2021 7th International Conference on Web Research (ICWR)最新文献

A method Based on an Attention Mechanism to Measure the Similarity of two Sentences 基于注意机制的两句相似度测量方法

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443135

Seyed Vahid Moravvej, Mehdi Joodaki, Mohammad Javad Maleki Kahaki, Moein Salimi Sartakhti

引用次数: 18

Detecting Adverse Drug Reactions from Social Media Based on Multichannel Convolutional Neural Networks Modified by Support Vector Machine 基于支持向量机修正的多通道卷积神经网络的社交媒体药物不良反应检测

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443128

Mahsa Rakhsha, M. Keyvanpour, Seyed Vahab Shojaedini

{"title":"Detecting Adverse Drug Reactions from Social Media Based on Multichannel Convolutional Neural Networks Modified by Support Vector Machine","authors":"Mahsa Rakhsha, M. Keyvanpour, Seyed Vahab Shojaedini","doi":"10.1109/ICWR51868.2021.9443128","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443128","url":null,"abstract":"Prescribing medication is a task that physicians face every day. However, when prescribing medication, physicians should be aware of all possible side effects of the drug. Drug-related side effects or Adverse Drug Reactions (ADR) may have profound effects on patients' quality of life as well as putting more pressure on the health care system. Due to the complexity of the diagnosis process, there are still a number of important unknown ADRs. Social media collects large amounts of information about drug use from patients, therefore may be a useful way for extracting ADRs. As a result, the social media becomes one of the effective tool for ADR because users share their experiences and opinions in different fields every day, such as their health, unknown side effects of a drug, and so on. In this study, we propose a new method for identifying ADRs. To meet the challenge of displaying data from multiple sources as well as identifying text containing drug reaction information, a new deep learning architecture is proposed which is based on multichannel convolutional neural networks. The obtained results from applying the proposed architecture on real data obtained from Twitter demonstrates its potential in recognizing ADRs.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"38 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":"114694778","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

Automatic Re-modularization of Clustered Codes Considering Invocation Types 考虑调用类型的集群代码自动重模块化

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443121

Alireza Khalilipour, Moharram Challenger

{"title":"Automatic Re-modularization of Clustered Codes Considering Invocation Types","authors":"Alireza Khalilipour, Moharram Challenger","doi":"10.1109/ICWR51868.2021.9443121","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443121","url":null,"abstract":"One of the approaches for software re-modularization is to transform sequential codes into distributed ones. Code clustering as a technique for software re-modularization is used on object-oriented codes. By clustering, the classes in a program are separated into several clusters, each of which contains cohesive classes. In the state of the art, the type of invocation or message passing between classes and objects is not considered in the clustering, which can impact the quality of the clusters. This paper presents an approach in which the types of invocations between classes and objects such as synchronous, one-way, or asynchronous will also be considered in the clustering. The aim is to separate asynchronous and one-way services from the clusters and move them to new clusters, automatically. So that, by running these services in parallel, extra waiting times for service invocations can be eliminated. Also, by transferring these services to new clusters, load distribution has taken place, which can reduce response time. The experiment results confirm that the proposed approach has a significant effect in reducing the overall waiting time.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"31 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":"130689606","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 Crowdsourcing and Gamification based Product Ranking Method for E-Commerce 基于众包和游戏化的电子商务产品排名方法

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443122

Mohammad Hajarian, Sara Hemmati

{"title":"A Crowdsourcing and Gamification based Product Ranking Method for E-Commerce","authors":"Mohammad Hajarian, Sara Hemmati","doi":"10.1109/ICWR51868.2021.9443122","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443122","url":null,"abstract":"Most of the time, when users want to decide whether to buy a product from an e-commerce website or not, they ask questions about a product or read the other users’ comments to decide. Users’ comments about a product, when positive, bring exceptional opportunity for sellers because it will act as word of mouth (WOM) advertisement and can increase their sales. On the other hand, users trust in WOM because the person who comments is not motivated to benefit from the sales of the products, but sharing experiences about a product with other customers is a motive. Hence WOM helps customers decide better to buy a product, and if it is positive, it can lead to a sale increase. However, the quality of the comments is another essential factor to be considered in e-commerce websites. Because wrong and misleading comments or answers to users’ questions can increase sales at the cost of decreasing user satisfaction. In this study, for the first time, we have proposed a gamification method to increase the effectiveness of WOM by considering the quality of the user comments. To this end, an influence metric was proposed to calculate the products’ ranking in a new fashion. To evaluate the proposed method, we collected data from an e-commerce website and compared it with classic product rankings methods. Experimental results show a significant difference between the product ranking using the proposed method compared with the classic product ranking methods, and overall it decreases product rankings.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"365 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":"124582023","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: 1

