Pub Date : 2023-01-01DOI: 10.12720/jait.14.5.876-882
Yota Kurokawa, Masaru Fukushi
—This paper proposes a simple and effective evaluation method for fault-tolerant routing methods developed for Network-on-Chip (NoC)-based many-core processors. To cope with faults which significantly degrade the reliability of communication among cores, a variety of fault-tolerant routing methods have been studied. Those methods have been mainly evaluated in terms of communication performance such as latency and throughput by computer simulations of packet routing. However, such evaluations are not practical in that they cannot reveal the performance difference in executing parallel applications with the fault-tolerant routing methods. The proposed method obtains the information of the target parallel application such as task execution time, communication pattern, and communication amount and incorporates it in the conventional packet routing simulations. With the proposed evaluation method, computer simulations have been conducted to evaluate the performance of four famous fault-tolerant routing methods, i.e., Fcube4, Position Route, Passage-Y, and Passage-XY, using NAS Parallel Benchmarks and the performance difference is revealed in executing parallel programs named Integer Sort (IS) and Fast Fourier Transform (FFT). The results show that, Passage-XY outperforms other methods in both IS and FT, and for the case of IS, Passage-XY can reduce the program execution time by up to about 39%, 56%, and 26% compared with Fcube4, Position Route, and Passage-Y, respectively.
{"title":"A Simple and Effective Evaluation Method for Fault-Tolerant Routing Methods in Network-on-Chips","authors":"Yota Kurokawa, Masaru Fukushi","doi":"10.12720/jait.14.5.876-882","DOIUrl":"https://doi.org/10.12720/jait.14.5.876-882","url":null,"abstract":"—This paper proposes a simple and effective evaluation method for fault-tolerant routing methods developed for Network-on-Chip (NoC)-based many-core processors. To cope with faults which significantly degrade the reliability of communication among cores, a variety of fault-tolerant routing methods have been studied. Those methods have been mainly evaluated in terms of communication performance such as latency and throughput by computer simulations of packet routing. However, such evaluations are not practical in that they cannot reveal the performance difference in executing parallel applications with the fault-tolerant routing methods. The proposed method obtains the information of the target parallel application such as task execution time, communication pattern, and communication amount and incorporates it in the conventional packet routing simulations. With the proposed evaluation method, computer simulations have been conducted to evaluate the performance of four famous fault-tolerant routing methods, i.e., Fcube4, Position Route, Passage-Y, and Passage-XY, using NAS Parallel Benchmarks and the performance difference is revealed in executing parallel programs named Integer Sort (IS) and Fast Fourier Transform (FFT). The results show that, Passage-XY outperforms other methods in both IS and FT, and for the case of IS, Passage-XY can reduce the program execution time by up to about 39%, 56%, and 26% compared with Fcube4, Position Route, and Passage-Y, respectively.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135649055","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 : 2023-01-01DOI: 10.12720/jait.14.5.1012-1018
Hongfei Jia, Yunpeng Qi, Chao Liu, Ruiyi Wu
—The dedicated Connected Autonomous Vehicle (CAV) lanes can avoid the interference of human-driven vehicles and create relatively safe operating conditions for CAVs. Besides, the dedicated CAV lanes can give full advantages of the connectivity and controllability to further improve the capacity of links. However, the consequent problem is unfairness among the traffic network users due to the higher priority right of CAVs in some links. This paper develops a bi-level programming model to design the CAV dedicated lanes deployment scheme considering the user fairness issue. In the lower-level model, we define the road resistance functions under various scenarios by investigating the effect of the dedicated lane on link capacity and construct the traffic assignment model which is solved by the diagonalized Frank-Wolfe method. The upper-level model aims to solve the multi-objective optimization problem that integrates user fairness and total system travel cost. The user fairness problem determines the fairness index using the Wilson entropy model, and the travel cost problem considers different users’ travel time value coefficients.
