Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference最新文献

Understanding SyncMap’s Dynamics and Its Self-organization Properties: A Space-time Analysis 理解SyncMap的动力学及其自组织特性:一个时空分析

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582102

Heng Zhang, Danilo Vasconcellos Vargas

{"title":"Understanding SyncMap’s Dynamics and Its Self-organization Properties: A Space-time Analysis","authors":"Heng Zhang, Danilo Vasconcellos Vargas","doi":"10.1145/3582099.3582102","DOIUrl":"https://doi.org/10.1145/3582099.3582102","url":null,"abstract":"Human are shown able to rapidly recognize patterns in sequences by detecting and chunking together the patterns found, without supervised signals. Recently, inspired by how neuron groups act in quickly switching behaviors, SyncMap was proposed to solve chunking problems based solely on self-organization. The idea is to create dynamical equations that maintain an equilibrium state by dynamically updating with positive and negative feedback loops. When the underlying structure changes, the system can quickly adapt to the new structure. Although SyncMap can solve chunking problems effectively, the properties of its dynamics during training, is still underexplored. Here, we give a detailed investigation of SyncMap’s dynamics by using several experiments to demonstrate the behaviors of SyncMap from the perspectives of space and time, in which a problem that causes imprecise results in the original work was identified. We then propose a solution call SyncMap with moving average (i.e., SyncMap-MA), which surpasses the original work and the baselines in all experiments, suggesting that the modification here is effective and can be integrated in the future version of the algorithm.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121554824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Faster-RCNN Based Cloud Region Recognition Algorithm for Single Images 基于快速rcnn的单幅图像云区域识别算法

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582114

Feng Wu, Xifang Zhu, Chen Wang, Ruxi Xiang, Shanlin Ke, Jiapeng Lu

{"title":"Faster-RCNN Based Cloud Region Recognition Algorithm for Single Images","authors":"Feng Wu, Xifang Zhu, Chen Wang, Ruxi Xiang, Shanlin Ke, Jiapeng Lu","doi":"10.1145/3582099.3582114","DOIUrl":"https://doi.org/10.1145/3582099.3582114","url":null,"abstract":"Remote sensing imaging is frequently disturbed by clouds which leads to unclear images with low contrast and poor resolution. Cloud obstacles usually arouse the valuable information loss of remote sensing images. Technology of cloud removal from single remote sensing images has attracted worldwide interests since only one image is available. When clouds are distributed unevenly and only a small portion of the images is covered by clouds, it is expected to preserve image information outside of the clouds as much as possible during cloud removal. In this paper, an algorithm based on Faster-RCNN was proposed to detect the cloud regions in the images before cloud removal. The principle of cloud region recognition was analyzed. The structure of Faster-RCNN was introduced. Three convolutional networks i.e. MobilenetV2, Resnet50 and VGG16 were introduced and applied as the backbone of Faster-RCNN respectively. Cloud region recognition algorithm was developed based on Faster-RCNN. Training and testing data sets were established and labeled by applying the proposed remote sensing imaging simulation algorithm to add clouds to the clear remote sensing images. Some experiments were carried out when Faster-RCNN selected MobilenetV2, Resnet50 and VGG16 as its backbone and was trained to optimize the parameters. Their results were compared. It proved the proposed algorithm with MobilenetV2 as the backbone achieved a successful recognition rate of 95% which supports the following cloud removal.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127047218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient CNN-based Automated Leukemia diagnosis Using microscopic blood smear images and Subtypes Classification 基于cnn的基于显微血液涂片图像和亚型分类的高效白血病自动诊断

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582117

Junaid Khan, Kyungsup Kim

引用次数: 1

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582107

A. Elouali, H. Mora, Francisco J. Mora Gimeno

引用次数: 0

Endangered Tujia language Speech Recognition Research based on Audio-Visual Fusion 基于视听融合的濒危土家族语言语音识别研究

