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arXiv 2022年

作者： Zhang, Kai Zhang, Kun Liu, Qi Huang, Zhenya Zhang, Mengdi Wu, Wei Cheng, Mingyue Chen, Enhong Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China State Key Laboratory of Cognitive Intelligence Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Meituan Beijing China Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China Hefei China

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models. © 2022, CC BY.

关键词： Sentiment analysis

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The State-of-the-Art Review on applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and analysis

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engineering 2021年第6期7卷 845-856页

作者： Yue Hou Qiuhan Li Chen Zhang Guoyang Lu Zhoujing Ye Yihan Chen Linbing Wang Dandan Cao Beijing Key Laboratory of Traffic Engineering Beijing University of TechnologyBeijing 100124China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China National Center for Materials Service Safety University of Science and Technology BeijingBeijing 100083China School of Transportation Southeast UniversityNanjing 211189China Department of Civil and Environmental Engineering Virginia TechBlacksburgVA 24061USA

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and *** service quality and service life are of great importance for civil engineers as they directly affect the regular service for the ***,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation *** pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface *** technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning *** review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

关键词： Pavement monitoring and analysis The state-of-the-art review Intrusive sensing Image processing techniques Machine learning methods

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Delay recovery model for high-speed trains with compressed train dwell time and running time

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Railway engineering Science 2020年第4期28卷 424-434页

作者： Yafei Hou Chao Wen Ping Huang Liping Fu Chaozhe Jiang National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong UniversityChengdu 610031China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong UniversityChengdu 610031China Intelligent Transport Systems Center Wuhan University of TechnologyWuhan 430070China

Modeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of *** this study,the effects of two train operation adjustment actions on train delay recovery were explored using train operation records from scheduled and actual train ***,the modeling data were sorted to extract the possible influencing factors under two typical train operation adjustment actions,namely the compression of the train dwell time at stations and the compression of the train running time in *** regression methods were then employed to determine the importance of the influencing factors corresponding to the train delay recovery time,namely the delay time,the scheduled supplement time,the running interval,the occurrence time,and the place where the delay occurred,under the two train operation adjustment ***,the gradient-boosted regression tree(GBRT)algorithm was applied to construct a delay recovery model to predict the delay recovery effects of the train operation adjustment actions.A comparison of the prediction results of the GBRT model with those of a random forest model confirmed the better performance of the GBRT prediction model.

关键词： High-speed train Delay recovery Train operation adjustment actions Gradient-boosted regression tree

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Modeling Offline Knowledge Evolution Effect for Online Knowledge Tracing

Modeling Offline Knowledge Evolution Effect for Online Knowl...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiaonan Zeng Shuanghong Shen Zhenya Huang Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei China Hefei Comprehensive National Science Center School of Computer Science and Technology Hefei Normal University & Institute of Artificial Intelligence Hefei China State Key Laboratory of Cognitive Intelligence & iFLYTEK AI Research (Central China) iFLYTEK Co. Ltd Hefei China

Knowledge Tracing (KT) focuses on tracing stu-dents' evolving knowledge state in the process of question-answering, which has been widely applied to online learning systems. Existing KT methods leverage students' online question-answering logs to infer their knowledge state and predict their subsequent performance. However, these methods only focus on students' online behaviors without considering the fact that students' knowledge also evolves offline. Specifically, students have plenty of offline learning actions that also make changes in their knowledge state, which is called the offline knowledge evolution effect in this paper. However, these offline actions are not recorded by online learning systems, therefore leading to unreliable knowledge tracing results. To alleviate this problem, we propose a novel model named offline-aware knowledge tracing (OKT) that considers students' offline knowledge evolution for KT. In O KT, we introduce a novel concept named offline trait to represent student's offline learning action preference. Based on the preference, the model evaluates offline knowledge evolution, and apply the evolution to generate a new knowledge state for students' future performance prediction. To the end, we conduct experiments on three real-world benchmark datasets and demonstrate that OKT achieves state-of-the-art performance. Besides, the experimental results indicate that considering offline actions can help interpret the KT results.

