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arXiv 2025年

作者： Kafle, Swatantra Joseph, Geethu Varshney, Pramod K. ANDRO Computational Solutions NY United States Electrical Engineering and Computer Science Department Syracuse University NY United States Signal Processing Systems group Electrical Engineering Mathematics and Computer Science faculty the Delft University of Technology Netherlands

This paper addresses the classical problem of one-bit compressed sensing using a deep learning-based reconstruction algorithm that leverages a trained generative model to enhance the signal reconstruction performance. The generator, a pretrained neural network, learns to map from a low-dimensional latent space to a higher-dimensional set of sparse vectors. This generator is then used to reconstruct sparse vectors from their one-bit measurements by searching over its range. The presented algorithm provides an excellent reconstruction performance because the generative model can learn additional structural information about the signal beyond sparsity. Furthermore, we provide theoretical guarantees on the reconstruction accuracy and sample complexity of the algorithm. Through numerical experiments using three publicly available image datasets, MNIST, Fashion-MNIST, and Omniglot, we demonstrate the superior performance of the algorithm compared to other existing algorithms and show that our algorithm can recover both the amplitude and the direction of the signal from one-bit measurements. © 2025, CC BY.

关键词： Compressed sensing

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Introducing Multilingual Phonetic Information to Speaker Embedding for Speaker Verification

Introducing Multilingual Phonetic Information to Speaker Emb...

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

作者： Zhida Song Liang He Penghao Wang Ying Hu Hao Huang School of Computer Science and Technology Xinjiang University China Xinjiang Key Laboratory of Signal Detection and Processing China Department of Electronic Engineering Tsinghua University China

Incorporating frame-level phonetic information during the extraction of speaker embeddings has been shown to enhance the performance of speaker verification systems. However, previous studies have primarily relied on phonetic information obtained from pre-trained models of monolingual automatic speech recognition (ASR). Considering that speaker verification datasets typically consist of multiple languages, there are instances where speakers are proficient in multiple languages, resulting in discrepancies between the languages used in the enrolled and test utterances. To address these challenges, we employ a pre-trained multilingual ASR Conformer encoder to initialize the MFA-Conformer network for speaker verification. Experimental results on the VoxCeleb dataset demonstrate a significant improvement in the performance of the system that incorporates multilingual phonetic information across different evaluation sets, including VoxCeleb1-O, E, and H, as well as the VoxSRC21 validation set, which focuses on multilingual verification. The source code is released at https://***/zds-potato/multilingual-phonetic-sv.

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Blockchain-Based Resilient Pairing and Bonding of BLE Devices Using Deep Reinforcement Learning

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IEEE Transactions on Consumer Electronics 2024年

作者： Devi, Aguru Aswani Babu, Erukala Suresh Rathore, Rajkumar Singh Jhaveri, Rutvij H. Benedetto, Francesco SRM University AP Dept. of Computer Science and Engineering India National Institute of Technology Warangal Dept. of Computer Science and Engineering Telangana India Cardiff Metropolitan University Dept. of Computer Science Cardiff School of Technologies United Kingdom Pandit Deendayal Energy University Computer Science and Engineering School of Technology Gandhinagar India Roma Tre University Signal Processing for Telecommunications and Economics Department of Economics Rome Italy

In the day-to-day deployment of the Internet of Things (IoT), IPv6-over-Bluetooth Low Energy (BLE) devices became significant enablers due to their low power, low range, and effortless connectivity. To overcome the latency during BLE device migration, we have proposed a Blockchain-based resilient pairing and bonding of BLE devices using a lightweight authenticated encryption scheme. The bonding information is stored on the local ledger of the intermediate IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) for BLE (6LoWPAN for BLE or 6LoWBLE) over gateway. Whenever the BLE device migrates from one gateway to another, the bonding information is transferred to the local ledger of the current gateway through the global ledger with the Proof-of-Voting consensus algorithm. The resilience of the proposed authentication scheme is analyzed using BAN logic. In addition, we proposed a Man-in-the-Middle (MITM) detection framework using Deep Reinforcement Learning (DRL) due to the escalation of MITM attacks on BLE devices. The DRL-based model is implemented in Python, and its performance is evaluated using the Kitsune Network Attack Dataset. The performance of the overall framework is tested on the Hyperledger Fabric, which results in low latency and a low average pairing time in comparison with existing frameworks. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Securing Data in an Internet of Things Network Using Blockchain technology: Smart Home Case

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SN computer science 2022年第2期3卷 167页

作者： Tchagna Kouanou, Aurelle Tchito Tchapga, Christian Sone Ekonde, Michael Monthe, Valery Mezatio, Brice Anicet Manga, Josépha Simo, Gael R. Muhozam, Yves Department of Computer Engineering College of Technology University of Buea Buea Cameroon Department of Training Research Development and Innovation InchTech’s Solutions Yaoundé Cameroon Department of Electrical and Electronic Engineering College of Technology University of Buea Buea Cameroon Department of Computer Science Faculty of Sciences University of Yaoundé 1 Yaoundé Cameroon Research Unit Condensed Matter Electronics and Signal Processing Department of Physics Faculty of Science University of Dschang PO Box 67 Dschang Cameroon

