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International Conference on Computer and Communications (ICCC)

作者： Zhe Zhang Jie Liu Lihua Chi Xinhai Chen Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.

关键词： Recurrent neural networks Computational modeling Bit error rate Semantics Speech recognition Feature extraction Task analysis

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Improving unsupervised domain adaptation by reducing bi-level feature redundancy

arXiv

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arXiv 2020年

作者： Wang, Mengzhu Zhang, Xiang Lan, Long Wang, Wei Tan, Huibin Luo, Zhigang Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha China Institute for Quantum State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China DUT-RU International School of Information Science & Engineering Dalian University of Technology DalianLiaoning116000 China Department of Science and Technology on Parallel and Distributed Processing National University of Defense Technology Changsha China

Reducing feature redundancy has shown beneficial effects for improving the accuracy of deep learning models, thus it is also indispensable for the models of unsupervised domain adaptation (UDA). Nevertheless, most recent efforts in the field of UDA ignore this point. Moreover, main schemes realizing this in general independent of UDA purely involve a single domain, thus might not be effective for cross-domain tasks. In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way. For the first level, we try to ensure compact domain-specific features with a transferable decorrelated normalization module, which preserves specific domain information whilst easing the side effect of feature redundancy on the sequel domain-invariance. In the second level, domain-invariant feature redundancy caused by domain-shared representation is further mitigated via an alternative brand orthogonality for better generalization. These two novel aspects can be easily plugged into any BN-based backbone neural networks. Specifically, simply applying them to ResNet50 has achieved competitive performance to the state-of-the-arts on five popular benchmarks. Our code will be available at https://***/dreamkily/gUDA. © 2020, CC BY.

关键词： Deep learning

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BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images

arXiv

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arXiv 2022年

作者： Cong, Runmin Yang, Haowei Jiang, Qiuping Gao, Wei Li, Haisheng Wang, Cong Zhao, Yao Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China The Department of Computer Science City University of Hong Kong Hong Kong The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Information Science and Engineering Ningbo University Ningbo315211 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China The Distributed and Parallel Software Lab Huawei Technologies Shenzhen518129 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary-Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. © 2022, CC BY-NC-SA.

关键词： COVID-19

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Multi-agent Fault-tolerant Reinforcement Learning with Noisy Environments

Multi-agent Fault-tolerant Reinforcement Learning with Noisy...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Canhui Luo Xuan Liu Xinning Chen Juan Luo College of Computer Science and Electronic Engineering Hunan University P. R. China The Science and Technology on Parallel and Distributed Processing Laboratory (PDL) P. R. China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Multi-agent reinforcement learning system is used to solve the problem that agents achieve specific goals in the interaction with the environment through learning policies. Almost all existing multi-agent reinforcement learning methods assume that the observation of the agents is accurate during the training process. It does not take into account that the observation may be wrong due to the complexity of the actual environment or the existence of dishonest agents, which will make the agent training difficult to succeed. In this paper, considering the limitations of the traditional multi-agent algorithm framework in noisy environments, we propose a multi-agent fault-tolerant reinforcement learning (MAFTRL) algorithm. Our main idea is to establish the agent's own error detection mechanism and design the information communication medium between agents. The error detection mechanism is based on the autoencoder, which calculates the credibility of each agent's observation and effectively reduces the environmental noise. The communication medium based on the attention mechanism can significantly improve the ability of agents to extract effective information. Experimental results show that our approach accurately detects the error observation of the agent, which has good performance and strong robustness in both the traditional reliable environment and the noisy environment. Moreover, MAFTRL significantly outperforms the traditional methods in the noisy environment.

关键词： Training Fault tolerance Working environment noise Fault tolerant systems Reinforcement learning Robustness Noise measurement

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Secure and Fast Decision Tree Evaluation on Outsourced Cloud Data 2nd

Secure and Fast Decision Tree Evaluation on Outsourced Cloud...

引用

2nd International Conference on Machine Learning for Cyber Security, ML4CS 2019

作者： Liu, Lin Su, Jinshu Chen, Rongmao Chen, Jinrong Sun, Guangliang Li, Jie School of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China National University of Defense Technology Changsha China

ISBN: (纸本)9783030306182

Decision trees are famous machine learning classifiers which have been widely used in many areas, such as healthcare, text classification and remote diagnostics, etc. The service providers usually host a decision tree model on the cloud server and provide some classification service for clients to use such a model remotely. In such a scenario, the model is a valuable asset to the cloud which should not be disclosed to the clients, while the query data and classification results are private to the client. To solve such a problem, we propose several building blocks, i.e., secure comparison and secure polynomial calculation, in a two-cloud model. Based on these building blocks, we design a privacy-preserving decision tree evaluation scheme. Compared with the most recent works, our scheme can fully protect the tree model and clients’ data privacy simultaneously. Besides, our scheme also supports offline service users which is essential to the system’s scalability. Moreover, through theoretical analysis and real-world experimental test, it is oblivious that our scheme is quite efficient. © 2019, Springer Nature Switzerland AG.

关键词： Decision trees

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CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer Topology 33rd

CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer T...

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33rd International Conference on Advanced Information Networking and Applications, AINA 2019

作者： Zheng, Li-ming Long, Wen-feng Liu, Yu-Jia Sun, Wei-dong Department of Message Communication Armed Police Officer Academy Chengdu China National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783030150341

Data distribution is a key technology for resources convergence and sharing in distributed environment. To better meet the requirement for real time data distribution in the dynamic network, a trace routing algorithm named CRAWL based on the hybrid two-layered topology is put forward. The algorithm contains an overlay topology named CBDLO, upper of which consists of multiple distributed balanced binary trees corresponding to different properties and the lower of which is an unstructured topology. CRAWL forwards the data on the lower unstructured topology in the form of random walk, so that the data can be sent to the corresponding upper topology entry. It also includes a matching algorithm named CDM for the parallel matching data properties on the upper distributed and balanced binary tree and transmitting the matched data to the nodes that are interested in the data. The experimental results show that the algorithm can effectively support large-scale data distribution in a dynamical network, reduce distribution overhead and matching delays. © 2019, Springer Nature Switzerland AG.

