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Using ParsBert on Augmented Data for Persian News Classification 利用ParsBert对增强数据进行波斯语新闻分类
2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443119
Mohammadreza Varasteh, A. Kazemi
{"title":"Using ParsBert on Augmented Data for Persian News Classification","authors":"Mohammadreza Varasteh, A. Kazemi","doi":"10.1109/ICWR51868.2021.9443119","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443119","url":null,"abstract":"Text classification is a fundamental task in Natural Language Processing (NLP). Although many works have been done to perform text classification in English, the number of studies on Persian text classification is limited. Previous works on Persian text classification often use classic machine learning methods such as Naive Bayes, Support Vector Machines, Decision Trees, etc. While these methods are fast and straightforward, they need feature engineering, and their performance heavily depends on the selected features. In this paper, we first augment the input words with their stem form and then use a pre-trained language model for the Persian language (ParsBERT) to classify the text. Augmenting the input words with their stem form enables the proposed classifier to generalize well to the new unseen data. We compare the performance of our proposed model with that of traditional machine learning algorithms. The results show that the proposed model achieves a 0.91 accuracy and outperforms the traditional machine learning algorithm by at least +0.4 absolute on both accuracy and F1 score.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116418164","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
GBKM: A New Genetic Based K-Means Clustering Algorithm 一种新的基于遗传的k -均值聚类算法
2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443113
Mahnaz Mardi, M. Keyvanpour
{"title":"GBKM: A New Genetic Based K-Means Clustering Algorithm","authors":"Mahnaz Mardi, M. Keyvanpour","doi":"10.1109/ICWR51868.2021.9443113","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443113","url":null,"abstract":"Clustering is an unsupervised classification method that focused on grouping data into clusters. The objects in each cluster are very similar but different from the objects in the other clusters. As clustering methods deal with the massive amount of information, many intelligent software agents have been widely utilized clustering techniques to filter, retrieve, and categorize documents that exist on the World Wide Web. Web mining is generally classified under data mining. In data mining, one of the significant clustering centroid-based partitioning methods is the K-Means algorithm. One of the K-Means algorithm's challenges is its extreme sensitivity to initial cluster centers' choice, which may yield get stuck in the local optimum if the initial centers are selected randomly. A variant of the K-Means method is the K-Means++ algorithm, which improves the algorithm's performance by smart choices of initialization of the cluster centroids. Evolutionary techniques, widely utilized for optimizing clustering methods by providing their prerequisite parameters. The Genetic Algorithm is stochastic and population-based, that applied in optimization problem-solving. This paper proposed a Genetic-based K-Means (GBKM) clustering algorithm where the clusters' centroids are encoded by chromosomes rather than random initial cluster centroids. The best cluster centers gave by the Genetic algorithm that maximizes the fitness function, as initial points of the K-Means algorithm. The results show this model helps increase the K-Means algorithm's performance by appropriate choice of initialization of the cluster centroids, compared to four other clustering algorithms.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114492648","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
Evaluating a Blockchain-based Method for Industrial IoT Data Confidentiality: Proof of Concept 评估基于区块链的工业物联网数据保密方法:概念验证
2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443118
E. Ashena
{"title":"Evaluating a Blockchain-based Method for Industrial IoT Data Confidentiality: Proof of Concept","authors":"E. Ashena","doi":"10.1109/ICWR51868.2021.9443118","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443118","url":null,"abstract":"Utilizing the Internet of Things in the industry has led to an event called IIoT (Industrial Internet of Things) due to make smart cities, communication routes, smart grids, etc. IIoT deals with various sensors, devices scattered on the edges, and cloud servers by identified standards and protocols in decentralized networks. Besides all benefits the IIoT has carried out, the data stream’s security and privacy remain a debatable subject of this technology. There are many solutions to overcome security issues and confidentiality breaches, but some do not completely consider the purpose. Factors like speed, integrity, security, and power consumption must be considered, and of course, the cost factor is a significant role in achieving the goal. The purpose of this article is to introduce a new scheme evolved from Blockchain methodology to overcome privacy and data confidentiality challenges.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129765942","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
A Scalable Pattern Mining Method Using Apache Spark Platform 基于Apache Spark平台的可扩展模式挖掘方法
2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443111
Samaneh Samiei, Mehdi Joodaki, Nasser Ghadiri
