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arXiv 2023年

作者： Liu, Peiyuan Wu, Beiliang Li, Naiqi Dai, Tao Lei, Fengmao Bao, Jigang Jiang, Yong Xia, Shu-Tao College of Computer Science and Software Engineering Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University China Ping An Bank Co. Ltd China WeBank Institute of Financial Technology Shenzhen University China

Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourier transform, which cannot capture fine-grained and local frequency structure. In this paper, we propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. WFTNet utilizes both Fourier and wavelet transforms to extract comprehensive temporal-frequency information from the signal, where Fourier transform captures the global periodic patterns and wavelet transform captures the local ones. Furthermore, we introduce a Periodicity-Weighted Coefficient (PWC) to adaptively balance the importance of global and local frequency patterns. Extensive experiments on various time series datasets show that WFTNet consistently outperforms other state-of-the-art baseline. Code is available at https://***/Hank0626/WFTNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Wavelet transforms

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Lexicon-enhanced Pre-trained Language Models for Chinese Ethics-related Tasks

TechRxiv

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TechRxiv 2023年

作者： Gu, TianLong Bao, Tong Li, Long Chang, Liang Feng, Xuan The Engineering Research Center of Trustworthy AI Ministry of Education College of Information Science and Technology College of Cyber Security Jinan University 601 Huangpu Avenue West Tianhe District Guangzhou510632 China The School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China The Guangxi Key Laboratory of Trusted Software School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China The College of Information Science and Technology College of Cyber Security Jinan University Guangzhou510632 China Jinan University Guilin University Of Electronic Technology China

The proposal and rapid development of pre-trained language models have led to unprecedented breakthroughs in natural language processing techniques. Although pre-trained language models have strong text understanding capabilities, their performance in handling ethical issues is unsatisfactory due to the complexity of ethical connotations. To improve the ethical understanding and generation capabilities of pre-trained language models, a novel knowledge enhancement solution based on ethics lexicon is proposed. First, a Chinese ethics lexicon containing ethical polarity and ethical strength is constructed, with a scale of over 33k words. Second, an ethics lexicon adapter (ELA) for Transformers is proposed, which serves as the underlying encoder to embed ethical knowledge into Transformers, thereby enhancing the sensitivity and accuracy of ethical content recognition. Third, ELA-based machine learning models are proposed for ethics-related tasks, including ethical behavior discrimination, moral story understanding, and moral generation. Finally, with the help of ETH-News and STORAL-ZH datasets, comprehensive experimental comparisons and analyses are conducted with multiple SOTA models targeting different ethical tasks, demonstrating the rationality and effectiveness of the proposed solution. © 2023, CC BY.

关键词： Natural language processing systems

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A self-supervised CNN for image watermark removal

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zheng, Menghua Jiao, Tiancai Zuo, Wangmeng Zhang, Yanning Lin, Chia-Wen the School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China the School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China the Peng Cheng Laboratory Laboratory Guangdong Shenzhen518055 China the School of Computer Science Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China the Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu300 Taiwan

Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal. However, watermarked images do not have reference images in the real world, which results in poor robustness of image watermark removal techniques. In this paper, we propose a self-supervised convolutional neural network (CNN) in image watermark removal (SWCNN). SWCNN uses a self-supervised way to construct reference watermarked images rather than given paired training samples, according to watermark distribution. A heterogeneous U-Net architecture is used to extract more complementary structural information via simple components for image watermark removal. Taking into account texture information, a mixed loss is exploited to improve visual effects of image watermark removal. Besides, a watermark dataset is conducted. Experimental results show that the proposed SWCNN is superior to popular CNNs in image watermark removal. Codes can be obtained at https://***/hellloxiaotian/SWCNN. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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Dynamic Programming-Based Optimal Charging Scheduling for Electric Vehicles

Dynamic Programming-Based Optimal Charging Scheduling for El...

