2021 26th International Computer Conference, Computer Society of Iran (CSICC)最新文献_第6页

An Algorithm for Optimizing Small-Large Outer Join in Cloud Computing Environment 云计算环境下小-大外连接优化算法

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420579

Farshad Delavarpour, A. Ahmadi

引用次数: 0

Prediction of protein–peptide-binding amino acid residues regions using machine learning algorithms 利用机器学习算法预测蛋白质肽结合氨基酸残基区域

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420568

S. Shafiee, Abdolhossein Fathi

引用次数: 1

A Geometric Algorithm for Fault-Tolerant Classification of COVID-19 Infected People 新型冠状病毒感染者容错分类的几何算法

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420595

Farnaz Sheikhi, Sharareh Alipour

{"title":"A Geometric Algorithm for Fault-Tolerant Classification of COVID-19 Infected People","authors":"Farnaz Sheikhi, Sharareh Alipour","doi":"10.1109/CSICC52343.2021.9420595","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420595","url":null,"abstract":"As the world is struggling against COVID-19 pandemic, and unfortunately no certain treatments are discovered yet, prevention of further transmission by isolating infected people has become an effective strategy to overcome this outbreak. That is why scaling up COVID-19 testing is strongly recommended. However, depending on the time tests are performed, they may have a high rate of false-negative results. This inaccuracy of COVID-19 testing is a challenge against controlling the pandemic. Therefore, in this paper we propose a geometric classification algorithm that is fault-tolerant to handle the inaccuracy of tests. So, in a metropolis of n people, let w + r be the number of cases that are tested, where r is the number of positive, while w is the number of negative COVID-19 cases, and k is an upper bound on the number of false-negative COVID-19 cases. The proposed algorithm takes O(r • (log r + log w) + w3 + w log(hR)) time for isolating all positive cases together with at most k (according to the rate of error of testing) possibly positive (false-negative) cases from the rest of the people. The term hR in the time complexity is the size of convex hull of the set of positive cases, and obviously k ∈ O(w). For simplicity of this isolation, we consider a simple convex shape (a triangle) for this classification algorithm.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"1 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":"128212048","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

Speed up Cassandra read path by using Coordinator Cache 使用协调缓存加速Cassandra读取路径

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420593

Latifa Azizi Vakili, N. Yazdani

{"title":"Speed up Cassandra read path by using Coordinator Cache","authors":"Latifa Azizi Vakili, N. Yazdani","doi":"10.1109/CSICC52343.2021.9420593","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420593","url":null,"abstract":"The fast increasing amount of massive and complex data in today’s Internet, called Big Data, requires sophisticated, comprehensive and highly operational databases. NoSQL databases are designed to fulfill Big Data requirements. Choosing an appropriate NoSQL database among various solutions to cover and manage big volume of data in Big Data, both in quantity and quality, itself is a big challenge. Cassandra is one of the distributed NoSQL databases mastered for managing very large amounts of structured and unstructured data spread out across many commodity servers, while providing highly available services with no single point of failure. Cassandra system was designed to run on cheap commodity hardware and handle high write through-put while not sacrificing read efficiency. This Paper will first present an overview of NoSQL databases, Big Data and IoT data as a controversial and complicated source of data in Big Data. Then, focuses on Cassandra database read request issues in its read path and suggests a model to reduce the time of read request (read query) coming from client side to Cassandra database. In this model we added a cache called Coordinator cache in Cassandra controlling nodes. Using a real dataset, we perform an analysis of Cassandra existing read path with suggested read path model and then compare the time of a read query before and after this model. The result shows that using Coordinator cache together with key cache offered by Cassandra database speedup data read request. Coordinator cache requires no extra memory because Cassandra Coordinator node does not store anything when doing controlling tasks over replica nodes and its potential memory space can be used for the introduced Coordinator cache.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"84 2 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":"128434640","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

Content-based Clothing Recommender System using Deep Neural Network 基于内容的深度神经网络服装推荐系统

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420544

Narges Yarahmadi Gharaei, Chitra Dadkhah, Lorence Daryoush

{"title":"Content-based Clothing Recommender System using Deep Neural Network","authors":"Narges Yarahmadi Gharaei, Chitra Dadkhah, Lorence Daryoush","doi":"10.1109/CSICC52343.2021.9420544","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420544","url":null,"abstract":"A recommender system primary purpose is to provide a series of item suggestions on a topic to its user. Deep learning is used in many fields and solved difficult and complex problems with large volumes of data. Deep learning can also be used in referral systems. Today, online shopping systems are looking for a method that can recommend items according to the user preference and interest in order to increase their sales. Clothing sales systems offer a set of recommendation based on the needs and interests of the users. Today, due to the current situation caused by the Coronavirus, the majority of tasks are done online. In this paper, we propose a content-based clothing recommender system using deep neural network. In content-based systems, product features are required for prediction of unobserved items ratings. In our proposed system by using a deep neural network, the cloth category is obtained and the need to manually extract the product features is eliminated by producing the required features with a large and useful volume. The advantage of this system is that it uses the same network to specify gender as a feature in making suggestions then shows the results to the user. Different machine learning algorithms are tested and analyzed with and without considering demographic information such as gender. The experimental results show that the loss of our proposed system is lower than the other related systems and solves the cold start problem for new items. Our proposed system also recommends novel, relevant and unexpected items.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"27 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":"130141387","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

