2021 26th International Computer Conference, Computer Society of Iran (CSICC)最新文献_第3页

The Effect of Using Masked Language Models in Random Textual Data Augmentation 在随机文本数据增强中使用掩码语言模型的效果

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420616

M. A. Rashid, Hossein Amirkhani

引用次数: 1

A Semantic-based Feature Extraction Method Using Categorical Clustering for Persian Document Classification 一种基于语义的波斯语文档分类聚类特征提取方法

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420602

Saeedeh Davoudi, S. Mirzaei

{"title":"A Semantic-based Feature Extraction Method Using Categorical Clustering for Persian Document Classification","authors":"Saeedeh Davoudi, S. Mirzaei","doi":"10.1109/CSICC52343.2021.9420602","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420602","url":null,"abstract":"Natural Language Processing (NLP) is one of the promising fields of artificial intelligence. In recent decades, high volume of text data has been generated through the Internet. This kind of data is a valuable source of information which can be used in various fields such as information retrieval, search engines, recommender systems, etc. One practical task of text mining is document classification. In this paper, we mainly focus on Persian document classification. We introduce a new feature extraction approach derived from the combination of K-means clustering and Word2Vec to acquire semantically relevant and discriminant word representations. We call our proposed approach CC-Word2Vec (Categorical Clustering-Word2Vec) since we retrain the Word2Vec model using the word clusters of each category obtained by K-Means algorithm. We use 200 documents of 5 most frequent categories of Hamshahri news dataset to evaluate our method. We pass the extracted word vectors to Multi-Layer Perceptron (MLP) and Gradient Boosting (GB) classifiers to compare the performance of the proposed approach with Term Frequency Inverse Document Frequency (TF-IDF) and Word2Vec methods. Our new approach resulted in an improvement in the obtained accuracy of Gradient Boosting and Multi-Layer Perceptron models in comparison with TF-IDF and Word2Vec techniques.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122046548","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

GITCBot: A Novel Approach for the Next Generation of C&C Malware GITCBot:下一代C&C恶意软件的新方法

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420590

Saeid Ghasemshirazi, Ghazaleh Shirvani

引用次数: 2

A Multi-Classifier System for Rock Mass Crack Segmentation Based on Convolutional Neural Networks 基于卷积神经网络的岩体裂纹分割多分类器系统

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420613

M. Asadi, M. Sadeghi, A. Y. Bafghi

引用次数: 5

Automatic Personality Perception Using Autoencoder And Hierarchical Fuzzy Classification 基于自编码器和层次模糊分类的自动人格感知

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420627

E. J. Zaferani, M. Teshnehlab, Mansoor Vali

引用次数: 1

Stochastic Spintronic Neuron with Application to Image Binarization 随机自旋电子神经元在图像二值化中的应用

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420559

Abdolah Amirany, M. Meghdadi, M. H. Moaiyeri, Kian Jafari

{"title":"Stochastic Spintronic Neuron with Application to Image Binarization","authors":"Abdolah Amirany, M. Meghdadi, M. H. Moaiyeri, Kian Jafari","doi":"10.1109/CSICC52343.2021.9420559","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420559","url":null,"abstract":"The hardware implementation of neural network has always been of interest to the researchers as it can significantly increase the efficiency and application of neural networks due to the distributed nature of Artificial Neural Networks (ANNs) in both memory and computation. Direct implementation of ANNs also offer large gains when scaling the network sizes. Stochastic neurons are among the most significant aspects of machine learning algorithms and are very important in different neural networks. In this paper, a hardware model for the stochastic neuron based on the magnetic tunnel junction (MTJ) in subcritical current switching regime is proposed. Functional evaluation of the proposed model demonstrates that the behavior of the proposed model is comparable to the mathematical description of the stochastic neuron, and it has a negligible error in comparison with the theoretical model. The simulation results of image binarization over 10,000 images indicate that the proposed hardware model has only 0.25% pack signal to noise ratio (PSNR) and 0.02% structural similarity (SSIM) variation compared to its software-based counterpart.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123005440","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

Designing a New Method for Detecting Money Laundering based on Social Network Analysis 基于社会网络分析的洗钱检测新方法设计

