Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096777
Amal Fahad Al-aqel, Murtaza Ali Khan
Human motion prediction aims to forecast the most likely future frames of motion conditioned on a given sequence of frames. Because of its importance to many applications especially robotics, it has received a lot of interest and has become an active area of research. Since humans are very flexible in nature, human motion prediction is very challenging Recently, deep learning methods have been dominant in many tasks due to their successful results. Particularly, Recurrent Neural Networks (RNNs) have shown excellent performance on human motion prediction task and other tasks that depend on sequential data, where preserving the order of the sequence items is crucial. The well-known Sequence-to-Sequence (Seq2Seq) architectures have been used for sequence learning where two RNNs namely the encoder and the decoder work cooperatively to transform one sequence to another. In the context of neural machine translation, the use of attention decoders yields state-of-the-art results. This work employs a simple but efficient Seq2Seq model with attention decoder. Both encoder and decoder are trained jointly to predict 15 different categories of human motion. Our experiments have shown that the attention decoder clearly outperforms earlier methods and achieves state-of-the-art results in the short-term (< 500ms) motion prediction task. Contrary to earlier methods that show progressive deterioration as the time of prediction increases, our model shows high quality long-term (> 500ms) motion prediction which stays as high even after 1000ms of prediction.
{"title":"Attention Mechanism for Human Motion Prediction","authors":"Amal Fahad Al-aqel, Murtaza Ali Khan","doi":"10.1109/ICCAIS48893.2020.9096777","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096777","url":null,"abstract":"Human motion prediction aims to forecast the most likely future frames of motion conditioned on a given sequence of frames. Because of its importance to many applications especially robotics, it has received a lot of interest and has become an active area of research. Since humans are very flexible in nature, human motion prediction is very challenging Recently, deep learning methods have been dominant in many tasks due to their successful results. Particularly, Recurrent Neural Networks (RNNs) have shown excellent performance on human motion prediction task and other tasks that depend on sequential data, where preserving the order of the sequence items is crucial. The well-known Sequence-to-Sequence (Seq2Seq) architectures have been used for sequence learning where two RNNs namely the encoder and the decoder work cooperatively to transform one sequence to another. In the context of neural machine translation, the use of attention decoders yields state-of-the-art results. This work employs a simple but efficient Seq2Seq model with attention decoder. Both encoder and decoder are trained jointly to predict 15 different categories of human motion. Our experiments have shown that the attention decoder clearly outperforms earlier methods and achieves state-of-the-art results in the short-term (< 500ms) motion prediction task. Contrary to earlier methods that show progressive deterioration as the time of prediction increases, our model shows high quality long-term (> 500ms) motion prediction which stays as high even after 1000ms of prediction.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117248055","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096842
Abdulmohsen Alsulaimi, Tariq Abdullah
The study centers on examining communication among stakeholders in information technology projects, exploring measures for application by project managers to ensure success. In projects, the correct management of the stakeholders and ensuring their effective communication is a key challenge. Issues of diversity, varying objectives and other communication barriers adversely affect project success. Other challenges entail resistance and weaknesses in shareholders in sharing information, challenge of reaching multiple audiences, heterogeneous goals and concerns, and resource limitations. The study objective is depicting a need for workflow process automation for effective stakeholder communication, identify challenges in communication and develop a useful workflow automation application for implementation in IT projects using Microsoft Visual Studio (ASP.net). Through the app, there is the defining of stakeholders and communications within the project with the inclusion of tasks, their status, and specific milestones. Finally, we aim in examining communication variation across age, educational level, and gender of stakeholder and explore how response times vary across these facets. The research applies a descriptive-survey method and reviews in expanding on the study, relying on information from reviewers and past studies. In the attainment of the project aims, the design and development of a communication software/application were undertaken. The prototype app was made with Microsoft Visual Studio (ASP.net). The design considers an app platform for adequate coverage and communication of stakeholders on different tasks. The prototype app is vital in supporting IT Project Manager in a dynamic stakeholder communication process. With the app, project managers can conveniently plan and control IT projects. The study shows challenges faced by project managers in communication. This entails resistance in data sharing, inability to reach multiple stakeholder needs and audiences, varied needs among various stakeholders, and limited resources or communication tools. The verification and validation procedures were conducted for measuring the product's performance, its functionalities, and customer perceptions. A sample of 102 reviewers was vital in the verification process during the inspection meeting. The data analysis from stakeholders showed that the average response time on project task communication was 300.20 minutes. From the analyzed demographic data collected from the reviewers, it was demonstrated that age affects the communication process and involvement level during the task in the stakeholders. Education and gender were shown as not affecting communication and task involvement.
