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Design of People Flow Monitoring System in Public Place based on MD-MCNN

Journal of Physics: Conference Series 2020年第1期1606卷

作者： Xiong Tianmin Lei Xiaochun Zheng Xingwen Chen Junyan Feng Yizhou School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields.

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Stable and unstableperiodic solutions for quadratic contact Hamiltonians with a small parameter

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Journal of Physics: Conference Series 2019年第1期1324卷

作者： Yanan Huang Leilei Jia Xiuyu Sun Zhanyong Li School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China School of Electronic Information and Automation Guilin University of Aerospace Technology Guilin China

In this paper, we study the existence of periodic solutions for a class of quadratic contact Hamiltonian system with a small parameter by averaging *** have proved that the quadratic contact Hamiltonian system has a stable periodic solution and an unstable periodic solution when the corresponding parameter is small enough.

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Exploiting Gabor Feature Extraction Method for Chinese Character Writing Quality Evaluation

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhixiao Wang Wenyao Yan Mingtao Guo Jiulong Zhang School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048 China Shaanxi Key Laboratory Xi'an of Network Computing and Security Technology China School of Data Science and Computer Xi'an innovation college of Yan'an University Xi'an 710100 China

The automatic evaluation of Chinese character writing quality has a wide application prospect. Most of the existing evaluation methods of Chinese character writing quality are based on radical segmentation and feature judgment, which require the high accuracy of Chinese character segmentation. However, there are many problems in the real handwriting, such as continuous writing, uneven strength of writing, personalized writing style and so on, which lead to the difficulty of segmentation in the ordinary handwriting. To solve the above problems, we propose an effective method based on image texture where the uniformity of writing lines and writing style is taken as an effective criterion. In our method, Gabor transform is used to extract the image features of writing samples, and finally the statistical learning method of support vector machine is used to effectively evaluate the writing quality. Experiments on multiple real datasets including CHAED show that our method is effective and accurate. The advantage of this method is that it does not need to segment fonts, and the cost of global feature extraction is small.

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SPECT bone imaging thyroid lesion segmentation

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Journal of Physics: Conference Series 2021年第1期1994卷

作者： Mingyang-Luo Qiang-Lin Tongtong-Li Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Lanzhou 730030 Key Laboratory of Streaming Data Computing and Application Northwest Minzu University Lanzhou 730124 School of Mathematics and Computer Science Northwest Minzu University Lanzhou 730030

Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exophthalmos, eyelid edema, and vision loss. In order to accurately segment thyroid lesions from SPECT thyroid data, a U-net-based thyroid lesion segmentation model was constructed. First, mirror, rotate, and translate the existing data. At the same time, use the generative adversarial network to expand the data. Then build a segmentation model based on U-net. Finally, perform experimental evaluation and analysis based on a set of real SPECT thyroid data. The results show that: in the traditional data expansion, the IoU value of the improved RS-U-net model is 66.83.

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Delay-Driven Layer Assignment Algorithm Based on Integrating Multi-Strategy for Advanced Technology Nodes

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics 2024年第4期36卷 625-635页

作者： Liu, Genggeng Jiang, Lieqiu Li, Zepeng Wu, Ruoxin Xu, Ning College of Computer and Data Science Fuzhou University Fuzhou350116 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China College of Maynooth International Engineering Fuzhou University Fuzhou350116 China School of Information Engineering Wuhan University of Technology Wuhan430070 China

By introducing layer assignment algorithm, it can effectively optimize multiple important indicators such as delay and via counts in physical design, and then improve the chip performance. Therefore, a delay optimization layer assignment algorithm integrating multiple strategies is proposed, which considers both non-default-rule wire and coupling effect. First, aiming at the problem that the difference of nets is not carefully considered in the previous work, the differentiated net strategy is proposed. Second, in order to solve the problem of unreasonable evaluation of grid edge congestion in previous work, the segment classifying strategy is proposed. Third, aiming at the problem that the existing work pays more attention to consider congestion constraint when reassigning illegal nets, which leads to high delay of routing results, an effective reassignment and adjustment strategy is proposed. Finally, aiming at the problem of unreasonable routing order in the previous work, a variety of novel methods to determine the routing order are designed, and then a multi-objective driven sorting strategy is proposed. The algorithm runs in the Linux environment with a 2.60 GHz CPU and 64 GB of RAM. The validation experiments use the DAC12 circuit, and the experimental results show that the proposed algorithm can effectively optimize both delay and via counts without overflow. © 2024 Institute of computing Technology. All rights reserved.

