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16th International Conference on Digital Image Processing, ICDIP 2024

作者： Zhang, Qian Ran, Yaqin Luo, Di Xu, Kaili Shi, Jilaing School of Data Science and Information Engineering GuiZhou MinZu University Guiyang550025 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang550025 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

It's very meaningful to conduct the driver to the parking space available in the parking lot clearly and accurately by computer vision and computational intelligence. While it is an extremely difficult task, because of the scene complexity in the parking lot. In this paper, a new computational intelligence technology is proposed to detect parking spaces available in the parking lot outdoor based on fusing of Region Convolution Neural Network (RCNN) and random forests (RF), and we called it as RCNN-RF. Within the RCNN-RF, a new activation function is design. And with the help of random forests, RCNN-RF overcomes scene complexity includes illumination, weather, and slight occlusion successfully. Extensive experimental results on the benchmark dataset named PKLot and the self-built dataset named GZMU-LOT outperform those of the state-of-the-art, and it shows the superiority of our proposed RCNN-RF method. © 2024 SPIE.

关键词： Random forests

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Sequence-Based Predicting Bacterial Essential ncRNAs Algorithm by Machine Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 2731-2741页

作者： Yuan-Nong Ye Ding-Fa Liang Abraham Alemayehu Labena Zhu Zeng Bioinformatics and Biomedical Big data Mining Laboratory Department of Medical InformaticsSchool of Big HealthGuizhou Medical UniversityGuiyang550025China Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical EngineeringSchool of Biology and EngineeringGuizhou Medical UniversityGuiyang550025China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of EducationGuizhou Medical UniversityGuiyang550025China College of Computational and Natural Sciences Dilla UniversityDilla419Ethiopia

Essential ncRNA is a type of ncRNAwhich is indispensable for the sur-vival of *** essential ncRNAs cannot encode proteins,they are as important as essential coding genes in *** have got wide variety of applications such as antimicrobial target discovery,minimal genome construction and evolution *** present,the number of species required for the deter-mination of essential ncRNAs in the whole genome scale is still very few due to the traditional methods are time-consuming,laborious and *** addition,tra-ditional experimental methods are limited by the organisms as less than 1%of bacteria can be cultured in the ***,it is important and necessary to develop theories and methods for the recognition of essential non-coding *** this paper,we present a novel method for predicting essential ncRNA by using both compositional and derivative features calculated by information theory of ncRNA *** method was developed with Support Vector Machine(SVM).The accuracy of the method was evaluated through cross-species cross-vali-dation and found to be between 0.69 and *** shows that the features we selected have good performance for the prediction of essential ncRNA using ***,the method can be applied for discovering essential ncRNAs in bacteria.

关键词： Bioinformatics biological information theory biomedical informatics

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Helmet Detection Algorithm of Electric Bicycle Riders Based on YOLOv5 with CBAM Attention Mechanism Integration 8th

Helmet Detection Algorithm of Electric Bicycle Riders Based ...

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8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Fu, Si-Yue Wei, Dong Zhou, Liu-Ying School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission Beijing100044 China

ISBN: (纸本)9789819975921

Object detection algorithms can assist in detecting the helmet-wearing status of electric bicycle riders, thereby saving regulatory manpower costs. However, there is currently a lack of standardized and publicly available datasets. Additionally, the basic YOLOv5s object detection algorithm, due to its limited feature extraction capabilities, may lead to numerous instances of both false negative and false positive. To enhance the model’s focus on critical information within the feature maps, this paper introduces the CBAM attention mechanism module into the Backbone section of YOLOv5s. This module sequentially infers attention maps from the input feature maps along both channel and spatial dimensions independently, and then multiplies these attention maps with the input feature maps to achieve adaptive feature optimization. This paper have established self-built dataset for experimental research, and the results indicate that compared to the original YOLOv5s model, the proposed method has improved the model’s overall mAP score by 1.89%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Grounded Multimodal Procedural Entity Recognition for Procedural Documents: A New dataset and Baseline 30

Grounded Multimodal Procedural Entity Recognition for Proced...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ren, Haopeng Zeng, Yushi Cai, Yi Ye, Zhenqi Yuan, Li Zhu, Pinli School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

