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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Fei Shen, Yuchen Wu, Zhen Dai, Xinyu National Key Laboratory for Novel Software Technology Nanjing University China School of Information and Software Engineering University of Electronic Science and Technology of China China

Multi-Label Few-Shot Aspect Category Detection (FS-ACD) is a new sub-task of aspect-based sentiment analysis, which aims to detect aspect categories accurately with limited training instances. Recently, dominant works use the prototypical network to accomplish this task, and employ the attention mechanism to extract keywords of aspect category from the sentences to produce the prototype for each aspect. However, they still suffer from serious noise problems: (1) due to lack of sufficient supervised data, the previous methods easily catch noisy words irrelevant to the current aspect category, which largely affects the quality of the generated prototype;(2) the semantically-close aspect categories usually generate similar prototypes, which are mutually noisy and confuse the classifier seriously. In this paper, we resort to the label information of each aspect to tackle the above problems, along with proposing a novel Label-Driven Denoising Framework (LDF). Extensive experimental results show that our framework achieves better performance than other state-of-the-art methods. Our code and datasets are available at https://***/1429904852/LDF. © 2022 Association for Computational Linguistics.

关键词： Sentiment analysis

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Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning

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Fundamental Research 2023年第4期3卷 544-556页

作者： Wei Wang Kuo Chen Xing Ma Jinhong Guo School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Materials Science and Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China The M.O.E.Key Laboratory of Laboratory Medical Diagnostics The College of Laboratory MedicineChongqing Medical UniversityChongqing 400016China

The combination of upconverting nanoparticles(UCNPs)and immunochromatography has become a widely used and promising new detection technique for point-of-care testing(POCT).However,their low luminescence efficiency,non-specific adsorption,and image noise have always limited their progress toward practical ***,artificial intelligence(AI)has demonstrated powerful representational learning and generalization capabilities in computer *** report for the first time a combination of AI and upconversion nanoparticle-based lateral flow assays(UCNP-LFAs)for the quantitative detection of commercial internet of things(IoT)*** universal UCNPs quantitative detection strategy combines high accuracy,sensitivity,and applicability in the field detection *** using transfer learning to train AI models in a small self-built database,we not only significantly improved the accuracy and robustness of quantitative detection,but also efficiently solved the actual problems of data scarcity and low computing power of POCT ***,the trained AI model was deployed in IoT devices,whereby the detection process does not require detailed data preprocessing to achieve real-time inference of quantitative *** validated the quantitative detection of two detectors using eight transfer learning models on a small *** AI quickly provided ultra-high accuracy prediction results(some models could reach 100%accuracy)even when strong noise was ***,the high flexibility of this strategy promises to be a general quantitative detection method for optical *** believe that this strategy and device have a scientific significance in revolutionizing the existing POCT technology landscape and providing excellent commercial value in the in vitro diagnostics(IVD)industry.

关键词： Upconverting nanoparticles Lateral flow assays Transfer learning Internet of medical things Portable fluorescent sensor

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ScIAMC:Single-Cell Imputation via adaptive matrix completion 10

ScIAMC:Single-Cell Imputation via adaptive matrix completion

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, Shuai Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

Single-cell sequencing techniques are often impacted by technical noise, leading to the generation of very sparse expression matrices. This technical noise is referred to as dropouts and poses as a major challenge for downstream analysis. In this study, we introduce scIAMC (single-cell imputation via adaptive parameter matrix completion), which is based on matrix completion theory to recover missing values in expression matrices. To expedite the algorithm's running time and avoid any parameter tuning on data, we formulated an optimization problem. Our approach led to an enhanced cell population identification and minimal errors, while also restoring biological landscapes that were damaged by these dropouts. © 2023 IEEE.

关键词： Cell proliferation

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ESR: Optimizing Gene Feature Selection for scRNA-seq Data 10

ESR: Optimizing Gene Feature Selection for scRNA-seq Data

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Huang, Tao Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis. © 2023 IEEE.

