Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284338
O. Parlangeli, Stefano Guidi, E. Marchigiani, P. Palmitesta, A. Andreadis, S. Roncato
Safety may depends crucially on making moral judgments. To date we have a lack of knowledge about the possibility of intervening in the processes that lead to moral judgments in relation to the behavior of artificial agents. The study reported here involved 293 students from the University of Siena who made moral judgments after reading the description of an event in which a person or robot killed other people or robots. The study was conducted through an online questionnaire. The results suggest that moral judgments essentially depend on the type of victim and that are different if they involve human or artificial agents. Furthermore, some characteristics of the evaluators, such as the greater or lesser disposition to attribute mental states to artificial agents, have an influence on these evaluations. On the other hand, the level of familiarity with these systems seems to have a limited effect.
{"title":"How guilty is a robot who kills other robots?","authors":"O. Parlangeli, Stefano Guidi, E. Marchigiani, P. Palmitesta, A. Andreadis, S. Roncato","doi":"10.1109/IISA50023.2020.9284338","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284338","url":null,"abstract":"Safety may depends crucially on making moral judgments. To date we have a lack of knowledge about the possibility of intervening in the processes that lead to moral judgments in relation to the behavior of artificial agents. The study reported here involved 293 students from the University of Siena who made moral judgments after reading the description of an event in which a person or robot killed other people or robots. The study was conducted through an online questionnaire. The results suggest that moral judgments essentially depend on the type of victim and that are different if they involve human or artificial agents. Furthermore, some characteristics of the evaluators, such as the greater or lesser disposition to attribute mental states to artificial agents, have an influence on these evaluations. On the other hand, the level of familiarity with these systems seems to have a limited effect.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116659453","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284147
J. Han, Chulhee Lee
In this paper, we propose a color lane line detection algorithm that can be used with moving vehicles. First, to reduce false detection and processing time, we considered the geometric relationship of the camera and the vanishing point. To effectively utilize color images for color line detection, we used the edge detection method based on the Bhattacharyya distance and a morphological operation. The proposed method was tested under various conditions (highway, urban, others; sunny, cloudy, early evening) and the experimental results show promising performance.
{"title":"Color Lane Line Detection Using the Bhattacharyya Distance","authors":"J. Han, Chulhee Lee","doi":"10.1109/IISA50023.2020.9284147","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284147","url":null,"abstract":"In this paper, we propose a color lane line detection algorithm that can be used with moving vehicles. First, to reduce false detection and processing time, we considered the geometric relationship of the camera and the vanishing point. To effectively utilize color images for color line detection, we used the edge detection method based on the Bhattacharyya distance and a morphological operation. The proposed method was tested under various conditions (highway, urban, others; sunny, cloudy, early evening) and the experimental results show promising performance.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123307238","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284370
Nikolaos I. Papandrianos, E. Papageorgiou, Athanasios Anagnostis, Konstantinos Papageorgiou, Anna Feleki, D. Bochtis
Focusing on prostate cancer patients, this research paper addresses the problem of bone metastasis diagnosis, investigating the capabilities of convolutional neural networks (CNN) and transfer learning. Considering the wide applicability of CNNs in medical image classification, VGG16 and DenseNet, as being two efficient types of deep neural networks, are exploited for images recognition, being used to properly classify an image by extracting its insightful features. The purpose of this study is to explore the capabilities of transfer learning in VGG16 and DenseNet application process, which will be able to classify bone scintigraphy images in patients suffering from prostate cancer. Efficient VGG16 and DenseNet architectures were built based on a CNN exploration process for bone metastasis diagnosis and then were employed to identify the metastasis from the bone scintigraphy image data. The classification task is a three-class problem, which classifies images as normal, malignant, and healthy images with degenerative changes. The results revealed that both methods are sufficiently accurate to differentiate the metastatic bone from degenerative changes as well as from normal tissue.
