal for battery-powered IoT devices. In terms of performance, our chip also achieves the highest evaluation score for audio quality (PESQ), outperforming previous works. © 2025 IEEE.

关键词： CMOS integrated circuits

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Enhanced Multi-Scale Ship Detection Through Joint Multi-Source Image Training 12

Enhanced Multi-Scale Ship Detection Through Joint Multi-Sour...

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12th IEEE International conference on Information and Communication Networks, ICICN 2024

作者： Huang, Yuan Cao, Minghua Zhang, Yue Ning, Cheng School of Computer and Communication Lanzhou University of Technology Lanzhou China

ISBN: (纸本)9798350355802

In order to improve the detection performance of traditional single-source remote sensing image ship detection methods in terms of anti-interference capability and multi-scale targets. We develop an improved algorithm based on YOLOv8, called Sea-YOLOv8, and obtain the multi-source remote sensing image detection model SAR-YOLOv8 through migration ***, Efficient Multi-Scale Convolution (EMSConv) is used to improve the ability of the C2f module in capturing the features of ships at different scales by using different sizes of convolution kernels. scale ship features using different sized convolution kernels. Secondly, Large Separable Kernel Attentio (LSKAtt) is applied to the SPPF module to decompose the 2D convolution kernel into horizontal and vertical 1D kernels, which improves the computational efficiency while maintaining the high efficiency image processing capability. Experimental results on the SeaShips and SSDD datasets show that Sea-YOLOv8 has a good performance in terms of R, mAP, model parameters and FPS have achieved satisfactory results. Finally, the optimal transfer learning strategy was determined through layer-by-layer freezing, training and validation, which led to the construction of the multi-source remote sensing image recognition model SAR-YOLOv8. © 2024 IEEE.

关键词： Remote sensing

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FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models 24

FedDEO: Description-Enhanced One-Shot Federated Learning wit...

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32nd ACM International conference on Multimedia, MM 2024

作者： Yang, Mingzhao Su, Shangchao Li, Bin Xue, Xiangyang School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400706868

In recent years, the attention towards One-Shot Federated Learning (OSFL) has been driven by its capacity to minimize communication. With the development of the diffusion model (DM), several methods employ the DM for OSFL, utilizing model parameters, image features, or textual prompts as mediums to transfer the local client knowledge to the server. However, these mediums often require public datasets or the uniform feature extractor, significantly limiting their practicality. In this paper, we propose FedDEO, a Description-Enhanced One-Shot Federated Learning Method with DMs, offering a novel exploration of utilizing the DM in OSFL. The core idea of our method involves training local descriptions on the clients, serving as the medium to transfer the knowledge of the distributed clients to the server. Firstly, we train local descriptions on the client data to capture the characteristics of client distributions, which are then uploaded to the server. On the server, the descriptions are used as conditions to guide the DM in generating synthetic datasets that comply with the distributions of various clients, enabling the training of the aggregated model. Theoretical analyses and sufficient quantitation and visualization experiments on three large-scale real-world datasets demonstrate that through the training of local descriptions, the server is capable of generating synthetic datasets with high quality and diversity. Consequently, with advantages in communication and privacy protection, the aggregated model outperforms compared FL or diffusion-based OSFL methods and, on some clients, outperforms the performance ceiling of centralized training. © 2024 ACM.

关键词： Federated learning

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Low Complexity Convolutional Neural Network Digital Predistortion for Radio Frequency Power Amplifiers 24

Low Complexity Convolutional Neural Network Digital Predisto...

