Pub Date : 2021-01-01DOI: 10.5220/0010534501170122
Guy Toko, Kagisho Losaba
In a world of instant information, information privacy and security are under constant attack. With that being the case, organisations are expected to comply with regulations of securing and ensuring that information assets are protected. Employees are also expected to operate within the set frameworks that have been adopted by the organisation, which brings about the question of digital literacy among the workforce in order to achieve the set goals. The security of information alludes to the manner in which information is stored, processed and transmitted in order to comply with the organisation’s information systems frameworks. The privacy of information can be described as the safeguarding of information related to a particular subject’s identity. In addition, the security of information is a significant instrument for ensuring information resources and business goals, while privacy is centred on the safety of a person's rights and privileges concerning similar
{"title":"Improving Information Privacy and Security: Strengthening Digital Literacy in Organisations","authors":"Guy Toko, Kagisho Losaba","doi":"10.5220/0010534501170122","DOIUrl":"https://doi.org/10.5220/0010534501170122","url":null,"abstract":"In a world of instant information, information privacy and security are under constant attack. With that being the case, organisations are expected to comply with regulations of securing and ensuring that information assets are protected. Employees are also expected to operate within the set frameworks that have been adopted by the organisation, which brings about the question of digital literacy among the workforce in order to achieve the set goals. The security of information alludes to the manner in which information is stored, processed and transmitted in order to comply with the organisation’s information systems frameworks. The privacy of information can be described as the safeguarding of information related to a particular subject’s identity. In addition, the security of information is a significant instrument for ensuring information resources and business goals, while privacy is centred on the safety of a person's rights and privileges concerning similar","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"119 1","pages":"117-122"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78134035","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 : 2021-01-01DOI: 10.1007/978-3-031-37320-6_3
B. Ferreira, B. Lima, Tiago F. Vieira
{"title":"Evaluating Deep Learning Models for the Automatic Inspection of Collective Protective Equipment","authors":"B. Ferreira, B. Lima, Tiago F. Vieira","doi":"10.1007/978-3-031-37320-6_3","DOIUrl":"https://doi.org/10.1007/978-3-031-37320-6_3","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"45 1","pages":"49-66"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91198709","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 : 2021-01-01DOI: 10.1007/978-3-031-37320-6_6
Ondrej Lukás, S. García
{"title":"Disrupting Active Directory Attacks with Deep Learning for Organic Honeyuser Placement","authors":"Ondrej Lukás, S. García","doi":"10.1007/978-3-031-37320-6_6","DOIUrl":"https://doi.org/10.1007/978-3-031-37320-6_6","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"6 1","pages":"111-133"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80712460","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 : 2021-01-01DOI: 10.1007/978-3-031-37320-6_7
M. H. Talukder, Shuhei Ota, M. Takanokura, N. Ishii
{"title":"Crack Detection on Brick Walls by Convolutional Neural Networks Using the Methods of Sub-dataset Generation and Matching","authors":"M. H. Talukder, Shuhei Ota, M. Takanokura, N. Ishii","doi":"10.1007/978-3-031-37320-6_7","DOIUrl":"https://doi.org/10.1007/978-3-031-37320-6_7","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"11 1","pages":"134-150"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73072239","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 : 2021-01-01DOI: 10.1007/978-3-031-37320-6_5
Yassir Alharbi, Daniel Arribas-Bel, F. Coenen
{"title":"Forecasting the UN Sustainable Development Goals","authors":"Yassir Alharbi, Daniel Arribas-Bel, F. Coenen","doi":"10.1007/978-3-031-37320-6_5","DOIUrl":"https://doi.org/10.1007/978-3-031-37320-6_5","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"25 1","pages":"88-110"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73878532","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 : 2021-01-01DOI: 10.5220/0010601301850190
Yan-Yu Lin, C. Yu, Chuen-Horng Lin
: This study is segmentation the car parts in a car model data collection and then use the segment car parts to generate large car texture images to provide automatic detection and classification of future 3D car models. The segmentation of car parts proposed in this study is divided into simple and fine car parts segmentation. Since there are few texture images of car parts, this study produces various parts to generate many automobile texture images. First, segment the parts after texture images in an automated method, change the RGB arrangement, change the color, and rotate the parts differently. Also, this study made various changes to the background, and then it randomly combined large texture images with various parts and the background. In the experiment, the car parts were divided into 6 categories: the left door, the right door, the roof, the front body, the rear body, and the wheels. In the performance of automated car parts segmentation technology, the simple and fine car parts segmentation has good results in texture images. Next, the segment car parts and use multiple groups to generate large car texture images automatically. It is hoped that we can practically apply these results to simulation systems.
