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RGB Color Model Aware Computational Color Naming and Its Application to data Augmentation

RGB Color Model Aware Computational Color Naming and Its App...

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2022 IEEE international conference on big data, big data 2022

作者： Yan, Zipei Xu, Linchuan Suzuki, Atsushi Wang, Jing Cao, Jiannong Huang, Jun The Hong Kong Polytechnic University Department of Computing Kowloon Hung Hom Hong Kong King's College London Department of Informatics London United Kingdom University of Greenwich Department of Computing and Mathematical Sciences London United Kingdom Anhui University of Technology School of Computer Science and Technology Ma'anshan China

ISBN: (纸本)9781665480451

Computational color naming (CCN) aims to learn a mapping from pixels into semantic color names, e.g., red, green and blue. CCN has wide applications including color vision deficiency assistance and color image retrieval. Existing research on CCN mainly studies pixels collected under laboratory settings or studies images collected from the web. However, laboratory pixels are very limited such that the learned mapping may not generalize well on unseen pixels, and the mapping discovered from images is usually data-specific. In this paper, we aim to learn a universal mapping by studying pixels collected from the web. To this end, we formulate a novel classification problem that incorporates both the pixels and the RGB color model. The RGB color model is beneficial for learning the mapping because it characterizes the production of colors, e.g., the addition of red and green produces yellow. However, the characterization is rather qualitative. To solve this problem, we propose ColorMLP, which is a multilayer perceptron (MLP) embedded with graph attention networks (GATs). Here, the GATs are designed to capture color relations that we construct by referring to the RGB color model. In this way, the parameters of the MLP can be regularized to comply with the RGB model. We conduct comprehensive experiments to demonstrate the superiority of ColorMLP to alternative *** expand the application of CCN, we design a novel data augmentation method named partial color jitter (PCJ), which performs color jitter (CJ) on a subset of pixels belonging to the same color of an image. In this way, PCJ partially changes the color properties of images, thereby significantly increasing images' diversity. We conduct extensive experiments on CIFAR10/100 and ImageNet datasets, showing that PCJ can consistently improve the classification performance. Our data and software can be found at https://https://***/yanzipei/CCN_and_ItsApp. © 2022 IEEE.

关键词： Mapping

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Research on small object detection methods based on deep learning 4

Research on small object detection methods based on deep lea...

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4th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2022

作者： Kong, Yandan Liu, Kai Liang, Zhihong Liu, Tiancheng Huang, Yuxiang Qin, Mingming Institute of Big Data and Artificial Intelligence Southwest Forestry University Kunming China Yunnan Provincial Company of China National Tobacco Corporation Kunming China E-Visible Supply Chain Management Co. Kunming China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

The efficiency and accuracy of object detection are steadily improving due to the development and widespread application of deep learning. However, small object detection remains a challenge. When employing mainstream object detection algorithms, small objects have low resolution, little feature information, and weak expressiveness, which leads to missed false detection and poor detection accuracy. This paper systematically describes on small object detection methods based on deep learning, divides them into four categories based on small object detection optimization methods, such as data augmentation, multi-scale feature fusion, contextual features, and optimized backbone networks, and analyzes the benefits and drawbacks of each method, and offers a forecast on future research directions. © 2022 IEEE.

关键词： Object detection

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A Probabilistic Model for JSON data 2

A Probabilistic Model for JSON Data

引用

2nd international conference on Computational Modeling, Simulation and data Analysis, CMSDA 2022

作者： Lv, Teng Yan, Ping School of Big Data and Artificial Intelligence Anhui Xinhua University China

ISBN: (纸本)9798350345759

JSON is an exchange data format for applications nowadays. To manage JSON data efficiently, it is important to use a proper model for JSON data. A straightforward model for JSON is a tree-structured model, in which nodes are types of JSON data, and edges are values of the nodes. As uncertainties are common and natural in real environments, it is realistic for a JSON model to deal with uncertainties of JSON data. This paper first analyzes the state-of-art uncertainty in data management fields, including relational database, XML data, and graph data, etc. Then based on the above observations, a probabilistic JSON model is proposed based on the tree model of JSON data is proposed, in which probability settings are incorporated in the (key, value) pairs of JSON model. Finally, JSONPath is extended to deal with probability computing of JSON data queries. © 2022 IEEE.

关键词： Trees (mathematics)

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Edge intelligence-Based UAV Human Target Recognition with Improved YOLOV5 Algorithm 23

Edge Intelligence-Based UAV Human Target Recognition with Im...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Cai, Zeyu Qu, Yuben Dong, Chao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665494571

Edge intelligent computing is often combined with the tasks of Unmanned Aerial Vehicle (UAV). Most applications of UAV require the location and identity information of human targets. Traditional human target detection and face recognition algorithms require a lot of time in edge intelligent computing. In addition, the final recognition result is not accurate enough. To this end, this paper proposes an improved YOLOV5-Cozy target detection algorithm to detect human targets and faces in a fast and accurate way. The algorithm uses MobileNetV3, depthwise separable convolution and shuffleconv to improve the original YOLOV5 (You Only Look Once version 5) algorithm. Compared with the original algorithm, the improved algorithm has an average detection accuracy drop of less than 5%, and its calculation time has been reduced by 20%. In terms of face recognition, we use the FaceNet algorithm with the addition of a face augmentation module. The module enhances the face recognition ability of the FaceNet algorithm in different environments, and the accuracy of face recognition has increased by 20%. We implement the proposed algorithm on the embedded computer Jetson Agx Xavier for UAV edge intelligent computing, and the average FPS achieves at about 23.6. © 2021 IEEE.