关键词： Delay circuits

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Contrastive Learning based Speech Spoofing Detection for Multimedia Security in Edge Intelligence

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ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Jiaqi Sun Xianjun Deng Shenghao liu Xiaoxuan Fan Yongling Huang Yuanyuan He Celimuge Wu Jong Hyuk Park Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China The University of Electro-Communications Japan Seoul National University of Science and Technology South Korea

Artificial intelligence (AI) empowered edge computing has given rise to a new paradigm and effectively facilitated the promotion and development of multimedia applications. The speech assistant is one of the significant services provided by multimedia applications, which aims to offer intelligent interactive experiences between humans and machines. However, malicious attackers may exploit spoofed speeches to deceive speech assistants, posing great challenges to the security of multimedia applications. The limited resources of multimedia terminal devices hinder their ability to effectively load speech spoofing detection models. Furthermore, processing and analyzing speech in the cloud can result in poor real-time performance and potential privacy risks. Existing speech spoofing detection methods rely heavily on annotated data and exhibit poor generalization capabilities for unseen spoofed speeches. To address these challenges, this paper first proposes the Coordinate Attention Network (CA2Net) that consists of coordinate attention blocks and Res2Net blocks. CA2Net can simultaneously extract temporal and spectral speech feature information and represent multi-scale speech features at a granularity level. Besides, a contrastive learning-based speech spoofing detection framework named GEMINI is proposed. GEMINI can be effectively deployed on edge nodes and autonomously learn speech features with strong generalization capabilities. GEMINI first performs data augmentation on speech signals and extracts conventional acoustic features to enhance the feature robustness. Subsequently, GEMINI utilizes the proposed CA2Net to further explore the discriminative speech features. Then, a tensor-based multi-attention comparison model is employed to maximize the consistency between speech contexts. GEMINI continuously updates CA2Net with contrastive learning, which enables CA2Net to effectively represent speech signals and accurately detect spoofed speeches. Extensive experiments on

关键词： Edge intelligence Multimedia applications Speech spoofing detection Contrastive learning Coordinate attention

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

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Engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

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CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy

arXiv

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arXiv 2023年

作者： Qiao, Mengyun Wang, Shuo Qiu, Huaqi de Marvao, Antonio O’Regan, Declan P. Rueckert, Daniel Bai, Wenjia Department of Computing Department of Brain Sciences Data Science Institute Imperial College London LondonSW7 2AZ United Kingdom Department of Computing Imperial College London United Kingdom Klinikum rechts der Isar Technical University of Munich Munich Germany The MRC Laboratory of Medical Sciences Imperial College London LondonW12 0HS United Kingdom The Department of Women and Children’s Health British Heart Foundation Centre of Research Excellence School of Cardiovascular and Metabolic Medicine and Sciences King’s College London London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai Key Laboratory of MICCAI Shanghai China

Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images;and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases. While the first question can often be addressed by image segmentation and motion tracking algorithms, our capability to model and answer the second question is still limited. In this work, we propose a novel conditional generative model to describe the 4D spatio-temporal anatomy of the heart and its interaction with non-imaging clinical factors. The clinical factors are integrated as the conditions of the generative modelling, which allows us to investigate how these factors influence the cardiac anatomy. We evaluate the model performance in mainly two tasks, anatomical sequence completion and sequence generation. The model achieves high performance in anatomical sequence completion, comparable to or outperforming other state-of-the-art generative models. In terms of sequence generation, given clinical conditions, the model can generate realistic synthetic 4D sequential anatomies that share similar distributions with the real data. The code and the trained generative model are available at https://***/MengyunQ/CHeart. © 2023, CC BY.

关键词： Heart

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Correlation-aware Cross-modal Attention Network for Fashion Compatibility Modeling in UGC Systems

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ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Kai Cui Shenghao Liu Wei Feng Xianjun Deng Liangbin Gao Minmin Cheng Hongwei Lu Laurence T. Yang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China China Nuclear Power Operation Technology Corporation Ltd. China

Empowered by the continuous integration of social multimedia and artificial intelligence, the application scenarios of information retrieval (IR) progressively tend to be diversified and personalized. Currently, User-Generated Content (UGC) systems have great potential to handle the interactions between large-scale users and massive media contents. As an emerging multimedia IR, Fashion Compatibility Modeling (FCM) aims to predict the matching degree of each given outfit and provide complementary item recommendation for user queries. Although existing studies attempt to explore the FCM task from a multimodal perspective with promising progress, they still fail to fully leverage the interactions between multimodal information or ignore the item-item contextual connectivities of intra-outfit. In this paper, a novel fashion compatibility modeling scheme is proposed based on Correlation-aware Cross-modal Attention Network. To better tackle these issues, our work mainly focuses on enhancing comprehensive multimodal representations of fashion items by integrating the cross-modal collaborative contents and uncovering the contextual correlations. Since the multimodal information of fashion items can deliver various semantic clues from multiple aspects, a modality-driven collaborative learning module is presented to explicitly model the interactions of modal consistency and complementarity via a co-attention mechanism. Considering the rich connections among numerous items in each outfit as contextual cues, a correlation-aware information aggregation module is further designed to adaptively capture significant intra-correlations of item-item for characterizing the content-aware outfit representations. Experiments conducted on two real-world fashion datasets demonstrate the superiority of our approach over state-of-the-art methods.

关键词： User-Generated Contents fashion intelligent analysis complementary compatibility modeling multimodal representation learning correlation-aware integration strategy

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