2020 6th International Conference on Web Research (ICWR)最新文献

Real-time Event Detection in Twitter: A Case Study Twitter中的实时事件检测:一个案例研究

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122281

A. Sani, A. Moeini

引用次数: 3

Human Action Recognition in Video Using DB-LSTM and ResNet 基于DB-LSTM和ResNet的视频人体动作识别

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122304

Akram Mihanpour, Mohammad J. Rashti, S. E. Alavi

{"title":"Human Action Recognition in Video Using DB-LSTM and ResNet","authors":"Akram Mihanpour, Mohammad J. Rashti, S. E. Alavi","doi":"10.1109/ICWR49608.2020.9122304","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122304","url":null,"abstract":"Human action recognition in video is one of the most widely applied topics in the field of image and video processing, with many applications in surveillance (security, sports, etc.), activity detection, video-content-based monitoring, man-machine interaction, and health/disability care. Action recognition is a complex process that faces several challenges such as occlusion, camera movement, viewpoint move, background clutter, and brightness variation. In this study, we propose a novel human action recognition method using convolutional neural networks (CNN) and deep bidirectional LSTM (DB-LSTM) networks, using only raw video frames. First, deep features are extracted from video frames using a pre-trained CNN architecture called ResNet152. The sequential information of the frames is then learned using the DB-LSTM network, where multiple layers are stacked together in both forward and backward passes of DB-LSTM, to increase depth. The evaluation results of the proposed method using PyTorch, compared to the state-of-the-art methods, show a considerable increase in the efficiency of action recognition on the UCF 101 dataset, reaching 95% recognition accuracy. The choice of the CNN architecture, proper tuning of input parameters, and techniques such as data augmentation contribute to the accuracy boost in this study.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123407842","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: 11

Analyzing the Robustness of Web Service Networks Web服务网络鲁棒性分析

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122272

Alireza Khalilipour, Moharram Challenger

引用次数: 0

Website Evaluation Frameworks: IS oriented vs. Business Oriented Models 网站评估框架:面向信息系统与面向业务的模型

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122316

H. Keshavarz, M. E. Givi

引用次数: 2

Intrusion Detection in IoT-Based Smart Grid Using Hybrid Decision Tree 基于混合决策树的物联网智能电网入侵检测

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122320

Seyedeh Mahsan Taghavinejad, Mehran Taghavinejad, Lida Shahmiri, M. Zavvar, M. Zavvar

引用次数: 22

RePersian:An Efficient Open Information Extraction Tool in Persian 一种有效的波斯语开放信息提取工具

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122301

Raana Saheb-Nassagh, Majid Asgari, B. Minaei-Bidgoli

{"title":"RePersian:An Efficient Open Information Extraction Tool in Persian","authors":"Raana Saheb-Nassagh, Majid Asgari, B. Minaei-Bidgoli","doi":"10.1109/ICWR49608.2020.9122301","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122301","url":null,"abstract":"Relation extraction is the task of extracting semantic information from raw data. One of the key points in the area of open information extraction systems is the ability to extract relation information automatically for any domains, especially in web mining and web research. Many researches have been done in this field for relation extraction in different languages. Many relation extraction algorithms work based on parsing trees. The Persian language, as a low-resource language, has a dependency grammar and lexical structure which makes the dependency parsing difficult or time-consuming, and it affects the speed of relation extraction in many cases. In this paper, we will introduce RePersian which is a fast method for relation extraction in Persian. Our proposed work is based on part-of-speech (POS) tags of a sentence and particular relation patterns. To achieve these patterns, we have analyzed sentence structures in the Persian language. RePersian searches through the POS-tags for finding the relation patterns, which are given in regular expression forms. In this way, RePersian finds semantic relations by matching the correct POS pattern to a relation pattern. We test and evaluate our method on the Dadegan, Persian dependency tree dataset, with two different POS tag-sets. Our approach had on average a precision of 78.05% on finding the first argument of a relation, a precision of 80.4% in finding the second argument and precision of 54.85% on finding the right relation between the arguments.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114730442","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 Dynamic Task Scheduling Algorithm Improved by Load Balancing in Cloud Computing 云计算中基于负载均衡改进的动态任务调度算法

