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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Pusheng Wu, Lianwei Wang, Linyong Guo, Sensen Liu, Yang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China Research & Development Institute of Northwestern Polytechnical University Shenzhen China Chongqing Science & Technology Innovation Center of Northwestern Polytechnical University China State Key Laboratory of Integrated Service Networks Xidian University China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University China

ISBN: (纸本)9782493814104

Recently, the autoregressive framework based on large language models (LLMs) has achieved excellent performance in controlling the generated text to adhere to the required style. These methods guide LLMs through prompt learning to generate target text in an autoregressive manner. However, this manner possesses lower controllability and suffers from the challenge of accumulating errors, where early prediction inaccuracies might influence subsequent word generation. Furthermore, existing prompt-based methods overlook specific region editing, resulting in a deficiency of localized control over input text. To overcome these challenges, we propose a novel three-stage prompt-based approach for specific region editing. To alleviate the issue of accumulating errors, we transform the text style transfer task into a text infilling task, guiding the LLMs to modify only a small portion of text within the editing region to achieve style transfer, thus reducing the number of autoregressive iterations. To achieve an effective specific editing region, we adopt both prompt-based and word frequency-based strategies for region selection, subsequently employing a discriminator to validate the efficacy of the selected region. Experiments conducted on several publicly competitive datasets for text style transfer task confirm that our proposed approach achieves state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Design and Implementation of AS6802 Clock Synchronization System in TTE thernet 6

Design and Implementation of AS6802 Clock Synchronization Sy...

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6th IEEE International Conference on computer and Communication Systems, ICCCS 2021

作者： Dong, Jinlong Qiu, Zhiliang Pan, Weitao Wu, Min Cao, Jialiang State Key Laboratory of the Integrated Services Networks Xidian University Xi'an710071 China Beijing Institute of Computer Technology and Application

ISBN: (纸本)9780738126043

Based on the research of the standard AS6802 protocol standard [1] and the interpretation of the existing Ethernet time synchronization solution on the FPGA hardware design platform, we designed and implemented a set of Ethernet clock synchronization system that supports the AS6802 standard. The system has been tested in a network environment, and the synchronization accuracy is less than 60 nanoseconds. Use FPGA to record the transparent clock, replacing the MAC IP core time stamping method in the previous Ethernet time synchronization solution. Therefore, this system is more flexible for the reception and transmission of Ethernet frame data, and reduces the dynamic delay introduced by the MAC IP core, which makes the synchronization accuracy higher, and has the characteristics of low cost and easy transplantation. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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High Wind Speed Retrieval from SAR Images Using Random Forest Algorithm

High Wind Speed Retrieval from SAR Images Using Random Fores...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Yang, Wenjin Yu, Peng Cai, Xiaoying Zhong, Xiaojing He, Yuanrong College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural Resources P.R. China Beijing100081 China College of Harbour and Coastal Engineering Jimei University Xiamen361021 China

ISBN: (纸本)9781510668898

This study provides a random forest (RF) based method to retrieve high wind speeds, which takes advantage of RF's powerful nonlinear fitting ability and prevents overfitting. The high wind speed data from the Stepping Frequency Microwave Radiometer (SFMR) are used as the reference data. Compared with two traditional GMF models, the C-band cross-polarized ocean model (C-2PO) and the C-band cross-polarized coupled parameter ocean model (C-3PO), the wind speed inversion results using the RF method have been greatly improved. © 2023 SPIE.

关键词： Synthetic aperture radar

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Hierarchical Knowledge Representation of Complex Tasks Based on Dynamic Motion Primitives 7th

Hierarchical Knowledge Representation of Complex Tasks Based...