Every day, innovative technologies emerge (5G networks, quantum computing) or change to existing technologies (big data, deep learning, blockchain, etc.). These technologies are expected to play an essential role on the Internet of Things (IoT) systems and applications. The IoT architecture generates substantial amounts of diverse types of data at extremely high speeds. All these generated data need to be secure. Scalable and secure data sharing is essential for all technological domains. Taking into consideration of blockchain technology today in data security, we can also think about a method to secure IoT data based on blockchain. In this paper, we proposed a blockchain method to secure data in an IoT architecture. We used EOS Blockchain for its scalability, flexibility and native enhanced throughput. Architecture in a concept of a smart home using blockchain is proposed. We combine Arduino, Raspberry Pi and sensors to create an IoT ecosystem for a smart home. Data are collected and stored in EOS blockchain for better security and integrity. We use Raspberry Pi as a client to get data in our developed blockchain. The real implementation is performed and the obtained results show us that we can send more than 500 data per second in our EOS blockchain. We discuss our results and saw that our blockchain method outperformed those in related works in terms of the number of data stored per millisecond in blockchain and the scalability. The proposed EOS blockchain pipeline for securing Smart Home data is provided at the end of this paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.

关键词： Architecture Blockchain Data security IoT Smart home

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Survey on depth and RGB image-based 3D hand shape and pose estimation

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Virtual Reality & Intelligent Hardware 2021年第3期3卷 207-234页

作者： Lin HUANG Boshen ZHANG Zhilin GUO Yang XIAO Zhiguo CAO Junsong YUAN Department of Computer Science and Engineering State University of New York at BuffaloNY 14260USA YouTu Lab TencentShanghai 201101China Department of Computer Science Fu Foundation School of Engineering and Applied ScienceColumbia UniversityNY 10027USA National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

The field of vision-based human hand three-dimensional(3D)shape and pose estimation has attracted significant attention recently owing to its key role in various applications,such as natural human computer *** the availability of large-scale annotated hand datasets and the rapid developments of deep neural networks(DNNs),numerous DNN-based data-driven methods have been proposed for accurate and rapid hand shape and pose ***,the existence of complicated hand articulation,depth and scale ambiguities,occlusions,and finger similarity remain *** this study,we present a comprehensive survey of state-of-the-art 3D hand shape and pose estimation approaches using RGB-D *** RGB-D cameras,hand datasets,and a performance analysis are also discussed to provide a holistic view of recent *** also discuss the research potential of this rapidly growing field.

关键词： Hand survey 3D hand pose estimation Hand shape reconstruction Hand-object interactions RGB-D cameras

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A REFERENCE-FREE METRIC FOR LANGUAGE-QUERIED AUDIO SOURCE SEPARATION USING CONTRASTIVE LANGUAGE-AUDIO PRETRAINING

arXiv

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arXiv 2024年

作者： Xiao, Feiyang Guan, Jian Zhu, Qiaoxi Liu, Xubo Wang, Wenbo Qi, Shuhan Zhang, Kejia Sun, Jianyuan Wang, Wenwu College of Computer Science and Technology Harbin Engineering University Harbin China University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science University of Sheffield Sheffield United Kingdom

Language-queried audio source separation (LASS) aims to separate an audio source guided by a text query, with the signal-to-distortion ratio (SDR)-based metrics being commonly used to objectively measure the quality of the separated audio. However, the SDR-based metrics require a reference signal, which is often difficult to obtain in real-world scenarios. In addition, with the SDR-based metrics, the content information of the text query is not considered effectively in LASS. This paper introduces a reference-free evaluation metric using a contrastive language-audio pretraining (CLAP) module, termed CLAPScore, which measures the semantic similarity between the separated audio and the text query. Unlike SDR, the proposed CLAPScore metric evaluates the quality of the separated audio based on the content information of the text query, without needing a reference signal. Experiments show that the CLAPScore provides an effective evaluation of the semantic relevance of the separated audio to the text query, as compared to the SDR metric, offering an alternative for the performance evaluation of LASS systems. The code for evaluation is publicly available1 Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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GlobalMapNet: An Online Framework for Vectorized Global HD Map Construction

arXiv

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arXiv 2024年

作者： Shi, Anqi Cai, Yuze Chen, Xiangyu Pu, Jian Fu, Zeyu Lu, Hong Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Department of Computer Science University of Exeter United Kingdom

High-definition (HD) maps are essential for autonomous driving systems. Traditionally, an expensive and labor-intensive pipeline is implemented to construct HD maps, which is limited in scalability. In recent years, crowdsourcing and online mapping have emerged as two alternative methods, but they have limitations respectively. In this paper, we provide a novel methodology, namely global map construction, to perform direct generation of vectorized global maps, combining the benefits of crowdsourcing and online mapping. We introduce GlobalMapNet, the first online framework for vectorized global HD map construction, which updates and utilizes a global map on the ego vehicle. To generate the global map from scratch, we propose GlobalMapBuilder to match and merge local maps continuously. We design a new algorithm, Map NMS, to remove duplicate map elements and produce a clean map. We also propose GlobalMapFusion to aggregate historical map information, improving consistency of prediction. We examine GlobalMapNet on two widely recognized datasets, Argoverse2 and nuScenes, showing that our framework is capable of generating globally consistent results. Copyright © 2024, The Authors. All rights reserved.