关键词： Routing algorithms

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An Effective Design to Improve the Efficiency of DPUs on FPGA

An Effective Design to Improve the Efficiency of DPUs on FPG...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Yutian Lei Qingyong Deng Saiqin Long Shaohui Liu Sangyoon Oh The Key Laboratory of Hunan Province for Internet of Things and Information Security The School of Computer Science Science and Technology on Parallel and Distributed Processing Laboratory Xiangtan University Xiangtan China The Artificial Intelligence Center PengCheng Laboratory shenzhen China Ajou University Suwon-si Republic of Korea

The following topics are dealt with: cloud computing; learning (artificial intelligence); resource allocation; security of data; mobile computing; neural nets; optimisation; parallel processing; scheduling; and storag... 详细信息

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

关键词： Deep learning Schedules Multicore processing Logic gates Throughput Acceleration Field programmable gate arrays

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Succinct representations in collaborative filtering: A case study using wavelet tree on 1,000 cores 20

Succinct representations in collaborative filtering: A case ...

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20th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Peng, Xiangjun Wang, Qingfeng Sun, Xu Gong, Chunye Wang, Yaohua User-Centric Computing Group University of Nottingham Ningbo China China Department of Computer Science and Technology National University of Defense Technology China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China

ISBN: (纸本)9781728126166

User-Item (U-I) matrix has been used as the dominant data infrastructure of Collaborative Filtering (CF). To reduce space consumption in runtime and storage, caused by data sparsity and growing need to accommodate side information in CF design, one needs to go beyond the U-I Matrix. In this paper, we took a case study of Succinct Representations in Collaborative Filtering, rather than using a U-I Matrix. Our key insight is to introduce Succinct Data Structures as a new infrastructure of CF. Towards this, we implemented a User-based K-Nearest-Neighbor CF prototype via Wavelet Tree, by first designing a Accessible Compressed Documents (ACD) to compress U-I data in Wavelet Tree, which is efficient in both storage and runtime. Then, we showed that ACD can be applied to develop an efficient intersection algorithm without decompression, by taking advantage of ACD's characteristics. We evaluated our design on 1,000 cores of Tianhe-II supercomputer, with one of the largest public data set ml-20m. The results showed that our prototype could achieve 3.7 minutes on average to deliver the results. © 2019 IEEE.

关键词： Collaborative filtering

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Towards to real world vehicle privacy protection: A new dataset and benchmark

引用

Pattern Recognition 2026年 169卷

作者： Jiayi Lin Chengming Zou Long Lan Yong Luo Yue Yu Yaowei Wang Wei Zeng Yonghong Tian School of Computer Science and Technology Wuhan University of Technology Wuhan 430070 Hubei China Hubei Key Laboratory of Transportation Internet of Things Technology Wuhan University of Technology Wuhan 430070 Hubei China Peng Cheng Laboratory Shenzhen 518000 Guangdong China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha 410073 Hunan China School of Computer Science and Engineering Nanyang Technological University 639798 Singapore Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha 410073 Hunan China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

The vehicle privacy protection plays a vital role in releasing or sharing of traffic videos. License plate, as the identifiable mark of a vehicle, contains the most sensitive information for a vehicle. Therefore, masking the license plates is a common way to protect the privacy of corresponding vehicles. However, in the real world scenarios, it is often hard to locate the small and shifting license plates, and therefore precise and cost-effective privacy protection is quite challenging. To address this problem in surveillance video, we fully explore all available spatio-temporal cues and design bidirectional Kalman filter model in the consecutive frames to locate missing license plates. To verify effectiveness of the proposed benchmark, we build a new License Plates Privacy-preserving Dataset (LPPD) collected from various scenes with diverse privacy and utility annotations. We demonstrate that our proposed method show very promising capability of privacy protection on the real world dataset without sacrificing its utility.

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Audio Tagging by Cross Filtering Noisy Labels

arXiv

引用

arXiv 2020年

作者： Zhu, Boqing Xu, Kele Kong, Qiuqiang Wang, Huaimin Peng, Yuxing Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha410073 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

—High quality labeled datasets have allowed deep learning to achieve impressive results on many sound analysis tasks. Yet, it is labor-intensive to accurately annotate large amount of audio data, and the dataset may contain noisy labels in the practical settings. Meanwhile, the deep neural networks are susceptive to those incorrect labeled data because of their outstanding memorization ability. In this paper, we present a novel framework, named CrossFilter, to combat the noisy labels problem for audio tagging. Multiple representations (such as, Logmel and MFCC) are used as the input of our framework for providing more complementary information of the audio. Then, though the cooperation and interaction of two neural networks, we divide the dataset into curated and noisy subsets by incrementally pick out the possibly correctly labeled data from the noisy data. Moreover, our approach leverages the multitask learning on curated and noisy subsets with different loss function to fully utilize the entire dataset. The noisy-robust loss function is employed to alleviate the adverse effects of incorrect labels. On both the audio tagging datasets FSDKaggle2018 and FSDKaggle2019, empirical results demonstrate the performance improvement compared with other competing approaches. On FSDKaggle2018 dataset, our method achieves state-of-the-art performance and even surpasses the ensemble models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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