{"title":"Development of Convolutional Neural Networkbased models for bone metastasis classification in nuclear medicine","authors":"Nikolaos I. Papandrianos, E. Papageorgiou, Athanasios Anagnostis, Konstantinos Papageorgiou, Anna Feleki, D. Bochtis","doi":"10.1109/IISA50023.2020.9284370","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284370","url":null,"abstract":"Focusing on prostate cancer patients, this research paper addresses the problem of bone metastasis diagnosis, investigating the capabilities of convolutional neural networks (CNN) and transfer learning. Considering the wide applicability of CNNs in medical image classification, VGG16 and DenseNet, as being two efficient types of deep neural networks, are exploited for images recognition, being used to properly classify an image by extracting its insightful features. The purpose of this study is to explore the capabilities of transfer learning in VGG16 and DenseNet application process, which will be able to classify bone scintigraphy images in patients suffering from prostate cancer. Efficient VGG16 and DenseNet architectures were built based on a CNN exploration process for bone metastasis diagnosis and then were employed to identify the metastasis from the bone scintigraphy image data. The classification task is a three-class problem, which classifies images as normal, malignant, and healthy images with degenerative changes. The results revealed that both methods are sufficiently accurate to differentiate the metastatic bone from degenerative changes as well as from normal tissue.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116025526","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284358
Xenofon Pournaras, Dimitrios A. Koutsomitropoulos
In the present paper we make a comparative study and evaluation of frameworks and libraries for deep learning purposes on the client-side, considering libraries such as TensorFlow.js, brain.js, Keras.js, ConvNet.js and others. It is examined how feasible and efficient it is to execute deep learning tasks, using client-side libraries and frameworks in contrast to the conventional approach. Moreover, we focus on the computer vision field of object detection and we examine the problem of object detection through different state-of-the-art approaches and object detectors. At the same time, we evaluate whether it is feasible and efficient to detect objects in the browser environment using a prototype implementation based on some of the libraries that are studied.
{"title":"Deep Learning on the Web: State-of-the-art Object Detection using Web-based Client-side Frameworks","authors":"Xenofon Pournaras, Dimitrios A. Koutsomitropoulos","doi":"10.1109/IISA50023.2020.9284358","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284358","url":null,"abstract":"In the present paper we make a comparative study and evaluation of frameworks and libraries for deep learning purposes on the client-side, considering libraries such as TensorFlow.js, brain.js, Keras.js, ConvNet.js and others. It is examined how feasible and efficient it is to execute deep learning tasks, using client-side libraries and frameworks in contrast to the conventional approach. Moreover, we focus on the computer vision field of object detection and we examine the problem of object detection through different state-of-the-art approaches and object detectors. At the same time, we evaluate whether it is feasible and efficient to detect objects in the browser environment using a prototype implementation based on some of the libraries that are studied.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123367711","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}
As optical networks urgently seek for extra capacity, Space Division Multiplexing (SDM) seems to be able to provide it. SDM can deploy multiple spatial channels in multi-core (MCFs) and/or multi-mode fibers (MMFs), and so, can increase the total transmission capacity. Specific integrated and scalable components will be needed to support the new multiplexing technology. In this paper we present a brief survey of recent progress in SDM and a taxonomy of up to date SDM experimental demonstrations and SDM network components. Finally we are discussing SDM research challenges and address fields of future interest.
{"title":"Progress and Demonstrations on Space Division Multiplexing","authors":"Charalampos Papapavlou, Konstantinos Paximadis, Giannis Tzimas","doi":"10.1109/IISA50023.2020.9284353","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284353","url":null,"abstract":"As optical networks urgently seek for extra capacity, Space Division Multiplexing (SDM) seems to be able to provide it. SDM can deploy multiple spatial channels in multi-core (MCFs) and/or multi-mode fibers (MMFs), and so, can increase the total transmission capacity. Specific integrated and scalable components will be needed to support the new multiplexing technology. In this paper we present a brief survey of recent progress in SDM and a taxonomy of up to date SDM experimental demonstrations and SDM network components. Finally we are discussing SDM research challenges and address fields of future interest.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128935600","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284373
A. Chorianopoulos, Ioannis Daramouskas, I. Perikos, F. Grivokostopoulou, I. Hatzilygeroudis
Breast Cancer is one of the most common cancers among women that affects about 10% of women worldwide. Although there are available treatments for bread cancer, the real challenge is to be properly detected it in early stages, a challenge that doctors and patients encounter constantly. In this study, we examine the performance of different deep learning models and depth-wise convolutional neural networks in medical imaging and assess their performance on breast cancer detection from ultrasounds and breast histopathology images. Experimental results suggest that the proposed deep learning models can effectively recognize breast cancer from ultrasound and histopathology images. The performance of the Convolutional Neural Network models reached 96.82% accuracy on ultrasounds, 88.23% on breast histology with cases of Invasive Ductal Carcinoma (IDC) and 91.04% on cancer-free tissue. The results are very promising and point out that deep-learning methods and depth-wise convolutional neural networks are very assistive in the diagnosis of breast cancer from ultrasound and histopathology images.
