Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference最新文献

Trajectory tracking control strategy of manipulator based on improved particle swarm optimization algorithm 基于改进粒子群算法的机械臂轨迹跟踪控制策略

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508293

Mingyi Gang, Xiao-hai Pan, Kaiyuan Tang, Xingguo Xia, Ben Feng

引用次数: 1

Conditional Drive Environment Translation using StarGAN with CBIN 使用StarGAN和CBIN的条件驱动环境转换

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508267

Rina Komatsu, Keisuke Yamazaki

{"title":"Conditional Drive Environment Translation using StarGAN with CBIN","authors":"Rina Komatsu, Keisuke Yamazaki","doi":"10.1145/3508259.3508267","DOIUrl":"https://doi.org/10.1145/3508259.3508267","url":null,"abstract":"Automatic driving without human's control requires a lot of training to be able to adopt any situation. About situation, the environment at driving has the variety such as dark at night, wet road because of rain and the stack of snow. Preparing the drive image dataset with a lot of environment situations is the difficult task in collecting. This study tried solving the preparing problem by constructing deep learning model with limited data and translating single image to multi environment situations. For accomplishing this subject, we employed N domains translation model called StarGAN. In this paper, we investigated the StarGAN with enough conditional translation performance through comparing visualized results and FID score. We trained StarGANs including original to translate single image to 6 kinds of drive environment domain: daytime & clear, daytime & rainy, daytime & snowy, night & clear, night & rainy and night & snowy. Through experiments, we found the StarGAN employing “CBIN: Central Biasing Instance Normalization” and “AdaLIN: Adaptive Layer-Instance Normalization” at Generator, and “the adversarial loss of CAM Logit” at Discriminator could mark the lower FID score than original StarGAN.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117216571","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

Research on Influencing Factors of Government Audit Big Data Capability 政府审计大数据能力的影响因素研究

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508284

Yu Sun, Yanfang Niu, L. Lu

引用次数: 0

University Students’ Perception on the Usefulness of the Incorporation of Conversational Agents in Mathematics Learning 大学生对融入会话主体在数学学习中有用性的认知

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508292

Choo-Peng Tan, C. K. Yeap, Oi Leng Chong, Yann Sheng Chan

{"title":"University Students’ Perception on the Usefulness of the Incorporation of Conversational Agents in Mathematics Learning","authors":"Choo-Peng Tan, C. K. Yeap, Oi Leng Chong, Yann Sheng Chan","doi":"10.1145/3508259.3508292","DOIUrl":"https://doi.org/10.1145/3508259.3508292","url":null,"abstract":"The open-ended interactions between the educator and student could definitely achieve a great success of teaching and learning. However, this is not easy to apply in the university classes as most classes are large size. Conversational agents, automated computer software that interact with human users through human language conversations, may implement into education to support the teaching and learning process. Unfortunately, this application in education domain is found scarce even though it has about 60 years’ history. This study is to design, develop and incorporate a conversational agent in the teaching and learning of undergraduate's mathematics subject. A quasi experimental design with a convenient sample was used. The students were brief and used it for learning outside the classroom as a blended learning tool for a month when they were taught by the lecturer in face-to-face classes. After that, they participated a 5 Likert-scale adapted questionnaire on perception toward this incorporation. Results shown that majority of them perceived positively on the aspect of usefulness of incorporation in their learning. The results cannot be generalized in accordance for all subjects, undergraduates and universities, but, may serve as a reference to design and develop educational conversational agent to be blended into learning for other subjects or universities.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129627822","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: 1

Resource Allocation Strategy during COVID-19 Period and Linear Programming Model Based on Three Meta-Heuristic Algorithms 新冠肺炎期间资源分配策略及基于三种元启发式算法的线性规划模型

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508289

Gula Da, Jinxing Zhao

{"title":"Resource Allocation Strategy during COVID-19 Period and Linear Programming Model Based on Three Meta-Heuristic Algorithms","authors":"Gula Da, Jinxing Zhao","doi":"10.1145/3508259.3508289","DOIUrl":"https://doi.org/10.1145/3508259.3508289","url":null,"abstract":"English Teachers resource allocation problem (TRAP) which is a highly complex multi-level system is a talent scheduling problem (TSP) with limited human, material and financial resources. It is of great significance to study the allocation of teacher resource in a century-long plan based on education. In this paper, under the effective control of COVID-19, taking the Bayannur City of Inner Mongolia as an example, teaching sites are set up to study the TRAP for the resumption of classes in the graduating grade. In order to minimize the total cost of the whole distribution system, a multi-objective linear hybrid model (MOLHM) is proposed based on the fact about different demands on the number of teachers in each site, the different daily salary of teachers with different teaching experience and degree level, and the different cost of transporting teachers to respective destination. And three heuristic algorithms, ant colony optimization algorithm (ACOA), tabu search algorithm (TSA) and particle swarm optimization algorithm (PSOA) are used to solve the model. Through numerical experiments, the feasibility of them is verified, and the performances of them is compared in terms of optimization results and running time. In the system of the paper, the optimization result of ACOA is optimal, and TSA has better performance of running time. Under the condition that the equal number of ants and particles, the running time of PSOA is better than that of ACOA.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123670764","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: 1