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7th International Conference on Cognitive Systems and Information Processing, ICCSIP 2022

作者： Miao, Shengyi Zhong, Daming Miao, Runqing Sun, Fuchun Wen, Zhenkun Huang, Haiming Zhang, Xiaodong Wang, Na College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Modern Post Beijing University of Posts and Telecommunications Beijing100083 China Department of Computer Science and Technology Tsinghua University Beijing100083 China College of Computer Science and Software Shenzhen University Shenzhen518060 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Application Beijing100083 China

ISBN: (纸本)9789819906161

Current service robots without learning ability are not qualified for many complex tasks. Therefore, it is very significant to decompose the complex task into repeatable execution unit. In this paper, we propose a complex task representation method based on dynamic motion primitives, and use hierarchical knowledge graph to represent the analytic results of complex tasks. To realize the execution of complex robot manipulation tasks, we decompose the semantic tasks into the minimum motion units that can be executed by the robot and combine the multi-modal information: posture, force and robot joint parameters, obtained by the sensors. We use the knowledge graph to record the end-effector required by the robot to perform different tasks and make appropriate selection of end-effector according to different needs. Finally, Taking the long sequence complex task of service scene as an example, we use UR5 robot to verify the effectiveness and feasibility of this design. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： End effectors

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A Method for Classification and Evaluation of Pilot’s Mental States Based on CNN

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computer Systems Science & Engineering 2023年第8期46卷 1999-2020页

作者： Qianlei Wang Zaijun Wang Renhe Xiong Xingbin Liao Xiaojun Tan Chengdu Institute of Computer Application Chinese Academy of SciencesChengdu610041SichuanChina School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100080China Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of ChinaGuanghan618307SichuanChina School of Air Traffic Management Civil Aviation Flight University of ChinaGuanghan618307SichuanChina Neurology Department Deyang Second People’s HospitalGuanghan618307SichuanChina

How to accurately recognize the mental state of pilots is a focus in civil aviation *** mental state of pilots is closely related to their cognitive ability in *** the cognitive ability meets the standard is related to flight ***,the pilot’s working state is unique,which increases the difficulty of analyzing the pilot’s mental *** this work,we proposed a Convolutional Neural Network(CNN)that merges attention to classify the mental state of pilots through electroencephalography(EEG).Considering the individual differences in EEG,semi-supervised learning based on improvedK-Means is used in themodel training to improve the generalization ability of the *** collected the EEG data of 12 pilot trainees during the simulated flight and compared the method in this paper with other methods on this *** method in this paper achieved an accuracy of 86.29%,which is better than 4D-aNN and HCNN *** emotion will increase the probability of fatigue appearing,and emotion recognition is also meaningful during the *** we conducted experiments on the public dataset SEED,and our method achieved an accuracy of 93.68%.In addition,we combine multiple parameters to evaluate the results of the classification network on a more detailed level and propose a corresponding scoring mechanism to display the mental state of the pilots directly.

关键词： Pilot mental state EEG attention CNN semi-supervised learning

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Laser tracking leader-follower automatic cooperative navigation system for UAVs

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International Journal of Agricultural and Biological Engineering 2022年第2期15卷 165-176页

作者： Rui Ming Zhiyan Zhou Zichen Lyu Xiwen Luo Le Zi Cancan Song Yu Zang Wei Liu Rui Jiang College of Engineering South China Agricultural University/Guangdong Laboratory for Lingnan Modern AgricultureGuangzhou 510642China College of Computer and Control Engineering Minjiang University/Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlFuzhou 350108China Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence(GDKL-AAI) Guangzhou 510642China Guangdong Engineering Research Center for Agricultural Aviation Application(ERCAAA) Guangzhou 510642China Key Laboratory of Key Technology on Agricultural Machine and Equipment South China Agricultural UniversityMinistry of Education of P.R.ChinaGuangzhou 510642China

Currently,small payload and short endurance are the main problems of a single UAV in agricultural applications,especially in large-scale *** is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative *** study proposed a laser tracking leader-follower automatic cooperative navigation system for *** leader in the cluster fires a laser beam to irradiate the follower,and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking *** on the existing kernel correlation filter(KCF)tracking algorithm,an improved KCF real-time spot tracking method was *** with the traditional KCF tracking algorithm,the recognition and tracking rate of the optimized algorithm was increased from 70%to 95%in indoor environment,and was increased from 20%to 90%in outdoor *** navigation control method was studied from two aspects:the distance coordinate transformation model based on micro-gyroscope and navigation control *** error of spot position was reduced from the maximum(3.12,−3.66)cm to(0.14,0.12)cm by correcting the deviation distance of the spot at different angles through a coordinate correction *** image coordinate conversion model was established for a complementary metal-oxide-semiconductor(CMOS)camera and laser receiving device at different mounting *** laser receiving device was divided into four regions,S0-S3,and the speed of the four regions is calculated using an uncontrollable discrete Kalman *** outdoor flight experiments of two UAVs were carried out outdoors using this *** experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm,and the coefficient of variation is *** average flight error on the Z-axis is 7.3 cm,and the coefficient of variation is *** study demonstrated the possibility

