2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)最新文献

Chinese Character Font Classification in Calligraphy and Painting Works Based on Decision Fusion 基于决策融合的书画作品汉字字体分类

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00117

Zimu Zeng, Pengchang Zhang, Jia Wang, Xingjia Tang, Xuebin Liu

引用次数: 0

Multiple Linear Combination Approaches for Information Search in Ranking 排序信息搜索的多重线性组合方法

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00119

Yizheng Huang, L. Zeng

引用次数: 0

Using Multi-task Deep Neural Network to Explore Person Interaction from Social Media 利用多任务深度神经网络探索社交媒体中的人际互动

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00061

Yung-Chun Chang, Tzu-Ying Chen, Ting-Yu Lin, Yu-Lun Hsieh

引用次数: 0

Deep Model Pruning By Parallel Pooling Attention Block 基于并行池化注意力块的深度模型剪枝

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00138

Junnan Wu, Liming Zhang

{"title":"Deep Model Pruning By Parallel Pooling Attention Block","authors":"Junnan Wu, Liming Zhang","doi":"10.1109/WI-IAT55865.2022.00138","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00138","url":null,"abstract":"Deep neural network models have achieved great success in various fields, including computer vision and recommendation systems. However, deep models are usually large and computationally time-consuming. Reducing the size of deep models and speeding up the learning process without a sharp drop in accuracy becomes a promising goal, both in research and practice. The channel pruning is one of the most effective methods, which can not only compress deep model size, but also directly speed up inference. In this paper, we propose a novel channel attention block called parallel pooling attention block (PPAB) that is designed on top of the squeeze and excitation (SE) blocks. There are two optimization improvements of PPAB. First, parallel max-pooling branch is added on top of SE blocks. Second, we avoid dimensionality reduction during the excitation phase. Both of these optimizations improve the channel importance measurement capabilities of PPAB. Experiment results show that PPAB outperforms general SE blocks on the channel attention objective. The proposed pruning method could be applied efficiently both in computer vision and recommendation systems.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115082667","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

Detecting Adverse Drug Reactions from User-Generated Twitter Data: A Case Study 从用户生成的Twitter数据中检测药物不良反应:案例研究

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00087

M. Shah, Maitry Patel, Priyank Patel, Xing Tan

{"title":"Detecting Adverse Drug Reactions from User-Generated Twitter Data: A Case Study","authors":"M. Shah, Maitry Patel, Priyank Patel, Xing Tan","doi":"10.1109/WI-IAT55865.2022.00087","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00087","url":null,"abstract":"Adverse Drug Reactions (ADRs) are defined as unwanted drug effects that cause induced mortality and morbidity in health-care. Health-related subjects can be discussed throughout the broad span of social media conversations. Plethora of information available in social media and health-related forums, as well as the rich expression of public opinion, has recently piqued the public health community’s interest in using these sources for pharmacovigilance. We investigate the role of sentiment analysis characteristics in detecting ADR mentions based on user generated dataset obtained from Twitter online streaming API. Our proposed model uses BERT-CNN model with final layer of Support vector machine (SVM) to classify the ADRs mentions. In our study, we extracted tweets from tweeter using Tweepy API and performed data pre-processing, data annotation and data augmentation to create a strong corpus. For data augmentation, we used Marian MT model for to increase the number of tweets with the help of back translation. We passed this corpus to BERT-Base model to get word embeddings and then used CNN model to get important features from data. To get the better efficiency, we used SVM which classifies a tweet. The evaluation study reveals that our proposed model achieved 92% accuracy and 78% F1score. Data augmentation and BERT pre-trained model are the main keys of our proposed model which help us to achieve better result than other machine learning models.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122392867","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 Multi-Dimensional Semantic Pseudo-Relevance Feedback Information Retrieval Model 多维语义伪相关反馈信息检索模型

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00141

Min Pan, Yu Liu, Quanli Pei, Huixian Mao, Aoqun Jin, Sheng Huang, Yinhan Yang

{"title":"A Multi-Dimensional Semantic Pseudo-Relevance Feedback Information Retrieval Model","authors":"Min Pan, Yu Liu, Quanli Pei, Huixian Mao, Aoqun Jin, Sheng Huang, Yinhan Yang","doi":"10.1109/WI-IAT55865.2022.00141","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00141","url":null,"abstract":"Recently neural information retrieval systems have spurred many successful applications. Retrieval model to obtain a candidate document collection in the first retrieval stage, then use BERT to sort the candidate documents. Generally, the sentence score or paragraph score obtained using BERT is integrated into the document score to get the final ranking result. Semantic similarity is less often used to select query extensions and integrate semantic information into pseudo-relevance feedback. We propose a new strategy in this paper, selecting query extensions with semantic information using the BERT model. Incorporating semantic information weights into traditional pseudo-relevance feedback can better alleviate problems such as word polysemy and multi-word synonymy. Improve the performance of the retrieval system and return more accurate documents. The experimental results demonstrate that the query extensions selected by incorporating semantic information can help return more accurate results and improve the accuracy of the retrieval system, and the results of MAP and P@10 can prove the validity and feasibility of our proposed model.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128738896","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