Still Image Action Recognition Using Ensemble Learning 基于集成学习的静止图像动作识别

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443021

Hojat Asgarian Dehkordi, Ali Soltani Nezhad, Seyed Sajad Ashrafi, S. B. Shokouhi

{"title":"Still Image Action Recognition Using Ensemble Learning","authors":"Hojat Asgarian Dehkordi, Ali Soltani Nezhad, Seyed Sajad Ashrafi, S. B. Shokouhi","doi":"10.1109/ICWR51868.2021.9443021","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443021","url":null,"abstract":"In recent years, human action recognition in still images has become a challenge in computer vision. Most methods in this field use annotations such as human and object bounding boxes to determine human-object interaction and pose estimation. Preparing these annotations is time-consuming and costly. In this paper, an ensembling-based method is presented to avoid any additional annotations. According to this fact that a network performance on fewer classes of a dataset is often better than its performance on whole classes; the dataset is first divided into four groups. Then these groups are applied to train four lightweight Convolutional Neural Networks (CNNs). Consequently, each of these CNNs will specialize on a specific subset of the dataset. Then, the final convolutional feature maps of these networks are concatenated together. Moreover, a Feature Attention Module (FAM) is trained to identify the most important features among concatenated features for final prediction. The proposed method on the Stanford40 dataset achieves 86.86% MAP, which indicates this approach can obtain promising performance compared with many existing methods that use annotations.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"5 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":"115106751","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

Swarm-based Algorithms Using Chaos for Task Scheduling in Cloud 基于混沌的云任务调度群算法

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443157

A. Zandvakili, N. Mansouri, M. Javidi

引用次数: 1

Impression Management Strategies Employed by Former Muslims on Social Media 前穆斯林在社交媒体上的印象管理策略

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443117

U. Hashmi, R. A. Rashid, Mohd Aftar Abu Bakar

引用次数: 1

Influence Maximization in Social Media: Network Embedding for Extracting Structural Feature Vector 社交媒体中的影响最大化:用于提取结构特征向量的网络嵌入

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443150

Narges Vafaei, M. Keyvanpour, Seyed Vahab Shojaedini

{"title":"Influence Maximization in Social Media: Network Embedding for Extracting Structural Feature Vector","authors":"Narges Vafaei, M. Keyvanpour, Seyed Vahab Shojaedini","doi":"10.1109/ICWR51868.2021.9443150","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443150","url":null,"abstract":"In parallel with the development of online social networks, the number of active users in these media is increased, which mainly use these media as a tool to share their opinions and obtaining information. Propagation of influence on social networks arises from a common social behavior called \"mouth-to-mouth\" diffusion among society members. The Influence Maximization (IM) problem aims to select a minimum set of users in a social network to maximize the spread of influence. In this paper, we propose a method in order to solve the IM problem on social media that uses the network embedding concept to learn the feature vectors of nodes. In the first step of the proposed method, we extract a structural feature vector for each node by network embedding. Afterward, according to the similarity between the vectors, the seed set of influential nodes is selected in the second step. The investigation of the results obtained from applying the proposed method on the real datasets indicates its significant advantage against its alternatives. Specifically, the two properties of being submodular and monotonic in the proposed method, which lead to an optimal solution with the ratio of (1–1/e) approximation, make this method considered a tool with high potential in order to address the IM problem.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"20 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":"130421068","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 Technology Tree for Internet of Things 物联网的技术树

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443132

A. Mansouri, Masoud Mohammadzadeh Qaratlu, Zahra Moezkarimi, Zahra Kalatehaei, Zahra Golmirzaei

引用次数: 0

A Framework For Scalable Similarity Evaluation in Text Graphs 文本图中可扩展相似度评价框架

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443144

Mahdi Samani, Nasser Ghadiri

{"title":"A Framework For Scalable Similarity Evaluation in Text Graphs","authors":"Mahdi Samani, Nasser Ghadiri","doi":"10.1109/ICWR51868.2021.9443144","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443144","url":null,"abstract":"Graphs and graph databases are applicable over a wide range of domains, including text mining and web mining. Using graphs to represent relationships between entities provides enriched models for emerging tasks of web search and information retrieval. Natural language processing algorithms use graphs to model structural relationships of texts efficiently, resulting in improved performance. However, the need to increase the accuracy of graph construction and weight allocation remains a fundamental challenge. Existing methods for these tasks provide limited efficiency and lack scalability for large graphs. In this study, we propose a novel graph-based method for text modeling and running a query to evaluate the similarity of text segments. In this method, the graph corresponding to the text is first created by modeling words and named entities by the state-of-the-art pre-trained BERT model. Graph nodes are then weighted in two stages. In the first stage, the nodes with more generalization obtain higher weights. The second weighting stage is done by the graph obtained from the query text. In this weighting step, nodes are considered important if they are specifically related to the query text. After determining the important nodes in the graph, the semantic similarity between the query text and the texts in the database is measured. The whole process of this framework uses a natural language processing pipeline in Apache Spark scalable platform. The efficiency of the model was evaluated for both distributed and non-distributed configuration and its scalability on a Spark cluster. Evaluation of the accuracy using the Pearson correlation coefficient shows that the proposed method performs higher performance than its competitors.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"53 1-2 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":"126942337","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