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2021 12th International Conference on Information and Knowledge Technology (IKT)最新文献

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User Preferences Elicitation in Bilateral Automated Negotiation Using Recursive Least Square Estimation 基于递归最小二乘估计的双边自动协商中用户偏好的提取
2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685496
Farnaz Salmanian, H. Jazayeriy, J. Kazemitabar
{"title":"User Preferences Elicitation in Bilateral Automated Negotiation Using Recursive Least Square Estimation","authors":"Farnaz Salmanian, H. Jazayeriy, J. Kazemitabar","doi":"10.1109/IKT54664.2021.9685496","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685496","url":null,"abstract":"The negotiating agents are trying to reach a quality agreement during the process of automated negotiation. While each agent tries to improve its own utility, the agreement yields when the opponent reach in an acceptable utility as well. Therefore, learning the opponent's preference during the negotiation is a challenging area of research. The opponent preferences modeled by two parameter vectors: the importance of negotiation issues, and the scoring value of each negotiation issue. In this study, the opponent model is updated by using an incremental recursive least square estimator. As time passes, the estimator reaches calculates the more accurate outcomes. By examining different negotiation domains, the computational experiments show the proposed method outperforms the recent studies.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116335343","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
Statistical Disorder Parameters Computing For Hyperspectral Image Anomaly Detection 高光谱图像异常检测的统计失序参数计算
2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685269
M. Imani
{"title":"Statistical Disorder Parameters Computing For Hyperspectral Image Anomaly Detection","authors":"M. Imani","doi":"10.1109/IKT54664.2021.9685269","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685269","url":null,"abstract":"Two statistical disorder parameters are defined for hyperspectral anomaly detection in this paper. While the background information is usually located in principal components of the hyperspectral data containing the most energy, the low variance components contain anomaly or noise signals. Two introduced parameters are computed based on the principal components. The first parameter called as entropy contains the randomness value of the spectral measurements while the second parameter called as anisotropy contains the relative importance of the consecutive components of the hyperspectral image. The extracted features can be given to any arbitrary anomaly detector. The experimental results show that feeding entropy and anisotropy features to the RX detector provides a significant improvement in hyperspectral anomaly detection.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130350974","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
AOV-IDS: Arithmetic Optimizer with Voting classifier for Intrusion Detection System 带有投票分类器的入侵检测系统算法优化器
2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685429
Amir Soltany Mahboob, Mohammad Reza Ostadi Moghaddam, S. Yousefi
{"title":"AOV-IDS: Arithmetic Optimizer with Voting classifier for Intrusion Detection System","authors":"Amir Soltany Mahboob, Mohammad Reza Ostadi Moghaddam, S. Yousefi","doi":"10.1109/IKT54664.2021.9685429","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685429","url":null,"abstract":"Intrusion Detection System (IDS) has been an imperative challenge in Computer Networks. Commonly, based on the network traffic and large amount of transmitted data in the network, solutions preventing misdiagnosis of attacks and increasing the accuracy of intrusion detection has been proposed by researchers. To address the mentioned challenge, in this paper we propose a hybrid IDS using an Arithmetic Optimizer Algorithm (AOA) and Majority Vote Classifier (MVC) for computer networks. First, the optimal feature subset is selected by the Arithmetic Optimizer Algorithm and then a MVC is used to classify the samples. MVC utilizes Naive Bayes (NB), Decision Tree (DT), and k-nearest neighbors (KNN). The efficiency of the proposed method has been evaluated using the UNSW-NB15 dataset and the results have been compared with other methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) as well as similar methods. Experimental results show better performance of the proposed method in terms of higher intrusion detection accuracy and fewer features compared to other similar studies.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121405275","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: 4
Violence Detection Using One-Dimensional Convolutional Networks 基于一维卷积网络的暴力检测
2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685835
Narges Honarjoo, Ali Abdari, Azadeh Mansouri