关键词： Target recognition Smart cities Convolution Face recognition Image edge detection Computational modeling Object detection

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Object Detection Algorithm Based on Channel-Spatial Fusion Cascade Attention 8

Object Detection Algorithm Based on Channel-Spatial Fusion C...

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8th IEEE international conference on Cloud computing and intelligence Systems, CCIS 2022

作者： Mao, Guojun Liao, Guanyi Zhu, Hengliang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Fujian University of Technology Fuzhou350118 China

ISBN: (纸本)9781665477352

To solve the problem of low accuracy and high leakage rate of small object detection in a one-stage object detection model, a cascade attention mechanism (CAtt) is proposed. Cascading attention integrates channel attention and spital attention mechanisms that can be flexibly embedded into any object detection model, such as YoloV4, to improve the performance of the object detection model. The attention mechanism is perfected from two aspects: one is to integrate channel attention and spatial attention respectively so that the two are more complementary. Secondly, by introducing the skipping connection mechanism, the shallow detail information is connected with the deep semantic information, to solve the problem of information loss caused by the multiple convolutions of the model. Four typical attention mechanisms were selected to compare public datasets such as PASCAL VOC2007 and PASCAL VOC2012. A large number of experimental results show that this method significantly improves the performance of the object detection model, especially for the PASCAL VOC2007. Embedding the method in YoloV4 resulted in a 4.77% increase in the mAP index of the original model. © 2022 IEEE.

关键词： Object detection

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RtSNN: Building a Real-time Siamese Neural Networks Model for Few-shot Action Recognition 8

RtSNN: Building a Real-time Siamese Neural Networks Model fo...

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8th international conference on big data computing and Communications (bigCom)

作者： Guo, Yao Song, Junyang Guo, Yu Bie, Rongfang Jiao, Libin Umek, Anton Kos, Anton Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China China Univ Min & Technol Sch Mech Elect & informat Engn Beijing Peoples R China Univ Ljubljana Fac Elect Engn Ljubljana Slovenia

ISBN: (纸本)9781665473842

The technology of real-time action recognition based on wearable sensors has been widely used by organizations and individuals to provide high-quality healthcare services. For instance, real-time action recognition using sports sensors can be used to predict irregular actions of athletes to avoid serious sports injuries. However, the collection and annotation of action data are always expensive, and only few high-quality labeled data are available. Moreover, existing studies on few-shot action recognition mainly focus on videos analysis, rather than targeting real-time action recognition. Therefore, how to enable real-time action recognition with few-shot data is still challenging and remains to be fully explored. In this paper, we propose the first siamese neural networks for real-time few-shot action recognition. Our main idea is to use augmented data from the original minority labeled action data to recognize real-time actions. To address the challenge of overfitting in few-shot learning tasks, we devise a scheme to effectively augment the original data. The proposed model, named Real-time Siamese Neural Networks (RtSNN), can learn a feature space in which all real-time action data are transformed into feature vectors. By leveraging the design philosophy of metric learning, RtSNN can classify different actions by the distance learned in the feature space. We perform extensive experiments on real-world action datasets to evaluate the RtSNN model. The evaluation results confirm that RtSNN can effectively recognize real-time actions. It can maintain the highest classification accuracy compared with baseline models.

关键词： real-time action recognition siamese neural networks few-shot learning

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Vocational Education Development Model Based on Intelligent Management System 11

Vocational Education Development Model Based on Intelligent ...

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11th international conference on Educational and Information Technology (ICEIT)

作者： Ni, Li Huang, Qiang Ye, Jingyi Hu, Bin Zhang, Ni Chang, Xiangqian Su, Zhihao Guangzhou Coll Appl Sci & Technol Guangzhou Peoples R China Guangzhou Huali Coll Guangzhou Peoples R China Natl Univ Malaysia Fac Informat Sci & Technol Kuala Lumpur Malaysia