{"title":"A Scalable Pattern Mining Method Using Apache Spark Platform","authors":"Samaneh Samiei, Mehdi Joodaki, Nasser Ghadiri","doi":"10.1109/ICWR51868.2021.9443111","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443111","url":null,"abstract":"The amount of data is growing sharply on the Internet. Some data like log files are enormous and entail valuable and precious hidden patterns. In other words, a log file is a set of recorded events that carry beneficial and vital information to develop web server performance, stability server loads, control, and rush up user response operations. However, analyzing massive data take a long time and require powerful hardware. Also, the performance of sequential pattern mining methods is usually unsatisfactory to deal with such data. This paper proposes a novel and advanced parallel method for finding the log file patterns, such as frequent patterns (e.g., URL, IP, Status Code), how users accessed files, the number of errors, and the most common errors by applying the Apache Spark platform. Experiment results demonstrate that the proposed method's run time on three datasets is significantly less than its four rival pattern mining methods.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117284923","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 Novel Data Filtering for a Modified Cuckoo Search Based Movie Recommender 一种基于改进布谷鸟搜索的电影推荐数据过滤方法
2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443116
Zahra Haghgu, Seyed Mohammad Hossein Hasheminejad, R. Azmi
{"title":"A Novel Data Filtering for a Modified Cuckoo Search Based Movie Recommender","authors":"Zahra Haghgu, Seyed Mohammad Hossein Hasheminejad, R. Azmi","doi":"10.1109/ICWR51868.2021.9443116","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443116","url":null,"abstract":"Nowadays, recommender systems are inseparable parts of e-commerce businesses and help in personalizing the offers. Clustering is an unsupervised tool to divide a given dataset into clusters based on a similarity metric. Hybrid recommendations based on clustering and metaheuristic optimization can improve predictions significantly. In our approach, the K-means algorithm applies for clustering the data, and the modified cuckoo search algorithm optimizes the clustering by moving some items into better clusters. the modified cuckoo search optimization we have used here replaces the random selection with tournament selection, which results in better clustering and prevents the algorithm from immature convergence. We also tried new filtering on data instead of modifying the clustering algorithm. With this new filtering, we made the recommendations more focused on the most interesting nearest movies. We compared the performance of our method to the existing methods, and the results show a significant improvement.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124101460","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
Finding Similar sub-lattice of A5 Lattice 寻找A5格的相似子格
2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443123
Faramarz Hashempour, Ehsan Akhtarkavan
{"title":"Finding Similar sub-lattice of A5 Lattice","authors":"Faramarz Hashempour, Ehsan Akhtarkavan","doi":"10.1109/ICWR51868.2021.9443123","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443123","url":null,"abstract":"Undoubtedly, the current developments in the world of technology are very closely related to the basic sciences, and these advances are due to the results that these sciences have achieved and provided to us over the years. Linear algebra and its laws have long solved many technical problems for us, one of which is to perform complex calculations to implement efficient models for encrypting digital data of any kind by performing some techniques in the field of algebra. Linearly, effective steps have been taken to optimize the results obtained. One of the cryptographic techniques is the use of lattice properties, which has recently received a lot of attention and effective measures have been taken in this field. In this article, we demonstrate the very important features of A5 lattices, and we introduce a new method for finding the generators of similar sub lattices of A5 Lattice.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129229771","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
A Shallow Deep Neural Network for Selection of Migration Candidate Virtual Machines to Reduce Energy Consumption 一种用于迁移候选虚拟机选择的浅深度神经网络以降低能耗
2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443133
Zeinab Khodaverdian, H. Sadr, S. A. Edalatpanah
{"title":"A Shallow Deep Neural Network for Selection of Migration Candidate Virtual Machines to Reduce Energy Consumption","authors":"Zeinab Khodaverdian, H. Sadr, S. A. Edalatpanah","doi":"10.1109/ICWR51868.2021.9443133","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443133","url":null,"abstract":"In recent years, the widespread growth of cloud computing has surprisingly increased the energy consumption in data centers. In this regard, employing energy reduction techniques is changed to one of the prominent challenges for cloud service providers and includes both dynamic and static techniques. Although by utilizing static techniques along with creating data centers energy consumption is relatively reduced, the rapid growth of cloud computing due to the increasing demands of users for these resources has changed energy consumption to a potential challenge. Utilizing dynamic energy reduction techniques which can be possible through the integration of the virtual machine into at least one physical server can be considered as an effective solution to this problem. This is done through live virtual machine migration and selecting the migration candidate virtual machine is a key step in this technique. In this paper, the combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is used to choose the appropriate migration candidate virtual machine which leads to the diagnosis of whether a virtual machine is sensitive to latency or not. The proposed model was validated on the workload of Microsoft Azure virtual machines as a dataset. According to the empirical results, the proposed model has higher classification accuracy compared to other existing models for selecting the migration candidate virtual machines.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129306351","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