{"title":"A Model for Deployment of Dedicated Connected Autonomous Vehicle Lanes Considering User Fairness","authors":"Hongfei Jia, Yunpeng Qi, Chao Liu, Ruiyi Wu","doi":"10.12720/jait.14.5.1012-1018","DOIUrl":"https://doi.org/10.12720/jait.14.5.1012-1018","url":null,"abstract":"—The dedicated Connected Autonomous Vehicle (CAV) lanes can avoid the interference of human-driven vehicles and create relatively safe operating conditions for CAVs. Besides, the dedicated CAV lanes can give full advantages of the connectivity and controllability to further improve the capacity of links. However, the consequent problem is unfairness among the traffic network users due to the higher priority right of CAVs in some links. This paper develops a bi-level programming model to design the CAV dedicated lanes deployment scheme considering the user fairness issue. In the lower-level model, we define the road resistance functions under various scenarios by investigating the effect of the dedicated lane on link capacity and construct the traffic assignment model which is solved by the diagonalized Frank-Wolfe method. The upper-level model aims to solve the multi-objective optimization problem that integrates user fairness and total system travel cost. The user fairness problem determines the fairness index using the Wilson entropy model, and the travel cost problem considers different users’ travel time value coefficients.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136305462","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 : 2023-01-01DOI: 10.12720/jait.14.6.1151-1158
Abdellah El Zaar, Rachida Assawab, Ayoub Aoulalay, Nabil Benaya, Toufik Bakir, Smain Femmam, Abderrahim El Allati
—Artificial Intelligence and Deep Learning applications are well-developed as a part of human life. In the field of object recognition, Convolutional Neural Network (CNN) based methods are getting more and more important and challenging. However, existing CNN methods do not perform well on datasets that exhibit high similarities, resulting in confusion between different classes. In this study, we propose a new Deep Learning approach for recognizing date fruit categories based on the Deep Convolutional Neural Network (DCNN). The modified fine-tuning (MFTs-Net) approach can recognize with high accuracy the different date fruit categories. In order to train and to test the robustness of our proposed method, we have collected a dataset that takes into account different date fruit categories. The presented dataset is challenging as it contains classes of a unique object and presents high similarities concerning the shape, color and texture of date fruit. We show that the MFTs-Net CNN we implemented, trained and tested using the collected dataset can recognize with high accuracy the different date categories compared with state-of-the-arts works. The presented methodology works perfectly with very small datasets, which is one of the main strengths of the proposed method. Our MFTs-Net architecture performs perfectly on test data with an accuracy of 98%. 1
{"title":"MFTs-Net: A Deep Learning Approach for High Similarity Date Fruit Recognition","authors":"Abdellah El Zaar, Rachida Assawab, Ayoub Aoulalay, Nabil Benaya, Toufik Bakir, Smain Femmam, Abderrahim El Allati","doi":"10.12720/jait.14.6.1151-1158","DOIUrl":"https://doi.org/10.12720/jait.14.6.1151-1158","url":null,"abstract":"—Artificial Intelligence and Deep Learning applications are well-developed as a part of human life. In the field of object recognition, Convolutional Neural Network (CNN) based methods are getting more and more important and challenging. However, existing CNN methods do not perform well on datasets that exhibit high similarities, resulting in confusion between different classes. In this study, we propose a new Deep Learning approach for recognizing date fruit categories based on the Deep Convolutional Neural Network (DCNN). The modified fine-tuning (MFTs-Net) approach can recognize with high accuracy the different date fruit categories. In order to train and to test the robustness of our proposed method, we have collected a dataset that takes into account different date fruit categories. The presented dataset is challenging as it contains classes of a unique object and presents high similarities concerning the shape, color and texture of date fruit. We show that the MFTs-Net CNN we implemented, trained and tested using the collected dataset can recognize with high accuracy the different date categories compared with state-of-the-arts works. The presented methodology works perfectly with very small datasets, which is one of the main strengths of the proposed method. Our MFTs-Net architecture performs perfectly on test data with an accuracy of 98%. 1","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135610240","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 : 2023-01-01DOI: 10.12720/jait.14.1.94-101
Afaf Tareef, Hayat Al-Dmour, Afnan Al-Sarayreh
Automated detection of human identity and gender offers several industrial applications in near future, such as monitoring, surveillance, commercial profiling and human computer interaction. In this paper, deep learning techniques have been used to investigate the problem of human identity and gender classification using hand images. First, pre-processing techniques have been applied to enhance the appearance of the hand images. The pre-processed image is passed through the convolution neural network to determine the gander. For identity detection, the network has been trained on the images for the determined gender for better recognition. To further enhance the result, the framework has been implemented using different optimizers and k fold cross-validation. Experimental results have shown that highly effective performance is achieved in both the human identification and gender classification objectives. High average accuracy of 97.75% using the dorsal hand side for human identification and 96.79% has been obtained for gender classification using the palm hand side. Conclusively, the proposed method has achieved more accuracy than the previous methods both for identification and gender classification.
