Proceedings of the 3rd International Conference on Applications of Intelligent Systems最新文献

Application of data anonymization in Learning Analytics 数据匿名化在学习分析中的应用

Proceedings of the 3rd International Conference on Applications of Intelligent Systems Pub Date : 2020-01-07 DOI: 10.1145/3378184.3378229

Janneth Chicaiza, Ma. Carmen Cabrera-Loayza, Rene Elizalde, Nelson Piedra

{"title":"Application of data anonymization in Learning Analytics","authors":"Janneth Chicaiza, Ma. Carmen Cabrera-Loayza, Rene Elizalde, Nelson Piedra","doi":"10.1145/3378184.3378229","DOIUrl":"https://doi.org/10.1145/3378184.3378229","url":null,"abstract":"Thanks to the proliferation of academic services on the Web and the opening of educational content, today, students can access a large number of free learning resources, and interact with value-added services. In this context, Learning Analytics can be carried out on a large scale thanks to the proliferation of open practices that promote the sharing of datasets. However, the opening or sharing of data managed through platforms and educational services, without considering the protection of users' sensitive data, could cause some privacy issues. Data anonymization is a strategy that should be adopted during lifecycle of data processing to reduce security risks. In this research, we try to characterize how much and how the anonymization techniques have been used in learning analytics proposals. From an initial exploration made in the Scopus database, we found that less than 6% of the papers focused on LA have also covered the privacy issue. Finally, through a specific case, we applied data anonymization and learning analytics to demonstrate that both technique can be integrated, in a reliably and effectively way, to support decision making in educational institutions.","PeriodicalId":437507,"journal":{"name":"Proceedings of the 3rd International Conference on Applications of Intelligent Systems","volume":"35 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128228723","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: 3

Control architecture for embedding reinforcement learning frameworks on industrial control hardware 在工业控制硬件上嵌入强化学习框架的控制体系结构

Proceedings of the 3rd International Conference on Applications of Intelligent Systems Pub Date : 2020-01-07 DOI: 10.1145/3378184.3378198

Alexander Schmidt, Florian Schellroth, O. Riedel

引用次数: 5

Improved Computation of Affine Dynamic Time Warping 仿射动态时间翘曲的改进计算

Proceedings of the 3rd International Conference on Applications of Intelligent Systems Pub Date : 2020-01-07 DOI: 10.1145/3378184.3378226

Jiaqi Zhang, Xiaoyi Jiang

引用次数: 0

An Experimental Study of the Impact of Pre-Training on the Pruning of a Convolutional Neural Network 预训练对卷积神经网络修剪影响的实验研究

Proceedings of the 3rd International Conference on Applications of Intelligent Systems Pub Date : 2020-01-07 DOI: 10.1145/3378184.3378224

Nathan Hubens, M. Mancas, M. Decombas, M. Preda, T. Zaharia, B. Gosselin, T. Dutoit

{"title":"An Experimental Study of the Impact of Pre-Training on the Pruning of a Convolutional Neural Network","authors":"Nathan Hubens, M. Mancas, M. Decombas, M. Preda, T. Zaharia, B. Gosselin, T. Dutoit","doi":"10.1145/3378184.3378224","DOIUrl":"https://doi.org/10.1145/3378184.3378224","url":null,"abstract":"In recent years, deep neural networks have known a wide success in various application domains. However, they require important computational and memory resources, which severely hinders their deployment, notably on mobile devices or for real-time applications. Neural networks usually involve a large number of parameters, which correspond to the weights of the network. Such parameters, obtained with the help of a training process, are determinant for the performance of the network. However, they are also highly redundant. The pruning methods notably attempt to reduce the size of the parameter set, by identifying and removing the irrelevant weights. In this paper, we examine the impact of the training strategy on the pruning efficiency. Two training modalities are considered and compared: (1) fine-tuned and (2) from scratch. The experimental results obtained on four datasets (CIFAR10, CIFAR100, SVHN and Caltech101) and for two different CNNs (VGG16 and MobileNet) demonstrate that a network that has been pre-trained on a large corpus (e.g. ImageNet) and then fine-tuned on a particular dataset can be pruned much more efficiently (up to 80% of parameter reduction) than the same network trained from scratch.","PeriodicalId":437507,"journal":{"name":"Proceedings of the 3rd International Conference on Applications of Intelligent Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127908412","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: 6

Towards Service-Oriented and Intelligent Microgrids 面向服务型和智能微电网

Proceedings of the 3rd International Conference on Applications of Intelligent Systems Pub Date : 2020-01-07 DOI: 10.1145/3378184.3378214

Ilche Georgievski, L. Fiorini, Marco Aiello

引用次数: 2

Effective Mutation and Recombination for Evolving Convolutional Networks 进化卷积网络的有效突变和重组

Proceedings of the 3rd International Conference on Applications of Intelligent Systems Pub Date : 2020-01-07 DOI: 10.1145/3378184.3378187

