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arXiv 2024年

作者： Wang, Zihao Ma, Le Feng, Yongsheng Pan, Xin Jin, Yuhang Zhang, Kejun Department of Computer Science Zhejiang University Hangzhou China Department of AI Music and Music Information Technology Central Conservatory of Music Beijing China Institute of Remote Sensing and Applications Beijing University of Civil Engineering and Architecture Beijing China Department of Computer Science Zhejiang University Hangzhou310027 China Department of Computer Science Zhejiang Key Laboratory of Intelligent Systems and Equipment for Digital Creativity Innovation Center of Yangtze River Delta Zhejiang University Hangzhou China

Singing voice conversion (SVC) aims to convert a singer’s voice to another singer’s from a reference audio while keeping the original semantics. However, existing SVC methods can hardly perform zero-shot due to incomplete feature disentanglement or dependence on the speaker look-up table. We propose the first open-source high-quality zero-shot SVC model SaMoye that can convert singing to human and non-human timbre. SaMoye disentangles the singing voice’s features into content, timbre, and pitch features, where we combine multiple ASR models and compress the content features to reduce timbre leakages. Besides, we enhance the timbre features by unfreezing the speaker encoder and mixing the speaker embedding with top-3 similar speakers. We also establish an unparalleled large-scale dataset to guarantee zero-shot performance, which comprises more than 1,815 hours of pure singing voice and 6,367 speakers. We conduct objective and subjective experiments to find that SaMoye outperforms other models in zero-shot SVC tasks even under extreme conditions like converting singing to animals’ timbre. The weights, code, dataset, and documents of SaMoye are publicly available on https://***/CarlWangchina/*** Codes 68Txx(Primary)14F05, 91Fxx(Secondary) Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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FusTP-FL: Enhancing Differential Federated Learning through Personalized Layers and Data Transformation

FusTP-FL: Enhancing Differential Federated Learning through ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xiong Yan Kedong Yan Chanying Huang Dan Yin Shan Xiao School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Comp. Science & Engineering Nanjing Univ. of Science & Technology Nanjing China School of Elec. & Inf. Engineering Beijing Univ. of Civil Engineering & Architecture Beijing China Fiberhome Comm. Tech. Co. Wuhan China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Federated Learning enables multiple clients to collaboratively train a model without sharing their individual data, thereby protecting local data privacy. However, attackers, such as untrusted servers, can still compromise the privacy of clients’ local training data through various inference attacks. One feasible approach to protect client privacy during training is the incorporation of differential privacy. Nevertheless, achieving an ideal level of privacy protection with differential privacy often degrades the model’s performance, significantly reducing its accuracy. To enhance model accuracy while minimizing the additional client heterogeneity introduced by differential privacy, this paper proposes a method that integrates personalized layers and data transformations, FusTP-FL. The core of our FusTP-FL is the incorporation of personalized layers and personalized data transformations within the client’s local training model, which further reduces client heterogeneity and improves model accuracy. We evaluated the model’s accuracy on six common datasets; experimental results demonstrate that the proposed FusTP-FL effectively enhances model accuracy across two different differential privacy modes (CDP and LDP), increasing it by up to 45%. Furthermore, we show that compared to PRIVATEFL, our method achieves lower client heterogeneity.

关键词： Training Differential privacy Privacy Accuracy Federated learning Computational modeling Training data Data models Servers Security

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AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks

arXiv

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arXiv 2023年

作者： Gong, Cheng Lu, Ye Dai, Surong Qian, Deng Du, Chenkun Li, Tao College of Software Nankai University Tianjin300350 China College of Computer Science Nankai University Tianjin300350 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Exploring the expected quantizing scheme with suitable mixed-precision policy is the key point to compress deep neural networks (DNNs) in high efficiency and accuracy. This exploration implies heavy workloads for domain experts, and an automatic compression method is needed. However, the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios. In this paper, we propose an end-to-end framework named AutoQNN, for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor. AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques: quantizing scheme search (QSS), quantizing precision learning (QPL), and quantized architecture generation (QAG). QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search, and then uses the differentiable neural architecture search (DNAS) algorithm to seek the layer- or model-desired scheme from the set. QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes, to the best of our knowledge. QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint. QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention, to facilitate end-to-end neural network quantization. We have implemented AutoQNN and integrated it into Keras. Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization. For 2-bit weight and activation of AlexNet and ResNet18, AutoQNN can achieve the accuracy results of 59.75% and 68.86%, respectively, and obtain accuracy improvements by up to 1.65% and 1.74%, respectively, compared

关键词： Deep neural networks

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Unleashing the Low-Precision Computation Potential of Tensor Cores on GPUs

Unleashing the Low-Precision Computation Potential of Tensor...