研究的重点是检查信息技术项目中利益相关者之间的沟通，探索项目经理应用的措施，以确保成功。在项目中，正确管理干系人并确保他们之间的有效沟通是一个关键的挑战。多样性问题、不同的目标和其他沟通障碍会对项目的成功产生不利影响。其他挑战包括股东在分享信息方面的阻力和弱点、接触多个受众的挑战、不同的目标和关注点以及资源限制。研究目标是描述工作流过程自动化对利益相关者有效沟通的需求，识别沟通中的挑战，并开发一个有用的工作流自动化应用程序，用于使用Microsoft Visual Studio (ASP.net)在IT项目中实现。通过该应用程序，可以定义项目内的利益相关者和沟通，包括任务、状态和特定的里程碑。最后，我们旨在研究利益相关者在年龄、教育水平和性别方面的沟通差异，并探讨响应时间在这些方面的差异。本研究采用描述性调查法和综述法对研究进行扩展，依赖于审稿人和以往研究的信息。为了达到计划的目标，我们进行了通讯软件/应用程序的设计和开发。原型应用程序是用Microsoft Visual Studio (ASP.net)制作的。该设计考虑了一个应用程序平台，以充分覆盖和沟通不同任务的利益相关者。原型应用程序在支持IT项目经理进行动态的利益相关者沟通过程中至关重要。有了这个应用程序，项目经理可以方便地计划和控制IT项目。该研究显示了项目经理在沟通方面面临的挑战。这导致数据共享方面的阻力，无法满足多个利益相关者的需求和受众，不同利益相关者之间的需求不同，以及资源或沟通工具有限。进行验证和确认程序是为了测量产品的性能、功能和顾客的看法。在检查会议期间的验证过程中，102名审查人员的样本至关重要。干系人的数据分析表明，项目任务沟通的平均响应时间为300.20分钟。从评估者收集的人口统计数据分析中，我们发现年龄影响了利益相关者在任务中的沟通过程和参与程度。教育程度和性别对沟通和任务投入没有影响。
{"title":"Management of Stakeholder Communications in IT Projects","authors":"Abdulmohsen Alsulaimi, Tariq Abdullah","doi":"10.1109/ICCAIS48893.2020.9096842","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096842","url":null,"abstract":"The study centers on examining communication among stakeholders in information technology projects, exploring measures for application by project managers to ensure success. In projects, the correct management of the stakeholders and ensuring their effective communication is a key challenge. Issues of diversity, varying objectives and other communication barriers adversely affect project success. Other challenges entail resistance and weaknesses in shareholders in sharing information, challenge of reaching multiple audiences, heterogeneous goals and concerns, and resource limitations. The study objective is depicting a need for workflow process automation for effective stakeholder communication, identify challenges in communication and develop a useful workflow automation application for implementation in IT projects using Microsoft Visual Studio (ASP.net). Through the app, there is the defining of stakeholders and communications within the project with the inclusion of tasks, their status, and specific milestones. Finally, we aim in examining communication variation across age, educational level, and gender of stakeholder and explore how response times vary across these facets. The research applies a descriptive-survey method and reviews in expanding on the study, relying on information from reviewers and past studies. In the attainment of the project aims, the design and development of a communication software/application were undertaken. The prototype app was made with Microsoft Visual Studio (ASP.net). The design considers an app platform for adequate coverage and communication of stakeholders on different tasks. The prototype app is vital in supporting IT Project Manager in a dynamic stakeholder communication process. With the app, project managers can conveniently plan and control IT projects. The study shows challenges faced by project managers in communication. This entails resistance in data sharing, inability to reach multiple stakeholder needs and audiences, varied needs among various stakeholders, and limited resources or communication tools. The verification and validation procedures were conducted for measuring the product's performance, its functionalities, and customer perceptions. A sample of 102 reviewers was vital in the verification process during the inspection meeting. The data analysis from stakeholders showed that the average response time on project task communication was 300.20 minutes. From the analyzed demographic data collected from the reviewers, it was demonstrated that age affects the communication process and involvement level during the task in the stakeholders. Education and gender were shown as not affecting communication and task involvement.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127777994","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096776