{"title":"An Automated Deep Learning Framework for Human Identity and Gender Detection","authors":"Afaf Tareef, Hayat Al-Dmour, Afnan Al-Sarayreh","doi":"10.12720/jait.14.1.94-101","DOIUrl":"https://doi.org/10.12720/jait.14.1.94-101","url":null,"abstract":"Automated detection of human identity and gender offers several industrial applications in near future, such as monitoring, surveillance, commercial profiling and human computer interaction. In this paper, deep learning techniques have been used to investigate the problem of human identity and gender classification using hand images. First, pre-processing techniques have been applied to enhance the appearance of the hand images. The pre-processed image is passed through the convolution neural network to determine the gander. For identity detection, the network has been trained on the images for the determined gender for better recognition. To further enhance the result, the framework has been implemented using different optimizers and k fold cross-validation. Experimental results have shown that highly effective performance is achieved in both the human identification and gender classification objectives. High average accuracy of 97.75% using the dorsal hand side for human identification and 96.79% has been obtained for gender classification using the palm hand side. Conclusively, the proposed method has achieved more accuracy than the previous methods both for identification and gender classification.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66329821","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 : 2023-01-01DOI: 10.12720/jait.14.3.606-615
Fatoumata Wongbé Rosalie Tokpa, B. H. Kamagaté, Vincent Monsan, S. Oumtanaga
— Social media refers to communication channels on Internet that enable the creation and publication of content generated by the user and interaction between users. Given the accessibility to these means of communication and their rapidity, people resort more to them comparatively to the traditional media including radio, television and newspapers. However, dubious pieces of information such as fake news are often disseminated for malicious purposes. The proliferation of fake news has a strong negative impact on a society such as damage to the reputation of a personality, an organization or the aggravation of conflicts between its members. Due to the proliferation of fake news on these websites, the notion of veracity of information becomes a crucial issue. Research based on machine learning is promising. However, one of the main limitations is the efficiency of predictions. As a solution to detect fake news, we have proposed two models based on hybrid deep learning and evaluated our models on the two real datasets, namely ISOT and FA-KES. An experience of the proposed models to detect fake news, allowed to obtain on ISOT an accuracy of 99% for both models and on FA-KES, we obtain an accuracy of 68% for one the models and an accuracy of 63% for other. Other experiments in generalizing models on these data sets have proposed. The results obtained are better than other machine learning models.