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582128

Chongchong Yu, Jiaqi Yu, Zhaopeng Qian, Yuchen Tan

{"title":"Endangered Tujia language Speech Recognition Research based on Audio-Visual Fusion","authors":"Chongchong Yu, Jiaqi Yu, Zhaopeng Qian, Yuchen Tan","doi":"10.1145/3582099.3582128","DOIUrl":"https://doi.org/10.1145/3582099.3582128","url":null,"abstract":"As an endangered language, Tujia language is a non-renewable intangible cultural resource. Automatic speech recognition (ASR) uses artificial intelligence technology to facilitate manually label Tujia language, which is an effective means to protect this language. However, due to the fact that Tujia language has few native speakers, few labeled corpus, and much noise in the corpus. The acoustic models thus suffer from over fitting and lowe noise immunity, which seriously harms the accuracy of recognition. To tackle the deficiencies, an approach of audio-visual speech recognition (AVSR) based on Transformer-CTC is proposed, which reduces the dependence of acoustic models on noise and the quantity of data by introducing visual modality in-formation including lip movements. Specifically, the new approach enhances the expression of speakers’ feature space through the fusion of audio and visual information, thus solving the problem of less available information for single modality. Experiment results show that the optimal CER of AVSR is 8.2% lower than that of traditional models, and 11.8% lower than that for lip reading. The proposed AVSR tackles the issue of low accuracy in recognizing endangered languages. Therefore, AVSR is of great significance in studying the protection and preservation of endangered languages through AI.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125180051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Microservices Containerization in SBCs (Single Board Computers): A Cloud Edge Computing Approach sbc(单板计算机)中的微服务容器化:云边缘计算方法

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582108

Othmane Dahi, Maryem Aboulfoujja, Mohammed Akiour, Bilal Elbouardi, Anass Choukri, M. Abid

{"title":"Microservices Containerization in SBCs (Single Board Computers): A Cloud Edge Computing Approach","authors":"Othmane Dahi, Maryem Aboulfoujja, Mohammed Akiour, Bilal Elbouardi, Anass Choukri, M. Abid","doi":"10.1145/3582099.3582108","DOIUrl":"https://doi.org/10.1145/3582099.3582108","url":null,"abstract":"With the recent and unprecedented increase in demand for Cloud services, furtherly promoted by 5G, Edge computing is emerging as an indispensable technology. Tailored to mitigate the continuously growing load on Cloud data centers and cope with the rising proliferation of IoT (Internet of Things), Single Board Computers (SBCs), embedded systems, and microservices-based applications, edge computing is turning into an integral technology enabler in 5G. Arguably, most of edge microservices will be deployed using virtualization, and specifically using containers instead of VMs (Virtual Machines). Dubbed 5G-MEC (Multi-Access Edge Computing), the 5G edge has to cope with 3 major services: eMBB (enhanced Mobile Broadband), mMTC (Massive Machine Type Communication), and URLLC (Ultra Reliable Low Latency Communication). In this paper, we shed further light on the fundamentals of cloud edge computing and present the subtleties of deploying a real-world SBC-based distributed edge application. The latter is an AI-based application, embedding an image recognition microservice running in containers, deployed in Raspberry PI SBCs, and orchestrated using Kubernetes.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125803132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An evaluation of classifiers for reading resistor colors 对读取电阻颜色的分类器的评价