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International Symposium on Code Generation and Optimization (CGO)

作者： Guangli Li Jingling Xue Lei Liu Xueying Wang Xiu Ma Xiao Dong Jiansong Li Xiaobing Feng School of Computer Science and Technology University of Chinese Academy of Sciences China School of Computer Science and Engineering University of New South Wales Australia SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China College of Computer Science and Technology Jilin University China

ISBN: (数字)9781728186139

ISBN: (纸本)9781728186146

Tensor-specialized hardware for supporting low-precision arithmetic has become an inevitable trend due to the ever-increasing demand on computational capability and energy efficiency in intelligent applications. The main challenge faced when accelerating a tensor program on tensor-specialized hardware is how to achieve the best performance possible in reduced precision by fully utilizing its computational resources while keeping the precision loss in a controlled manner. In this paper, we address this challenge by proposing QUANTENSOR, a new approach for accelerating general-purpose tensor programs by replacing its tensor computations with low-precision quantized tensor computations on NVIDIA Tensor Cores. The key novelty is a new residual-based precision refinement technique for controlling the quantization errors, allowing tradeoffs between performance and precision to be made. Evaluation with GEMM, deep neural networks, and linear algebra applications shows that QUANTENSOR can achieve remarkable performance improvements while reducing the precision loss incurred significantly at acceptable overheads.

关键词： Tensors Quantization (signal) Neural networks Market research Hardware Acceleration Optimization

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Landslide Disaster Assessment with Integration of Seismic, Social Media and Remote Sensing Data 6

Landslide Disaster Assessment with Integration of Seismic, S...

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6th International Conference on Signal Processing and Information Security, ICSPIS 2023

作者： Rawat, Deepak Sharma, M.L. Kumar, Roshan Saxena, Nishant Kumar, Atul Kadir, Abdul Indian Institute of Technology Roorkee Centre of Excellence in Disaster Mitigation and Management Uttarakhand Roorkee India Indian Institute of Technology Roorkee Department of Earthquake Engineering Roorkee Uttarakhand Roorkee India Miami College Henan University Department of Electronics and Information Technology Kaifeng Henan China Indian Institute of Technology Roorkee Department of Architecture and Planning Uttarakhand Roorkee India Indian Institute of Technology Roorkee Computer Science and Engineering Department Uttarakhand Roorkee India

ISBN: (纸本)9798350329599

On February 7, 2021, a rockfall in the Rishiganga Valley killed about 200 people. For the integration of seismic, social media, and remote sensing, a case study on this tragedy has been conducted. This study explores the role of social media in managing Himalayan landslides in conjunction with remote sensing data analytics. We look at how these technologies can enhance damage management, quick mobilization, and early warning systems. A literature review and case studies of the successful use of these technologies in other disaster-prone areas will support these suggestions. The intention is to equip decision-makers and experts in disaster management with the practical knowledge they need to effectively prepare for and respond to Himalayan landslide events, saving lives and minimizing the impact of natural hazards. © 2023 IEEE.

关键词： Remote sensing

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Employment Data Acquiring Method Based on WeChat Scan Code

Employment Data Acquiring Method Based on WeChat Scan Code

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International Conference on Systems and Informatics (ICSAI)

作者： Bianping Su Yunsong Wang Yantao He Longqing Zhang Jiayu He Jieming Lin School of Computer Guangdong University of Science & Technology Dongguan Guangdong China Xi'an International Inland Port Investment and Development Group Co. Ltd. Xi'an Chan-Ba International Port Management Committee Xi'an China School of Science Xi'an University of Architecture and Technology Xi'an Chin

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Based on the current employment situation and methods for obtaining unemployment and employment data in china, a new method for obtaining unemployment and employment data was constructed using the Naive Bayes classification principle in machine learning by WeChat scan data of travelers. This method divides the unemployment or employment status of individuals using prior and posterior probabilities. The evaluation index values of the experimental results on the dataset demonstrate the effectiveness of the method, and the accuracy of determining the employment status of travelers through mobile phone calls has increased by 22.9%. A strategy of data preprocessing combined with this method was proposed for large-scale data and preliminary discussions were conducted. The new method of obtaining unemployment and employment data has opened up new avenues for traditional statistical data acquisition, providing strong intelligent support for the government to dynamically grasp the employment situation in real time.

关键词： Social networking (online) Posterior probability Employment Machine learning Message services Real-time systems Mobile handsets Naive Bayes methods Unemployment Informatics

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Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data

Cox-ResNet: A Survival Analysis Model Based on Residual Neur...