Raja H. Alyaffer, D. Alboaneen, Nourah F. Alqahtani
News organisations that use social media sites (such as Twitter) rapidly generate a large volume of data every day. Data visualisation is an effective way to represent microblogging data graphically in order to increase understanding of what news organisations are reporting over a given time period. This paper focuses on visualising the tweets (posts on Twitter) of a broad cross-section of English-language news outlets over time, with the goal of developing an interactive web-based visualisation system that summarises the output of news outlets’ tweets as a scatter plot where each point refers to a news outlet. Such a visualisation could enable interested parties to explore similarities and differences among what news outlets report based on the frequency with which specific words are used.
{"title":"NewsScatter: Topic Similarity in Social Media","authors":"Raja H. Alyaffer, D. Alboaneen, Nourah F. Alqahtani","doi":"10.1109/ICCAIS48893.2020.9096776","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096776","url":null,"abstract":"News organisations that use social media sites (such as Twitter) rapidly generate a large volume of data every day. Data visualisation is an effective way to represent microblogging data graphically in order to increase understanding of what news organisations are reporting over a given time period. This paper focuses on visualising the tweets (posts on Twitter) of a broad cross-section of English-language news outlets over time, with the goal of developing an interactive web-based visualisation system that summarises the output of news outlets’ tweets as a scatter plot where each point refers to a news outlet. Such a visualisation could enable interested parties to explore similarities and differences among what news outlets report based on the frequency with which specific words are used.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130510457","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096722
S. Z. Iqbal, Dania Huzam, N. Aleliwi, Asma Alabdulkareem, Shikah Alalyani, Shaikha Alfaris, H. Gull
Reading is an essential part of our life, and it is a way by which we acquire new knowledge, discover ourselves and develop our skills. A book fair is an event that provides visitors with the opportunity to further enhance their knowledge, to obtain rare books and to meet their favorite authors. In this event, the staff aim to achieve visitor’s satisfaction, and to increase the fair’s value. Every year Kingdom of Saudi Arabia alos arrange such events to promote knowledge. These events provide readers a chance to acquire international books that may not be available in the Kingdom. The Book Fair organizers are always seeking to reach a better level of organization to gain visitors’ satisfaction and avoid losing the basic value of the Book Fair. SmartFair Guide is a web- based and application-based system that will manage the Kingdom’s book fair event and that will enhance the experience of all individuals involved. The reason the system is referred to as smart is its outstanding features and most specifically, the system includes implementing number of data mining techniques anticipated to have a direct effect on the books classification process and the recommendation system. In this research paper the proposed model structure will display the main procedures and the user’s role behavior in the cycle of the system. The cycle includes seven of the system’s processes which are the donation, ticket trade, barcode scanning, crowd deduction, recommendation, classification and section locating processes. The system analysis demonstrates the diagrams which includes the Use Case, DFDs, ER, Activity, Sequence diagrams and their description.