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122287

F. Ebadifard, S. M. Babamir, Sedighe Barani

{"title":"A Dynamic Task Scheduling Algorithm Improved by Load Balancing in Cloud Computing","authors":"F. Ebadifard, S. M. Babamir, Sedighe Barani","doi":"10.1109/ICWR49608.2020.9122287","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122287","url":null,"abstract":"Task scheduling is the main challenge for the service provider in cloud Computing. One of the most critical objective in the scheduling is to assign tasks to virtual machines so that some machines do not overload or under load. To do this, load balancing plays a crucial role in the scheduling problem. Using an appropriate load balancing method can reduce response time and increase resource utilization. In this paper, we present a dynamic method for scheduling a task to virtual machines to increase load balancing and reliability in cloud computing. The proposed method reduces the makespan, increases the degree of load balancing, and improves the system's reliability. The proposed method, in contrast to previous work, has been able to increase the reliability of the task scheduling based on previous experience of the virtual machines in addition to the fair distribution of workload among the virtual machines. We have compared the proposed algorithm with other task scheduling algorithms such as the honeybee load balancing and dynamic scheduling without load balancing. Simulation results show that the proposed method improves the reliability and degree of imbalance.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124315465","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: 18

Review Helpfulness Prediction Using Convolutional Neural Networks and Gated Recurrent Units 回顾使用卷积神经网络和门控循环单元的有用性预测

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122297

Mohammad Ehsan Basiri, Shirin Habibi

{"title":"Review Helpfulness Prediction Using Convolutional Neural Networks and Gated Recurrent Units","authors":"Mohammad Ehsan Basiri, Shirin Habibi","doi":"10.1109/ICWR49608.2020.9122297","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122297","url":null,"abstract":"Product reviews are one of the most important types of user-generated contents that are becoming more and more available. These reviews are valuable sources of knowledge for users who want to make purchasing decisions and for producers who want to improve their products and services. However, not all product reviews are equally helpful and this makes the process of finding helpful reviews among the massive number of similar reviews very challenging. To address this problem, automatic review helpfulness prediction systems are designed to classify reviews according to their content. In this study, a deep model is proposed to utilize content-based, semantic, sentiment, and metadata features of reviews for predicting review helpfulness. In the proposed method, convolution layer is used for learning feature maps and gated recurrent units are employed for exploiting sequential context. The results of comparing the proposed method with five traditional learning methods and two deep models trained on the same types of features shows that the proposed method outperforms other methods by 4% and 2% in terms of F1-measure and accuracy. Moreover, results reveal that both textual and metadata features are important in detecting helpful reviews. The findings of this study may help online retailers to efficiently rank the product reviews.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"2022 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125778031","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

A Combination of Machine Learning and Lexicon Based Techniques for Sentiment Analysis 情感分析的机器学习和基于词汇的技术结合

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122298

Seydeh Akram Saadat Neshan, R. Akbari

引用次数: 7

Review of Apriori based Frequent Itemset Mining Solutions on Big Data 基于Apriori的大数据频繁项集挖掘方法综述

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122295

Mohammad Javad Shayegan Fard, Parsa Asgari Namin

{"title":"Review of Apriori based Frequent Itemset Mining Solutions on Big Data","authors":"Mohammad Javad Shayegan Fard, Parsa Asgari Namin","doi":"10.1109/ICWR49608.2020.9122295","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122295","url":null,"abstract":"The data being generated today is massive in terms of volume, velocity, and variety. It is a great challenge to derive knowledge from data in this condition. Researchers, therefore have proposed ways to deal with this challenge. frequent itemset mining is one of the proposed ways to distinguish itemsets inside the vast amount of data to aid the operation of a variety of pursuits and businesses, a process termed ‘Association Rule Mining'. However, there are a variety of works done in this area. The introduction of different algorithms, frameworks, and applications throughout the recent decade has produced many interesting approaches. One of the algorithms in this area is the Apriori algorithm. It is a simple yet powerful algorithm. However, the original Apriori is not suitable for big data and due to this reason, researchers have attempted to introduce ways and schemes to adapt it to this new age of data. Because of the number of efforts in this area, having a bird's eye view of the past works is of value. This review aims to present an insight into the works done in the intersection of two matters: big data and the Apriori algorithm. It is concerned with Aprioribased algorithms presented in the recent decade with a focus on the three popular big data platforms: Apache Hadoop, Spark, and Flink. Also, major points of each approach and solution is presented. A conclusion in the end summarizes the points discussed in this paper.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127127847","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