2020 14th International Conference on Innovations in Information Technology (IIT)最新文献

IoT applications for monitoring companion animals: A systematic literature review 物联网应用于伴侣动物监测:系统文献综述

2020 14th International Conference on Innovations in Information Technology (IIT) Pub Date : 2020-11-17 DOI: 10.1109/IIT50501.2020.9299045

Roberto Alves Lima Junior

引用次数: 9

Aspect-Based Sentiment Analysis of Arabic Tweets in the Education Sector Using a Hybrid Feature Selection Method 利用混合特征选择方法对教育领域阿拉伯语推文进行面向情感分析

2020 14th International Conference on Innovations in Information Technology (IIT) Pub Date : 2020-11-17 DOI: 10.1109/IIT50501.2020.9299026

Manar Alassaf, A. M. Qamar

{"title":"Aspect-Based Sentiment Analysis of Arabic Tweets in the Education Sector Using a Hybrid Feature Selection Method","authors":"Manar Alassaf, A. M. Qamar","doi":"10.1109/IIT50501.2020.9299026","DOIUrl":"https://doi.org/10.1109/IIT50501.2020.9299026","url":null,"abstract":"Sentiment analysis can be applied in many domains given the abundance of views in social networks, including the education sector, reflecting how cultures and nations grow and develop. In this context, aspect-based sentiment analysis with its two main tasks; aspect detection and aspect-opinion classification, might provide an accurate picture of many educational institutions ’ strengths and weaknesses. In this research, a real-world Twitter dataset was collected, containing approximately 7,934 Arabic tweets related to Qassim University in Saudi Arabia. In the text classification task, the high dimensionality problem is usually faced, and the feature selection methods contribute to tackling this problem. Accordingly, the purpose of the experimental study is to investigate the effectiveness of using a hybrid feature selection method in improving the results of aspect-based sentiment analysis by reducing the number of features. The proposed hybrid feature method consists of a one-way analysis of variance to examine the relationship between each feature and classes, and the ridge regression that calculates the importance of features together during the learning phase. Several experiments were conducted to study the effects of the proposed feature selection method on improving the Support Vector Machine classifier ’s performance. The experimental results demonstrate that the hybrid method successfully enhances the classifier’s performance in both subtasks.","PeriodicalId":128526,"journal":{"name":"2020 14th International Conference on Innovations in Information Technology (IIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124979577","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: 8

Dynamic Reference Information: Formalising Contextual Actionable Information for Contested Environments 动态参考信息:为有争议的环境形式化上下文可操作信息

2020 14th International Conference on Innovations in Information Technology (IIT) Pub Date : 2020-11-17 DOI: 10.1109/IIT50501.2020.9298988

Angela Consoli, Andrew Walters

{"title":"Dynamic Reference Information: Formalising Contextual Actionable Information for Contested Environments","authors":"Angela Consoli, Andrew Walters","doi":"10.1109/IIT50501.2020.9298988","DOIUrl":"https://doi.org/10.1109/IIT50501.2020.9298988","url":null,"abstract":"Acquiring situation awareness and understanding within contested and dynamic environments requires the most up-to-date information that is both actionable and contextual. Information fusion systems currently use static a-priori information and knowledge bases, and in some cases, incomplete or stale symbolic information, which is not situational or contextual. This paper formalises Dynamic Reference Information (DRI), which is defined as temporal, contextual, situational and actionable information that can update a-priori information. Furthermore, DRI semantically represents new objects, attributes and their values from situations within an observable space. The formalisation of DRI was used on unstructured information generated during the Heimdall Experiment. During the analysis, two main results were reported: i) novel Objects of Interest within an Observable Space were able to be defined from unstructured information ii) the implicit nature of context in DRI allowed an enhanced situational understanding of all a-priori and novel objects perceived in a situation represented within a tactical display.","PeriodicalId":128526,"journal":{"name":"2020 14th International Conference on Innovations in Information Technology (IIT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123584442","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

Efficient Base-Centric/User-Centric Clustering Algorithms Based on Thresholding and Sorting 基于阈值和排序的高效基数中心/用户中心聚类算法

2020 14th International Conference on Innovations in Information Technology (IIT) Pub Date : 2020-11-17 DOI: 10.1109/IIT50501.2020.9299058

S. Moghaddam, Kiaksar Shirvani Moghaddam

引用次数: 4

Studying Data Privacy Management in Small and Medium-Sized IT Companies 研究中小型IT公司的数据隐私管理

2020 14th International Conference on Innovations in Information Technology (IIT) Pub Date : 2020-11-17 DOI: 10.1109/IIT50501.2020.9299050