{"title":"Automatically Segmentation the Car Parts and Generate a Large Car Texture Images","authors":"Yan-Yu Lin, C. Yu, Chuen-Horng Lin","doi":"10.5220/0010601301850190","DOIUrl":"https://doi.org/10.5220/0010601301850190","url":null,"abstract":": This study is segmentation the car parts in a car model data collection and then use the segment car parts to generate large car texture images to provide automatic detection and classification of future 3D car models. The segmentation of car parts proposed in this study is divided into simple and fine car parts segmentation. Since there are few texture images of car parts, this study produces various parts to generate many automobile texture images. First, segment the parts after texture images in an automated method, change the RGB arrangement, change the color, and rotate the parts differently. Also, this study made various changes to the background, and then it randomly combined large texture images with various parts and the background. In the experiment, the car parts were divided into 6 categories: the left door, the right door, the roof, the front body, the rear body, and the wheels. In the performance of automated car parts segmentation technology, the simple and fine car parts segmentation has good results in texture images. Next, the segment car parts and use multiple groups to generate large car texture images automatically. It is hoped that we can practically apply these results to simulation systems.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"47 1","pages":"185-190"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90042774","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 : 2021-01-01DOI: 10.5220/0010616000590066
Stanley T. Yu, Gangming Zhao
Predicting the malignancy of pulmonary nodules found in chest CT images have become much more accurate due to powerful deep convolutional neural networks. However, attributes, such as lobulation, spiculation, and texture, as well as the correlations and dependencies among such attributes have rarely been exploited in deep learning-based algorithms albeit they are frequently used by human experts during nodule assessment. In this paper, we propose a hybrid machine learning framework consisting of two relation modeling modules: Attribute Graph Network and Bayesian Network, which effectively take advantage of attributes and the correlations and dependencies among them to improve the classification performance of pulmonary nodules. According to experiments on the LIDC−IDRI benchmark dataset, our method achieves an accuracy of 93.59%, which gains a 4.57% improvement over the 3D Dense-FPN baseline.
{"title":"Attribute Relation Modeling for Pulmonary Nodule Malignancy Reasoning","authors":"Stanley T. Yu, Gangming Zhao","doi":"10.5220/0010616000590066","DOIUrl":"https://doi.org/10.5220/0010616000590066","url":null,"abstract":"Predicting the malignancy of pulmonary nodules found in chest CT images have become much more accurate due to powerful deep convolutional neural networks. However, attributes, such as lobulation, spiculation, and texture, as well as the correlations and dependencies among such attributes have rarely been exploited in deep learning-based algorithms albeit they are frequently used by human experts during nodule assessment. In this paper, we propose a hybrid machine learning framework consisting of two relation modeling modules: Attribute Graph Network and Bayesian Network, which effectively take advantage of attributes and the correlations and dependencies among them to improve the classification performance of pulmonary nodules. According to experiments on the LIDC−IDRI benchmark dataset, our method achieves an accuracy of 93.59%, which gains a 4.57% improvement over the 3D Dense-FPN baseline.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"25 1","pages":"59-66"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89411962","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 : 2021-01-01DOI: 10.5220/0010519101090116
Kassem Dia, V. L. Coli, L. Blanc-Féraud, J. Leblond, L. Gomart, D. Binder
Archaeological studies involve more and more numerical data analyses. In this work, we are interested in the analysis and classification of ceramic sherds tomographic images in order to help archaeologists in learning about the fabrication processes of ancient pottery. More specifically, a particular manufacturing process (spiral patchwork) has recently been discovered in early Neolithic Mediterranean sites, along with a more traditional coiling technique. It has been shown that the ceramic pore distribution in available tomographic images of both archaeological and experimental samples can reveal which manufacturing technique was used. Indeed, with the spiral patchwork, the pores exhibit spiral-like behaviours, whereas with the traditional one, they are distributed along parallel lines, especially in the experimental samples. However, in archaeological samples, these distributions are very noisy, making analysis and discrimination hard to process. Here, we investigate how Learning Methods (Deep Learning and Support Vector Machine) can be used to answer these numerically difficult problems. In particular, we study how the results depend on the input data (either raw data at the output of the tomographic device, or after a preliminary pore segmentation step), and the quality of the information they could provide to archaeologists.