关键词： Crowdsourcing

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Beyond Euclidean Structures: Collaborative Topological Graph Learning for Multiview Clustering

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10606-10618页

作者： Cheng Liu Rui Li Hangjun Che Man-Fai Leung Si Wu Zhiwen Yu Hau-San Wong Department of Computer Science Shantou University Shantou China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China School of Computing and Information Science Faculty of Science and Engineering Anglia Ruskin University Cambridge U.K. School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Computer Science City University of Hong Kong Hong Kong China

Graph-based multiview clustering (MVC) approaches have demonstrated impressive performance by leveraging the consistency properties of multiview data in an unsupervised manner. However, existing methods for graph learning heavily rely on either Euclidean structures or the manifold topological structures derived from fixed view-specific graphs. Unfortunately, these approaches may not accurately reflect the consensus topological structure in a multiview setting. To address this limitation and enhance the intrinsic graph learning process, an adaptive exploration of a more appropriate consistency topological structure is required. Toward this end, we propose a novel approach called collaborative topological graph learning (CTGL) for MVC. The key idea is to adaptively discover the consistent topological structure to guide intrinsic graph learning. We achieve this by introducing an auxiliary consistency graph that formulates the topological relevance learning function. However, estimating the auxiliary consistency graph is not straightforward, as it is based on the learned view-specific graphs and requires prior availability. To overcome this challenge, we develop a collaborative learning strategy that simultaneously learns both the auxiliary consistency graph and view-specific graphs using tensor learning techniques. This strategy enables the adaptive exploration of the consistency topological structure during graph learning, resulting in more accurate clustering outcomes. Extensive experiments are provided to show the effectiveness of the proposed method. The source code can be found at https://***/CLiu272/CTGL .

关键词： Manifolds Tensors Collaboration Accuracy Learning systems Federated learning Kernel computer science Silicon Adaptive learning

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UltraEdit: instruction-based fine-grained image editing at scale 24

UltraEdit: instruction-based fine-grained image editing at s...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Haozhe Zhao Xiaojian Ma Liang Chen Shuzheng Si Rujie Wu Kaikai An Peiyu Yu Minjia Zhang Qing Li Baobao Chang National Key Laboratory for Multimedia Information Processing Peking University and School of Software and Microelectronics Peking University State Key Laboratory of General Artificial Intelligence BIGAI National Key Laboratory for Multimedia Information Processing Peking University and School of Computer Science Peking University China Department of Computer Science and Technology Tsinghua University School of Computer Science Peking University China UCLA UIUC National Key Laboratory for Multimedia Information Processing Peking University

ISBN: (纸本)9798331314385

This paper presents UltraEdit, a large-scale (~4M editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2Pix [10] and MagicBrush [71], and provide a systematic approach to producing massive and high-quality image editing samples. UltraEdit offers several distinct advantages: 1) It features a broader range of editing instructions by leveraging the creativity of large language models (LLMs) alongside in-context editing examples from human raters; 2) Its data sources are based on real images, including photographs and artworks, which provide greater diversity and reduced bias compared to datasets solely generated by text-to-image models; 3) It also supports region-based editing, enhanced by high-quality, automatically produced region annotations. Our experiments show that canonical diffusion-based editing baselines trained on UltraEdit set new records on MagicBrush and Emu-Edit benchmarks. Our analysis further confirms the crucial role of real image anchors and region-based editing data. The dataset, code, and models are available in ***/pkunlp-icler/UltraEdit.

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Incomplete multi-view clustering via local and global co-regularization

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science China(Information sciences) 2022年第5期65卷 96-111页

作者： Jiye LIANG Xiaolin LIU Liang BAI Fuyuan CAO Dianhui WANG Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Artificial Intelligence Research Institute China University of Mining and Technology Department of Computer Science and Computer Engineering La Trobe University

The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods, which try to obtain a complete and consensus clustering result from a latent subspace, have been developed to overcome this problem, most methods excessively rely on views-public instances to bridge the connection with view-private instances. When lacking sufficient views-public instances, existing methods fail to transmit the information among incomplete views effectively. To overcome this limitation, we propose an incomplete multi-view clustering algorithm via local and global co-regularization(IMVC-LG). In this algorithm, we define a new objective function that is composed of two terms: local clustering from each view and global clustering from multiple views, which constrain each other to exploit the local clustering information from different incomplete views and determine a global consensus clustering result, ***, an iterative optimization method is proposed to minimize the objective function. Finally, we compare the proposed algorithm with other state-of-the-art incomplete multi-view clustering methods on several benchmark datasets to illustrate its effectiveness.

关键词： incompleteness multi-view clustering local clustering global clustering co-regularization

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