2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)最新文献

A re-engineering approach for extension of the Tourist Guide Knowledge Base 导游知识库扩展的再造方法

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) Pub Date : 2020-11-24 DOI: 10.1109/CloudTech49835.2020.9365875

Asya Stojanova-Doycheva, T. Glushkova, E. Doychev, N. Moraliyska

引用次数: 0

Secure, Efficient and Dynamic Data Search using Searchable Symmetric Encryption 使用可搜索对称加密的安全，高效和动态数据搜索

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) Pub Date : 2020-11-24 DOI: 10.1109/CloudTech49835.2020.9365907

M. S. Shaikh, Jatna Bavishi, Reema Patel

{"title":"Secure, Efficient and Dynamic Data Search using Searchable Symmetric Encryption","authors":"M. S. Shaikh, Jatna Bavishi, Reema Patel","doi":"10.1109/CloudTech49835.2020.9365907","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365907","url":null,"abstract":"Fog computing, which works complementary to cloud computing, is being developed to overcome the issue of cloud computing such as latency in the cases where the data is to be retrieved immediately. But, along with solving the problem of latency, fog computing brings along with it different set of security issues than cloud computing. The storage and processing capabilities of fog computing is limited and hence, the security issues must be solved with these constrained resources. One of the problems faced when the data is stored outside the internal network is loss of confidentiality. For this, the data must be encrypted. But, whenever document needs to be searched, all the related documents must be decrypted first and later the required document is to be fetched. Within this time frame, the document data can be accessed by an unauthorized person. So, in this paper, a searchable symmetric encryption scheme is proposed wherein the authorized members of an organization can search over the encrypted data and retrieve the required document in order to preserve the security and privacy of the data. Also, the searching complexity of the algorithm is much less so that it suitable to fog computing environment.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126973807","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

An Artificial Neural Networks Based Ensemble System to Forecast Bitcoin Daily Trading Volume 基于人工神经网络的集成系统预测比特币日交易量

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) Pub Date : 2020-11-24 DOI: 10.1109/CloudTech49835.2020.9365913

S. Lahmiri, R. Saadé, Danielle Morin, F. Nebebe

{"title":"An Artificial Neural Networks Based Ensemble System to Forecast Bitcoin Daily Trading Volume","authors":"S. Lahmiri, R. Saadé, Danielle Morin, F. Nebebe","doi":"10.1109/CloudTech49835.2020.9365913","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365913","url":null,"abstract":"Cryptocurrencies are digital assets gaining popularity and generating huge transactions on electronic platforms. We develop an ensemble predictive system based on artificial neural networks to forecast Bitcoin daily trading volume level. Indeed, although ensemble forecasts are increasingly employed in various forecasting tasks, developing an intelligent predictive system for Bitcoin trading volume based on ensemble forecasts has not been addressed yet. Ensemble Bitcoin trading volume are forecasted using two specific artificial neural networks; namely, radial basis function neural networks (RBFNN) and generalized regression neural networks (GRNN). They are adopted to respectively capture local and general patterns in Bitcoin trading volume data. Finally, the feedforward artificial neural network (FFNN) is implemented to generate Bitcoin final trading volume after having aggregated the forecasts from RBFNN and GRNN. In this regard, FFNN is executed to merge local and global forecasts in a nonlinear framework. Overall, our proposed ensemble predictive system reduced the forecasting errors by 18.81% and 62.86% when compared to its components RBFNN and GRNN, respectively. In addition, the ensemble system reduced the forecasting error by 90.49% when compared to a single FFNN used as a basic reference model. Thus, the empirical outcomes show that our proposed ensemble predictive model allows achieving an improvement in terms of forecasting. Regarding the practical results of this work, while being fast, applying the artificial neural networks to develop an ensemble predictive system to forecast Bitcoin daily trading volume is recommended to apply for addressing simultaneously local and global patterns used to characterize Bitcoin trading data. We conclude that the proposed artificial neural networks ensemble forecasting model is easy to implement and efficient for Bitcoin daily volume forecasting.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122182837","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

Multi-task Offloading and Computational Resources Management in a Mobile Edge Computing Environment 移动边缘计算环境下的多任务卸载与计算资源管理

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) Pub Date : 2020-11-24 DOI: 10.1109/CloudTech49835.2020.9365903

Mohamed El Ghmary, Youssef Hmimz, T. Chanyour, Ali Ouacha, Mohammed Ouçamah Cherkaoui Malki