A Neural Network Model and Framework for an Automatic Evaluation of Image Descriptions based on NCAM Image Accessibility Guidelines 基于NCAM图像可及性准则的图像描述自动评价的神经网络模型与框架

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508269

R. Shrestha

{"title":"A Neural Network Model and Framework for an Automatic Evaluation of Image Descriptions based on NCAM Image Accessibility Guidelines","authors":"R. Shrestha","doi":"10.1145/3508259.3508269","DOIUrl":"https://doi.org/10.1145/3508259.3508269","url":null,"abstract":"Millions of people who are either blind or visually impaired have difficulty understanding the content in an image. To address the problem textual image descriptions or captions are provided separately or as alternative texts on the web so that the users can read them through a screen reader. However, most of the image descriptions provided are inadequate to make them accessible enough. Image descriptions could be written either manually or automatically generated using software tools. There are tools, methods, and metrics used to evaluate the quality of the generated text. However, almost all of them are word-similarity-based and generic. Even though there are standard guidelines such as WCAG2.0 and NCAM image accessibility guidelines, they are rarely used in the evaluation of image descriptions. In this paper, we propose a neural network-based framework and models for an automatic evaluation of image descriptions in terms of compliance with the NCAM guidelines. A custom dataset was created from a widely used Flickr8K dataset to train and test the models. The experimental results show the proposed framework performing very well with an average accuracy of above 98%. We believe that the framework could be helpful and useful for the authors of image descriptions in writing accessible image descriptions for the users.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121667110","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: 1

Location of Regions of Interest in Tepscan images: Using Entropy Thresholding Associated with a Direction Vector and Related Component Analysis 扫描图像中感兴趣区域的定位:使用与方向向量相关的熵阈值和相关成分分析

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508263

Karen Zig

{"title":"Location of Regions of Interest in Tepscan images: Using Entropy Thresholding Associated with a Direction Vector and Related Component Analysis","authors":"Karen Zig","doi":"10.1145/3508259.3508263","DOIUrl":"https://doi.org/10.1145/3508259.3508263","url":null,"abstract":"Positron emission tomography (PET) is a commonly used examination nowadays, especially in cancerology. Thus, many methods of segmentation of Regions Of Interest (ROI) on PET images have been proposed in the literature. Among these methods, we can note iterative approaches, considering the characteristics of the patient, others based on pattern recognition, watersheds, etc. These methods have one major inconvenience: they require a calibration step on each device and each PET image reconstruction method. One can also mention the great algorithmic complexity that they induce. The aim of this work is to highlight hypermetabolic foci, our ROIs. To this end, we present an adaptation of image segmentation by two-dimensional entropy maximization, based on \"recuit microcanonique\". The search for segmentation thresholds, to which we add a direction, is carried out in steps of decreasing energy. In this process, the computation time as well as the localization of the ROI improves. The algorithm is tested on Tepscan images in DICOM format and compared to images where the area of interest has been manually marked.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128860418","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

A Deep Neural Network Based Financial Statement Fraud Detection Model: Evidence from China 基于深度神经网络的财务报表舞弊检测模型:来自中国的证据

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508280

Yurou Wang, Ruixue Li, Yanfang Niu

引用次数: 0

Time-to-Failure Prediction of Electronic Devices Based on Hawkes Point Process 基于Hawkes点过程的电子器件失效时间预测

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508285

L. Guan, Jinglong Guan, Jiacheng Li

引用次数: 0

An Approach to Assess Air Quality using Deep Learning with BRB 基于BRB的深度学习空气质量评估方法

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508294

Asfia Kawnine, Z. Sultana, L. Nahar

{"title":"An Approach to Assess Air Quality using Deep Learning with BRB","authors":"Asfia Kawnine, Z. Sultana, L. Nahar","doi":"10.1145/3508259.3508294","DOIUrl":"https://doi.org/10.1145/3508259.3508294","url":null,"abstract":"Air quality estimation is very important to maintain a sustainable world. In this industrial world the environment has adverse effects due to air pollution. Incidents of air pollution is increasing day by day, there is a necessity to predict such occurrence to save human lives. Many expensive sensors have been used to measure this caution; different methods have been applied to solve this problem. Deep learning is a data driven approach which is successfully used to maintain a sustainable environment and also capable to find out hidden features by analyzing enormous data. To assess air quality using deep learning this research used sensor data which may have different kinds of uncertainty. To handle this uncertainty here deep learning technique is integrated with belief Rule based Expert System (BRBES). BRBES is a rule based approach which gives exact prediction based on knowledge base and inference engine. This paper proposed a Convolutional Neural Network (CNN) as a deep learning method which is a combination of convolutional layers and pooling layers to determine multiclass feature, using softmax function. To predict air quality this integrated approach gives remarkable result.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130077147","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