{"title":"Violence Detection Using One-Dimensional Convolutional Networks","authors":"Narges Honarjoo, Ali Abdari, Azadeh Mansouri","doi":"10.1109/IKT54664.2021.9685835","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685835","url":null,"abstract":"Violence detection in surveillance video processing is a useful capability helping discover abnormal events in a variety of places. Utilizing methods considering the accuracy and complexity simultaneously can provide systems suitable for real-time applications. In this paper, the traditional approach of extracting temporal features has been investigated, while by exploiting one-dimensional convolutional networks, a new approach is proposed, which extracts these features across consecutive frames properly. This low-complexity convolutional-based approach represents a series of frames with a robust feature vector, which can be applied for real-time applications. The experimental results on Hockey, ViolentFlow reveal the efficiency of this proposed method.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122963577","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
A Perceptual Loss For Screen Content Image Super-Resolution 屏幕内容图像超分辨率的感知损失
2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685780
Hossein Sekhavaty, Marzieh Hosseinkhani, Azadeh Mansouri
{"title":"A Perceptual Loss For Screen Content Image Super-Resolution","authors":"Hossein Sekhavaty, Marzieh Hosseinkhani, Azadeh Mansouri","doi":"10.1109/IKT54664.2021.9685780","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685780","url":null,"abstract":"The acceptable results of deep learning led to the use of the deep neural network on a wide range of models, including image super-resolution. The performance of the deep neural network is directly affected by its loss function. Most methods use intensity loss, such as MSE, which computes the difference between the predicted image and the ground truth. Because the structural information of a scene is more sensitive to the human visual system, it is desired that the loss function could measure the impact of the structural error. In addition, the use of screen content images has become widespread because of many applications such as desktop-sharing and remote computing. As a result, super-resolution of screen content images becomes a crucial technique to enhance the quality of low-resolution images. In the presented loss function, the structural error is weighted employing DCT components. The model is trained and tested using the screen content images, and the experimental subjective and objective results illustrate the effectiveness of the presented loss for screen content images.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121517490","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
UltraLearn: Next-Generation CyberSecurity Learning Platform ultrallearn:下一代网络安全学习平台
2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685940
Saeed Raisi, Saeid Ghasemshirazi, Ghazaleh Shirvani
{"title":"UltraLearn: Next-Generation CyberSecurity Learning Platform","authors":"Saeed Raisi, Saeid Ghasemshirazi, Ghazaleh Shirvani","doi":"10.1109/IKT54664.2021.9685940","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685940","url":null,"abstract":"Learning was always an essential part of our lives to acquire knowledge and move forward. However, the increasing wages of on-campus learning leads to an increasing need for an alternate way of learning. With the outbreak of COVID-19, people needed to stay at home for safety. Therefore, finding an answer to this need became more dominant. Another major problem, especially in the ongoing decade, is the lack of enough cybersecurity knowledge by the common folk. This paper aims to look at a new platform that is carefully designed to teach cybersecurity to learners with any background. We design this system using gamification to increase the efficiency of learning. Because modern technologies are ubiquitous in our lives, particularly during the coronavirus outbreak, including practical work in teaching will be quite beneficial. The practical activity has a clear advantage, such as promoting experimental learning and improving student abilities and skills for the future professional career. Finally, we assess this method with a classic learning approach using two different groups.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116978222","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
An efficient task scheduling in cloud computing based on ACO algorithm 基于蚁群算法的云计算高效任务调度
2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685674
Zahra Shafahi, Alireza Yari
{"title":"An efficient task scheduling in cloud computing based on ACO algorithm","authors":"Zahra Shafahi, Alireza Yari","doi":"10.1109/IKT54664.2021.9685674","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685674","url":null,"abstract":"Resource allocation as a NP-hard problem is a very important part of cloud computing and is examined in the form of scheduling algorithms. An Ant Colony Optimization (ACO) algorithm was proposed in this study to improve the load balancing performance and makespan time parameters. Most of the tasks scheduling algorithms have been proposed to improve one of the service quality parameters for service providers or users and do not address the needs of both at the same time. Since an appropriate scheduling algorithm should be able to consider the quality requirements of users and service providers simultaneously, for this purpose in this paper we have proposed a new algorithm for scheduling tasks in cloud environment. The proposed algorithm is based on the ACO algorithm and studied in comparison to a Particle Swarm Optimization (PSO) algorithm, a Genetic Algorithm (GA) and also another research based on ACO. The proposed algorithm has showed the significant improvements concerning the makespan time, load balancing, execution time and resource utilization against the compared algorithms.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133133061","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
