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Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Guan Quecairang Yang Maojia Rou Te Se Chajia Ci Zhenjiacuo School of Computer Science Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Question intent classification is an important part of the Tibetan Q&A system. It is a challenging task to use deep learning methods to classify the intent of Tibetan questions. This paper aims to: (1) The traditional Tibetan question intent classification scheme is too coarse, which is not conducive to the problem intent recognition task in the question answering system. (2) The Tibetan question intent type is highly dependent on the local spatial characteristics with interrogative virtual words as the core, which leads to the problem of poor classification effect, and a novel Tibetan question intent classification scheme is proposed, and each question intent type is defined. On this basis, through a detailed analysis of the semantic features and writing rules of Tibetan interrogators, a semantic information labeling algorithm for Tibetan interrogators was designed, and an BERT_RCNN intentional classification model for Tibetan questions was trained by combining the advantages of Tibetan Pre-Training (TiBERT) and recurrent convolutional neural network. The F1 value of this method can reach 94.87% on the test set, which can accurately identify user needs, compress the search space and optimize model decision-making, and significantly improve the performance of the Tibetan Q&A system.

关键词： Deep learning Intent recognition Semantics Decision making Writing Question answering (information retrieval) Classification algorithms Labeling Convolutional neural networks Robots

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Improving Automatic Piano Transcription by Refined Feature Fusion and Weighted Loss 9th

Improving Automatic Piano Transcription by Refined Feature ...

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9th Conference on Sound and Music Technology, CSMT 2021

作者： Zhao, Jiahao Wu, Yulun Wen, Liang Ma, Lianhang Ruan, Linping Wang, Wantao Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China CETHIK Group Co. Ltd Hangzhou311199 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Automatic Piano Transcription is to transcribe raw audio files into annotated piano rolls. In recent studies, jointly estimating pitch, onset, offset, and velocity of each note is commonly used. The previous state-of-the-art "Onsets and Frames" model chooses to concatenate the output of both onset and offset sub-tasks with extracted features to improve the frame-wise pitch detection, which is, however, low-efficiency in our opinion. In this paper, we proposed an improved piano transcription model by doing feature fusion and loss weighting. Our proposed model outperforms the baselines by a large margin. It also shows comparable performance with the state-of-the-art "High-Res PT" model in note metrics and outperforms it in frame metrics with an F1 score of 90.27%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Musical instruments

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AUITestAgent: Automatic Requirements Oriented GUI Function Testing

arXiv

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arXiv 2024年

作者： Hu, Yongxiang Wang, Xuan Wang, Yingchuan Zhang, Yu Guo, Shiyu Chen, Chaoyi Wang, Xin Zhou, Yangfan Meituan Shanghai China School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

The Graphical User Interface (GUI) is how users interact with mobile apps. To ensure it functions properly, testing engineers have to make sure it functions as intended, based on test requirements that are typically written in natural language. While widely adopted manual testing and script-based methods are effective, they demand substantial effort due to the vast number of GUI pages and rapid iterations in modern mobile apps. This paper introduces AUITestAgent, the first automatic, natural language-driven GUI testing tool for mobile apps, capable of fully automating the entire process of GUI interaction and function verification. Since test requirements typically contain interaction commands and verification oracles. AUITestAgent can extract GUI interactions from test requirements via dynamically organized agents. Then, AUITestAgent employs a multi-dimensional data extraction strategy to retrieve data relevant to the test requirements from the interaction trace and perform verification. Experiments on customized benchmarks 1 demonstrate that AUITestAgent outperforms existing tools in the quality of generated GUI interactions and achieved the accuracy of verifications of 94%. Moreover, field deployment in Meituan has shown AUITestAgent’s practical usability, with it detecting 4 new functional bugs during 10 regression tests in two months. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphical user interfaces

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Two-stage Vehicle Pair Dispatch in Multi-hop Ridesharing 26

Two-stage Vehicle Pair Dispatch in Multi-hop Ridesharing

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wei, Xiaobo Li, Peng Huang, Weiyi Liu, Zhiyuan Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350331684

Ridesharing benefits the economy and the environment. In multi-hop ridesharing, passengers are permitted to switch vehicles within a single trip, extending the flexibility of conventional ridesharing. Nonetheless, vehicle dispatch is a difficult issue in multi-hop ridesharing. We subdivide the vehicle dispatching problem into the vehicle pairing problem and the request selection problem within a vehicle pair. To address these subproblems, we propose a two-stage framework for vehicle pair dispatching. In the initial stage, we model the vehicle pairing problem as a maximum vehicle-vehicle matching problem in a general graph, which differs from the conventional vehicle-request matching problem in a bipartite graph. The vehicle pairing algorithm is proposed to efficiently solve the vehicle pairing problem. In the second stage, we model the request selection problem as a multidimensional knapsack problem (d-KP) and propose an LP-relaxation request selection algorithm with an approximation ratio 1/5. Experiments conducted on a real-world dataset demonstrate the economic benefit of our proposed two-stage framework. © 2023 IEEE.

