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3rd International Symposium on Advances in Informatics, Electronics and Education, ISAIEE 2022

作者： Zhang, Yueqing Yu, Zhi Wang, Wei Bu, Jiajun Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China School of Software Technology Zhejiang University Zhejiang Hangzhou China

ISBN: (纸本)9781665463577

One kind of traffic light recognition method based on super resolution technology applied in intelligent eyeglasses system is proposed in this paper. Take advantage of the high recognition accuracy of the latest hot target detectors and the ability of super-resolution technology which can increase image resolution dramatically, so as to achieve the accurate identification of pedestrian traffic lights. The lightweight adjustment operation can ensure the real-time performance of the model on the mobile terminal and correctly assist the safe passage of visually impaired people at traffic intersections. © 2022 IEEE.

关键词： Glass

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A Sentiment Classification Model Based on Attention TIG-CNN-BiGRU

A Sentiment Classification Model Based on Attention TIG-CNN-...

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2023 International Conference on Culture-Oriented science and technology, CoST 2023

作者： Li, Peng Liang, Tianmian An, Ning Zhang, Lichen Wu, Xiaojun Wang, Xiaoming Lu, Junling Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China School of Computer Science Shaanxi Normal University Xi'an710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi'an710119 China Xi'An Key Laboratory of Cultural Tourism Resources Development and Utilization Xi'an710062 China

ISBN: (纸本)9798350357998

The uniqueness of folk song expression forms and complex semantic relationships, and the existing models are not good at recognizing the emotion category of folk song lyrics, so an attention TIG-CNN-BiGRU based emotion classification model is proposed. That is, we first propose a text vector representation based on TF-IDF feature weighted pretrained Glove word embedding to extract important features by assigning different weights, and then combine CNN, BiGRU and attention mechanism to extract local features using multi-scale convolutional kernel CNN, BiGRU to capture semantic dependencies of long sequences and attention mechanism to assign high weights to key features to make the model have better lyrics recognition ability. Experiments comparing the model with various baseline methods on the lyrics dataset show that the model outperforms other lyrics sentiment classification models in terms of sentiment classification accuracy and various other metrics. © 2023 IEEE.

关键词： Deep neural networks

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News-driven financial warning based on label information attention 5

News-driven financial warning based on label information att...

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

作者： Zhilin, Ren Long, Chen Xiaohua, Huang Wenjing, Wang Cai, Xu Wei, Zhao Ziyu, Guan Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Most existing news-driven stock market prediction methods ignore the potential relationship between financial news and stocks. The complex relationship can help us to improve the accuracy of algorithmic trading systems. Therefore, we propose a deep learning method for financial warning by fusing Stock Label Information (SLI). We extract events from news texts and fuse stock information together as feature vectors, using neural networks to model the underlying relationship between news and stocks. Experimental results show that our method outperforms other baseline methods in experiments on TPX500 and TPX100 datasets. CCS CONCEPTS •Computing methodologies •Artificial intelligence •Natural language processing © 2022 ACM.

关键词： Deep learning

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Imperceptible and Reliable Adversarial Attack 1

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4th International Conference on Frontiers in Cyber Security, FCS 2021

作者： Zhang, Jiawei Wang, Jinwei Luo, Xiangyang Ma, Bin Xiong, Naixue School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou450001 China Shandong Provincial Key Laboratory of Computer Networks and Qilu University of Technology Jinan250353 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States

ISBN: (数字)9789811905230

ISBN: (纸本)9789811905223

Deep neural networks are vulnerable to adversarial examples, which can fool classifiers by adding small perturbations. Various adversarial attack methods have been proposed in the past several years, and most of them add the perturbation in a "sparse" or "global" way. Since the number of pixels perturbed by the "sparse" method and the perturbation intensity of each pixel added by the "global" method are both small, the adversarial property can be destroyed easily. Finally, it makes the adversarial attack and the adversarial training based on these samples unreliable. To address this issue, we present an "pixel-wise" method which is somewhere in between the "sparse" or "global" way. First, the perception of human eyes to the error of different image regions is different. Second, image processing methods have different effects on the different areas of the image. Based on these two considerations, we propose an imperceptible and reliable adversarial attack method, which projects the perturbation to the different areas differently. Extensive experiments demonstrate our method can preserve the attack ability while maintaining good view quality. More importantly, the proposed projection can be combined with existing attack methods to generate a stronger generation algorithm which improves the robustness of adversarial examples. Based on the proposed method, the reliability of adversarial attacks can be greatly improved. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Pixels

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DI-MVS: Learning Efficient Multi-View Stereo With Depth-Aware Iterations

DI-MVS: Learning Efficient Multi-View Stereo With Depth-Awar...

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

作者： Jianfei Jiang Mingwei Cao Jun Yi Chenglong Li Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University School of Computer Science and Information Engineering Hefei University of Technology

Learning-based Multi-View Stereo (MVS) methods aim to reconstruct 3D scenes from a set of 2D calibrated images. However, existing learning-based MVS methods often overlook depth maps that include the geometric shapes of the scene when constructing the cost volume. This can result in suboptimal reconstructions, particularly in low-texture or repetitive-texture regions where valuable geometric information is absent. To address this issue, we develop DI-MVS, a coarse-to-fine framework that effectively incorporates context-guided depth geometry into the cost volume using a depth-aware iterator. First, we employ the proposed depth-aware cost completion module to update the cost volume, followed by 2D ConvGRUs to iteratively optimize depth maps efficiently. Second, we propose a hybrid loss strategy that combines two loss functions’ strengths to improve depth estimation’s robustness. Extensive experiments demonstrate that DI-MVS outperforms state-of-the-art methods on the DTU dataset and the Tanks & Temples benchmark. The source code is available at: https://***/JianfeiJ/DI-MVS.

