社会计算(英文)最新文献_第3页

Enhancing Next-Item Recommendation Through Adaptive User Group Modeling 通过自适应用户组建模增强下一项推荐

社会计算(英文) Pub Date : 2023-06-01 DOI: 10.23919/JSC.2023.0013

Nengjun Zhu;Lingdan Sun;Jian Cao;Xinjiang Lu;Runtong Li

{"title":"Enhancing Next-Item Recommendation Through Adaptive User Group Modeling","authors":"Nengjun Zhu;Lingdan Sun;Jian Cao;Xinjiang Lu;Runtong Li","doi":"10.23919/JSC.2023.0013","DOIUrl":"10.23919/JSC.2023.0013","url":null,"abstract":"Session-based recommender systems are increasingly applied to next-item recommendations. However, existing approaches encode the session information of each user independently and do not consider the interrelationship between users. This work is based on the intuition that dynamic groups of like-minded users exist over time. By considering the impact of latent user groups, we can learn a user's preference in a better way. To this end, we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user groups. Specifically, we utilize two network units to learn users' long and short-term sessions, respectively. Meanwhile, we employ two additional units to determine the affiliation of users with specific latent groups, followed by an aggregation of these latent group representations. Finally, user preference representations are shaped comprehensively by considering all these four aspects, based on an attention mechanism. Moreover, to avoid setting the number of groups manually, we further incorporate an adaptive learning unit to assess the necessity for creating a new group and learn the representation of emerging groups automatically. Extensive experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall, mean average precision (mAP), and area under curve (AUC) metrics.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 2","pages":"112-124"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/10239701/10239705.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43754789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Message from Editors-in-Chief 主编寄语

社会计算(英文) Pub Date : 2023-06-01 DOI: 10.23919/JSCTUP.2023.10241325

引用次数: 0

Manager Mobility and Private Equity Syndications from the Perspective of Coupling Networks: Evidence from China's Private Equity Industry 耦合网络视角下的经理人流动与私募股权联合——来自中国私募股权行业的证据

社会计算(英文) Pub Date : 2023-06-01 DOI: 10.23919/JSC.2023.0012

Jie Ren;Xibao Li;Likun Cao

{"title":"Manager Mobility and Private Equity Syndications from the Perspective of Coupling Networks: Evidence from China's Private Equity Industry","authors":"Jie Ren;Xibao Li;Likun Cao","doi":"10.23919/JSC.2023.0012","DOIUrl":"10.23919/JSC.2023.0012","url":null,"abstract":"This study explores whether manager mobility can influence syndications between private equity (PE) firms by constructing coupling network models. Using data from China's private equity market from 1993 to 2017, we found that driving forces, resistant forces, and network structure play significant roles in determining resource flows between PE firms. Specifically, driving forces indicate that managers moving from domestic and foreign PE firms to state-owned PE firms are more likely to induce syndications. Furthermore, if the manager is promoted when changing jobs, mobility is likely to enhance the flow of resources. Resistant forces indicate that increased geographical distance reduces syndications. As for the influence of structure, if managers leave PE firms with higher status, they are more likely to induce syndications. This study contributes to the coupling network literature by providing a clarified three-factor framework. By exploring the characteristic of managers in state-owned private equity firms, we specified the syndication theory in China. This study can help private equity firms hire valuable managers and expand syndication networks in practice.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 2","pages":"150-167"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/10239701/10241349.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43675483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Human-AI Collaboration in Crowd-Powered Source Search: A Preliminary Study 在众源搜索中利用人类-人工智能协作:初步研究

社会计算(英文) Pub Date : 2023-06-01 DOI: 10.23919/JSC.2023.0002

Yong Zhao;Zhengqiu Zhu;Bin Chen;Sihang Qiu

{"title":"Leveraging Human-AI Collaboration in Crowd-Powered Source Search: A Preliminary Study","authors":"Yong Zhao;Zhengqiu Zhu;Bin Chen;Sihang Qiu","doi":"10.23919/JSC.2023.0002","DOIUrl":"10.23919/JSC.2023.0002","url":null,"abstract":"Source search is an important problem in our society, relating to finding fire sources, gas sources, or signal sources. Particularly, in an unexplored and potentially dangerous environment, an autonomous source search algorithm that employs robotic searchers is usually applied to address the problem. Such environments could be completely unknown and highly complex. Therefore, novel search algorithms have been designed, combining heuristic methods and intelligent optimization, to tackle search problems in large and complex search spaces. However, these intelligent search algorithms were not designed to address completeness and optimality, and therefore commonly suffer from the problems such as local optimums or endless loops. Recent studies have used crowd-powered systems to address the complex problems that cannot be solved by machines on their own. While leveraging human intelligence in an AI system has been shown to be effective in making the system more reliable, whether using the power of the crowd can improve autonomous source search algorithms remains unanswered. To this end, we propose a crowd-powered source search approach enabling human-AI collaboration, which uses human intelligence as external supports to improve existing search algorithms and meanwhile reduces human efforts using AI predictions. Furthermore, we designed a crowd-powered prototype system and carried out an experiment with both experts and non-experts, to complete 200 source search scenarios (704 crowdsourcing tasks). Quantitative and qualitative analysis showed that the sourcing search algorithm enhanced by crowd could achieve both high effectiveness and efficiency. Our work provides valuable insights in human-AI collaborative system design.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 2","pages":"95-111"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/10239701/10241351.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47111075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cover 盖

