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arXiv 2021年

作者： Liu, Jing Zhuang, Bohan Chen, Peng Shen, Chunhua Cai, Jianfei Tan, Mingkui The School of Software Engineering South China University of Technology China The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China The Pazhou Laboratory Guangzhou China The School of Computer Science the University of Adelaide Australia The Faculty of Information Technology Monash University Australia The College of Computer Science and Technology Zhejiang University Hangzhou China

Network pruning and quantization are proven to be effective ways for deep model compression. To obtain a highly compact model, most methods first perform network pruning and then conduct quantization based on the pruned model. However, this strategy may ignore that the pruning and quantization would affect each other and thus performing them separately may lead to sub-optimal performance. To address this, performing pruning and quantization jointly is essential. Nevertheless, how to make a trade-off between pruning and quantization is non-trivial. Moreover, existing compression methods often rely on some pre-defined compression configurations (i.e., pruning rates or bitwidths). Some attempts have been made to search for optimal configurations, which however may take unbearable optimization cost. To address these issues, we devise a simple yet effective method named Single-path Bit Sharing (SBS) for automatic loss-aware model compression. To this end, we consider the network pruning as a special case of quantization and provide a unified view for model pruning and quantization. We then introduce a single-path model to encode all candidate compression configurations, where a high bitwidth value will be decomposed into the sum of a lowest bitwidth value and a series of re-assignment offsets. Relying on the single-path model, we introduce learnable binary gates to encode the choice of configurations and learn the binary gates and model parameters jointly. More importantly, the configuration search problem can be transformed into a subset selection problem, which helps to significantly reduce the optimization difficulty and computation cost. In this way, the compression configurations of each layer and the trade-off between pruning and quantization can be automatically determined. Extensive experiments on CIFAR-100 and ImageNet show that SBS significantly reduces computation cost while achieving promising performance. For example, our SBS compressed MobileNetV2 achieves

关键词： Encoding (symbols)

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Non-rigid 3D shape classification based on low-level features

Non-rigid 3D shape classification based on low-level feature...

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Chinese Intelligent Systems Conference, CISC 2018

作者： Wu, Yujuan Li, Haisheng Du, Yujia Cai, Qiang School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China

ISBN: (纸本)9789811322877

Non-rigid 3D shape classification is an important issue in digital geometry processing. In this paper, we propose a novel non-rigid 3D shape classification method using Convolutional Neural Networks (CNNs) based on the scale-invariant heat kernel signature (SIHKS). Firstly, SIHKS feature is extracted and we can get a matrix for every 3D shape. Then CNNs is employed to shape classification. The matrix of 3D shapes can be the input of CNNs. Finally, we can obtain the category probability of 3D shapes. Experimental results demonstrate the proposed method can get better results compared with SVM. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Support vector machines

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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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Research on function-behavior-structure-based software engineering methodology for software defined systems

Space Habitation

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Space Habitation 2025年第1期1卷

作者： Yang Liu Jiuhao Wang Yahui Li Weichuan Dong Lanxue Dang School of Computer and Information Engineering Henan University Kaifeng 475004 PR China Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng 475004 PR China School of Information Engineering Zhengzhou College of Finance and Economics Zhengzhou 450000 PR China School of Medicine Case Western Reserve University Cleveland OH 44022 USA

Software Defined Systems (SDS) separate hardware from software, allowing for rapid adaptation to demand changes via reconfiguration. Traditional software engineering methods struggle with function reuse, system reconfiguration, and maintaining and extending systems, failing to meet the diverse needs of software defined applications. This paper introduces the Function-Behavior-Structure-based Software engineering (FBS-SE) design method for platform and intelligent SDS, based on the “function-behavior-structure” model. Leveraging structured and object-oriented software engineering principles, we design systems at three levels function, behavior, and structure tailored to the specific characteristics and requirements of SDS. To accommodate multi-business requirements, we decompose basic functional units at the functional layer, enabling function-to-behavior mapping. At the behavioral layer, we implement a fine-grained division, defining both the sequence and relationship of algorithmic steps to facilitate behavior-to-structure mapping. Furthermore, we refine our system design to enhance reconfiguration capabilities and functional reuse. The software defined satellite (also known as intelligent satellite) prototype demonstrates that the FBS-SE-based design and optimization method effectively adapts to diverse and changing system requirements. It significantly reduces hardware-software coupling, enhances system function scalability, improves system behavior maintainability, and boosts system structure reusability. This has important theoretical significance for the intelligent software engineering, and has important theoretical and practical value for optimizing the development of SDS.