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Research on key Techniques of Text Recognition under Strong Light Noise 2

Research on Key Techniques of Text Recognition under Strong ...

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2nd Asia-Pacific Conference on Communications technology and computer science, ACCTCS 2022

作者： Wang, Yongpan Yu, Zhi Xing, Hangdi Wang, Wei College of Computer Science Zhejiang University Alibaba Group Hangzhou China School of Software Technology Zhejiang University Hangzhou China Zhejiang University Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Hangzhou China

ISBN: (纸本)9781665400343

Text recognition technology based on deep learning has achieved great success in recent years. However, these recognition models have poor robustness under the influence of strong light noise. The existing solution to this problem leverages preprocessing methods to remove uneven lighting, which shows just passable effects on noise reduction for strong light. In this paper, we propose a text image recognition algorithm with attention-based strong light region perception module to solve this problem. The model is trained to detect strong light regions and guide the generated feature maps to focus on the clear part of the input image. Experimental results show that this model mitigates the impact of strong light noise pollution and improves the robustness of recognition technology on multiple datasets. © 2022 IEEE.

关键词： Generative adversarial networks

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A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

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ACM Transactions on Evolutionary Learning and Optimization 2023年第4期3卷 1-25页

作者： Yazdani, Delaram Yazdani, Danial Yazdani, Donya Omidvar, Mohammad Nabi Gandomi, Amir H. Yao, Xin Department of Computer Engineering Mashhad Branch Azad University Mashhad*** Iran Faculty of Engineering & Information Technology University of Technology Sydney Ultimo2007 Australia AI Lab British Antarctic Survey CambridgeCB3 0ET United Kingdom School of Computing Leeds University Business School University of Leeds LeedsLS2 9JT United Kingdom Obuda University Budapest1034 Hungary Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method. © 2023 Copyright held by the owner/author(s).

关键词： Resource allocation

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Enhancing Video Quality Assessment through Semantic Guidance

Enhancing Video Quality Assessment through Semantic Guidance

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Futian Wang Zeyu Li School of Computer Science and Technology Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

With the rapid advancement of network and information technologies, user-generated content (UGC) platforms like TikTok have become essential mainstream media channels. However, video quality often varies significantly due to factors during shooting, compression, and transmission, inconveniencing users. While existing methods assess video quality using objective standards, human subjective perception remains crucial. This paper introduces a simple and effective no-reference video quality assessment model that integrates human perception into the complex video generation process to enhance UGC quality evaluation. We develop a trainable FastVQA branch to extract technical features and incorporate a ConvNeXt branch to capture semantic features. These features are combined and processed through a multi-layer perceptron (MLP) network to predict video quality scores. Our findings demonstrate that incorporating semantic features allows the network to better leverage human perception of video content quality, thereby improving the accuracy of technical assessments. This method not only optimizes traditional quality assessment models but also offers a new research direction, highlighting the importance of human perception in automated video quality evaluation.

关键词： Accuracy Video on demand Technical management Semantics User-generated content Feature extraction Quality assessment Web sites Video recording System analysis and design

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Multisequences with High Joint Nonlinear Complexity from Function Fields

Multisequences with High Joint Nonlinear Complexity from Fun...

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2022 International Conference on Electronic Information Engineering and computer science, EIECS 2022

作者： Yan, Yang Wang, Qiuyan Wu, Chenghuang Zhao, Chun'e Liu, Lin School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China School of Computer Science and Technology Tiangong University Tianjin China Provincial Key Laboratory of Applied Mathematics Putian University Fujian Putian China College of Science University of Petroleum Shandong Qingdao China Tianjin LINHAITEC Ltd Tianjin China

ISBN: (纸本)9781510663312

Multisequences over finite fields play an important role in applications that related to parallelization, such as word-based stream ciphers and pseudorandom vector generators. It is interesting to study complexity measures for multisequences. In this paper, we propose three constructions of multisequences over finite fields from rational function fields and Hermitian function fields. We also analyze the joint nonlinear complexity of these multisequences. Moreover, the length and dimension of these multisequences are flexible. © 2023 SPIE.

关键词： Rational functions

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Leveraging Static Relationships for Intra-Type and Inter-Type Message Passing in Video Question Answering

arXiv

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arXiv 2025年

作者： Liang, Lili Sun, Guanglu School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Heilongjiang Provincial Key Laboratory of Intelligent Information Processing and Application Harbin150080 China

Video Question Answering (VideoQA) is an important research direction in the field of artificial intelligence, enabling machines to understand video content and perform reasoning and answering based on natural language questions. Although methods based on static relationship reasoning have made certain progress, there are still deficiencies in the accuracy of static relationship recognition and representation, and they have not fully utilized the static relationship information in videos for in-depth reasoning and analysis. Therefore, this paper proposes a reasoning method for intra-type and inter-type message passing based on static relationships. This method constructs a dual graph for intra-type message passing reasoning and builds a heterogeneous graph based on static relationships for inter-type message passing reasoning. The intra-type message passing reasoning model captures the neighborhood information of targets and relationships related to the question in the dual graph, updating the dual graph to obtain intra-type clues for answering the question. The inter-type message passing reasoning model captures the neighborhood information of targets and relationships from different categories related to the question in the heterogeneous graph, updating the heterogeneous graph to obtain inter-type clues for answering the question. Finally, the answers are inferred by combining the intra-type and inter-type clues based on static relationships. Experimental results on the ANetQA and Next-QA datasets demonstrate the effectiveness of this method. © 2025, CC BY.

关键词： Question answering

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