A Community-Based Method for Identifying Influential Nodes using Network Embedding 基于社区的基于网络嵌入的影响节点识别方法
2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685292
Narges Vafaei, M. Keyvanpour
{"title":"A Community-Based Method for Identifying Influential Nodes using Network Embedding","authors":"Narges Vafaei, M. Keyvanpour","doi":"10.1109/IKT54664.2021.9685292","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685292","url":null,"abstract":"People's influence on their friends' personal opinions and decisions is an essential feature of social networks, which has led to many businesses using social media to convince a small number of users to increase awareness and ultimately maximize sales to the maximum number of users. This issue is typically expressed as the influence maximization problem. In this paper, we will identify the most influential nodes in the social network during two phases. In the first phase, we offer a community detection approach based on the Node2Vec method to detect the potential communities. In the second phase, larger communities are chosen as candidate communities, and then the heuristic-based measurement approach is utilized to identify influential nodes within candidate communities. Evaluations of the proposed method on two real datasets show the superiority of this method over other compared methods.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128245817","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
Short-Term Traffic Flow Prediction Based on a Recurrent Deep Neural Network: a Study in Tehran 基于递归深度神经网络的短期交通流预测:德黑兰研究
2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685122
M. Abdoos, Taha Vajedsamiei
{"title":"Short-Term Traffic Flow Prediction Based on a Recurrent Deep Neural Network: a Study in Tehran","authors":"M. Abdoos, Taha Vajedsamiei","doi":"10.1109/IKT54664.2021.9685122","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685122","url":null,"abstract":"With growing of population, the issue of optimal mobility between two points of the city has become one of the most important problems. There are various tools to suggest the optimal route, but due to the momentary changes in traffic in cities, especially large cities, providing the optimal route without predicting the traffic load will not be accurate. In this regard, it can be noted that one of the most widely used up-to-date methods is the use of deep neural networks to predict the future. In this paper, while examining some of the most widely used deep neural networks to predict traffic sequence, a method is presented based on one of recurrent neural networks. The method has been evaluated on real traffic data on a part of Tehran. The results show that the proposed method outperforms the other similar neural networks.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130386826","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
Analyzing effect of news polarity on stock market prediction: a machine learning approach 新闻极性对股市预测的影响分析:一种机器学习方法
2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685403
Golshid Ranibaran, M. Moin, S. H. Alizadeh, A. Koochari
{"title":"Analyzing effect of news polarity on stock market prediction: a machine learning approach","authors":"Golshid Ranibaran, M. Moin, S. H. Alizadeh, A. Koochari","doi":"10.1109/IKT54664.2021.9685403","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685403","url":null,"abstract":"In finance, the stock market and its trends are volatile in nature. In the stock market, which is dynamic, complex, nonlinear and non-parametric, accurate forecasting is crucial for trading strategy. This need attracted researchers to detect fluctuations and to predict the next move. It is assumed that news articles affect the stock market. In this work, non-measurable data like financial news headlines has been transferred into the measurable data. We investigated the relationship between news and their impact on stock prices. To show this relationship, we applied the sentiment analysis data and the price difference between the day before the news was published and the day of the news to the classic machine learning models such as SVR, BayesianRidge, LASSO, Decision tree and Random forest. The observations showed that SVM performs well in all tests. The prediction error in this model is 0.28, which is much less than that of the random news tagging. Also based on our tests, using a computer for tagging is as good as manual tagging.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134619707","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
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