关键词： Entropy

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YOLOv5-LW: Lightweight UAV Object Detection Algorithm Based on YOLOv5 13th

YOLOv5-LW: Lightweight UAV Object Detection Algorithm Based ...

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13th EAI International Conference on Mobile Networks and Management, MONAMI 2023

作者： Xiao, He Zhao, Kai Xie, Xiaomei Song, Peilong Dong, Siwen Yang, Jiahui School of Software Engineering Jiangxi University of Science and Technology Jiangxi Nanchang330013 China Nanchang Key Laboratory of Virtual Digital Factory and Cultural Communications Nanchang330013 China

ISBN: (纸本)9783031554704

UAV object detection task is a highly popular computer vision task, where algorithms can be deployed on unmanned aerial vehicles (UAVs) for real-time object detection. However, YOLOv5’s performance for UAV object detection is not entirely satisfactory due to the small size of the detected objects and the problem of occlusion. To address these two issues in the YOLOv5 algorithm, we propose the YOLOv5-LW algorithm model. Building upon YOLOv5, we replace the FPN-PAN network structure with the FPN-PANS structure. This modification helps mitigate the issue of feature disappearance for small objects during the training process while reducing the model parameters and computational complexity. Additionally, within the FPN-PANS structure, we employ a multistage feature fusion approach instead of the original feature fusion module. This approach effectively corrects the erroneous information generated during the upsampling stage for certain objects. Finally, we replace the SPPF module with the SPPF-W module to further increase the receptive field while maintaining almost unchanged parameters. We conducted multiple experiments and demonstrate that YOLOv5-LW performs exceptionally well in lightweight small object detection tasks using the VisDrone dataset. Compared to YOLOv5, YOLOv5-LW achieves a 4.7% improvement in mean average precision (mAP), reduces the model size by 40%, and decreases the parameters by 40%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Object detection

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A Comprehensive Review on Deep Learning System Testing 24th

A Comprehensive Review on Deep Learning System Testing

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Ying Shan, Chun Liu, Zhen Liao, Shuyan Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789819615476

Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Improved YOLOv5-Based Method for Tiny Defect Detection on Steel Plate 12

Improved YOLOv5-Based Method for Tiny Defect Detection on St...

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12th International Conference on Intelligent Control and Information Processing, ICICIP 2024

作者： Yu, Jiwei Ji, Zhihang Yu, Yang Liu, Muhua Wang, Lin Zhao, Xuhui Zheng, Ruijuan School of Information Engineering Henan University of Science and Technology Luoyang China 360 Digital Security Technology Group Co. Ltd Longmen Laboratory Luoyang China School of Software Henan University of Science and Technology Luoyang China

ISBN: (纸本)9798350308020

In this paper, a novel detection method based YOLOv5 is proposed, which is suitable for detecting tiny defects on steel plate. First, we introduce the Bi-Level Routing Attention (BRA) module to learn global and local detail features in the im age simultaneously, which can more efficiently extract tiny defect feature information. Second, a feature enhancement module, Spatial Context Pyramid, is designed to enhance the tiny defect feature by contrasting the spatial context information obtained from feature fusion procedure. Extensive experimental results show that our method achieves comparable performance on the NEU-DET dataset. © 2024 IEEE.

关键词： Defects

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Non-Autoregressive Sign Language Translation: A Transformer Encoder-only Approach for Enhanced Multimodal Integration 24

Non-Autoregressive Sign Language Translation: A Transformer ...

