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arXiv 2024年

作者： Wei, Tong Li, Hao-Tian Li, Chun-Shu Shi, Jiang-Xin Li, Yu-Feng Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China

Recent research on fine-tuning vision-language models has demonstrated impressive performance in various downstream tasks. However, the challenge of obtaining accurately labeled data in real-world applications poses a significant obstacle during the fine-tuning process. To address this challenge, this paper presents a Denoising Fine-Tuning framework, called DEFT, for adapting vision-language models. DEFT utilizes the robust alignment of textual and visual features pre-trained on millions of auxiliary image-text pairs to sieve out noisy labels. The proposed framework establishes a noisy label detector by learning positive and negative textual prompts for each class. The positive prompt seeks to reveal distinctive features of the class, while the negative prompt serves as a learnable threshold for separating clean and noisy samples. We employ parameter-efficient fine-tuning for the adaptation of a pre-trained visual encoder to promote its alignment with the learned textual prompts. As a general framework, DEFT can seamlessly fine-tune many pre-trained models to downstream tasks by utilizing carefully selected clean samples. Experimental results on seven synthetic and real-world noisy datasets validate the effectiveness of DEFT in both noisy label detection and image classification tasks. Our source code is available at https://***/HotanLee/DeFT. © 2024, CC BY.

关键词： Visual languages

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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Research on Zenith Tropospheric Delay Model Based on TCN Improving HGPT2 Model 14th

Research on Zenith Tropospheric Delay Model Based on TCN Imp...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Shi, Danyang Zhao, Jumin Wu, Fanming Yan, Liangquan Feng, Ran Zhang, Xinfang Zhao, Jinhua College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

The zenith tropospheric delay (ZTD) obtained by global navigation satellite system (GNSS) atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3 (GPT3) model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3_ZTD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network (TCN) for improving the hourly global pressure and temperature 2 (HGPT2) model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2_ZTD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCN_ZTD obtained from the proposed model is higher than GPT3_ZTD and HGPT2_ZTD when using the GNSS_ZTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious. © 2024, Aerospace information Research Institute.

关键词： Fourier analysis

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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A general machine learning framework for solving real applications with a texture perception case study 6

A general machine learning framework for solving real applic...

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6th International Conference on Image, Vision and Computing, ICIVC 2021

作者： Zhang, Qin Liu, Jing Sun, Huali Liu, Jun College of Science and Information Qingdao Agricultural University Qingdao China Shandong Provincial Key Laboratory of Computer Network Jinan China

ISBN: (纸本)9781665443685

Machine learning, especially deep learning, has made great achievements in application domains such as computer vision, speech recognition, machine translation, etc. This great success not only depends on the progress of model design but also on the emergence of large-scale annotated benchmark datasets. However, in some application domains, it is difficult to apply machine learning methods to solve the new arising problems due to the lack of standardized and annotated datasets. People have to find their way from the very beginning of the data collecting, cleaning, and labeling. Unfortunately, there is not a guiding framework to deal with such a situation. This paper pays attention to it and proposes a general machine learning framework to solve the pratical problems from the scratch. It contains two main stages implemented by an unsupervised clustering method and a semi-supervised learning method representatively. In the first stage, an unsupervised clustering method is utilized to find representative data samples which are more important and can be used to do data cleaning and manual labeling. In the second stage, a semi-supervised method is adopted to predict the labels for the rest data samples and to construct a larger annotated dataset. A case study in the texture perception field has been done to confirm the effectiveness and efficiency of the proposed application framework. © 2021 IEEE.

关键词： Speech recognition

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Distributed Physical Device Connection Relationship Discovery Technology Based on Traffic information 7th

Distributed Physical Device Connection Relationship Discover...

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7th International Conference on information Science, Communication and Computing, ISCC 2023

作者： Wang, Jiaxing Rui, Junyan Niu, Huibo Chang, Yuan Hu, Jiawen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Haidian District Beijing100876 China School of Computer Science and Technology of Anhui University Anhui China China Aerospace Science and Industry Network Information Development Co. Ltd. Beijing China University of Waterloo ON Canada

ISBN: (纸本)9789819971602

Cloud data center networks have a complex structure, in which multiple network protocols are utilised to participate in the composition of the data center. How to achieve topology discovery and mapping in the face of complex, multi-structured cloud data centre network devices has become a hot research problem. network tomography (NT), as an end-to-end measurement technique of network boundaries, enables protocol-independent network measurements without the collaboration of internal nodes. The emergence of this technique provides a new research idea for cloud data center network topology mapping. In this paper, we propose a topology discovery technique based on the delay covariance matrix and a topology optimisation method based on the delay peak map, and conduct simulations for these two algorithms to verify the feasibility of the protocol-independent topology discovery technique applied to data center topology discovery. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Covariance matrix

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace information Research Institute.

关键词： Long short-term memory

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Research on the Distribution Characteristics of Standardized Big Data Resources Based on Data Visualization - - Taking Standardization Practice in Shandong Province as an Example 9

Research on the Distribution Characteristics of Standardized...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Sun, Jinyang Peng, Dongdong Lu, Zhe Zhai, Mengran Qi, Ning Qi, Yuanhua SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. Jinan China Jinan Municipal Bureau of Big Data Jinan China Shandong Institute of Economy and Informatization Development Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Standardized big data, as a product of the combination of big data and standardization work, objectively reflects the status quo and trends of standardization work. An in-depth study of the distribution characteristics of standardized big data resources has important value for improving the efficiency of standardization. Therefore, this paper explores the characteristics of the distribution of standardization resources from the dimensions of spatial and temporal distribution, industry distribution, standard type distribution, and distribution of think tank experts through visual analysis methods. The study shows that: (1) the distribution of standardization resources in Shandong Province presents significant hierarchical and geographical characteristics;(2) from the viewpoint of the distribution of industry types and standard types, the distribution of standardization resources in Shandong Province presents a typical "long-tailed distribution";(3) the distribution of think-tank experts presents obvious "agglomeration effect". © 2024 IEEE.

关键词： Data consistency

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM 14th

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models. © 2024, Aerospace information Research Institute.

关键词： Support vector machines

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Semantic-Aware Prompt Learning for Multimodal Sarcasm Detection

Semantic-Aware Prompt Learning for Multimodal Sarcasm Detect...

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

作者： Guangjin Wang Bao Wang Fuyong Xu Zhenfang Zhu Peipei Wang Ru Wang Peiyu Liu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model’s "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model’s ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method.

关键词： Semantics Benchmark testing Speech enhancement Signal processing Vectors Acoustics Context modeling

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