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IEEE International Conference on Networking, Sensing and Control

作者： Qingyan Yin Wangwang Chen Ruiping Wu Zhi Wei School of Science Xi'an University of Architecture and Technology Xi'an China Department of Computer Science New Jersey Institute of Technology Newark USA

ISBN: (纸本)9781665472449

Survival analysis with genomics data provides a deep understanding of biological processes related to prognosis and disease progression at the molecular level. However, high-dimensional small sample genome data causes computational challenges in survival analysis. To address this problem of overfitting and poor interpretation of existing models, we applied the deep learning technology to genome data and proposed a survival analysis model based on an image-based residual neural network model, called Cox-ResNet. High-dimensional gene expression data was embedded into 2D images according to gene positions on chromosomes, and then a residual network model based on Cox proportional hazards was introduced to perform survival analysis. We demonstrated the performance of Cox-ResNet on five datasets of different cancer types from TCGA, comparing it with the cutting-edge survival analysis methods. The Cox-ResNet model not only shows better performance in prediction accuracy, but also biologically interpretable, by generating heat-maps and prognostic genes for high-risk groups with the guided Grad-Cam visualization method. By performing protein-protein interaction network analysis, we examined hub genes and their biological functions for the bladder cancer. These findings confirm that Cox-ResNet model provides a new solution for discovering the driver genes of poor cancer prognosis.

关键词： Analytical models Biological system modeling Computational modeling Genomics Data models Gene expression Bioinformatics

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Towards Deep Application-Network Integration: architectures, Progress and Opportunities

arXiv

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arXiv 2024年

作者： Serracanta, Berta Gao, Kai Ros-Giralt, Jordi Rodriguez-Natal, Alberto Contreras, Luis M. Yang, Richard Cabellos, Albert UPC BarcelonaTech Spain The Department of Computer Science and Techonology Tsinghua University China Qualcomm Europe Inc. The Enterprise Networking CTO team at Cisco Technology Expert at Telefonica CTIO Computer Science and Electrical Engineeringsince The Computer Architecture Department Universitat Politècnica de Catalunya Spain

With the rise of a new generation of applications (e.g., virtual and augmented reality, artificial intelligence, etc) demanding stringent performance requirements, the need for networking solutions and architectures that can enable a higher Quality of Experience (QoE) is becoming increasingly important. While jointly optimizing application and network may increase the applications' QoE and simultaneously improve the utilization of network resources, such a paradigm has had limited success in real production networks. However, with the combination of revolutionary trends in (1) compute processing demands, (2) networking capabilities, and (3) sustainable business models, it is high time the community explores the full potential of deeper integration between application and network. In this paper, recent trends observed over the past few years are systematically reviewed. These include the paradigm shift in modern communication services towards computing-driven applications, such as on-site AI training, advances in programmable network technologies like Software Defined Networking (SDN), and new business models incentivizing collaboration and cooperation between parties. Following this, successful scenarios that benefit from various forms of deeper network-application integration are reported, highlighting their considerable potential. A unified framework is then introduced, providing an overview of possible architecture paradigms for network-application integration and bringing awareness to existing abstractions, mechanisms, tools, and their potential combinations. The paper concludes with a discussion of several remaining challenges in building practical network-application integrated systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Quality of service

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ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection

arXiv

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arXiv 2022年

作者： Yin, Junbo Zhou, Dingfu Zhang, Liangjun Fang, Jin Xu, Cheng-Zhong Shen, Jianbing Wang, Wenguan School of Computer Science Beijing Institute of Technology China Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China SKL-IOTSC CIS University of Macau China ReLER AAII University of Technology Sydney Australia

Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination. Scene-level methods tend to lose local details that are crucial for recognizing the road objects, while point/voxel-level methods inherently suffer from limited receptive field that is incapable of perceiving large objects or context environments. Considering region-level representations are more suitable for 3D object detection, we devise a new unsupervised point cloud pre-training framework, called ProposalContrast, that learns robust 3D representations by contrasting region proposals. Specifically, with an exhaustive set of region proposals sampled from each point cloud, geometric point relations within each proposal are modeled for creating expressive proposal representations. To better accommodate 3D detection properties, ProposalContrast optimizes with both inter-cluster and inter-proposal separation, i.e., sharpening the discriminativeness of proposal representations across semantic classes and object instances. The generalizability and transferability of ProposalContrast are verified on various 3D detectors (i.e., PV-RCNN, CenterPoint, PointPillars and PointRCNN) and datasets (i.e., KITTI, Waymo and ONCE). Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection 3rd

A Benchmark of Ocular Disease Intelligent Recognition: One S...

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3rd BenchCouncil International Symposium on Benchmarking, Measuring, and Optimizing, Bench 2020

作者： Li, Ning Li, Tao Hu, Chunyu Wang, Kai Kang, Hong College of Computer Science Nankai university Tianjin300350 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Science Beijing100190 China Beijing Shanggong Medical Technology Co. Ltd. Beijing100176 China

ISBN: (纸本)9783030710576

In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields. © 2021, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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