关键词： Learning systems Predictive models Benchmark testing Behavioral sciences

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Pushing the Performance Limit of Scene Text Recognizer without Human Annotation

arXiv

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arXiv 2022年

作者： Zheng, Caiyuan Li, Hui Rhee, Seon-Min Han, Seungju Han, Jae-Joon Wang, Peng School of Computer Science Ningbo Institute Northwestern Polytechnical University China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China China Korea Republic of

Scene text recognition (STR) attracts much attention over the years because of its wide application. Most methods train STR model in a fully supervised manner which requires large amounts of labeled data. Although synthetic data contributes a lot to STR, it suffers from the real-to-synthetic domain gap that restricts model performance. In this work, we aim to boost STR models by leveraging both synthetic data and the numerous real unlabeled images, exempting human annotation cost thoroughly. A robust consistency regularization based semi-supervised framework is proposed for STR, which can effectively solve the instability issue due to domain inconsistency between synthetic and real images. A character-level consistency regularization is designed to mitigate the misalignment between characters in sequence recognition. Extensive experiments on standard text recognition benchmarks demonstrate the effectiveness of the proposed method. It can steadily improve existing STR models, and boost an STR model to achieve new state-of-the-art results. To our best knowledge, this is the first consistency regularization based framework that applies successfully to STR. © 2022, CC BY.

关键词： Character recognition

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Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting

arXiv

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arXiv 2024年

作者： Xiong, Qi Tang, Kai Ma, Minbo Zhang, Ji Xu, Jie Li, Tianrui School of Computing and Artificial Intelligence Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education National Engineering Laboratory of Integrated Transportation Big Data Application Technology Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China School of Computing The University of Leeds LeedsLS2 9JT United Kingdom

Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in existing LTSF methods caused by the inadequate modeling of Temporal Dependencies within the Target (TDT). To address this issue, we propose a novel and generic temporal modeling framework, Temporal Dependency Alignment (TDAlign), that equips existing LTSF methods with TDT learning capabilities. TDAlign introduces two key innovations: 1) a loss function that aligns the change values between adjacent time steps in the predictions with those in the target, ensuring consistency with variation patterns, and 2) an adaptive loss balancing strategy that seamlessly integrates the new loss function with existing LTSF methods without introducing additional learnable parameters. As a plug-and-play framework, TDAlign enhances existing methods with minimal computational overhead, featuring only linear time complexity and constant space complexity relative to the prediction length. Extensive experiments on six strong LTSF baselines across seven real-world datasets demonstrate the effectiveness and flexibility of TDAlign. On average, TDAlign reduces baseline prediction errors by 1.47% to 9.19% and change value errors by 4.57% to 15.78%, highlighting its substantial performance improvements. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series analysis

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A cross Transformer for image denoising

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Information Fusion 2024年 102卷

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Xi'anShaanxi710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'anShaanxi710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi'an710129 China Department of Electrical Engineering and the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. © 2023 Elsevier B.V.

关键词： Image denoising

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Graph Attention Multi-Layer Perceptron

arXiv

引用

arXiv 2022年

作者： Zhang, Wentao Yin, Ziqi Sheng, Zeang Li, Yang Ouyang, Wen Li, Xiaosen Tao, Yangyu Yang, Zhi Cui, Bin School of CS & Key Laboratory of High Confidence Software Technologies Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Tencent Inc China Beijing Institute of Technology China

