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BIT Conference (BITCON), International

作者： I. Sapthami Ch. Srilakshmi Batta Saranya N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology VNR VJIET Bachupally Hyderabad India AIDS Department CBITGandipet Hyderabad India Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The death rate from skin cancer has radically grown due to a lack of understanding about the indications and prevention measures. It is one of the most lethal types of cancers, and the mortality rate due to skin cancer has risen sharply. Cancer must be diagnosed as early as possible to halt its growth and prevent metastasis. Even though there are numerous varieties of skin cancer, melanoma, basal cell carcinoma, and squamous cell carcinoma are considered the three most dangerous types of skin cancer. This research, using the CNN image-processing technology and machine learning, tries to detect and classify into the many kinds of skin cancer. Artificial intelligence (AI) based algorithms for automated skin cancer classification have consistently outperformed human professionals in several clinical studies, laying the groundwork for a future in which AI will be widely used.

关键词： Squamous cell carcinoma Prevention and mitigation Mortality Melanoma Machine learning Data augmentation Skin Classification algorithms Convolutional neural networks Skin cancer

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Attention-Based End-to-End Differentiable Particle Filter for Audio Speaker Tracking

TechRxiv

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TechRxiv 2023年

作者： Zhao, Jinzheng Xu, Yong Qian, Xinyuan Liu, Haohe Plumbley, Mark D. Wang, Wenwu Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom Tencent AI Lab BellevueWA98004 United States Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China

Particle filter (PF) has been widely used in speaker tracking due to its capability of modeling the non-linear process or the non-Gaussian environment. However, particle filter are penalized by many issues. For example, pre-defined handcrafted measurements are often used which can limit the model performance. In addition, the transition and update models are often preset which make PF less flexible to be adapted to different scenarios. To address these issues, we propose an end-to-end differentiable particle filter framework by employing the multi-head attention to model the long-range dependencies. The proposed model employs the self-attention as the learned transition model and the cross-attention as the learned update model. To our knowledge, this is the first proposal of combining particle filter and transformer for speaker tracking, which integrates the measurement extraction, transition and update steps into an end-to-end architecture. Experimental results show that the proposed model achieves superior performance over the recurrent baseline models. © 2023, CC BY.

关键词： Monte Carlo methods

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Audio-Visual Speaker Tracking: Progress, Challenges, and Future Directions

arXiv

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arXiv 2023年

作者： Zhao, Jinzheng Xu, Yong Qian, Xinyuan Berghi, Davide Wu, Peipei Cui, Meng Sun, Jianyuan Jackson, Philip J.B. Wang, Wenwu Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom Tencent AI Lab BellevueWA98004 United States Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Department of Computer Science University of Sheffield SheffieldS1 4DP United Kingdom

Audio-visual speaker tracking has drawn increasing attention over the past few years due to its academic values and wide application. Audio and visual modalities can provide complementary information for localization and tracking. With audio and visual information, the Bayesian-based filter can solve the problem of data association, audio-visual fusion and track management. In this paper, we conduct a comprehensive overview of audio-visual speaker tracking. To our knowledge, this is the first extensive survey over the past five years. We introduce the family of Bayesian filters and summarize the methods for obtaining audio-visual measurements. In addition, the existing trackers and their performance on AV16.3 dataset are summarized. In the past few years, deep learning techniques have thrived, which also boosts the development of audio-visual speaker tracking. The influence of deep learning techniques in terms of measurement extraction and state estimation is also discussed. At last, we discuss the connections between audio-visual speaker tracking and other areas such as speech separation and distributed speaker tracking. © 2023, CC BY.

关键词： Face recognition

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False Discovery Rate Control for Grouped Variable Selection in High-Dimensional Linear Models Using the T-Knock Filter

False Discovery Rate Control for Grouped Variable Selection ...

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European signal processing Conference (EUSIPCO)

作者： Jasin Machkour Michael Muma Daniel P. Palomar Signal Processing Group Technische Universität Darmstadt Darmstadt Germany Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

High-dimensional variable selection is a challenging task, especially when groups of highly correlated variables are present in the data, such as in genomics research, direction-of-arrival estimation, and financial engineering. Recently, the T-Knock filter, a new framework for fast variable selection in high-dimensional settings has been developed. It provably controls the false discovery rate (FDR) at a given target level. However, its current version does not consider groups of highly correlated variables, which can lead to a loss in the true positive rate (TPR), i.e., the power. Hence, we propose the T-Knock+GVS filter that allows for grouped variable selection with FDR control in such settings. This is achieved by modifying the forward variable selection algorithm within the T- Knock filter and by adjusting the knockoff generation process such that the generated sets of knockoffs mimic the group correlation structure within the original set of variables. For a special case, we prove that the proposed $\mathrm{T}-\text{Knock}+\text{GVS}$ filter possesses the grouped variable selection property. Through a simulated high-dimensional genome-wide association study (GWAS), we show that the proposed method significantly increases the TPR, while controlling the FDR at the target level.