- Social media是指在互联网上实现用户生成内容的创作和发布以及用户之间互动的传播渠道。考虑到这些传播手段的可及性和快速性，与传统媒体包括广播、电视和报纸相比，人们更多地使用它们。然而，虚假新闻等可疑信息往往出于恶意目的而传播。假新闻的泛滥对一个社会有很强的负面影响，比如损害一个人的声誉，一个组织或其成员之间的冲突加剧。由于这些网站上假新闻的泛滥，信息真实性的概念成为一个至关重要的问题。基于机器学习的研究很有前途。然而，主要的限制之一是预测的效率。作为假新闻检测的解决方案，我们提出了两个基于混合深度学习的模型，并在两个真实数据集ISOT和FA-KES上对我们的模型进行了评估。所提出的模型检测假新闻的经验，允许在ISOT和FA-KES上获得99%的准确率，我们获得一个模型的准确率为68%，另一个模型的准确率为63%。已经提出了在这些数据集上推广模型的其他实验。所得结果优于其他机器学习模型。
{"title":"Fake News Detection in Social Media: Hybrid Deep Learning Approaches","authors":"Fatoumata Wongbé Rosalie Tokpa, B. H. Kamagaté, Vincent Monsan, S. Oumtanaga","doi":"10.12720/jait.14.3.606-615","DOIUrl":"https://doi.org/10.12720/jait.14.3.606-615","url":null,"abstract":"— Social media refers to communication channels on Internet that enable the creation and publication of content generated by the user and interaction between users. Given the accessibility to these means of communication and their rapidity, people resort more to them comparatively to the traditional media including radio, television and newspapers. However, dubious pieces of information such as fake news are often disseminated for malicious purposes. The proliferation of fake news has a strong negative impact on a society such as damage to the reputation of a personality, an organization or the aggravation of conflicts between its members. Due to the proliferation of fake news on these websites, the notion of veracity of information becomes a crucial issue. Research based on machine learning is promising. However, one of the main limitations is the efficiency of predictions. As a solution to detect fake news, we have proposed two models based on hybrid deep learning and evaluated our models on the two real datasets, namely ISOT and FA-KES. An experience of the proposed models to detect fake news, allowed to obtain on ISOT an accuracy of 99% for both models and on FA-KES, we obtain an accuracy of 68% for one the models and an accuracy of 63% for other. Other experiments in generalizing models on these data sets have proposed. The results obtained are better than other machine learning models.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66332265","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 : 2023-01-01DOI: 10.12720/jait.14.4.838-845
Marwa A. Marzouk, M. Elkholy
—The continuous increment of malware and its complexity motivated researchers to implement techniques to detect and classify it. Manual detection of malicious files is time consuming and shows poor results. Recently, Deep Convolution Neural Networks (DCNN) shows promising results in malware detection. DCNNs include large number of fully connected layers that are capable to deal with fast iterations of Android malware. Compared to the existing approach, DCNN shows high performance and accuracy in detecting different types of malwares. The proposed work combines Scale-Invariant Feature Transform (SIFT) and DCNN to detect malware features. Combining SIFT with DCNN allow higher accuracy of features classification and overcome the problem of single-feature extraction. The proposed method is compared to existing approaches to malware detection in terms of anticipated time and detection accuracy. The experimental results showed the significant enhancement offered by the proposed work in terms of accuracy and performance.
{"title":"Deep Image: An Efficient Image-Based Deep Conventional Neural Network Method for Android Malware Detection","authors":"Marwa A. Marzouk, M. Elkholy","doi":"10.12720/jait.14.4.838-845","DOIUrl":"https://doi.org/10.12720/jait.14.4.838-845","url":null,"abstract":"—The continuous increment of malware and its complexity motivated researchers to implement techniques to detect and classify it. Manual detection of malicious files is time consuming and shows poor results. Recently, Deep Convolution Neural Networks (DCNN) shows promising results in malware detection. DCNNs include large number of fully connected layers that are capable to deal with fast iterations of Android malware. Compared to the existing approach, DCNN shows high performance and accuracy in detecting different types of malwares. The proposed work combines Scale-Invariant Feature Transform (SIFT) and DCNN to detect malware features. Combining SIFT with DCNN allow higher accuracy of features classification and overcome the problem of single-feature extraction. The proposed method is compared to existing approaches to malware detection in terms of anticipated time and detection accuracy. The experimental results showed the significant enhancement offered by the proposed work in terms of accuracy and performance.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66333797","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 : 2023-01-01DOI: 10.12720/jait.14.4.863-875
Abdul Jabbar, Manzoor Illahi, Sajid Iqbal, Amjad Rehman Khan, Narmine ElHakim, Tanzila Saba
.