{"title":"Deep Learning Methods in Medical Imaging for the Recognition of Breast Cancer","authors":"A. Chorianopoulos, Ioannis Daramouskas, I. Perikos, F. Grivokostopoulou, I. Hatzilygeroudis","doi":"10.1109/IISA50023.2020.9284373","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284373","url":null,"abstract":"Breast Cancer is one of the most common cancers among women that affects about 10% of women worldwide. Although there are available treatments for bread cancer, the real challenge is to be properly detected it in early stages, a challenge that doctors and patients encounter constantly. In this study, we examine the performance of different deep learning models and depth-wise convolutional neural networks in medical imaging and assess their performance on breast cancer detection from ultrasounds and breast histopathology images. Experimental results suggest that the proposed deep learning models can effectively recognize breast cancer from ultrasound and histopathology images. The performance of the Convolutional Neural Network models reached 96.82% accuracy on ultrasounds, 88.23% on breast histology with cases of Invasive Ductal Carcinoma (IDC) and 91.04% on cancer-free tissue. The results are very promising and point out that deep-learning methods and depth-wise convolutional neural networks are very assistive in the diagnosis of breast cancer from ultrasound and histopathology images.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132812159","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284356
Apostolos Xenakis, Georgios Papastergiou, V. Gerogiannis, G. Stamoulis
Plant diseases are major threat to green product quality and agricultural productivity. Agronomists and farmers often encounter great difficulties in early detection of plant diseases and controlling their potential production damages. Thus, it is of great importance for stakeholders to diagnose plant diseases at very early stages of plant growing by exploiting state-of-the art technologies, consider appropriate actions and avoid further economic losses. Artificial Intelligence (AI) techniques, field sensors, data analytics and inference algorithms are some contemporary tools which could be helpful for early plant disease diagnosis. In this paper, we present a plant Disease Diagnosis Support System (DDSS) that utilizes an Internet of Things platform to control a lightweight robotic system. The DDSS applies a Convolution Neural Network learning algorithm to perform early plant disease diagnosis and classification. The system can help farmers to apply appropriate precision agriculture actions and better control their production. The proposed DDSS achieves around 98% success classification rate, according to our demonstration case study.
{"title":"Applying a Convolutional Neural Network in an IoT Robotic System for Plant Disease Diagnosis","authors":"Apostolos Xenakis, Georgios Papastergiou, V. Gerogiannis, G. Stamoulis","doi":"10.1109/IISA50023.2020.9284356","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284356","url":null,"abstract":"Plant diseases are major threat to green product quality and agricultural productivity. Agronomists and farmers often encounter great difficulties in early detection of plant diseases and controlling their potential production damages. Thus, it is of great importance for stakeholders to diagnose plant diseases at very early stages of plant growing by exploiting state-of-the art technologies, consider appropriate actions and avoid further economic losses. Artificial Intelligence (AI) techniques, field sensors, data analytics and inference algorithms are some contemporary tools which could be helpful for early plant disease diagnosis. In this paper, we present a plant Disease Diagnosis Support System (DDSS) that utilizes an Internet of Things platform to control a lightweight robotic system. The DDSS applies a Convolution Neural Network learning algorithm to perform early plant disease diagnosis and classification. The system can help farmers to apply appropriate precision agriculture actions and better control their production. The proposed DDSS achieves around 98% success classification rate, according to our demonstration case study.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132926982","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284413
Andreas Kaloudis, D. Tsolis, Theodore Koutsobinas
The major perspective of this paper is to provide more evidence into the empirical determinants of capital structure choice by focusing and discussing the relative importance of firm-specific and macroeconomic variables from an alternative scope in U.S. This study extends the empirical research on the topic of capital structure by focusing on a quantile regression method in order to investigate the behavior of firm-specific characteristics and macroeconomic variables across all quantiles of distribution of leverage (total debt, long-terms debt and short-terms debt). We thus based on a partial adjustment model, find that long-term and short-term debt ratios varying regarding their partial adjustment speeds; the short-term debt raised up while the long-term debt ratio slows down for same periods.