关键词： Function-behavior-structure Model Software Defined System Software engineering Intelligent Satellite Software Defined Satellite

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Investigating the influence of wetland area change on environmental sustainable development goals: evidence from 31 provinces of China

Environmental and Sustainability Indicators

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Environmental and Sustainability Indicators 2025年

作者： Shisong Cao Xiaoxuan Li Jie Jiang Linlin Lu Mingyi Du Wen Song School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing 100044 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China

Wetland ecosystems play an important role in maintaining ecological balance and regulating climate, as well as are crucial for the sustainable development of our environment. However, how variations in wetland types and area impact the attainment of Environmental Sustainable Development Goals (E-SDGs), e.g., SDGs 6 (clean water and sanitation), SDG 13 (climate action), and SDG 15 (life on land) are still not well understood. With this in mind, the present study investigates the facilitating and inhibiting effects of the rate of change of wetland area ( RCW ) on E-SDGs. We analyzed the spatial and temporal characteristics of RCW over 31 provinces in China from 2005 to 2020 using global 30 m annual wetland data. In addition, the Spearman coefficient model was utilized to explore the interaction and lag effect between RCW and E-SDG target achievement. Furthermore, how geo-economic settings and wetland type influence the interaction between RCW and E-SDGs was investigated. Results show that increases in RCW can promote achieving E-SDGs, except for saline type. The influence of RCW on SDG 6.6 (aquatic ecosystem protection) between 2010 and 2020 showed a promoting effect in general ( ρ -value = 0.248**), especially in 2015 ( ρ -value = 0.352**). The impact of wetland area changes on SDG 6.3 (water quality improvement, t -value = 0.033) may not be immediately discernible but will gradually manifest over time. RCW exhibited a positive correlation with SDG 6.3 (improved water quality) in West ( ρ -value = 0.368*) and Northeast ( ρ -value = 0.619**) regions. Conversely, no significant correlation was observed in the East and Central regions. Our results emphasize the importance of rationalizing the structure and proportion of wetland types to promote sustainable development. Additionally, targeted regional strategies need to be developed to maximize the positive results of wetland area restoration in promoting sustainable development.

关键词： Environmental Sustainable Development Goals Wetland ecosystem Spatiotemporal changes Synergies and trade-offs

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Enhancing aspect-based sentiment analysis with BERT-driven context generation and quality filtering

Natural Language Processing Journal

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Natural Language Processing Journal 2024年 7卷

作者： Chuanjun Zhao Rong Feng Xuzhuang Sun Lihua Shen Jing Gao Yanjie Wang School of Information Shanxi University of Finance and Economics Taiyuan 030006 Shanxi China Economic Big Data Shanxi Province Key Laboratory Shanxi University of Finance and Economics Taiyuan 030006 Shanxi China Shanxi Information Technology Application Innovation Engineering Research Center Taiyuan 030006 Shanxi China

Fine-grained sentiment analysis, commonly referred to as aspect-based sentiment analysis (ABSA), has garnered substantial attention in both academic and industrial circles. ABSA focuses on unveiling the sentiment orientation associated with specific entities or attributes within textual data, resulting in a more precise depiction of intricate emotional nuances. However, due to the extensive range of applications for ABSA, certain domains face challenges such as constrained dataset sizes and the absence of exhaustive, high-quality corpora, leading to issues like few-shot learning and resource scarcity scenarios. To address the issue of limited training dataset sizes, one viable approach involves the utilization of text-based context generation to expand the dataset. In this study, we amalgamate Bert-based text generation with text filtering algorithms to formulate our model. Our model fully leverages contextual information using the Bert model, with a particular emphasis on the interrelationships between sentences. This approach effectively integrates the relationships between sentences and labels, resulting in the creation of an initial data augmentation corpus. Subsequently, filtering algorithms have been devised to enhance the quality of the initial augmentation corpus by eliminating low-quality generated data, ultimately yielding the final text-enhanced dataset. Experimental findings on the Semeval-2014 Laptop and Restaurant datasets demonstrate that the enhanced dataset enhances text quality and markedly boosts the performance of models for aspect-level sentiment classification.