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2024 International Conference on Advanced Robotics, Automation engineering and Machine Learning, ARAEML 2024

作者： Du, Yao Xie, Pan Peng, Taiying Hu, Xiaohui School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400717116

Sign language translation aims to transform continuous sign language videos into text, thereby mapping sign language actions onto natural language. Current research primarily focuses on the Transformer's encoder-decoder architecture, employing an autoregressive (AR) approach for SLT. This one-by-one token generation method has a slow speed to translate. We design a Non-Autoregressive Sign Language Translation (NA-SLT) model based on Transformer Encoder. The proposed model consists of three components: The text-encoder, sign-encoder, and sign-Text-encoder. Both the text-encoder and sign-encoder utilize the Encoder to model single-modality data. The sign-Text-encoder effectively engages with multi-modal data, producing high-quality corresponding text after minimal refinement iterations. Moreover, during model training, we devise a double-mask strategy. In addition to traditional random masking, the schedule allocates mask probabilities based on each word's frequency, ensuring long-Tail tokens have an increased chance of being masked and receiving extensive training. We evaluate the model on PHOENIX-2014T, and the results indicate that NA-SLT approaches the performance of AR models on BLEU (21.06) and ROUGE(49.59) metrics while significantly improving translation speed (5.4x-7.9x). © 2024 ACM.

关键词： Metadata

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Improving Pedestrian Attribute Recognition with Dense Feature Pyramid and Mixed Pooling 13th

Improving Pedestrian Attribute Recognition with Dense Featur...

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13th EAI International Conference on Mobile Networks and Management, MONAMI 2023

作者： Xiao, He Zou, Chen Chen, Yaosheng Gong, Sujia Dong, Siwen School of Software Engineering Jiangxi University of Science and Technology Jiangxi Nanchang330013 China Nanchang Key Laboratory of Virtual Digital Factory and Cultural Communications Nanchang330013 China

ISBN: (纸本)9783031554704

In the field of computer vision, pedestrian attribute recognition plays a crucial role in pedestrian detection and pedestrian re-identification. However, this task faces challenges such as blurry images, difficulty in recognizing fine-grained features, and overlooking relationships between pedestrian attributes. To address these challenges, we propose a novel method for pedestrian attribute recognition. Our method is based on convolutional neural networks and incorporates a feature pyramid structure that is specifically designed for the task of pedestrian attribute recognition (PAR). Additionally, we enhance feature information by employing multi-scale feature fusion. Furthermore, our proposed AIIM module facilitates interactions between different attributes by establishing both remote dependencies and short-range dependencies. Through comprehensive experimentation, we have validated the effectiveness of our method and achieved state-of-the-art results. Specifically, our method has achieved impressive average accuracies (mA) of 86.27%, 83.45%, and 81.56% on well-known datasets such as PETA, RAP, and PA100k, respectively. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Convolution

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Deep Learning Based Take-Over Performance Prediction and Its Application on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Liu, Weimin Li, Qingkun Wang, Wenjun Wang, Zhenyuan Zeng, Chao Cheng, Bo State Key Laboratory of Automotive Safety and Energy Center for Intelligent Connected Vehicles and Transportation School of Vehicle and Mobility Tsinghua University Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we aim to predict minimum anticipated collision time (min ACT), an indicator of drivers' take-over performance, in expectation of promoting safer take-overs via deep learning, so that drivers' state detriment of take-over safety could be adjusted accordingly with intelligent human-machine interaction algorithms predictably. By incorporating multi-source information including drivers' state, drivers' demographics, surrounding traffic features as well as driver-vehicle interaction characteristics, network model “ACTNet” was proposed to facilitate continuous estimation. Depthwise separable convolution and non-local self-attention were utilized to prevent overfitting and establish spatial dependency over fixation heatmap, respectively. To overcome data distribution imbalance, class balanced loss was used in conjunction with regression loss to realize more accurate predictions. Driving simulator experiment was conducted with dataset collected for the subsequent verification of the proposed algorithm. Potentialities of deep learning methods were highlighted for take-over studies, contributing to the design of intelligent human-machine interaction systems in conditional automation. Our findings present a valid method of deep learning in predicting drivers' take-over performance and meanwhile have implications for the development of intelligent adaptive take-over time budget regulation and dynamic drivers' state adjustment algorithms. IEEE

关键词： Automation

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