Binay Dahal, J. Zhan

{"title":"Effective Mutation and Recombination for Evolving Convolutional Networks","authors":"Binay Dahal, J. Zhan","doi":"10.1145/3378184.3378187","DOIUrl":"https://doi.org/10.1145/3378184.3378187","url":null,"abstract":"The major part of the success that we have had in deep learning is attributed to the proper design of the architecture of the network model. With all the hardware resources and mathematical foundation at hand, it is the clever assembling of the pieces that define the performance of that deep learning model. Almost all of the research in this field till date use the models that are designed by a human researcher using their past experiences or applying the iterative process of adding components to the model to see which one performs the best. As we strive towards the general Artificial Intelligence, this process of manually tuning the network to make it work on a specific task does not add up well for the cause. We are concerned with automating the task of network architecture design where the learning algorithm tries to discover the best performing model on itself. To do this, we employ an evolving algorithm in addition to the gradient descent to evolve and train the model. Specifically, we use a form of genetic algorithm with mutation and recombination operators to constantly change the architecture, while the commonly used gradient descent is used as a learning algorithm for each of the genetically procreated models. We propose a series of mutation operators and a method of recombination called Highest Varying k-Features Recombination(HVk-FR) to evolve the CNN models. Results show using our method of recombination on top of mutation yields the best accuracy.","PeriodicalId":437507,"journal":{"name":"Proceedings of the 3rd International Conference on Applications of Intelligent Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131811369","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: 3

Factory Maintenance Application Using Augmented Reality 使用增强现实的工厂维护应用程序

Proceedings of the 3rd International Conference on Applications of Intelligent Systems Pub Date : 2020-01-07 DOI: 10.1145/3378184.3378218

S. Coscetti, D. Moroni, G. Pieri, M. Tampucci

{"title":"Factory Maintenance Application Using Augmented Reality","authors":"S. Coscetti, D. Moroni, G. Pieri, M. Tampucci","doi":"10.1145/3378184.3378218","DOIUrl":"https://doi.org/10.1145/3378184.3378218","url":null,"abstract":"Tissue converting lines represent one of the key plant in the paper production field: with them, paper tissue is converted into its final form for domestic and sanitary usage. One of the key points of the tissue converting lines is the productivity and the possibility to follow conversion process at relativity low cost. Despite the actual lines have yet an high productivity, the study of the state of the art has shown that choke points still exist, caused by inadequate automation. In this paper, we present the preliminary results of a project which aims at removing such obstacle towards complete automation, by introducing a set of innovations based on ICT solutions applied to advanced automation. In detail, advanced computer vision and video analytics methods will be applied to pervasively monitor converting lines and to automatically extract process information in order to self-regulate specific machine and global parameters. Big data analysis methodologies will be also integrated to obtain new knowledge and infer optimal management models which could be used for the predictive maintenance. Augmented reality interfaces are being designed and developed to support converting line monitoring and maintenance, both ordinary and extraordinary. An Artificial Intelligence module provides suggestions and instructions to the operators in order to guarantee production level even in case of unskilled staff. The automation of such processes will improve factory safety, decrease manual interventions and, thus, will increase production line up-time and efficiency.","PeriodicalId":437507,"journal":{"name":"Proceedings of the 3rd International Conference on Applications of Intelligent Systems","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134583702","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: 12

From Object Detection to Room Categorization in Robotics 机器人学中从物体检测到房间分类

Proceedings of the 3rd International Conference on Applications of Intelligent Systems Pub Date : 2020-01-07 DOI: 10.1145/3378184.3378230

David Fernandez-Chaves, J. Ruiz-Sarmiento, N. Petkov, J. Gonzalez-Jimenez

引用次数: 9

To Survive in a CBRN Hostile Environment: Application of CAVE Automatic Virtual Environments in First Responder Training 在CBRN敌对环境中生存:CAVE自动虚拟环境在急救人员训练中的应用

Proceedings of the 3rd International Conference on Applications of Intelligent Systems Pub Date : 2020-01-07 DOI: 10.1145/3378184.3378212

P. Maciejewski, M. Gawlik-Kobylińska, J. Lebiedź, Witold Ostant, Doğan Aydın

{"title":"To Survive in a CBRN Hostile Environment: Application of CAVE Automatic Virtual Environments in First Responder Training","authors":"P. Maciejewski, M. Gawlik-Kobylińska, J. Lebiedź, Witold Ostant, Doğan Aydın","doi":"10.1145/3378184.3378212","DOIUrl":"https://doi.org/10.1145/3378184.3378212","url":null,"abstract":"This paper is of a conceptual nature and focuses on the use of a specific virtual reality environment in civil-military training. We analyzed the didactic potential of so-called CAVE automatic virtual environments for First Responder training, a type of training that fills the gap between First Aid training and the training received by emergency medical technicians. Since real training involves live drills based on unexpected situations, it is expensive and difficult to organize. We propose the application of virtual environments of three different sizes to increase the effectiveness of First Responder training. Our findings show that using the CAVEs allows for easier adaptation of the learning environment to a specific scenario while reducing costs from preparing training fields. The environments are also safer for trainees who must perform tasks related to hazardous materials. The analysis was based on the facilities at the Immersive 3D Visualization Lab (I3DVL) at Gdańsk University of Technology in Poland. We believe that, in the context of emerging threats of chemical, biological, radiological, and nuclear terrorism, such training is a necessity. The application of artificial intelligence can be considered as a further step in the facility development to increase the training fidelity.","PeriodicalId":437507,"journal":{"name":"Proceedings of the 3rd International Conference on Applications of Intelligent Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125693737","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: 5

A CNN based monitoring system to minimize medication errors during treatment process at home 一个基于CNN的监控系统，以尽量减少在家治疗过程中的药物错误

Proceedings of the 3rd International Conference on Applications of Intelligent Systems Pub Date : 2020-01-07 DOI: 10.1145/3378184.3378223

Muddasar Naeem, Giovanni Paragliola, A. Coronato, G. Pietro

引用次数: 12