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IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Kuan Li Ying Zhang Chenglie Du Tao You Lu Bai Jiaming Wu School of Software Northwestern Polytechnical University Xi’an China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Architecture and Civil Engineering Chalmers University of Technology Gothenburg Sweden

The charging scheduling of electric vehicles not only affects the operation of the grid system, but also affects the charging cost of vehicle users. This paper studies the charging scheduling problem of electric vehicles from the user’s perspective by considering the grid’s as well as the user’s needs. On the basis of real-time electricity prices, an electric vehicle charging scheduling system aiming at the lowest charging cost is designed and an optimal charging scheduling method based on dynamic programming is established. In order to reduce the unnecessary computational expenses, this paper optimizes the dynamic programming algorithm, and defines the state transition limit of the dynamic programming search space according to the actual constraints, which reduces the computational complexity. The simulation results show that, on the premise of satisfying the power constraints and user charging demands, the electric vehicles charging scheduling system can effectively reduce the charging cost and time. In addition, the charging process has little impact on the power system from the perspective of grid fluctuation.

关键词： Fuzzy logic Vehicle-to-grid Costs Fluctuations Processor scheduling Simulation Dynamic scheduling

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A mathematical model for efficient extraction of key locations from point-cloud data in track area

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Industrial Artificial Intelligence 2023年第1期1卷 1-14页

作者： Chen, Shuyue Wu, Jiaolv Lu, Jian Wang, Xizhao College of Mathematics and Statistics Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institue of Information Technology Shenzhen China Shenzhen No. 3 Vocational School of Technology Shenzhen China College of Engineering Huaqiao University Quanzhou China Shenzhen Key Lab. of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Key Lab. of Intelligent Information Process Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

During the construction of a metro system, it is inevitable that deviations will occur between the excavated tunnel and the original designed scheme. As such, it is necessary to adjust the designed scheme to accommodate these discrepancies. Specifically, the adjustment of the designed scheme involves a rigorous process of repeatedly selecting and verifying the feasibility of the proposed modifications using point-cloud data obtained from the tunnel. However, this process can be considerably time-consuming due to the large-scale and potentially redundant nature of the point-cloud data. This paper proposes a mathematical model for point-cloud data acquired in measuring a mined tunnel, which may deviate from the originally designed one. The modeling, which mainly includes determining its normal plane, and building the equation of tunnel point-cloud data, is to quickly extract several key locations in the tunnel surface for modifying the original design in order to achieve a minimum error between the modified design and the mined tunnel. In comparison with the conventional processing of extracting several key locations directly from point-cloud data, our model shows a significant promotion of extraction efficiency under an acceptable error bound. The model is tested in a real tunnel point-cloud data and the testing results confirm the increase of fitting accuracy and the decrease of computational load.

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Performance of OTFS-NOMA Scheme for Coordinated Direct and Relay Transmission Networks in High-Mobility Scenarios

arXiv

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arXiv 2023年

作者： Xu, Yao Du, Zhen Yuan, Weijie Jia, Shaobo Leung, Victor C.M. School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In this paper, an orthogonal time frequency space (OTFS) based non-orthogonal multiple access (NOMA) scheme is investigated for the coordinated direct and relay transmission system, where a source directly communicates with a near user with high mobile speed, and it needs the relaying assistance to serve the far user also having high mobility. Due to the coexistence of signal superposition coding and multi-domain transformation, the performance of OTFS-based NOMA is usually challenging to be measured from a theoretical perspective. To accurately evaluate the system performance of the proposed scheme, we derive the closed-form expressions for the outage probability and the outage sum rate by using the Inversion formula and characteristic function. Numerical results verify the performance superiority and the effectiveness of the proposed scheme. Copyright © 2023, The Authors. All rights reserved.

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Research on the Current Online Learning Status of Undergraduate computer science Students and its Influencing Factors and Optimization Strategies

SSRN

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SSRN 2023年

作者： Zhang, Wuwen Guan, Yurong Hu, Zhihua Wang, Chencheng Higher Education Computer Software & Applications Educational Theory & Management Secondary Education Huanggang Normal University China The Computer Science Department of Huanggang Normal University The Department Higher Education Educational Theory & Management Telecommunication Technology Huanggang Normal University Central University of the Philippines China The Computer Science Department of Huanggang Normal University The Department Higher Education Internet Technology Educational Theory & Management Huanggang Normal University China The School of Education Huanggang Normal University Computer Software & Applications Educational Theory & Management Secondary Education Huanggang Normal University China