ISBN: (纸本)9782493814104

Much of commonsense knowledge in real world is in the form of procudures or sequences of steps to achieve particular goals. In recent years, knowledge extraction on procedural documents has attracted considerable attention. However, they often focus on procedural text but ignore a common multimodal scenario in the real world. Images and text can complement each other semantically, alleviating the semantic ambiguity suffered in text-only modality. Motivated by these, in this paper, we explore a problem of grounded multimodal procedural entity recognition (GMPER), aiming to detect the procedural entity and the corresponding bounding box groundings in images (i.e., visual entities). A new dataset (Wiki-GMPER) is built and extensive experiments are conducted to evaluate the effectiveness of our proposed model. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Visualizing Program Behavior: A Study of Enhanced Program Diagrams Using LLM 54

Visualizing Program Behavior: A Study of Enhanced Program Di...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Li, Ying Yang, Runze Gui, ShiJie Shi, Peng Huang, Xuefei Yang, Da Zhang, Xiaozhou Gai, Yiming School of Computer Science and Engineering Beihang University Beijing China International Innovation Institute Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou China

ISBN: (纸本)9798350351507

This paper aims to address the difficulties faced by novice programmers in grasping code structure and execution flow, improving programming thinking, and pinpointing code errors with accuracy. It proposes providing students with program behavior diagrams based on large language models (LLMs) and visualization techniques to achieve personalized guidance. Specifically, these program behavior diagrams include programming thinking visualization diagrams and code vulnerability visualization diagrams. A programming thinking visualization diagram employs static code analysis to gather code structure information, combined with the structured chain-of-thought method to collectively optimize the LLM. This enables the LLM to explain each interpretable part of the code from top to bottom, detailing the programming concepts, and displaying them on a modularized code structure diagram. The code vulnerability visualization diagram primarily utilizes the fine-tuned LLM, optimizing it based on program analysis and clustering analysis methods to accurately identify vulnerabilities in student code and display them on a code flow diagram. Its feature is to visually display to students the error location, error information, and the impact of errors on program flow, rather than providing the programming answers. Lastly, through experiments and statistical analysis of actual teaching data, this paper serves a demonstration that the enhanced models used in the visualization diagram generation process have a noticeable effect on mainstream LLMs, and that visualization diagrams hold significant value for students at different stages of learning. © 2024 IEEE.

关键词： Students

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Research on Metaverse Security and Forensics

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Computers, Materials & Continua 2023年第10期77卷 799-825页

作者： Guangjun Liang Jianfang Xin Qun Wang Xueli Ni Xiangmin Guo Pu Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjingChina School of Intelligent Engineering Nanjing Institute of Railway TechnologyNanjingChina

As a subversive concept,the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic,industrial,and investment ***,while the metaverse brings unprecedented opportunities for transformation to human society,it also contains related *** is a digital living space with information infrastructure,interoperability system,content production system,and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and *** social incentives and governance rules,the metaverse reflects the digital migration of human *** article will conduct an in-depth analysis of the metaverse from the perspective of electronic data ***,from the perspective of Internet development,the background and development process of the metaverse is *** systematically elaborating on the concept and connotation of the metaverse,this paper summarizes the different views of current practitioners,experts,and scholars on the ***,from the perspective of metaverse security,the social risk and crime risks of the metaverse are *** the importance of metaverse forensics is ***,from the perspective of blockchain,smart wearable devices,and virtual reality devices,the objects and characteristics of metaverse forensics have been studied in *** smart wearable devices as an example,this paper gives the relevant experimental process of smart bracelet ***,many challenges faced by metaverse forensics are summarized by us which provide readers with some exploratory guidance.

关键词： Metaverse forensics blockchain smart wear virtual reality

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Multi-Label Learning with Label-Specific Feature Selection via Local Positive and Negative Correlation 5

Multi-Label Learning with Label-Specific Feature Selection v...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Xia, Shuxin School of Information Engineering Zhe Jiang Ocean University Zhoushan316022 China Zhejiang Province School of Information Engineering Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application Zhoushan316022 China

ISBN: (纸本)9798350385557

High dimension data increasingly emerged in many application areas. Feature selection methods are widely studied to solve the issues, playing a crucial role in eliminating redundant or irrelevant feature. Previous feature selection algorithms seldom consider the fact that each label might be determined by its specific features, which has its own characteristics. Meanwhile, they often neglect mutual exclusion among labels, named 'negative label correlations' and assume labels share global correlation in exploring label correlation. To address the above problem, we propose a novel algorithm called 'Multi-label learning with label-specific feature selection via local positive and negative correlation' (LLCPN-MIFS). Multiple datasets manifest that the effectiveness of the proposed method. © 2024 IEEE.