ISBN: (纸本)9781665445467

Intelligent management system centered on artificial intelligence algorithms, cloud computing technology, mobile Internet technology, and big data applications have made remarkable achievements in various industries. The intelligent management system designed in this project is to apply these technologies to explore a vocational education development model that adapts to the current Internet society. Vocational education needs to be oriented to the professional needs of the society, it needs to be oriented to students' vocational choices, and it needs to be oriented to the skill range of teachers. This project collects students' learning data and teaches students in accordance with their aptitude in the course of students' daily classes, matches students with appropriate vocational and technical elective courses and teachers, and records students' feedback during their studies, and in the future career development of students, to help students make better career choices. At the same time, the needs of students, companies, and teachers in vocational education will be collected, and artificial intelligence algorithms will be used to process student data and corporate recruitment data, automatically matching the most suitable combination of students and companies. The biggest core innovation of this system is the latest deep learning algorithm, and according to the actual situation of vocational education, the residual neural network is improved to create an improved residual neural network suitable for students' professional development recommendations. The final result of this algorithm, the auxiliary role in the process of student vocational education and development is far greater than the traditional vocational education management system, and in the C University student education and professional development database, there is about 90% satisfaction rate and the 85% occupational matching accuracy rate is far higher than the performance of the traditional vocation

关键词： intelligence management system deep learning algorithms recommendation system

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Emerging Technologies and Space 8th

Emerging Technologies and Space

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8th international conference on Modelling and Simulation for Autonomous Systems (MESAS)

作者： Carlo, Antonio Roux, Lucille Tallinn Univ Technol Tallinn Estonia Univ Paris Saclay Paris France

ISBN: (纸本)9783030982607;9783030982591

The fourth industrial revolution and breakthroughs in emerging technologies, namely in big data, Artificial intelligence (AI) and Quantum computing, have the potential to change the nature of space activities, ranging from space exploration, space tourism, to space activities. The practical applications of AI are manifold and include the automation of repetitive tasks in the manufacturing of satellites and spacecrafts, image recognition of satellite data and provision of assistance to astronauts in task and behaviour management. While emerging technologies offer new opportunities, the side effects of these promises rest on the negative issues associated. Ensuring the accessibility of space datasets, while also ensuring the identification and mitigation of cybersecurity implications of AI-powered devices and quantum computers, remains essential. These are some of the few challenges that scientists, design engineers, and decision-makers will have to face in both the short and long-term. Based on the above, this paper will discuss the possibilities presented by emerging technologies, their associated risks in the space sector, and finally the potential mitigating measures addressing the degree of coordination that is necessary to ensure that space activities remain peaceful and beneficial for all humankind.

关键词： EDTs Artificial intelligence Quantum computing

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Impact and Application of Block Chain Technology on Urban Traffic Based on Artificial intelligence 1

引用

11th international conference on Frontier computing, FC 2021

作者： Bian, Zhenxing Software Engineering Department Shandong Polytechnic College Shandong Jining272000 China

ISBN: (数字)9789811680526

ISBN: (纸本)9789811680519

In recent years, with the development of artificial intelligence, blockchain technology has been used in many industries. The development of the financial industry is the fastest and most mature. Now in the transportation field, blockchain technology and big data technology are combined;it can solve some of the pain points in traffic. Blockchain technology is a decentralized technology. Decentralization means that each node in the network is independent of each other and carries out point-to-point information data transmission. In this process, there is no organization or individual pairing and transmission process. Using blockchain technology in traffic scenarios can put information and data on the chain to ensure that information and data will not be tampered with and can be traced back at all times. This article proposes a method to build a big data platform for urban intelligent transportation using blockchain technology. The method takes data as the core, eliminates the centralized computer management of multiple organizations, changes the methods of data collection, data processing and analysis, completes the data storage modules and methods, and realizes the complete platformization of the urban intelligent transportation system. The results show that the urban intelligent transportation big data platform architecture using blockchain technology overcomes the limitations of multiple organizations, discovers urban intelligent transportation data, and further solves the problem of insufficient data in existing network archives. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Urban transportation

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A Method for Scholars' Influence Evaluation Based on Representative Papers 4

A Method for Scholars' Influence Evaluation Based on Represe...

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4th international conference on Machine Learning, big data and Business intelligence, MLBDBI 2022

作者： Wang, Jie Li, Yajie Jia, Ying Zhang, Peng Shanxi University of Finance and Economics School of Information Taiyuan China Bank of China Shanxi Branch Taiyuan China

ISBN: (纸本)9798350333947

[Purpose/Significance] Evaluating the academic influence of scholars by computing methods is an important task in literature data analysis. At present, the research on scholars' academic influence evaluation methods from the perspective of representative papers is still lacking. Representative papers can reflect the academic level of scientific researchers more accurately. [Methods/Process] This paper proposes a new indicator HAIF (High Author Impact Factor), which is based on representative papers to evaluate the influence of scholars. Specifically, HAIF is the maximum value of the author's single paper influence. The author's single paper influence combines the author contribution and quality of papers. The author contribution is calculated according to the author's signature order. The quality of papers considers two factors comprehensively, which are the journal impact factor and citation frequency. HAIF index was compared with other indicators, such as p-index, h-index, x-index, and v-index. [Results/Conclusion] The experiment results demonstrate that HAIF outperforms other indicators in terms of discrimination, volatility, and correlation. © 2022 IEEE.

关键词： data handling

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