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{"title":"PWMStem: A Corpus-Based Suffix Identification and Stripping Algorithm for Multi-lingual Stemming","authors":"Abdul Jabbar, Manzoor Illahi, Sajid Iqbal, Amjad Rehman Khan, Narmine ElHakim, Tanzila Saba","doi":"10.12720/jait.14.4.863-875","DOIUrl":"https://doi.org/10.12720/jait.14.4.863-875","url":null,"abstract":".","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66334125","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 : 2023-01-01DOI: 10.12720/jait.14.1.26-38
A. M. Syarif, A. Azhari, S. Suprapto, K. Hastuti
This study proposes a Gamelan melody generation system based on three characteristics, which are the melodic patterns recognition, composition meter rules that control the duration of notes, and the special notes (pitches) selection which represent ambiguous rules in determining the Gamelan musical mode system. Long-Short Term Memory (LSTM) networks were trained using the sequence prediction technique to generate symbolic based Gamelan melodies. The dataset collected from sheet music was converted into ABC notation format, added with codes representing the composition meter and special notes, and restructured into a character-based representation format. The LSTM network training showed good results in the melodic patterns recognition but the networks take less than 10 attempts for the LSTM network to successfully generate one melody. The evaluation was conducted using experts’ judgment. Three generated melodies were sent to experts to be read, hummed and judged. Overall, the evaluation results showed that the generated melodies can comply with the characteristics of the Gamelan melodic patterns, the composition meter and the special notes.
{"title":"Gamelan Melody Generation Using LSTM Networks Controlled by Composition Meter Rules and Special Notes","authors":"A. M. Syarif, A. Azhari, S. Suprapto, K. Hastuti","doi":"10.12720/jait.14.1.26-38","DOIUrl":"https://doi.org/10.12720/jait.14.1.26-38","url":null,"abstract":"This study proposes a Gamelan melody generation system based on three characteristics, which are the melodic patterns recognition, composition meter rules that control the duration of notes, and the special notes (pitches) selection which represent ambiguous rules in determining the Gamelan musical mode system. Long-Short Term Memory (LSTM) networks were trained using the sequence prediction technique to generate symbolic based Gamelan melodies. The dataset collected from sheet music was converted into ABC notation format, added with codes representing the composition meter and special notes, and restructured into a character-based representation format. The LSTM network training showed good results in the melodic patterns recognition but the networks take less than 10 attempts for the LSTM network to successfully generate one melody. The evaluation was conducted using experts’ judgment. Three generated melodies were sent to experts to be read, hummed and judged. Overall, the evaluation results showed that the generated melodies can comply with the characteristics of the Gamelan melodic patterns, the composition meter and the special notes.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66329271","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 : 2023-01-01DOI: 10.12720/jait.14.1.77-84
Tuan Nguyen Kim, Duy Ho Ngoc, Nin Ho Le Viet, N. Moldovyan
Many types of digital signature schemes have been researched and published in recent years. In this paper, we propose two new types of collective signature schemes, namely i) the collective signature for several signing groups and ii) the collective signature for several individual signings and several signing groups. And then we used two difficult problems factoring and discrete logarithm to construct these schemes. To create a combination of these two difficult problems we use the prime module p with a special structure: p = Nn + 1 with n = rq, N is an even number, r and q are prime numbers of at least 512 bit. Schnorr’s digital signature scheme and the RSA key generation algorithm are used to construct related basic schemes such as the single signature scheme, the collective signature scheme, and the group signature scheme. The proposed collective signature schemes are built from these basic schemes. The correctness, security level and performance of the proposed schemes have also been presented in this paper.