{"title":"Smart Fair Guide: Requirements and Design of a Smart Fair Management System","authors":"S. Z. Iqbal, Dania Huzam, N. Aleliwi, Asma Alabdulkareem, Shikah Alalyani, Shaikha Alfaris, H. Gull","doi":"10.1109/ICCAIS48893.2020.9096722","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096722","url":null,"abstract":"Reading is an essential part of our life, and it is a way by which we acquire new knowledge, discover ourselves and develop our skills. A book fair is an event that provides visitors with the opportunity to further enhance their knowledge, to obtain rare books and to meet their favorite authors. In this event, the staff aim to achieve visitor’s satisfaction, and to increase the fair’s value. Every year Kingdom of Saudi Arabia alos arrange such events to promote knowledge. These events provide readers a chance to acquire international books that may not be available in the Kingdom. The Book Fair organizers are always seeking to reach a better level of organization to gain visitors’ satisfaction and avoid losing the basic value of the Book Fair. SmartFair Guide is a web- based and application-based system that will manage the Kingdom’s book fair event and that will enhance the experience of all individuals involved. The reason the system is referred to as smart is its outstanding features and most specifically, the system includes implementing number of data mining techniques anticipated to have a direct effect on the books classification process and the recommendation system. In this research paper the proposed model structure will display the main procedures and the user’s role behavior in the cycle of the system. The cycle includes seven of the system’s processes which are the donation, ticket trade, barcode scanning, crowd deduction, recommendation, classification and section locating processes. The system analysis demonstrates the diagrams which includes the Use Case, DFDs, ER, Activity, Sequence diagrams and their description.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134318633","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096667
Ahmad Alnafessah, G. Casale
Cloud computing, Artificial Intelligence, and Big Data technologies have recently become one of the most impactful forms of technology innovation. It is common to have multiple users share the same computing resources. This practice noticeably leads to performance anomalies. For instance, some applications can feature variability in processing time due to interference from other applications, or software contention from the other users, which may lead to unexpectedly long execution time and be considered anomalous. There is an urgent need for an automated effective performance anomaly detection method that can be used within the production environment for the streaming system to avoid any late detection of unexpected system failures. To address this challenge, we introduce a new black-box training workload configuration optimization with a neural network driven methodology to identify anomalous performance in an in-memory Spark streaming Big Data platform. The proposed methodology effectively uses Bayesian Optimization to find the ideal training dataset size and Spark streaming workload configuration parameters to train the anomaly detection model. The proposed model is validated on the Apache Spark streaming system. The results demonstrate that the proposed solution succeeds and accurately detects many types of performance anomalies. In addition, the training time for the machine learning model is reduced by more than 50%, which offers a fast anomaly detection deployment for system developers to utilize more efficient monitoring solutions.
{"title":"AI Driven Methodology for Anomaly Detection in Apache Spark Streaming Systems","authors":"Ahmad Alnafessah, G. Casale","doi":"10.1109/ICCAIS48893.2020.9096667","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096667","url":null,"abstract":"Cloud computing, Artificial Intelligence, and Big Data technologies have recently become one of the most impactful forms of technology innovation. It is common to have multiple users share the same computing resources. This practice noticeably leads to performance anomalies. For instance, some applications can feature variability in processing time due to interference from other applications, or software contention from the other users, which may lead to unexpectedly long execution time and be considered anomalous. There is an urgent need for an automated effective performance anomaly detection method that can be used within the production environment for the streaming system to avoid any late detection of unexpected system failures. To address this challenge, we introduce a new black-box training workload configuration optimization with a neural network driven methodology to identify anomalous performance in an in-memory Spark streaming Big Data platform. The proposed methodology effectively uses Bayesian Optimization to find the ideal training dataset size and Spark streaming workload configuration parameters to train the anomaly detection model. The proposed model is validated on the Apache Spark streaming system. The results demonstrate that the proposed solution succeeds and accurately detects many types of performance anomalies. In addition, the training time for the machine learning model is reduced by more than 50%, which offers a fast anomaly detection deployment for system developers to utilize more efficient monitoring solutions.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129981195","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096716
R. Yuwono, M. F. Edy Purnomo, Dandy Imam Zaki, A. Rafli
Efficient and easy-to-produce characteristics of microstrip antenna makes it very common of being applied on communication devices. This research used five clover-shaped patch array microstrip antenna with 1×1, 2×2, 3×3, 4×4, and 5×5 format to analyze the parameters on 2.4 GHz operation frequency. VSWR, return loss, bandwidth and gain of each antennas are analyzed on this research. In conclusion, the addition of radiating element (patch) of the antenna lower the antenna’s performance in VSWR and return loss, but widen the bandwidth and increasing gain of the antenna.