Graph neural networks (GNNs) have achieved great success in many graph-based applications. However, the enormous size and high sparsity level of graphs hinder their applications under industrial scenarios. Although some scalable GNNs are proposed for large-scale graphs, they adopt a fixed k-hop neighborhood for each node, thus facing the over-smoothing issue when adopting large propagation depths for nodes within sparse regions. To tackle the above issue, we propose a new GNN architecture — Graph Attention Multi-Layer Perceptron (GAMLP), which can capture the underlying correlations between different scales of graph knowledge. We have deployed GAMLP in Tencent with the Angel platform 1, and we further evaluate GAMLP on both real-world datasets and large-scale industrial datasets. Extensive experiments on these 14 graph datasets demonstrate that GAMLP achieves state-of-the-art performance while enjoying high scalability and efficiency. Specifically, it outperforms GAT by 1.3% regarding predictive accuracy on our large-scale Tencent Video dataset while achieving up to 50× training speedup. Besides, it ranks top-1 on both the leader-boards of the largest homogeneous and heterogeneous graph (i.e., ogbn-papers100M and ogbn-mag) of Open Graph Benchmark 2 Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Network analysis and spatial agglomeration of China’s high-speed rail: A dual network approach

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Chinese Physics B 2021年第1期30卷 612-622页

作者： Wei Wang Wen-Bo Du Wei-Han Li Lu(Carol)Tong Jiao-E Wang School of Electronic and Information Engineering Beihang UniversityBeijing 100191China National Engineering Laboratory for Big Data Application Technologies for Comprehensive Traffic Beijing 100191China School of Cyber Science and Technology Beihang UniversityBeijing 100191China Research Institute of Frontier Science Beihang UniversityBeijing 100191China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China

China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban *** HSR system can be represented as different types of networks in terms of the nodes and various relationships(i.e.,linkages) between them. In this paper, we first introduce a general dual network model, including a physical network(PN)and a logical network(LN) to provide a comparative analysis for China’s high-speed rail network via complex network theory. The PN represents a layout of stations and rail tracks, and forms the basis for operating all trains. The LN is a network composed of the origin and destination stations of each high-speed train and the train flows between them. China’s high-speed railway(CHSR) has different topological structures and link strengths for PN in comparison with the LN. In the study, the community detection is used to analyze China’s high-speed rail networks and several communities are found to be similar to the layout of planned urban agglomerations in China. Furthermore, the hierarchies of urban agglomerations are different from each other according to the strength of inter-regional interaction and intra-regional interaction, which are respectively related to location and spatial development strategies. Moreover, a case study of the Yangtze River Delta shows that the hub stations have different resource divisions and are major contributors to the gap between train departure and arrival flows.

关键词： China’s high-speed rail dual network network analysis urban agglomeration

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On the precise quantification of the impact of a single discretionary lane change on surrounding traffic

arXiv

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arXiv 2024年

作者： Hou, Kangning Zou, Jia Zheng, Fangfang Liu, Xiaobo He, Zhengbing School of Transportation and Logistics Southwest Jiaotong University Western Hi-tech Zone Chengdu Sichuan611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Western Hi-tech Zone Chengdu Sichuan611756 China Senseable City Lab Massachusettes Institute of Technology CambridgeMA02421 United States

Lane-changing is a critical maneuver of vehicle driving, and a comprehensive understanding of its impact on traffic is essential for effective traffic management and optimization. Unfortunately, existing studies fail to adequately distinguish the impact of lane changes from those resulting from natural traffic dynamics. Additionally, there is a lack of precise methods for measuring the spatial extent and duration of the impact of a single discretionary lane change, as well as a definitive metric to quantify the overall spatiotemporal impact. To address these gaps, this study introduces a quantitative indicator called the Corrected Travel Distance Bias (CTDB), which accounts for variable speeds due to inherent traffic dynamics, providing a more accurate assessment of lane-changing impacts. A comprehensive methodology is developed to compare vehicle trajectory data before and after lane-changing events, measuring both the magnitude and spatiotemporal extent of the lane-changing impact. The results, based on the Zen traffic data from Japan, indicate that the impact of a lane change in the target lane lasts an average of 23.8 seconds, affecting approximately 5.6 vehicles, with a CTDB value of -10.8 meters. In contrast, in the original lane, the impact lasts 25 seconds, affects 5.3 vehicles, and yields a CTDB value of 4.7 meters. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicles

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