Online education is increasingly becoming a primary method for teachers and students to conduct regular teaching and learning in the post-pandemic era. Consequently, understanding students' engagement in online learning and its influencing factors is especially pivotal. This research aims to investigate the current status of online learning and its influencing factors among undergraduate computer science students at universities and suggests optimization strategies for online education. The study employs the TPACK model, behaviorist learning theory, learning engagement theory, and situated cognition learning theory as its theoretical foundation. A research framework was devised, encompassing student characteristics , multidimensional online learning engagement and key influencing factors. Research on undergraduates revealed a significant positive correlation between the duration of online learning and online learning engagement, while the impacts of gender, home location, and major were limited. Self factors of students, such as subjective intent, attitudes, and motivation, played a crucial role in affecting engagement levels. Interactions with teachers and peers, teaching methodologies, and feedback were intimately linked with students' emotional engagement. Based on these findings, recommendations include establishing a module to foster students' self-motivation, enhancing online interactions between educators and students, refining technical support and platform experience, and bolstering the evaluation and feedback on learning outcomes. This research offers empirical evidence to further comprehend and refine online education. © 2023, The Authors. All rights reserved.

关键词： Students

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2D Animation - Q Version of the AIDS Prevention Short Video

2D Animation - Q Version of the AIDS Prevention Short Video

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IEEE International Conference on Orange Technologies (ICOT)

作者： Chun-Cheng Wei Hui-Ming Hsieh Long-Chen Hung Ru-Yi Chen Jiang-Fang Huang Xin-Chun Lu Yu-Shan Zhong School of Software and Big Data Changzhou College of Information Technology Changzhou China Department of Computer Science and Multimedia Design Taiwan Shoufu University Tainan Taiwan Department of Electronic Engineering Lunghwa University of Science and Technology Taiwan

ISBN: (纸本)9781665454209

AIDS is a sexually transmitted disease. The medical term is called Acquired Immune Deficiency Syndrome. Medical methods such as cocktail therapy and anticancer drugs can kill AIDS. Although there are currently cured cases, prevention is still required to prevent the infection from expanding; prevention Ways, like the AIDS vaccine or preventive safe sex, can reduce the risk of infection. We hope that through the production of animated short films for viewing, we can help reduce the HIV infection rate. This short film is presented as a story similar to a micro-film by ourselves. And using the modern scene and the character design of the Q version as the material, the story content is made into 2D animation with Adobe Flash CS5.

关键词： Uniform resource locators Video on demand Films Acquired immune deficiency syndrome Music Medical treatment Production

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Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today

arXiv

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arXiv 2023年

作者： Wang, Zhuo Li, Rongzhen Dong, Bowen Wang, Jie Li, Xiuxing Liu, Ning Mao, Chenhui Zhang, Wei Dong, Liling Gao, Jing Wang, Jianyong Department of Computer Science and Technology Tsinghua University China Department of Neurology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Chinese Academy of Medical Science Peking Union Medical College China The First Affiliated Hospital of Nanchang University China China University of Chinese Academy of Sciences China School of Computer Science and Technology East China Normal University China School of Software Shandong University China

Recent investigations show that large language models (LLMs), specifically GPT-4, not only have remarkable capabilities in common Natural Language Processing (NLP) tasks but also exhibit human-level performance on various professional and academic benchmarks. However, whether GPT-4 can be directly used in practical applications and replace traditional artificial intelligence (AI) tools in specialized domains requires further experimental validation. In this paper, we explore the potential of LLMs such as GPT-4 to outperform traditional AI tools in dementia diagnosis. Comprehensive comparisons between GPT-4 and traditional AI tools are conducted to examine their diagnostic accuracy in a clinical setting. Experimental results on two real clinical datasets show that, although LLMs like GPT-4 demonstrate potential for future advancements in dementia diagnosis, they currently do not surpass the performance of traditional AI tools. The interpretability and faithfulness of GPT-4 are also evaluated by comparison with real doctors. We discuss the limitations of GPT-4 in its current state and propose future research directions to enhance GPT-4 in dementia diagnosis. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting

An Image Analysis-Based Automated Method using Deep Learning...

引用

International Conference on Industrial and Information Systems, ICIIS

作者： Ramesh Ajikaran Ashen Iranga Hewarathna Vigneshwaran Palanisamy Charles Joseph Selvarajah Thuseethan Dept. of Computing and Infromation Systems Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sabaragamuwa University of Sri Lanka Dept. of Computer Science FAU Germany School of Information Technology Deakin University Australia

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.

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