{"title":"Applications of Learning Methods to Imaging Issues in Archaeology, Regarding Ancient Ceramic Manufacturing","authors":"Kassem Dia, V. L. Coli, L. Blanc-Féraud, J. Leblond, L. Gomart, D. Binder","doi":"10.5220/0010519101090116","DOIUrl":"https://doi.org/10.5220/0010519101090116","url":null,"abstract":"Archaeological studies involve more and more numerical data analyses. In this work, we are interested in the analysis and classification of ceramic sherds tomographic images in order to help archaeologists in learning about the fabrication processes of ancient pottery. More specifically, a particular manufacturing process (spiral patchwork) has recently been discovered in early Neolithic Mediterranean sites, along with a more traditional coiling technique. It has been shown that the ceramic pore distribution in available tomographic images of both archaeological and experimental samples can reveal which manufacturing technique was used. Indeed, with the spiral patchwork, the pores exhibit spiral-like behaviours, whereas with the traditional one, they are distributed along parallel lines, especially in the experimental samples. However, in archaeological samples, these distributions are very noisy, making analysis and discrimination hard to process. Here, we investigate how Learning Methods (Deep Learning and Support Vector Machine) can be used to answer these numerically difficult problems. In particular, we study how the results depend on the input data (either raw data at the output of the tomographic device, or after a preliminary pore segmentation step), and the quality of the information they could provide to archaeologists.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"2016 1","pages":"109-116"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86411349","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 : 2021-01-01DOI: 10.5220/0010559500480058
Yaguang Liu, Lisa Singh, Zeina Mneimneh
In order for social scientists to use social media as a source for understanding human behavior and public opinion, they need to understand the demographic characteristics of the population participating in the conversation. What proportion are female? What proportion are young? While previous literature has investigated this problem, this work presents a larger scale study that investigates inference techniques for predicting age and gender using Twitter data. We consider classic text features used in previous work and introduce new ones. Then we use a range of learning approaches from classic machine learning models to deep learning ones to understand the role of different language representations for demographic inference. On a data set created from Wikidata, we compare the value of different feature sets with different algorithms. In general, we find that classic models using statistical features and unigrams perform well. Neural networks also perform well, particularly models using sentence embeddings, e.g. a Siamese network configuration with attention to tweets and user biographies. The differences are marginal for age, but more significant for gender. In other words, it is reasonable to use simpler, interpretable models for some demographic inference tasks (like age). However, using richer language model is important for gender, highlighting the varying role language plays for demographic inference on social media.
{"title":"A Comparative Analysis of Classic and Deep Learning Models for Inferring Gender and Age of Twitter Users","authors":"Yaguang Liu, Lisa Singh, Zeina Mneimneh","doi":"10.5220/0010559500480058","DOIUrl":"https://doi.org/10.5220/0010559500480058","url":null,"abstract":"In order for social scientists to use social media as a source for understanding human behavior and public opinion, they need to understand the demographic characteristics of the population participating in the conversation. What proportion are female? What proportion are young? While previous literature has investigated this problem, this work presents a larger scale study that investigates inference techniques for predicting age and gender using Twitter data. We consider classic text features used in previous work and introduce new ones. Then we use a range of learning approaches from classic machine learning models to deep learning ones to understand the role of different language representations for demographic inference. On a data set created from Wikidata, we compare the value of different feature sets with different algorithms. In general, we find that classic models using statistical features and unigrams perform well. Neural networks also perform well, particularly models using sentence embeddings, e.g. a Siamese network configuration with attention to tweets and user biographies. The differences are marginal for age, but more significant for gender. In other words, it is reasonable to use simpler, interpretable models for some demographic inference tasks (like age). However, using richer language model is important for gender, highlighting the varying role language plays for demographic inference on social media.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"88 1","pages":"48-58"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75416878","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 : 2021-01-01DOI: 10.5220/0010618100670078
Jannes Magnusson, Ahmed J. Afifi, Shengjia Zhang, A. Ley, O. Hellwich
Automated semantic segmentation of medical imagery is a vital application using modern Deep Learning methods as they can support clinicians in their decision-making processes. However, training these models requires a large amount of training data which can be especially hard to obtain in the medical field due to ethical and data protection regulations. In this paper, we present a novel method to synthesize realistic retinal fundus images. The process mainly includes the vessel tree generation and synthesis of non-vascular regions (retinal background, fovea, and optic disc). We show that combining the (virtually) unlimited synthetic data with the limited real data during training boosts segmentation performance beyond what can be achieved with real data alone. We test the performance of the proposed method on the DRIVE and STARE databases. The results highlight that the proposed data augmentation technique achieves state-of-the-art performance and
{"title":"Synthesizing Fundus Photographies for Training Segmentation Networks","authors":"Jannes Magnusson, Ahmed J. Afifi, Shengjia Zhang, A. Ley, O. Hellwich","doi":"10.5220/0010618100670078","DOIUrl":"https://doi.org/10.5220/0010618100670078","url":null,"abstract":"Automated semantic segmentation of medical imagery is a vital application using modern Deep Learning methods as they can support clinicians in their decision-making processes. However, training these models requires a large amount of training data which can be especially hard to obtain in the medical field due to ethical and data protection regulations. In this paper, we present a novel method to synthesize realistic retinal fundus images. The process mainly includes the vessel tree generation and synthesis of non-vascular regions (retinal background, fovea, and optic disc). We show that combining the (virtually) unlimited synthetic data with the limited real data during training boosts segmentation performance beyond what can be achieved with real data alone. We test the performance of the proposed method on the DRIVE and STARE databases. The results highlight that the proposed data augmentation technique achieves state-of-the-art performance and","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"38 1","pages":"67-78"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87371973","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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