关键词： Maximum likelihood detection Input variables Genomics signal processing algorithms Europe Nonlinear filters Filtering algorithms

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E-Buddy for Rescue Child Labour

E-Buddy for Rescue Child Labour

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BIT Conference (BITCON), International

作者： Ajmeera Kiran Prithiviraj Ananth P Veera Prakash N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Sona College of Technology Salem Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Ebuddy is a ground breaking platform dedicated to rescuing and rehabilitating child laboures. By leveraging advanced technology, this initiative effectively connects with at-risk children, providing them with crucial education and support services. With a mission to break the vicious cycle of poverty and exploitation, Ebuddy collaborates with local communities and organizations to raise awareness about child labor and advocate for policies that safeguard children's rights. This platform offers comprehensive support services aimed at addressing the children's physical, emotional, and psychological well-being. By providing counseling, rehabilitation, and necessary medical assistance, Ebuddy empowers children to leave their past behind and embrace a better future.

关键词： Employee welfare Pediatrics Education Psychology Morphology Organizations

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ZCS-CDiff: A Zero-Shot Code-Switching TTS System with Conformer-Based Diffusion Model

ZCS-CDiff: A Zero-Shot Code-Switching TTS System with Confor...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ke Chen Zhihua Huang Liang He Yonghong Yan School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics CAS Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Code-Switching (CS) Text-To-Speech (TTS) models have gained attention due to the increasing prevalence of multilingual communication. However, existing models struggle to meet the growing demand for personalized CS TTS, particularly in Zero-Shot (ZS) scenarios where the model should generate speech for an unseen speaker from reference speech. In this paper, we propose ZCS-CDiff, a zero-shot code-switching TTS system with the Conformer-based diffusion model. We disentangle speech features and use the diffusion model to precisely model these disentangled attributes, resulting in high-quality ZS CS speech. Additionally, we introduce a Conformer-based WaveNet as the denoising network within the diffusion model to further improve the accuracy of modeling different attributes. We also designed a speaker-assist module to help the model better handle speaker information extracted from the reference speech, resulting in CS speech with higher speaker similarity. Experimental results and ablation studies demonstrate that the ZS CS speech generated by ZCS-CDiff has good speech naturalness, intelligibility, and speaker similarity while confirming the effectiveness of our design choices. Audio samples are available 1 .

关键词： Training Adaptation models Accuracy Speech coding Noise reduction Speech enhancement signal processing Diffusion models Probabilistic logic Text to speech

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Novel Personalized Multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

Novel Personalized Multimedia Recommendation Systems Using T...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin541004 China Guilin University of Electronic Technology Department of Information and Telecommunication Guilin541004 China Northwestern State University of Louisiana Department of Engineering Technology NatchitochesLA71497 United States Communications Research Centre Canada OttawaONK2H 8S2 Canada

ISBN: (纸本)9798350321524

Nowadays, multimedia data are often produced by various sources, e.g., Internet of Things (IoT), social media, customer databases, etc. As tremendous multimedia data are produced every day, users or E-commerce customers cannot infer information from such data by themselves and therefore recommender systems have been developed to help users to select the products or services which better fit users' preferences or requirements. Nonetheless, there exists few works on the incorporation of side information or multiple attributes about items into the design of a more robust recommender system. In this work, we propose a novel approach based on the third-order tensor singular-value-decomposition (T3-SVD) to design new personalized multimedia recommender systems (PMRSs) for internet users. A PMRS can dynamically adjust its recommendation strategy subject to a particular user's on-line transaction behavior. To evaluate the effectiveness of our proposed PMRS based on T3-SVD, we compare the performances of our proposed new PMRS and two other existing tensor-based recommendation systems over realworld data in terms of normalized root-mean-square error (NRMSE). As a result, our proposed new PMRS greatly outperforms the other two existing systems. © 2023 IEEE.