关键词： Low resources Context-based text generation Text filtering Aspect-level sentiment classification

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OSAF_e: One-Stage Anchor Free Object Detection Method Considering Effective Area 9th

OSAF_e: One-Stage Anchor Free Object Detection Method Consid...

引用

9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Zhang, Yong Zhang, Lizong Rao, Zhihong Duan, Guiduo Wang, Chunyu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China China Electronic Technology Cyber Security Co. Ltd. Chengdu610000 China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9783030596040

The task of object detection is to identify the bounding box of the object and its corresponding category in images. In this paper, we propose a new one-stage anchor free object detection algorithm OSAF_e, with the consideration of effective mapping area. A feature extraction network is used to obtain high level feature, and the true bounding box of the object in the original image is mapped to the grid of feature map, in order to perform category prediction and bounding box regression. The proposed algorithm is evaluated with the Pascal Voc dataset, and the experiments indicate that it has a better result. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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Research on the Assistant Decision-making of Gas Regulator Based on Case-based Reasoning

Research on the Assistant Decision-making of Gas Regulator B...

引用

第32届中国控制与决策会议

作者： Sijie Li Yahui Wang Yinxing Dong School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing North Hua chuang Vacuum Technology Co Ltd

In order to improve the efficiency of fault decision-making of gas surge tank, this paper proposes a casebased reasoning(CBR) for assistant decision-making of gas surge tank. On the basis of the real case of surge tank fault, the characteristic attributes of the case are extracted, and the case is represented by the frame representation method. Through analytic hierarchy process, local similarity calculation model and global similarity calculation model, the assistant decision-making method of gas surge tank based on case-based reasoning is established. Finally, the source case and target case are analyzed, and the results are proved. The application of this method in assistant decision-making of gas surge tank fault has practical significance.

关键词： gas surge tank assistant decision-making case-based reasoning similarity calculation analytic hierarchy process frame representation

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How does data augmentation affect privacy in machine learning?

arXiv

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arXiv 2020年

作者： Yu, Da Zhang, Huishuai Chen, Wei Yin, Jian Liu, Tie-Yan School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Microsoft Research Asia China

It is observed in the literature that data augmentation can significantly mitigate membership inference (MI) attack. However, in this work, we challenge this observation by proposing new MI attacks to utilize the information of augmented data. MI attack is widely used to measure the model's information leakage of the training set. We establish the optimal membership inference when the model is trained with augmented data, which inspires us to formulate the MI attack as a set classification problem, i.e., classifying a set of augmented instances instead of a single data point, and design input permutation invariant features. Empirically, we demonstrate that the proposed approach universally outperforms original methods when the model is trained with data augmentation. Even further, we show that the proposed approach can achieve higher MI attack success rates on models trained with some data augmentation than the existing methods on models trained without data augmentation. Notably, we achieve 70.1% MI attack success rate on CIFAR10 against a wide residual network while previous best approach only attains 61.9%. This suggests the privacy risk of models trained with data augmentation could be largely underestimated. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Heterogenous Multi-Source Fusion for Ship Trajectory Complement and Prediction with Sequence Modeling

Heterogenous Multi-Source Fusion for Ship Trajectory Complem...

引用

IEEE International Conference on data Science in Cyberspace (DSC)

作者： Changmeng Zheng Qi Peng Xuemiao Xu Key Laboratory of Big Data and Intelligent Robot School of Software Engineering South China University of Technology Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Effectively monitoring ships and discovering abnormal ship trajectory in time is necessary for marine traffic supervision. The basic work of discovering the ship's abnormal trajectory is to predict the ship's navigation dynamically. Previous works in ship trajectory prediction are basically concern on single-source data, for example, the AIS data. These methods ignore the relations between different sources which may improve the performance of predicting ship trajectory. We propose a neural sequence model based on heterogeneous multisource fusion for ship trajectory completion and prediction. Our method makes better utilization of AIS, GPS and ARPA radar information to predict ship trajectory precisely. We construct a dataset which contains about 8 million ship trajectory samples and the experiments demonstrate that our multi-source fusion model gains promising results.

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