{"title":"Multi-task Offloading and Computational Resources Management in a Mobile Edge Computing Environment","authors":"Mohamed El Ghmary, Youssef Hmimz, T. Chanyour, Ali Ouacha, Mohammed Ouçamah Cherkaoui Malki","doi":"10.1109/CloudTech49835.2020.9365903","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365903","url":null,"abstract":"In Mobile Cloud Computing, Smart Mobile Devices (SMDs) and Cloud Computing are combined to create a new infrastructure that allows data processing and storage outside the device. The Internet of Things refers to the billions of physical devices that are connected to the Internet. With the rapid development of these, it is clear that the requirements are largely based on the need for autonomous devices to facilitate the services required by applications that require rapid response time and flexible mobility. In this article, we study the management of computational resources and the trade-off between the consumed energy by an SMD and the processing time of its tasks. For this, we define a system model, a problem formulation and offer heuristic solutions for offloading tasks in order to jointly optimize the allocation of computing resources under limited energy and sensitive latency. In addition, we use the residual energy of the SMD battery and the sensitive latency of its tasks in defining the weighting factor of energy consumption and processing time.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129082066","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: 2

A Big Data Placement Strategy in Geographically Distributed Datacenters 地理分布数据中心中的大数据放置策略

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) Pub Date : 2020-11-24 DOI: 10.1109/CloudTech49835.2020.9365881

L. Bouhouch, M. Zbakh, C. Tadonki

{"title":"A Big Data Placement Strategy in Geographically Distributed Datacenters","authors":"L. Bouhouch, M. Zbakh, C. Tadonki","doi":"10.1109/CloudTech49835.2020.9365881","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365881","url":null,"abstract":"With the pervasiveness of the \"Big Data\" characteristic together with the expansion of geographically distributed datacenters in the Cloud computing context, processing large- scale data applications has become a crucial issue. Indeed, the task of finding the most efficient way of storing massive data across distributed locations is increasingly complex. Furthermore, the execution time of a given task that requires several datasets might be dominated by the cost of data migrations/exchanges, which depends on the initial placement of the input datasets over the set of datacenters in the Cloud and also on the dynamic data management strategy. In this paper, we propose a data placement strategy to improve the workflow execution time through the reduction of the cost associated to data movements between geographically distributed datacenters, considering their characteristics such as storage capacity and read/write speeds. We formalize the overall problem and then propose a data placement algorithm structured into two phases. First, we compute the estimated transfer time to move all involved datasets from their respective locations to the one where the corresponding tasks are executed. Second, we apply a greedy algorithm in order to assign each dataset to the optimal datacenter w.r.t the overall cost of data migrations. The heterogeneity of the datacenters together with their characteristics (storage and bandwidth) are both taken into account. Our experiments are conducted using Cloudsim simulator. The obtained results show that our proposed strategy produces an efficient placement and actually reduces the overheads of the data movement compared to both a random assignment and a selected placement algorithm from the literature.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"47 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129794931","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

Machine Learning for anomaly detection. Performance study considering anomaly distribution in an imbalanced dataset 异常检测的机器学习。考虑不平衡数据集中异常分布的性能研究

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) Pub Date : 2020-11-24 DOI: 10.1109/CloudTech49835.2020.9365887

S. E. Hajjami, Jamal Malki, M. Berrada, Bouziane Fourka

{"title":"Machine Learning for anomaly detection. Performance study considering anomaly distribution in an imbalanced dataset","authors":"S. E. Hajjami, Jamal Malki, M. Berrada, Bouziane Fourka","doi":"10.1109/CloudTech49835.2020.9365887","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365887","url":null,"abstract":"The continuous dematerialization of real-world data greatly contributes to the important growing of the exchanged data. In this case, anomaly detection is increasingly becoming an important task of data analysis in order to detect abnormal data, which is of particular interest and may require action. Recent advances in artificial intelligence approaches, such as machine learning, are making an important breakthrough in this area. Typically, these techniques have been designed for balanced data sets or that have certain assumptions about the distribution of data. However, the real applications are rather confronted with an imbalanced data distribution, where normal data are present in large quantities and abnormal cases are generally very few. This makes anomaly detection similar to looking for the needle in a haystack. In this article, we develop an experimental setup for comparative analysis of two types of machine learning techniques in their application to anomaly detection systems. We study their performance taking into account anomaly distribution in an imbalanced dataset.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126503651","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: 6

Deployment of Containerized Deep Learning Applications in the Cloud 在云端部署容器化深度学习应用

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) Pub Date : 2020-11-24 DOI: 10.1109/CloudTech49835.2020.9365868