{"title":"Design of Symmetric and Asymmetric Array Clover Patch Microstrip Antennas at 2.4 GHz Frequency","authors":"R. Yuwono, M. F. Edy Purnomo, Dandy Imam Zaki, A. Rafli","doi":"10.1109/ICCAIS48893.2020.9096716","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096716","url":null,"abstract":"Efficient and easy-to-produce characteristics of microstrip antenna makes it very common of being applied on communication devices. This research used five clover-shaped patch array microstrip antenna with 1×1, 2×2, 3×3, 4×4, and 5×5 format to analyze the parameters on 2.4 GHz operation frequency. VSWR, return loss, bandwidth and gain of each antennas are analyzed on this research. In conclusion, the addition of radiating element (patch) of the antenna lower the antenna’s performance in VSWR and return loss, but widen the bandwidth and increasing gain of the antenna.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"84 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133136175","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096727
Samira Yeasmin, Layla Abdulrahman Albabtain
Technology has become an integral part of our life. One of the most emerging technologies of the 21st century is Virtual Reality. It is being applied to medicine, education, engineering, architecture, training, entertainment and so on. Nowadays it is being used in the gaming field as well. Game developers are turning their games into VR games and it is gaining a lot of popularity. Virtual reality gaming is the application of a 3D artificial environment to games. A VR gaming accessory involves VR headsets, sensor-equipped gloves, hand controllers, etc. Virtual reality games can be played on standalone systems, on specialized game consoles, or using advanced laptops and PCs using Oculus Rift, HTC Vive and Lenovo Explorer [1]. An escape room game is a game where players have to ‘escape’ a room by solving challenges within a given time limit [2]. Sometimes the challenges are made inaccessible and must be found by completing puzzles. Therefore, this project proposes to create a VR escape room game that will help the players to enhance their skills. The game will have several rooms to escape representing different cultures of different countries to help the player interact with different cultures. The game will have puzzle-solving challenges to solve so that players can escape the room. The challenges will be based on math, arrangement, and pattern recognition. During the project, a survey will be carried out to gather information about playing VR games and the significance of a VR escape room game. Similar games around the world will also be reviewed. The goal of the project is to use people’s free time to teach them skills.
{"title":"Escape The Countries: A VR Escape Room Game","authors":"Samira Yeasmin, Layla Abdulrahman Albabtain","doi":"10.1109/ICCAIS48893.2020.9096727","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096727","url":null,"abstract":"Technology has become an integral part of our life. One of the most emerging technologies of the 21st century is Virtual Reality. It is being applied to medicine, education, engineering, architecture, training, entertainment and so on. Nowadays it is being used in the gaming field as well. Game developers are turning their games into VR games and it is gaining a lot of popularity. Virtual reality gaming is the application of a 3D artificial environment to games. A VR gaming accessory involves VR headsets, sensor-equipped gloves, hand controllers, etc. Virtual reality games can be played on standalone systems, on specialized game consoles, or using advanced laptops and PCs using Oculus Rift, HTC Vive and Lenovo Explorer [1]. An escape room game is a game where players have to ‘escape’ a room by solving challenges within a given time limit [2]. Sometimes the challenges are made inaccessible and must be found by completing puzzles. Therefore, this project proposes to create a VR escape room game that will help the players to enhance their skills. The game will have several rooms to escape representing different cultures of different countries to help the player interact with different cultures. The game will have puzzle-solving challenges to solve so that players can escape the room. The challenges will be based on math, arrangement, and pattern recognition. During the project, a survey will be carried out to gather information about playing VR games and the significance of a VR escape room game. Similar games around the world will also be reviewed. The goal of the project is to use people’s free time to teach them skills.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126825588","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096831
C. Farhat, N. Alhazmi, P. Avery, R. Tezaur, Y. Ghazi
In aerodynamic studies, models are essential tools for understanding complex fluid flow phenomena. However, their use can be expensive in terms of computer power and calculation time. Therefore, machine learning algorithms have become essential when it comes to analyzing uncertainty in modelling and predicting the values for new input parameters with sensitivity quantifications and in a reasonably short time. The aim of this paper is to predict the key factors in aircraft design by finding the best estimation of the dependent variable in the form of the lift to drag ratio, for any new input-dependent values in the form of Mach numbers and angle of attack. Therefore, different regressions of classical supervised learning algorithms have been applied. The statistical errors have been calculated for these regressions in order to choose the best fit for an unknown model. In addition, artificial neural networks (ANN) have been used to train the data, and to predict the ratio of lift to drag in a practical time compared to the use of experimental tests and the computational fluid dynamics (CFD) technique.