关键词： two-UAVs cooperative visual navigation laser tracking

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Msqat: A Multi-Dimension Non-Intrusive Speech Quality Assessment Transformer Utilizing Self-Supervised Representations

SSRN

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SSRN 2023年

作者： Shen, Kailai Yan, Diqun Dong, Li Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Convolutional neural networks (CNNs) have been widely utilized as the main building block for many \textit{non-intrusive} speech quality assessment (NISQA) methods. A new trend is to add a self-attention mechanism based on CNN to better capture long-term global content. However, it is not clear whether the pure attention-based network is sufficient to obtain good performance in NISQA. To this end, a framework named \textbf{M}ulti-dimension \textit{non-intrusive} \textbf{S}peech \textbf{Q}uality \textbf{A}ssessment \textbf{T}ransformer (MSQAT) is proposed. To strengthen the interactions of various speech regions between local and global, three multi-dimensional modules are devised. Additionally, speech quality varies not only in different frames, but also in different frequencies. Thus, a two-branch structure is designed to better evaluate the quality of speech. Experimental results demonstrate that the proposed MSQAT has state-of-the-art performance on three standard datasets (NISQA Corpus, Tencent Corpus, and PSTN Corpus). © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Non-Intrusive Speech Quality Assessment: A Survey

SSRN

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SSRN 2023年

作者： Shen, Kailai Yan, Diqun Hu, Jing Ye, Zhe Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Speech quality is a critical consideration for applications such as speech enhancement, coding, transmission, and synthesis. Accurately evaluating the quality of degraded speech without a reference is particularly challenging. As a result, non-intrusive speech quality assessment has been extensively researched due to its efficiency and ease of deployment. This survey comprehensively reviews these studies. Commonly used speech quality assessment databases are reviewed, as they serve as validation sets for objective metrics. Non-intrusive speech quality assessment methods are categorized and reviewed, and the performance of state-of-the-art and classical speech quality methods is compared. The survey presents an overview of classic algorithms and recent advancements in the field of non-intrusive speech quality assessment. © 2023, The Authors. All rights reserved.

关键词： Speech enhancement

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Bidirectional Weighted Co-Association-Based Clustering Ensemble Algorithm for High-Speed Train Delay Prediction 29th

Bidirectional Weighted Co-Association-Based Clustering Ensem...

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29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Zhao, Yi Bai, Muchen Ni, Panpan Jiang, Yunan Feng, Xuan Zhang, Xiaobo School of Design SouthWest Jiaotong University Chengdu611756 China School of Computing and Artificial Intelligence SouthWest Jiaotong University Chengdu611756 China Sichuan Institute of Computer Sciences Chengdu610042 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9783031425141

In recent years, high-speed trains are one of the popular research topics in the field of transportation and have attracted much attention from scholars at home and abroad in view of their high efficiency and safety features. High-speed trains are susceptible to unexpected situations of delayed arrival due to the disturbance of external factors including wind and temperature during the trip. The delay of high-speed trains has a vital impact on people's work efficiency and living standards. To solve the problem of intelligent forecast of high-speed train delay, we design and implement a clustering ensemble algorithm based on text data of bullet train operation environment monitoring. We firstly combine the concepts of cosine similarity, Jaccard similarity and information entropy, then fuse a bidirectional weighting formula, and further propose a novel Clustering Ensemble algorithm based on Bidirectional Weighted Co-association (CE-BWC). This approach consists of an inter-cluster weighting formula used to obtain the implicit generalized information by weighting each cluster between clusters, and a reliability weighting formula transferring the cluster information to the data item itself to measure the correlation degree of the data group. Experiments prove that our CE-BWC has better prediction performance for high-speed train delay and outperforms the state-of-the-art benchmark algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

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MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

MIANet: Aggregating Unbiased Instance and General Informatio...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yong Yang Qiong Chen Yuan Feng Tianlin Huang School of Computer Science and Engineering South China University of Technology Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5 i and COCO-20 i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at ***/Aldrich2y/MIANet.

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