Predicting response probability by embedding questions in online question recommendation 通过在在线问题推荐中嵌入问题来预测响应概率

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00042

Yuki Hoshino, Makoto Tasaki, Kota Ishizuka, Motoya Azami, Keisuke Mizutani, K. Nakata

{"title":"Predicting response probability by embedding questions in online question recommendation","authors":"Yuki Hoshino, Makoto Tasaki, Kota Ishizuka, Motoya Azami, Keisuke Mizutani, K. Nakata","doi":"10.1109/WI-IAT55865.2022.00042","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00042","url":null,"abstract":"Currently, there are many Q&A sites, including Yahoo! Answers, Quora, and StackOverflow. Although the number of questions posted on these sites is enormous, many remain unanswered. This is detrimental to the user experience, so service operators are motivated to obtain more answers to posted questions. It is also diffcult for users to find specific questions they can answer among the vast number that are asked. Therefore, a system that recommends questions that can be answered by the user is needed. In this study, we first propose a method for predicting response probability. Specifically, we propose a method for learning embedding vectors that takes into account cases in which the required answers are similar, even if the question texts are different, based on a contrastive learning method. We also implemented a recommendation method that increases respondent satisfaction by optimizing and analyzing the theoretical properties of our method. Finally, we conduct an experimental validation of these two methods to demonstrate their effectiveness using data from a Q&A service for child care.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130586353","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

Additive Consistency Analysis for Picture Fuzzy Preference Relation 图像模糊偏好关系的加性一致性分析

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00132

Xiaoyu Wu, Tingting Zheng, Weiwei Meng, Jung-Chang Liu

引用次数: 1

Question answering over knowledge graphs: a graph-driven approach 基于知识图的问题回答:图驱动的方法

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00050

Sareh Aghaei, Sepide Masoudi, Tek Raj Chhetri, A. Fensel

{"title":"Question answering over knowledge graphs: a graph-driven approach","authors":"Sareh Aghaei, Sepide Masoudi, Tek Raj Chhetri, A. Fensel","doi":"10.1109/WI-IAT55865.2022.00050","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00050","url":null,"abstract":"With the growth of knowledge graphs (KGs), question answering systems make the KGs easily accessible for end-users. Question answering over KGs aims to provide crisp answers to natural language questions across facts stored in the KGs. This paper proposes a graph-driven approach to answer questions over a KG through four steps, including (1) knowledge subgraph construction, (2) question graph construction, (3) graph matching, and (4) query execution. Given an input question, a knowledge subgraph, which is likely to include the answer is extracted to reduce the KG’s search space. A graph, named question graph, is built to represent the question’s intention. Then, the question graph is matched over the knowledge subgraph to find a query graph corresponding to a SPARQL query. Finally, the corresponding SPARQL is executed to return the answers to the question. The performance of the proposed approach is empirically evaluated using the 6th Question Answering over Linked Data Challenge (QALD-6). Experimental results show that the proposed approach improves the performance compared to the-state-of-art in terms of recall, precision, and F1-score.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"139 10‐12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132340198","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

On the Relationship Between the Self-efficacy and the English Achievement of English Majors: A Case Study of NingboTech University Students 英语专业学生自我效能感与英语成绩的关系研究——以宁波工业大学学生为例

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00066

Chen Ou, Lu Yang

{"title":"On the Relationship Between the Self-efficacy and the English Achievement of English Majors: A Case Study of NingboTech University Students","authors":"Chen Ou, Lu Yang","doi":"10.1109/WI-IAT55865.2022.00066","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00066","url":null,"abstract":"Self-efficacy refers to the hypothetical inference made by individuals about whether they can complete a specific behavior or task. Therefore, learners’ positive self-efficacy will positively impact on their learning behaviors and results. In order to explore the relationship between English Majors’ self-efficacy and English achievement, this study designs questionnaire to investigate the English Major juniors in NingboTech University. Based on the data collected from the questionnaire, this paper tries to analyze the overall level of self-efficacy of English majors and probe into the relationship between English majors’ self-efficacy and their English achievements. After analyzing the data by SPSS 26.0, the following results are obtained: (1) The respondents’ self-efficacy is at a medium level; (2) There is a significant positive correlation between the respondents’ English self-efficacy and their academic achievement, and the former can predict the latter. In view of the research results, this study puts forward some suggestions for teachers to improve students’ self-efficacy in order to enhance students’ learning effect in the teaching process.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131145249","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