近年来，人们研究和发表了许多类型的数字签名方案。本文提出了两种新的集体签名方案，即i)多个签名组的集体签名方案和ii)多个个人签名和多个签名组的集体签名方案。然后我们用两个难题分解和离散对数来构造这些格式。为了创建这两个难题的组合，我们使用具有特殊结构的素数模块p: p = Nn + 1, n = rq, n是偶数，r和q是至少512位的素数。使用Schnorr的数字签名方案和RSA密钥生成算法构建了相关的基本方案，如单个签名方案、集体签名方案和组签名方案。提出的集体签名方案是在这些基本方案的基础上构建的。本文还介绍了所提方案的正确性、安全性和性能。
{"title":"The New Collective Signature Schemes Based on Two Hard Problems Using Schnorr's Signature Standard","authors":"Tuan Nguyen Kim, Duy Ho Ngoc, Nin Ho Le Viet, N. Moldovyan","doi":"10.12720/jait.14.1.77-84","DOIUrl":"https://doi.org/10.12720/jait.14.1.77-84","url":null,"abstract":"Many types of digital signature schemes have been researched and published in recent years. In this paper, we propose two new types of collective signature schemes, namely i) the collective signature for several signing groups and ii) the collective signature for several individual signings and several signing groups. And then we used two difficult problems factoring and discrete logarithm to construct these schemes. To create a combination of these two difficult problems we use the prime module p with a special structure: p = Nn + 1 with n = rq, N is an even number, r and q are prime numbers of at least 512 bit. Schnorr’s digital signature scheme and the RSA key generation algorithm are used to construct related basic schemes such as the single signature scheme, the collective signature scheme, and the group signature scheme. The proposed collective signature schemes are built from these basic schemes. The correctness, security level and performance of the proposed schemes have also been presented in this paper.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330044","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 : 2023-01-01DOI: 10.12720/jait.14.2.178-184
Azeema Sadia, Fatima Bashir, Reema Qaiser Khan, Ammarah Khalid
—The Internet is used as a tool to offer people with endless knowledge. It is a global platform which is used for connectivity, communication, and sharing. At almost no cost, an individual can use the Internet to send email messages, update tweets, and Facebook messages to a vast number of people. These messages can also contain unsolicited advertisement which is identified as a spam. The company Twitter too is massively affected by spamming and it is an alarming issue for them. Twitter considers spam as actions that are unsolicited and repeated. These include tweet repetition, and the URLs that lead users to completely unrelated websites. The authors’ have worked with twitter’s dataset focusing on tweets about “iPhone”. It was collected by using an API which was further pre-processed. In this paper, content-based features have been selected that recognize the spamming tweet by using R. Multiple machine learning algorithms were applied to detect spamming tweets: Naive Bayes, Logistic Regression, KNN, Decision Tree, and Support Vector Machine. It was observed that the best performance was achieved by Naive Bayes Algorithm giving an accuracy of 89%.
{"title":"Comparison of Machine Learning Algorithms for Spam Detection","authors":"Azeema Sadia, Fatima Bashir, Reema Qaiser Khan, Ammarah Khalid","doi":"10.12720/jait.14.2.178-184","DOIUrl":"https://doi.org/10.12720/jait.14.2.178-184","url":null,"abstract":"—The Internet is used as a tool to offer people with endless knowledge. It is a global platform which is used for connectivity, communication, and sharing. At almost no cost, an individual can use the Internet to send email messages, update tweets, and Facebook messages to a vast number of people. These messages can also contain unsolicited advertisement which is identified as a spam. The company Twitter too is massively affected by spamming and it is an alarming issue for them. Twitter considers spam as actions that are unsolicited and repeated. These include tweet repetition, and the URLs that lead users to completely unrelated websites. The authors’ have worked with twitter’s dataset focusing on tweets about “iPhone”. It was collected by using an API which was further pre-processed. In this paper, content-based features have been selected that recognize the spamming tweet by using R. Multiple machine learning algorithms were applied to detect spamming tweets: Naive Bayes, Logistic Regression, KNN, Decision Tree, and Support Vector Machine. It was observed that the best performance was achieved by Naive Bayes Algorithm giving an accuracy of 89%.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330518","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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