M. Jäntti

{"title":"Studying Data Privacy Management in Small and Medium-Sized IT Companies","authors":"M. Jäntti","doi":"10.1109/IIT50501.2020.9299050","DOIUrl":"https://doi.org/10.1109/IIT50501.2020.9299050","url":null,"abstract":"Today, poorly implemented information security and data privacy measures may cause significant threats to companies’ existence and business continuity. Additionally, European Union has established strong data protection regulation rules for companies operating within EU. In order to be compliant with these new rules and regulations, organizations have to put a lot of resources to create data privacy policies and plans as well as to adjust tools to manage data privacy requests and fullfill privacy by design and privacy by default principles. Especially for small and medium-sized (SME) Information Technology (IT) firms and software development organizations with limited resources, new GDPR legislation and stricter requirements for information security have caused several challenges and uncertainty on what is adequate level of data privacy. In this paper, we focus on exploring Finnish IT SMEs and their actions and feelings on data privacy and information security. The research problem of this study is: How small and medium sized companies have prepared for growing data privacy and information security requirements? The main contribution of this paper is to show how small and medium sized IT companies in Northern Savo region did prepare for EU data privacy regulation and what types of challenges did exist in the GDPR preparation phase.","PeriodicalId":128526,"journal":{"name":"2020 14th International Conference on Innovations in Information Technology (IIT)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114790417","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

Application of Transfer Learning for Fruits and Vegetable Quality Assessment 迁移学习在果蔬品质评价中的应用

2020 14th International Conference on Innovations in Information Technology (IIT) Pub Date : 2020-11-17 DOI: 10.1109/IIT50501.2020.9299048

S. Turaev, A. Almisreb, M. Saleh

{"title":"Application of Transfer Learning for Fruits and Vegetable Quality Assessment","authors":"S. Turaev, A. Almisreb, M. Saleh","doi":"10.1109/IIT50501.2020.9299048","DOIUrl":"https://doi.org/10.1109/IIT50501.2020.9299048","url":null,"abstract":"In this paper, we utilize the concept of transfer learning in fruits and vegetable quality assessment. The transfer learning concept applies the idea of reuse the pre-trained Convolutional Neural Network to solve a new problem without the need for large-scale datasets for training. Eight pre-trained deep learning models namely AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, Vgg16, Vgg19, and NasNetMobile are fine-tuned accordingly to evaluate the quality of fruits and vegetable. To evaluate the training and validation performance of each fine-tuned model, we collect a dataset consists of images from 12 fruits and vegetable samples. The dataset builds over five weeks. For every week 70 images collected therefore the total number of images over five weeks is 350 and the total number of images in the dataset is (12*350) 4200 images. The overall number of classes in the dataset is (12*5) 60 classes. The evaluation of the models was conducted based on this dataset and also based on an augmented version. The model’s outcome shows that the Vgg19 model achieved the highest validation accuracy over the original dataset with 91.50% accuracy and the ResNet18 model scored the highest validation accuracy based on the augmented dataset with 91.37% accuracy.","PeriodicalId":128526,"journal":{"name":"2020 14th International Conference on Innovations in Information Technology (IIT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126538742","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

Towards General Purpose Object Detection: Deep Dense Grid Based Object Detection 面向通用目标检测:基于深度密集网格的目标检测

2020 14th International Conference on Innovations in Information Technology (IIT) Pub Date : 2020-11-17 DOI: 10.1109/IIT50501.2020.9299036

Solomon Negussie Tesema, E. Bourennane

引用次数: 2

IIT 2020 Index IIT 2020指数

2020 14th International Conference on Innovations in Information Technology (IIT) Pub Date : 2020-11-17 DOI: 10.1109/iit50501.2020.9299001

引用次数: 0

A survey on Arabic Image Captioning Systems Using Deep Learning Models

2020 14th International Conference on Innovations in Information Technology (IIT) Pub Date : 2020-11-17 DOI: 10.1109/IIT50501.2020.9299027

Anfal Attai, Ashraf Elnagar

引用次数: 4

Application of Containerized Microservice Approach to Airline Sentiment Analysis 容器化微服务方法在航空公司情绪分析中的应用

2020 14th International Conference on Innovations in Information Technology (IIT) Pub Date : 2020-11-17 DOI: 10.1109/IIT50501.2020.9299043

Sumbal Malik, H. El-Sayed, M. A. Khan, H. Ignatious

{"title":"Application of Containerized Microservice Approach to Airline Sentiment Analysis","authors":"Sumbal Malik, H. El-Sayed, M. A. Khan, H. Ignatious","doi":"10.1109/IIT50501.2020.9299043","DOIUrl":"https://doi.org/10.1109/IIT50501.2020.9299043","url":null,"abstract":"Containers are getting more popularity than the virtual machines by offering the benefits of virtualization along with the performance nearby bare metal. Standardizing support of Docker containers among various cloud providers has made them a trendy solution for developers. In this paper, we elaborate on containerized microservice, leveraging the lightweight Docker container technology. The evolution of microservice architecture allows applications to be structured into independent modular components making them easier to manage and scale. As a special case, the containerized sentiment analysis microservice is deployed using popular classification approaches. We implement and compare eight machine learning algorithms: Multinomial Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbour, AdaBoost, Support Vector Machine, Multilayer Perceptron, and Stochastic Gradient Descent to analyze and classify the tweets into positive, negative, and neutral sentiments. Experimental results procured for the Twitter US Airline Sentiment dataset show that Support Vector Machine, Multinomial Naive Bayes, Stochastic Gradient Descent, and Random Forest outperform the other algorithms. We believe that this research study will assist companies and organizations to improve their services by precisely analyzing Twitter data.","PeriodicalId":128526,"journal":{"name":"2020 14th International Conference on Innovations in Information Technology (IIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123889501","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