ParsBERT Post-Training for Sentiment Analysis of Tweets Concerning Stock Market 股票市场推文情绪分析的ParsBERT后训练

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420569

Mohammadjalal Pouromid, Arman Yekkehkhani, M. A. Oskoei, Amin Aminimehr

{"title":"ParsBERT Post-Training for Sentiment Analysis of Tweets Concerning Stock Market","authors":"Mohammadjalal Pouromid, Arman Yekkehkhani, M. A. Oskoei, Amin Aminimehr","doi":"10.1109/CSICC52343.2021.9420569","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420569","url":null,"abstract":"Social media has become a playground for users to share their ideas freely. Analyzing these data has become of special interest to authorities and consulting firms. They seek to choose right policies based on the insight acquired. Hence, sentiment analysis of data spread in social media has gained significant importance. There are two major approaches for sentiment analysis including lexicon-based and supervised methods. Among supervised methods, deep models have proven to be a better fit for the sentiment analysis task. Since, they are domain free and able to handle large volumes of data effectively. In particular, BERT’s state of the art performance on various natural language processing tasks has encouraged us to use this network architecture for sentiment analysis. In this research, over 12000 Persian tweets including the stock market keyword have been crawled from twitter. They are labeled manually in three different categories of positive, neutral and negative. Then a pre-trained ParsBERT model has been fine-tuned on these data. Our model is evaluated on the test dataset and compared to its counterpart, lexicon-based method using Polyglot as its lexicon. Accuracy of 82 percent has been achieved by our proposed model surpassing its lexicon-based contender.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"22 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":"121094747","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 Mobility-Aware Caching Scheme in Heterogeneous Cellular Networks 异构蜂窝网络中移动感知缓存方案

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420600

Seyyed Amir Ahmad Siahpoosh, F. Rezaei

引用次数: 2

Feature Selection in Multi-label Classification based on Binary Quantum Gravitational Search Algorithm 基于二元量子引力搜索算法的多标签分类特征选择

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420617

Hojat Noormohammadi, M. B. Dowlatshahi

引用次数: 1

Improving ranking function and diversification in interactive recommendation systems based on deep reinforcement learning 基于深度强化学习的交互式推荐系统中排名功能和多样性的改进

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420615

Vahid Baghi, Seyed Mohammad Seyed Motehayeri, A. Moeini, R. Abedian

{"title":"Improving ranking function and diversification in interactive recommendation systems based on deep reinforcement learning","authors":"Vahid Baghi, Seyed Mohammad Seyed Motehayeri, A. Moeini, R. Abedian","doi":"10.1109/CSICC52343.2021.9420615","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420615","url":null,"abstract":"Recently, interactive recommendation systems based on reinforcement learning have been attended by researchers due to the consider recommendation procedure as a dynamic process and update the recommendation model based on immediate user feedback, which is neglected in traditional methods. The existing works have two significant drawbacks. Firstly, inefficient ranking function to produce the Top-N recommendation list. Secondly, focusing on recommendation accuracy and inattention to other evaluation metrics such as diversity. This paper proposes a deep reinforcement learning based recommendation system by utilizing Actor-Critic architecture to model dynamic users' interaction with the recommender agent and maximize the expected longterm reward. Furthermore, we propose utilizing Spotify's ANNoy algorithm to find the most similar items to generated action by actor-network. After that, the Total Diversity Effect Ranking algorithm is used to generate the recommendation items concerning relevancy and diversity. Moreover, we apply positional encoding to compute representations of the user's interaction sequence without using sequence-aligned recurrent neural networks. Extensive experiments on the MovieLens dataset demonstrate that our proposed model is able to generate a diverse while relevance recommendation list based on the user's preferences.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"13 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":"124015099","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

Significantly improving human detection in low-resolution images by retraining YOLOv3 通过再训练YOLOv3显著提高低分辨率图像中的人体检测

2021 26th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2021-03-03 DOI: 10.1109/CSICC52343.2021.9420554

Shima Pouyan, M. Charmi, A. Azarpeyvand, H. Hassanpoor

引用次数: 1