Flower Image Classification Using Deep Convolutional Neural Network 基于深度卷积神经网络的花卉图像分类
2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443129
N. Alipour, Omid Tarkhaneh, M. Awrangjeb, Hongda Tian
{"title":"Flower Image Classification Using Deep Convolutional Neural Network","authors":"N. Alipour, Omid Tarkhaneh, M. Awrangjeb, Hongda Tian","doi":"10.1109/ICWR51868.2021.9443129","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443129","url":null,"abstract":"These days deep learning methods play a pivotal role in complicated tasks, such as extracting useful features, segmentation, and semantic classification of images. These methods had significant effects on flower types classification during recent years. In this paper, we are trying to classify 102 flower species using a robust deep learning method. To this end, we used the transfer learning approach employing DenseNet121 architecture to categorize various species of oxford-102 flowers dataset. In this regard, we have tried to fine-tune our model to achieve higher accuracy respect to other methods. We performed preprocessing by normalizing and resizing of our images and then fed them to our fine-tuned pretrained model. We divided our dataset to three sets of train, validation, and test. We could achieve the accuracy of 98.6% for 50 epochs which is better than other deep-learning based methods for the same dataset in the study.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133257565","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: 7
Adversarial Weakly Supervised Domain Adaptation for Few Shot Sentiment Analysis 针对少镜头情感分析的对抗弱监督域自适应
2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443023
Seyyed Ehsan Taher, M. Shamsfard
{"title":"Adversarial Weakly Supervised Domain Adaptation for Few Shot Sentiment Analysis","authors":"Seyyed Ehsan Taher, M. Shamsfard","doi":"10.1109/ICWR51868.2021.9443023","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443023","url":null,"abstract":"The ability of deep neural networks to generate state-of-the-art results on many NLP problems has been apparent to everyone for some years now. However, when there is not enough labeled data or the test dataset has domain shift, these networks face many challenges and results are getting worse.In this article, we present a method for adapting the domain from formal to colloquial (in sentiment classification). Our method uses two approaches, adversarial training and weak supervision, and only needs a few shots of labeled data.In the first stage, we label a crawled dataset (containing colloquial and formal sentences) with weakly supervised sentiment tags using a sentiment vocabulary network. Then we fine-tune a pre-trained model with adversarial training on this weak dataset to generate domain-independent representations. In the last stage, we train the above fine-tuned neural network with just 50 samples of data (formal domain) and test it on colloquial.Experimental results show that our method outperforms the state-of-the-art model (Pars BERT) on the same data with 15% higher F1 measure.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123735861","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
Autonomous Driving Systems: Developing an Approach based on A* and Double Q-Learning 自动驾驶系统:基于A*和双q学习的方法开发
2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443139
Faezeh Jamshidi, Lei Zhang, Fahimeh Nezhadalinaei
{"title":"Autonomous Driving Systems: Developing an Approach based on A* and Double Q-Learning","authors":"Faezeh Jamshidi, Lei Zhang, Fahimeh Nezhadalinaei","doi":"10.1109/ICWR51868.2021.9443139","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443139","url":null,"abstract":"Autonomous driving is the most attractive field to research by academic and industrial socials that intelligent transportation play a vital role in structure of autonomous driving systems. Artificial Intelligence (AI) is an infrastructure for autonomous driving by designing of intelligent machine. Deep Learning is one of subfields of Artificial Intelligence that create models by mimicking human brain’s functioning to make decision that it has shown great success in autonomous diving systems field. However, it performs very poorly in some stochastic environments caused by large overestimations of action values. Thus, we use the double estimator to Q-learning to construct Double Q-learning with a new off-policy reinforcement learning algorithm. By this algorithm, we can approximate the maximum expected value for any number of random variables and it underestimate rather than overestimate the maximum expected value. Moreover, we use an optimization method based on A* to improve routing in automation driving. Our proposed approach based on double Q-Learning and A* is evaluated on an example environment with random obstacles and compare results to use Q-Learning alone. Results show the proposed approach has better performance based on duration of trip to destination and collision to obstacles.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128698422","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: 5
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