{"title":"Parametric studies of aerodynamic properties of wings using various forms of machine learning","authors":"C. Farhat, N. Alhazmi, P. Avery, R. Tezaur, Y. Ghazi","doi":"10.1109/ICCAIS48893.2020.9096831","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096831","url":null,"abstract":"In aerodynamic studies, models are essential tools for understanding complex fluid flow phenomena. However, their use can be expensive in terms of computer power and calculation time. Therefore, machine learning algorithms have become essential when it comes to analyzing uncertainty in modelling and predicting the values for new input parameters with sensitivity quantifications and in a reasonably short time. The aim of this paper is to predict the key factors in aircraft design by finding the best estimation of the dependent variable in the form of the lift to drag ratio, for any new input-dependent values in the form of Mach numbers and angle of attack. Therefore, different regressions of classical supervised learning algorithms have been applied. The statistical errors have been calculated for these regressions in order to choose the best fit for an unknown model. In addition, artificial neural networks (ANN) have been used to train the data, and to predict the ratio of lift to drag in a practical time compared to the use of experimental tests and the computational fluid dynamics (CFD) technique.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114780324","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096848
Rolando Treviño-Lozano, Juan Raúl Martinez-Gutierrez, Francisco Javier Cantu Ortiz, Hector Gibran Ceballos Cancino
In 2018 a new regime arrived to Mexican administrative apparatus. This new regime has expressed plans to abolish public policies from previous administrations, energetic policy among them. This document tries to determine if hydrocarbon fuels imports have been affected by this new regime.
{"title":"Analysis of hydrocarbon fuels imports under a new Mexican regime","authors":"Rolando Treviño-Lozano, Juan Raúl Martinez-Gutierrez, Francisco Javier Cantu Ortiz, Hector Gibran Ceballos Cancino","doi":"10.1109/ICCAIS48893.2020.9096848","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096848","url":null,"abstract":"In 2018 a new regime arrived to Mexican administrative apparatus. This new regime has expressed plans to abolish public policies from previous administrations, energetic policy among them. This document tries to determine if hydrocarbon fuels imports have been affected by this new regime.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123894079","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096869
S. Yerima, Mohammed K. Alzaylaee
The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites. Based on the results of extensive experiments, our CNN based models proved to be highly effective in detecting unknown phishing sites. Furthermore, the CNN based approach performed better than traditional machine learning classifiers evaluated on the same dataset, reaching 98.2% phishing detection rate with an F1-score of 0.976. The method presented in this paper compares favourably to the state-of-the art in deep learning based phishing website detection.
{"title":"High Accuracy Phishing Detection Based on Convolutional Neural Networks","authors":"S. Yerima, Mohammed K. Alzaylaee","doi":"10.1109/ICCAIS48893.2020.9096869","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096869","url":null,"abstract":"The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites. Based on the results of extensive experiments, our CNN based models proved to be highly effective in detecting unknown phishing sites. Furthermore, the CNN based approach performed better than traditional machine learning classifiers evaluated on the same dataset, reaching 98.2% phishing detection rate with an F1-score of 0.976. The method presented in this paper compares favourably to the state-of-the art in deep learning based phishing website detection.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126338744","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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