News. Phi Delta Epsilon最新文献_第8页

Improving Information Privacy and Security: Strengthening Digital Literacy in Organisations 改善资讯私隐及保安:加强机构的数码素养

News. Phi Delta Epsilon Pub Date : 2021-01-01 DOI: 10.5220/0010534501170122

Guy Toko, Kagisho Losaba

引用次数: 0

Evaluating Deep Learning Models for the Automatic Inspection of Collective Protective Equipment 集体防护装备自动检测的深度学习模型评估

News. Phi Delta Epsilon Pub Date : 2021-01-01 DOI: 10.1007/978-3-031-37320-6_3

B. Ferreira, B. Lima, Tiago F. Vieira

引用次数: 0

Disrupting Active Directory Attacks with Deep Learning for Organic Honeyuser Placement 利用深度学习破坏有机蜂蜜用户放置的活动目录攻击

News. Phi Delta Epsilon Pub Date : 2021-01-01 DOI: 10.1007/978-3-031-37320-6_6

Ondrej Lukás, S. García

引用次数: 0

Crack Detection on Brick Walls by Convolutional Neural Networks Using the Methods of Sub-dataset Generation and Matching 基于子数据集生成和匹配方法的卷积神经网络砖墙裂缝检测

News. Phi Delta Epsilon Pub Date : 2021-01-01 DOI: 10.1007/978-3-031-37320-6_7

M. H. Talukder, Shuhei Ota, M. Takanokura, N. Ishii

引用次数: 0

Forecasting the UN Sustainable Development Goals 预测联合国可持续发展目标

News. Phi Delta Epsilon Pub Date : 2021-01-01 DOI: 10.1007/978-3-031-37320-6_5

Yassir Alharbi, Daniel Arribas-Bel, F. Coenen

引用次数: 0

Automatically Segmentation the Car Parts and Generate a Large Car Texture Images 自动分割汽车零件并生成大型汽车纹理图像

News. Phi Delta Epsilon Pub Date : 2021-01-01 DOI: 10.5220/0010601301850190

Yan-Yu Lin, C. Yu, Chuen-Horng Lin

{"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}

引用次数: 1

Attribute Relation Modeling for Pulmonary Nodule Malignancy Reasoning 肺结节恶性推理的属性关系建模

News. Phi Delta Epsilon Pub Date : 2021-01-01 DOI: 10.5220/0010616000590066

Stanley T. Yu, Gangming Zhao

引用次数: 0

Applications of Learning Methods to Imaging Issues in Archaeology, Regarding Ancient Ceramic Manufacturing 学习方法在考古成像问题中的应用，关于古代陶瓷制造

News. Phi Delta Epsilon Pub Date : 2021-01-01 DOI: 10.5220/0010519101090116

Kassem Dia, V. L. Coli, L. Blanc-Féraud, J. Leblond, L. Gomart, D. Binder

{"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}

引用次数: 0

A Comparative Analysis of Classic and Deep Learning Models for Inferring Gender and Age of Twitter Users 推特用户性别和年龄推断的经典和深度学习模型比较分析

News. Phi Delta Epsilon Pub Date : 2021-01-01 DOI: 10.5220/0010559500480058

Yaguang Liu, Lisa Singh, Zeina Mneimneh

{"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}

引用次数: 12

Synthesizing Fundus Photographies for Training Segmentation Networks 用于训练分割网络的眼底图像合成

News. Phi Delta Epsilon Pub Date : 2021-01-01 DOI: 10.5220/0010618100670078

Jannes Magnusson, Ahmed J. Afifi, Shengjia Zhang, A. Ley, O. Hellwich

引用次数: 0