关键词： Recommender systems

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An Integrated Web Solution for Planetary Data Visualization and Exploration

An Integrated Web Solution for Planetary Data Visualization ...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Ananya Maheshwari Love Lakhwani Pariav Naik Abhishek Kumar Jai G. Singla Amitabh Nitant Dube VijayPal Singh Dhaka School of Computing and Intelligent Systems Manipal University Jaipur Dehmi Kalan Jaipur Rajasthan India Department of Computer Engineering Charotar University of Science and Technology Anand Gujarat India Department of Electrical Engineering Indian Institute of Technology Bhubaneshwar Bhubaneshwar Odisha India Signal and Image Processing Area Space Applications Centre ISRO Ahmedabad Gujarat India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The exploration and study of planetary bodies have produced vast amounts of geospatial data, creating a need for effective visualization and analysis tools. Many existing solutions are constrained by platform limitations and are resource-intensive, reducing their accessibility for broader use. To overcome these challenges, we developed a lightweight, web-based platform that visualizes interplanetary data and supports multiple geospatial data formats, including shapefiles, KML, and CZML. Our solution includes unique features such as 2D and 2.5D terrain visualization, 3D tiles, and tools for navigating to specific coordinates and measuring distances. These enhancements allow users to explore and interact with geospatial data from various planetary bodies across multiple devices without requiring specialized hardware or software. By modifying the underlying system, we extended its application to planetary data while preserving accessibility and performance across different platforms. This paper discusses the methodology behind these customizations, the challenges we encountered, and the potential applications of our platform in scientific research and education.

关键词： Performance evaluation Three-dimensional displays Coordinate measuring machines Navigation Data visualization Geoscience and remote sensing Extraterrestrial measurements Software Hardware Geospatial analysis

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Unpaired Overwater Image Defogging Using Prior Map Guided Cycle-Consistent Generative Adversarial Network

SSRN

引用

SSRN 2024年

作者： Mo, Yaozong Li, Chaofeng Ren, Wenqi Wang, Wenwu Wu, Xiao-Jun Shanghai201306 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen518000 China Center for Vision Speech and Signal Processing Department of Electrical and Electronic Engineering University of Surrey Surrey SurreyGU2 7XH United Kingdom School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China

Existing image defogging approaches have made significant advancements. But their effectiveness in addressing overwater foggy images remains limited. Current methods are predominantly optimized for land scenes, which causes poor performance when applied to overwater images, since overwater scenes typically contain large expanses of sky and water. In this paper, we propose a prior map guided cycle-consistent generative adversarial network (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experimental results demonstrate that PG-CycleGAN is effective in overwater image defogging. © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Exploring Invariance Matters for Domain Generalization

引用

IEEE Transactions on Image processing 2025年 34卷 3336-3351页

作者： Wang, Shanshan He, Houmeng Yang, Xun Liu, Zhipu Zhong, Yuanhong Zhang, Xingyi Wang, Meng Anhui University National Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology Institutes of Physical Science and Information Technology Hefei230601 China University of Science and Technology of China School of Information Science and Technology Department of Electronic Engineering and Information Science Hefei230026 China Chongqing University of Technology School of Computer Science and Engineering Chongqing400054 China Chongqing University School of Microelectronics and Communication Engineering Chongqing400030 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China

Domain generalization (DG) aims to solve the problem of significant performance degradation when target domain data collected from the Out-Of-Distribution (O.O.D). Previous efforts try to exploit invariant features in the source domain through CNN networks. However, inspired by causal mechanisms, we find that the complex spurious-invariant information is still hidden in this view invariant features, and the impact of domain and class discrepancies on extracting invariance has not been effectively mitigated. To alleviate these issues, we propose a self-weighted multi-view mining invariance domain generalization framework (SMIDG). On the one hand, to make up for the insufficiency of traditional single-view convolutional feature extraction networks, we propose to mine features from another frequency view and use the self-adaptive adversarial masks to eliminate some spurious correlations, ensuring causal invariance in the coarse-grained generalization. However, due to inconsistencies in discriminative information between interdomain and intra-domain samples, as well as inter-class and intra-class samples, the coarse-grained elimination of spurious associations does not fully resolve this issue. On the other hand, we also consider the fine-grained generalization from two aspects. Firstly, to better tackle the domain discrepancies, we propose a novel progressive contrastive learning strategy that learns the underlying specific features of samples while gradually mitigating domain discrepancies, thereby ensuring domain invariance in fine-grained generalization. Secondly, due to the issue of feature inconsistency, we adopt a self-adaptive hard sample mining method with information gain to ensure that the model pays more attention on hard disentangled samples, thus maintaining feature invariance. Extensive experiments on five benchmark datasets demonstrate that our method outperforms state-of-the-art approaches. © 1992-2012 IEEE.

关键词： Contrastive learning Feature extraction Frequency-domain analysis Training Data models Data mining Correlation Data augmentation Convolutional neural networks computer science

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