{"title":"Capital Structure Determinants And Speed Of Adjustment In Us (including cultural industries). A Quantile Regression Approach","authors":"Andreas Kaloudis, D. Tsolis, Theodore Koutsobinas","doi":"10.1109/IISA50023.2020.9284413","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284413","url":null,"abstract":"The major perspective of this paper is to provide more evidence into the empirical determinants of capital structure choice by focusing and discussing the relative importance of firm-specific and macroeconomic variables from an alternative scope in U.S. This study extends the empirical research on the topic of capital structure by focusing on a quantile regression method in order to investigate the behavior of firm-specific characteristics and macroeconomic variables across all quantiles of distribution of leverage (total debt, long-terms debt and short-terms debt). We thus based on a partial adjustment model, find that long-term and short-term debt ratios varying regarding their partial adjustment speeds; the short-term debt raised up while the long-term debt ratio slows down for same periods.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133423751","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284384
Effrosyni Sigala, Efthymios Alepis, C. Patsakis
Smartphones are considered as indispensable parts of humans in modern life, both in terms of mobile computing and communication and also as valuable components in the IoT era. They consist of a plethora of sensors which, when combined and counted in the scale of thousands or even millions, realize crowdsensing and provide solutions and novel applications in many aspects of human and social life. This paper introduces an innovative mobile application that has been deployed and successfully evaluated by users, that targets in measuring the street surface quality of cities, providing useful and up to now unavailable information both for people and also for municipalities all around the globe.
{"title":"Measuring the Quality of Street Surfaces in Smart Cities through Smartphone Crowdsensing","authors":"Effrosyni Sigala, Efthymios Alepis, C. Patsakis","doi":"10.1109/IISA50023.2020.9284384","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284384","url":null,"abstract":"Smartphones are considered as indispensable parts of humans in modern life, both in terms of mobile computing and communication and also as valuable components in the IoT era. They consist of a plethora of sensors which, when combined and counted in the scale of thousands or even millions, realize crowdsensing and provide solutions and novel applications in many aspects of human and social life. This paper introduces an innovative mobile application that has been deployed and successfully evaluated by users, that targets in measuring the street surface quality of cities, providing useful and up to now unavailable information both for people and also for municipalities all around the globe.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124830027","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284341
Eirini Mougiakou, Spyros Papadimitriou, M. Virvou
The continuous evolution of technology affects various areas of people’s daily lives. One of them is the field of education, where the use of technological means allows alternative ways of teaching. The spread of the SARS-CoV-2 coronavirus, which has led to the closure of educational institutions in many countries, has increased the significance of educational platforms. Some platforms allow synchronous communication between the tutor and the student, i.e., the tutoring process takes place at a predetermined time, simulating conventional training. Asynchronous educational platforms enable the student to study and solve exercises in the time and at the pace of their own choice. However, there are questions about users’ data, especially considering the General Data Protection Regulation 1 (GDPR) in force since 25 May 2018. In this article, we describe the features that distinguish synchronous from asynchronous learning systems and identify their points of impact with specific GDPR elements, respectively. Particularly for the asynchronous method, we focus on platforms that process user data and appropriately adapt the educational material. Having identified the impact points, we address the issue by providing guidelines for similar system designers. We also compare the two methods in terms of their benefits, taking into account the design needed for GDPR compliance.
{"title":"Synchronous and Asynchronous Learning Methods under the light of General Data Protection Regulation","authors":"Eirini Mougiakou, Spyros Papadimitriou, M. Virvou","doi":"10.1109/IISA50023.2020.9284341","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284341","url":null,"abstract":"The continuous evolution of technology affects various areas of people’s daily lives. One of them is the field of education, where the use of technological means allows alternative ways of teaching. The spread of the SARS-CoV-2 coronavirus, which has led to the closure of educational institutions in many countries, has increased the significance of educational platforms. Some platforms allow synchronous communication between the tutor and the student, i.e., the tutoring process takes place at a predetermined time, simulating conventional training. Asynchronous educational platforms enable the student to study and solve exercises in the time and at the pace of their own choice. However, there are questions about users’ data, especially considering the General Data Protection Regulation 1 (GDPR) in force since 25 May 2018. In this article, we describe the features that distinguish synchronous from asynchronous learning systems and identify their points of impact with specific GDPR elements, respectively. Particularly for the asynchronous method, we focus on platforms that process user data and appropriately adapt the educational material. Having identified the impact points, we address the issue by providing guidelines for similar system designers. We also compare the two methods in terms of their benefits, taking into account the design needed for GDPR compliance.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"7 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129839884","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}
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