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420621

Maryam Mahootiha, S. Golpayegani, B. Sadeghian

{"title":"Designing a New Method for Detecting Money Laundering based on Social Network Analysis","authors":"Maryam Mahootiha, S. Golpayegani, B. Sadeghian","doi":"10.1109/CSICC52343.2021.9420621","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420621","url":null,"abstract":"Money laundering nowadays occurs as one of the most severe and common crimes with great potential to harm the economy. Discovering money laundering by different computer methods has always been necessary due to criminals' high tendency to launder money. This study has focused on catching a type of money laundering, which leaves a trace in the datasets where the process of money laundering has been done collaboratively. This crime can be uncovered merely by discovering the pattern of group behavior of individuals. In this research, the social networks analysis method has been employed to detect group behavior in money laundering. The data were simulated based on the real environment and by considering different states because of proper data inaccessibility. The patterns of placement, layering, and integration of money are initially explained in money laundering in this study, followed by drawing a social network of individuals' transactions. In the end, the main culprits and their collaborators will be introduced based on a combination of criteria of centrality and detecting communities. Three different types of data have been used aimed at assessing the accuracy of the proposed solution. The proposed solution has also been compared with essential solutions such as the support vector machine, decision tree, and deep learning.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117096354","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

A Novel Wireless Network-on-Chip Architecture for Multicore Systems 一种新的多核系统无线片上网络架构

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420564

E. Tahanian, Mohsen Rezvani, Mansoor Fateh

{"title":"A Novel Wireless Network-on-Chip Architecture for Multicore Systems","authors":"E. Tahanian, Mohsen Rezvani, Mansoor Fateh","doi":"10.1109/CSICC52343.2021.9420564","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420564","url":null,"abstract":"Using Wireless Network-on-Chip (WiNOC) for designing the multicore System-on-Chips can significantly decrease the latency and power dissipation of the network. This improvement is achieved by replacing the multi-hop paths between far apart cores with a wireless single-hop link. Due to space, power and cost limitations, it is crucial to determine both the optimum number of equipped wireless hubs and their proper positions. In this paper, we propose a novel approach to obtain the optimum configuration of a WiNOC by leveraging the Simulating Annealing algorithm. Simultaneous multiple communications in such a network can be achieved by using multiple access techniques such as Orthogonal Frequency Division Multiple Access (OFDMA) to create dedicated channels between a source and destination pair. This technique is more bandwidth efficient compared to previously used FDMA. Also, it can distribute the available bandwidth between wireless nodes according to the traffic demands. So, we introduce an adequate channel reallocation algorithm regards to the broadcasting nature of the OFDMA scheme. The introduced architecture shows better performance in comparison with the conventional WiNOCs. This improvement is especially observed for latency characteristics where an improvement of about 15 is obtained.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115227167","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

Transfer Learning for End-to-End ASR to Deal with Low-Resource Problem in Persian Language 端到端ASR迁移学习处理波斯语低资源问题

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420540

Maryam Asadolahzade Kermanshahi, A. Akbari, B. Nasersharif

{"title":"Transfer Learning for End-to-End ASR to Deal with Low-Resource Problem in Persian Language","authors":"Maryam Asadolahzade Kermanshahi, A. Akbari, B. Nasersharif","doi":"10.1109/CSICC52343.2021.9420540","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420540","url":null,"abstract":"End-to-end models are state of the art for Automatic Speech Recognition (ASR) systems. Despite all their advantages, they suffer a significant problem: huge amounts of training data are required to achieve excellent performance. This problem is a serious challenge for low-resource languages such as Persian. Therefore, we need some methods and techniques to overcome this issue. One simple, yet effective method towards addressing this issue is transfer learning. We aim to explore the effect of transfer learning on a speech recognition system for the Persian language. To this end, we first train the network on 960 hours of English LibriSpeech corpus. Then, we transfer the trained network and fine-tune it on only about 3.5 hours of training data from the Persian FarsDat corpus. Transfer learning exhibits better performance while needing shorter training time than the model trained from scratch. Experimental results on FarsDat corpus indicate that transfer learning with a few hours of Persian training data can achieve 31.48% relative Phoneme Error Rate (PER) reduction compared to the model trained from scratch.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114584799","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

Design and Simulation of OR Logic Gate Using RF MEMS Resonators 基于RF MEMS谐振器的OR逻辑门设计与仿真

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420607

M. Attar, R. A. Moghadam, A. Rezaee

引用次数: 0