关键词： Feature Selection

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A First Look at LLM-powered Smartphones 39

A First Look at LLM-powered Smartphones

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39th ACM/IEEE International Conference on Automated Software engineering Workshops, ASEW 2024

作者： Wu, Liangxuan Zhao, Yanjie Wang, Chao Liu, Tianming Wang, Haoyu Huazhong University of Science and Technology Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China

ISBN: (纸本)9798400712494

The integration of Large Language Models (LLMs) into edge devices such as smartphones represents a significant leap in mobile technology, promising enhanced user experiences and novel functionalities. This paper presents a first look at LLM-powered smartphones, addressing four key aspects: the current market landscape, core functions enabled by integrated LLMs, potential security risks, and user perceptions. The findings reveal a rapidly evolving market with major manufacturers competing to integrate LLMs, innovative features that improve user interaction, significant security challenges, and mixed user perceptions that balance enthusiasm for new capabilities with privacy concerns. This study contributes to understanding LLM integration in mobile devices and its implications for users, manufacturers, and the broader technological landscape. © 2024 ACM.

关键词： Smartphones

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A multi-level FW-UAV fault diagnosis solution based on multi-task learning under small samples 14

A multi-level FW-UAV fault diagnosis solution based on multi...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Zhang, Yizong Yang, Lei An, Xue Li, Shaobo Zhang, Ansi Liao, Zihao School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (纸本)9798350301359

Deep learning provides more advanced solutions for Unmanned Aerial Vehicle (UAV) fault diagnosis, but most existing deep learning methods are designed for a single fault diagnosis task. A UAV is a complex system with multiple coupled components, requiring the simultaneous execution of several different Faulty Component Diagnosis (FCD) level 1 tasks and Fault Level Detection (FLD) level 2 tasks. In addition, scarcity of fault samples is a recognized problem. Therefore, we propose a Multi-Level Fault Diagnosis (MLFD) method for Fixed-Wing UAV (FW-UAV) based on multi-task learning. MLFD jointly trains the FCD and FLD tasks by Cross-stitch units under small samples, adaptively sharing the public information of the two tasks, enriching the fault diagnosis knowledge, and at the same time improving the model's ability to handle the two tasks. Finally, we validate the effectiveness of the proposed method in real FW-UAV failure data. © 2023 IEEE.

关键词： deep learning fault diagnosis multi-task learning shared information UAV

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Overcoming Debye length limitations:Three-dimensional wrinkled graphene field-effect transistor for ultra-sensitive adenosine triphosphate detection

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Frontiers of physics 2023年第5期18卷 183-192页

作者： Yue Ding Chonghui Li Meng Tian Jihua Wang Zhenxing Wang Xiaohui Lin Guofeng Liu Wanling Cui Xuefan Qi Siyu Li Weiwei Yue Shicai Xu School of Physics and Electronics Shandong Normal UniversityJinan 250014China Shandong Key Laboratory of Biophysics Institute of BiophysicsDezhou UniversityDezhou 253023China Shandong Engineering Laboratory of Swine Herd Health Big Data and Intelligent Monitoring Dezhou UniversityDezhou 253023China

Adenosine triphosphate(ATP)is closely related to the pathogenesis of certain diseases,so the detection of trace ATP is of great significance to disease diagnosis and drug *** field-effect transistors(GFETs)have been proven to be a promising platform for the rapid and accurate detection of small molecules,while the Debye shielding limits the sensitive detection in real ***,a three-dimensional wrinkled graphene field-effect transistor(3D WG-FET)biosensor for ultra-sensitive detection of ATP is *** lowest detection limit of 3D WG-FET for analyzing ATP is down to 3.01 aM,which is much lower than the reported *** addition,the 3D WG-FET biosensor shows a good linear electrical response to ATP concentrations in a broad range of detection from 10 aM to 10 ***,we achieved ultra-sensitive(LOD:10 aM)and quantitative(range from 10 aM to 100 fM)measurements of ATP in human *** 3D WG-FET also exhibits high *** work may provide a novel approach to improve the sensitivity for the detection of ATP in complex biological matrix,showing a broad application value for early clinical diagnosis and food health monitoring.

关键词： ATP in complex human serum three-dimensional wrinkled graphene field effect transistor Debye shielding ultra-sensitive

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