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24th IEEE International conference on Communication Technology, ICCT 2024

作者： Xie, Qian Wang, Yong Ding, Jianyang Guo, Zihan Niu, Jiajun Xidian University State Key Laboratory of Integrated Services Networks Xi'an710061 China Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214000 China

ISBN: (纸本)9798350363760

The predistortion models for radio frequency (RF) power amplifiers (PAs), such as convolutional neural networks (CNNs) and deep neural networks (DNNs), has shown a significant improvement in predistortion performance. However, digital predistortion (DPD) models with neural network structures encounter challenges related to high computational complexity and prolonged training duration. In this study, we propose a DPD model utilizing a low complexity convolutional neural network (LCCNN). The proposed model simplifies the traditional CNN structure, employing an augmented real-valued cross-memorized terms (ARCT) matrix as input layer, and configuring convolutional layer to extract nonlinear features through macro convolutional kernels. Furthermore, migration learning and pruning operations are implemented to expedite model training and parameter reduction, thereby decreasing training duration and floating-point operations (FLOPs). The experiment results based on a 160 MHz Doherty PA indicate that the proposed model effectively reduces computational complexity while maintaining required predistortion performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Beyond the Gut Feeling: Machine-Learning Methods for Autism Screening 24

Beyond the Gut Feeling: Machine-Learning Methods for Autism ...

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24th IEEE International conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Zhusubalieva, Aigerim Oudah, Mai New York University Abu Dhabi Computer Science Program Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331518622

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that manifests itself as speech impairment and difficulties in social and cognitive skills. The onset of ASD is typically before the age of three, but many children are not diagnosed until later in life due to the current diagnosis being based on behavioral assessments. However, early intervention is crucial for minimizing the impact of ASD on both the child and their caregivers, and for developing an effective treatment plan for symptoms. We propose a gut microbiome-based screening tool for ASD utilizing feature selection, feature engineering, and machine learning algorithms. Our approach significantly reduces the feature space to 1-2% of its original size, emphasizing computational efficiency and informative attribute focus. Training and testing on two diverse datasets revealed minimal common features, underscoring the impact of regional differences on taxonomic behavior. Biomarker analysis confirmed known associations and unveiled novel connections, contributing valuable insights to the ongoing debate on ASD biomarkers. Notably, the genus Oscillospira has been identified by our Attribute Selection tools as being increased in ASD. Model performance, measured by Area Under the Curve, showcases the Bayes-Net algorithm on the Hierarchical Feature Engineering subset as the most optimal for ASD prediction. Our findings surpass existing literature, demonstrating the effectiveness of our approach in decoding the complex interplay between gut microbiome and ASD. © 2024 IEEE.

关键词： Diseases

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Face Anti-Spoofing in Face Recognition: Using Color Texture and Corner Features with Mtcnn and Facenet on Raspberry PI 21

Face Anti-Spoofing in Face Recognition: Using Color Texture ...

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21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Lamsis, Almer A. Osmena, John Daniel M. Linsangan, Noel B. School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (纸本)9798331519254

Face recognition, a dependable biometric technology, utilizes facial information to verify an individual's identity. In the field of research, the emphasis of face recognition has been on enhancing the recognition rate. Spoofing attacks pose serious threats to traditional face recognition systems, which rely primarily on face detection and identification. The study's objective is to apply an anti-spoofing model in an MTCNN and FaceNet face recognition model utilizing the limited computing resources of Raspberry Pi. To achieve this, three specific objectives must be met;(1) To implement an MTCNN and FaceNet face recognition model that detects face input from a Raspberry camera (2) To implement a Color Texture and Corner Feature anti-spoofing detection model that identifies and prevents spoofing attacks in the face recognition model (3) Determine the accuracy of the facial recognition model with Anti-Spoofing Detection using the ratio of the False Reject Rate (FRR) and False Accept Rate (FAR). This study successfully implemented face image anti-spoofing, face detection, and identification on a Raspberry Pi system. At threshold 0.5, the anti-spoofing model achieves an accuracy of 94.94%, an FRR of 7.78%, and a low FAR of 1.47%. The face identification model gave the best results at a threshold of 0.1, with 100% Accuracy, 0% FAR, and a low FRR of 5.26%. The system allowed usability balancing with a very low FAR and FRR overall. Further improvements toward the real-time performance of the face recognition and anti-spoofing model are recommended for future studies. © 2024 IEEE.

关键词： Face recognition

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