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582126

Y. Mitani, Wataru Yoshimura, Y. Hamamoto

{"title":"An evaluation of classifiers for reading resistor colors","authors":"Y. Mitani, Wataru Yoshimura, Y. Hamamoto","doi":"10.1145/3582099.3582126","DOIUrl":"https://doi.org/10.1145/3582099.3582126","url":null,"abstract":"A lot of effort has been devoted to reading resistor colors using image processing and pattern recognition techniques. It is not so clear which classifier or machine learning is effective for classifying colors in reading a resistance of a resistor. This paper presents an evaluation of classifiers for reading resistor's colors on an RGB color space under various illumination situations. Eight classifiers to be examined are k-nearest neighbor (k-NN) (k=1, 3, and 5), decision tree (DT), support vector machine (SVM), Gaussian naive Bayes (NB), artificial neural network (ANN), and random forest (RF). The classification performance of 8 classifiers is evaluated by the average error rate, respectively. From the experimental results, depending on the training sample size and illumination situations, the classifier to be used for reading resistor colors should be considered. Considering practical color pattern recognition problems with poor illumination conditions, the 1-NN classifier should be the more practical and usable classifier. This study will provide one of the ways for AI and robotics applications to accurately classify colors.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127952183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding SyncMap: Analyzing the components of Its Dynamical Equation 理解SyncMap:分析其动力学方程的组成部分

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582136

Tham Yik Foong, Danilo Vasconcellos Vargas

引用次数: 0

Artificial Intelligence Usability Evaluation of Entrepreneurial Mindfulness Application 创业正念应用的人工智能可用性评价

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582139

Given Name Sasmoko, Y. Indrianti, Given Name Angeline

{"title":"Artificial Intelligence Usability Evaluation of Entrepreneurial Mindfulness Application","authors":"Given Name Sasmoko, Y. Indrianti, Given Name Angeline","doi":"10.1145/3582099.3582139","DOIUrl":"https://doi.org/10.1145/3582099.3582139","url":null,"abstract":"Mindfulness is a condition that entrepreneurs have and is very important because it involves the alignment of mind, soul, and body so as to produce more appropriate intentions, actions, and strategies. The entrepreneurial mindfulness application was developed with the aim that entrepreneurs can find out their mindfulness capacity. To maximize application development, a usability test is needed so that application developers can find out the response of users to the quality of the application. This study aims to conduct usability testing on the entrepreneurial mindfulness application and find out how the system condition is described according to the user so that developers can find out what the shortcomings are in the application. This research will be conducted on applications that have been developed, namely entrepreneurial mindfulness applications. The method is carried out using Neuroresearch and will be carried out through three stages, namely exploratory, explanatory, and confirmatory. Exploratory research and qualitative research are carried out through theoretical studies and gain an understanding related to User Interface and User Experience. Followed by explanatory and confirmatory research as a form of quantitative research to deepen exploratory research by analyzing the variables obtained. The results of the study through confirmatory research showed that most of the respondents gave feedback in a good category with lower bound - upper bound = 63.5051 - 66.0568.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132929769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiple Robots Path Planning based on Reinforcement Learning for Object Transportation 基于强化学习的多机器人物体运输路径规划

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582133

M. Parnichkun

{"title":"Multiple Robots Path Planning based on Reinforcement Learning for Object Transportation","authors":"M. Parnichkun","doi":"10.1145/3582099.3582133","DOIUrl":"https://doi.org/10.1145/3582099.3582133","url":null,"abstract":"This paper proposes reinforcement learning methods to perform an object transportation task for multiple robots. This task consists of two main subtasks, path planning and motion control task. Double deep Q-learning (DDQN) model is selected to achieve path planning for an unknown environment. To increase the capability of reinforcement learning model, semi-supervised method by A* algorithm is applied during the training process. In motion control task, reinforcement learning model is designed to control a movement of a differential wheeled mobile robot. The actions of mobile robot consisting of linear and angular velocities are computed by agent. The models for motion control task are separately trained for two different purposes. The first agent is trained to deal with the path following task and the other agent is trained to handle the point following task. The agent of the point following task is utilized to control the group of robots to move with a specific formation. Proximal policy optimization (PPO) is selected for the path following task and deep deterministic policy gradient (DDPG) is selected for the point following task. Eventually, the integration of the proposed reinforcement learning models can accomplish the object transportation task for multiple robots successfully both in simulation and experiment.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129233034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0