Rim Doukha, S. Mahmoudi, M. Zbakh, P. Manneback

{"title":"Deployment of Containerized Deep Learning Applications in the Cloud","authors":"Rim Doukha, S. Mahmoudi, M. Zbakh, P. Manneback","doi":"10.1109/CloudTech49835.2020.9365868","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365868","url":null,"abstract":"During the last years, the use of Cloud computing environment has increased as a result of the various services offered by Cloud providers (Amazon Web Services, Google Cloud, Microsoft Azure, etc.). Many companies are moving their data and applications to the Cloud in order to tackle the complex configuration effort, for having more flexibility, maintenance, and resource availability. However, it is important to mention the challenges that developers may face when using a Cloud solution such as the variation of applications requirements (in terms of computation, memory and energy consumption) over time, which makes the deployment and migration a hard process. In fact, the deployment will not depend only on the application, but it will also rely on the related services and hardware for the proper functioning of the application. In this paper, we propose a Cloud infrastructure for automatic deployment of applications using the services of Kubernetes, Docker, Ansible and Slurm. Our architecture includes a script to deploy the application depending of its requirement needs. Experiments are conducted with the analysis and the deployment of Deep Learning (DL) applications and more particularly images classification and object localization.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122675899","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

An Image processing Player Acquisition and Tracking System for E-sports 一种电子竞技图像处理玩家获取与跟踪系统

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) Pub Date : 2020-11-24 DOI: 10.1109/CloudTech49835.2020.9365885

Joaquim Vieira, N. Luwes

引用次数: 1

A Review of Credit Card Fraud Detection Using Machine Learning Techniques 基于机器学习技术的信用卡欺诈检测综述

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) Pub Date : 2020-11-24 DOI: 10.1109/CloudTech49835.2020.9365916

Nadia Boutaher, A. Elomri, N. Abghour, K. Moussaid, M. Rida

{"title":"A Review of Credit Card Fraud Detection Using Machine Learning Techniques","authors":"Nadia Boutaher, A. Elomri, N. Abghour, K. Moussaid, M. Rida","doi":"10.1109/CloudTech49835.2020.9365916","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365916","url":null,"abstract":"Big Data technologies concern several critical areas such as Healthcare, Finance, Manufacturing, Transport, and E-Commerce. Hence, they play an indispensable role in the financial sector, especially within the banking services which are impacted by the digitalization of services and the evolvement of e-commerce transactions. Therefore, the emergence of the credit card use and the increasing number of fraudsters have generated different issues that concern the banking sector. Unfortunately, these issues obstruct the performance of Fraud Control Systems (Fraud Detection Systems & Fraud Prevention Systems) and abuse the transparency of online payments. Thus, financial institutions aim to secure credit card transactions and allow their customers to use e-banking services safely and efficiently. To reach this goal, they try to develop more relevant fraud detection techniques that can identify more fraudulent transactions and decrease frauds. The purpose of this article is to define the fundamental aspects of fraud detection, the current systems of fraud detection, the issues and challenges of frauds related to the banking sector, and the existing solutions based on machine learning techniques.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115168969","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

A Cross-Layered Interference in Multichannel MAC of VANET VANET多通道MAC中的跨层干扰

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) Pub Date : 2020-11-24 DOI: 10.1109/CloudTech49835.2020.9365870

Fadlallah Chbib, L. Khoukhi, W. Fahs, Jamal Haydar, R. Khatoun

{"title":"A Cross-Layered Interference in Multichannel MAC of VANET","authors":"Fadlallah Chbib, L. Khoukhi, W. Fahs, Jamal Haydar, R. Khatoun","doi":"10.1109/CloudTech49835.2020.9365870","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365870","url":null,"abstract":"Recently, with the various issues experienced in street mobility, the presence and improvement of the Vehicular Ad hoc Networks (VANETs) become a necessity. VANETs has distinctive positive effects on vehicular safety, street security, traffic the executives, drivers' comfort and passengers' convenience. Roads have encountered an increase in traffic congestion as a reason for the growth in population density. Thus, people face drastic delays that lead to a radical impact on the economics, society and environment. In this paper, we propose a novel Signal to Interference Routing (SIR) protocol that aims to discover the best routing path between source and destination. SIR differs from existing protocols by proposing a new metric that depends on Signal to Interference (SIR) at each Service Channel (SCH) of each node from source to destination. The proposed protocol aims to minimize interferences in vehicular environment considering both MAC and routing layers. We use Network Simulator NS-2.35 in order to implement and evaluate the proposed protocol. The results show significant improvements concerning an end to end delay, throughput and packet delivery ratio.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133949253","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: 2