关键词： Vehicles

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Quality-Oriented Task Assignment for Heterogeneous Users in Mobile Crowdsensing 26

Quality-Oriented Task Assignment for Heterogeneous Users in ...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Chen, Kang Li, Peng Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350331684

Mobile crowdsensing (MCS) is a potential technology for large-scale data collection. This technology requires the platform to recruit users to complete tasks in specific areas. A vital issue in MCS is task assignment, and most existing task assignment efforts consider only a single user type, which is not reasonable in real scenarios. Task assignment becomes more complicated when the platform tries to assign tasks to heterogeneous users with a limited budget in the platform. In this paper, we consider a quality-oriented task assignment for heterogeneous users problem. Professional users have a high sensing quality with a high cost, and normal users have a low sensing quality with a low cost First, we model the sensing capabilities and the costs of different users and formulate the quality-oriented task assignment for heterogeneous users problem. Then, we design the integer linear programming form and prove that the problem is NP-hard. By verifying the submodularity of the objective function, we present a greedy algorithm. Considering the inefficiency of the algorithm, we design two genetic algorithms to improve the total sensing quality. Finally, we evaluate the proposed algorithms under different cost cases based on a real dataset The results show that our proposed algorithm performs well under different cost distribution scenarios. © 2023 IEEE.

关键词： Genetic algorithms

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ViT-based Classification of Oracle Bones for Periodization of the Shang Dynasty

ViT-based Classification of Oracle Bones for Periodization o...

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Li, Bang Ding, Zengmao Zheng, Rongxin Han, Shengwei Zhang, Han Zhang, Zhan Guo, An Wang, Nan Gao, Feng Liu, Yongge School of Computer & Information Engineering Anyang Normal University Henan Anyang455000 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Henan Anyang455000 China International Joint Research Laboratory for Perception Data Intelligent Processing of Henan Anyang Normal University Henan Anyang455000 China

ISBN: (纸本)9781510671881

Oracle bone inscriptions are the oldest systematically written characters in China, and their periodization and chronology are essential tasks in historical research. However, current deep-learning models face significant challenges when they are used to recognize oracle bone images directly. These models can only achieve an accuracy of no more than 45%. As the research on the periodization of oracle bones mainly relies on the characters inscribed on the bones, a domain knowledge-based oracle bone classification model is proposed that improves the recognition accuracy of Vision Transformer (ViT) by 5.28%. The proposed approach incorporates results obtained from oracle bone character detection to provide a more effective recognition object. Moreover, a novel model with a style network and category embedding is proposed in this paper that focuses on characters, resulting in a notable improvement in recognition accuracy of 16.01%. Our study reveals the importance of domain knowledge in the classification of historical documents, and it presents new insights into using artificial intelligence for assisting in historical document organization tasks. © 2023 SPIE. All rights reserved.

关键词： Domain Knowledge

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The Research of Retinopathy Image Recognition Method Based on Vit 5

The Research of Retinopathy Image Recognition Method Based o...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Xu, Zongyu Xu, Xuebin Huang, Zihao School of Computer Science and Technology Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China

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Towards Combating Frequency Simplicity-biased Learning for Domain Generalization 38

Towards Combating Frequency Simplicity-biased Learning for D...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： He, Xilin Hu, Jingyu Lin, Qinliang Luo, Cheng Xie, Weicheng Song, Siyang Khan, Muhammad Haris Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at AdvFrequency. © 2024 Neural information processing systems foundation. All rights reserved.

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Efficient QoS Data Prediction Based on Tensor Kernel Paradigm-Tensor Decomposition

Efficient QoS Data Prediction Based on Tensor Kernel Paradig...

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International Conference on Networking and Network Applications (NaNA)

作者： Hong Xia Jing Xu Qingyi Dong Hui Jia Yanping Chen Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

In the Mobile Edge Computing (MEC) environment, the prediction efficiency is low when user recommendation is based on Quality of Service (QoS) data due to network environment and other factors. Traditional methods use Collaborative Filtering (CF) or tensor decomposition methods to mine the relationship between historical QoS data for prediction, but there are problems with prediction accuracy and efficiency. Given this, this paper proposes an efficient QoS data prediction method (CTT) combining tensor kernel paradigm-tensor decomposition. The method first introduces the tensor kernel norm to approximate the tensor rank function to better capture the spatio-temporal correlation of real QoS data; Second, tensor decomposition is combined to address inefficiencies. Experimental results on the public QoS dataset WS-Dream show that the method significantly improves the runtime while guaranteeing prediction accuracy and applies to scenarios with different data missing densities.

关键词： Tensors Accuracy Runtime Multi-access edge computing Computational modeling Quality of service Predictive models

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Named Entity Recognition of Electronic Medical Records based on BERT-BiLSTM-Biaffine Model

Named Entity Recognition of Electronic Medical Records based...

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2023 Asia-Pacific Conference on Image processing, Electronics and computers, IPEC 2023

作者： Wang, Peng Gu, Jinguang Institute of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Prov. Key Laboratory of Intelligent Information Processing and Real Time Industrial Systems Wuhan University of Science and Technology Wuhan China

In the case of the specific task of identifying named entities within electronic medical record, it is hard to determine the boundary of nested entities, and existing NER systems have insufficient decoding performance. Based on the pre training model BERT, this paper introduces a novel network structure called Biaffine Layer using a bidirectional LSTM layer. The network uses a dual affine attention mechanism for semantic information learning, which can better interact with the semantic information of entity heads and entity tails, thereby achieving better recognition results for entities. Due to the sparsity of named entity datasets and the uneven distribution of entity categories, traditional binary cross entropy loss functions require multiple rounds of training to decode entities. In this paper, we have modified the binary cross entropy loss function to make the proposed model faster decode the entities that need to be identified. The model performs well, according to the experimental findings. The approach suggested in this paper offers a fresh approach to the NER issue raised by electronic medical records, and it is anticipated to considerably boost the effectiveness and caliber of clinical medical research. © Published under licence by IOP Publishing Ltd.

关键词： Decoding

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