社会计算(英文) Pub Date : 2023-03-01 DOI: 10.23919/JSCTUP.2023.10184092

引用次数: 0

Automatic Real-Time Detection of Infant Drowning Using YOLOv5 and Faster R-CNN Models Based on Video Surveillance 基于视频监控的YOLOv5和更快R-CNN模型实时自动检测婴儿溺水

社会计算(英文) Pub Date : 2023-03-01 DOI: 10.23919/JSC.2023.0006

Qianen He;Zhiqiang Mei;Huisheng Zhang;Xiuying Xu

{"title":"Automatic Real-Time Detection of Infant Drowning Using YOLOv5 and Faster R-CNN Models Based on Video Surveillance","authors":"Qianen He;Zhiqiang Mei;Huisheng Zhang;Xiuying Xu","doi":"10.23919/JSC.2023.0006","DOIUrl":"10.23919/JSC.2023.0006","url":null,"abstract":"Infant drowning has occurred frequently in swimming pools recent years, which motivates the research on automatic real-time detection of the accident. Unlike youths or adults, swimming infants are small in terms of size and motion range, and unable to send out distress signals in emergencies, which exerts negative effects on the detection of drowning. Aiming at this problem, a new step is initialized towards detecting infant drowning automatically and efficiently based on video surveillance. Diverse live-scene videos of infant swimming and drowning are collected from a variety of natatoriums and labeled as datasets. A part of the datasets is downscaled or enlarged to enhance generalization ability of the model. On this basis, advantages of Faster R-CNN and a series of YOLOv5 models are specifically explored to enable fast and accurate detection of infant drowning in real-world. Supervised learning experiments are carried out, model test results show that mean Average Precision (mAP) of either Faster R-CNN or YOLOv5s of the series of YOLOv5 can be over 89%; the former can process merely 6 frames of videos per second with the precision of only 62.04%, while the latter can reach an average speed of 75 frames/s with the precision of about 86.6%. The YOLOv5s eventually stands out as an optimal model for detecting infant drowning in view of comprehensive performance, which is of great application value to reduce the accidents in swimming pools.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 1","pages":"62-73"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/10184064/10184087.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42707657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Manipify: An Automated Framework for Detecting Manipulators in Twitter Trends Manipify:在Twitter趋势中检测操纵者的自动框架

社会计算(英文) Pub Date : 2023-03-01 DOI: 10.23919/JSC.2023.0001

Soufia Kausar;Bilal Tahir;Muhammad Amir Mehmood

{"title":"Manipify: An Automated Framework for Detecting Manipulators in Twitter Trends","authors":"Soufia Kausar;Bilal Tahir;Muhammad Amir Mehmood","doi":"10.23919/JSC.2023.0001","DOIUrl":"10.23919/JSC.2023.0001","url":null,"abstract":"The rapid adoption of online social media platforms has transformed the way of communication and interaction. On these platforms, discussions in the form of trending topics provide a glimpse of events happening around the world in real-time. Also, these trends are used for political campaigns, public awareness, and brand promotions. Consequently, these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience. In this article, we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan. We propose “Manipify”-a framework for automatic detection and analysis of malicious users in Twitter trends. Our framework consists of three distinct modules: (1) user classifier, (2) hashtag classifier, and (3) trend analyzer. The user classifier module introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features. Also, the module classifies human and bot users. Next, the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories. Finally, the trend analyzer module examines users, hashtags, and tweets for hashtag reach, linguistic features, and user behaviour. Our user classifier module achieves 0.92 and 0.98 accuracy in classifying manipulators and bots, respectively. We further test Manipify on the dataset comprising 652 trending hashtags with 5.4 million tweets and 1.9 million users. The analysis of trends reveals that the trending panel is mostly dominated by political hashtags. In addition, our results show a higher contribution of human accounts in trend manipulation as compared to bots.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 1","pages":"46-61"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/10184064/10184140.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43238579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Call for Papers: Special Issue on Assessing Sentience of AI Systems 论文征集：人工智能系统感知评估特刊

社会计算(英文) Pub Date : 2023-03-01 DOI: 10.23919/JSC.2023.0009

引用次数: 0

Prediction of Academic Performance of Students in Online Live Classroom Interactions—An Analysis Using Natural Language Processing and Deep Learning Methods 在线课堂互动中学生学习成绩的预测——基于自然语言处理和深度学习方法的分析

社会计算(英文) Pub Date : 2023-03-01 DOI: 10.23919/JSC.2023.0007

Yuanyi Zhen;Jar-Der Luo;Hui Chen

{"title":"Prediction of Academic Performance of Students in Online Live Classroom Interactions—An Analysis Using Natural Language Processing and Deep Learning Methods","authors":"Yuanyi Zhen;Jar-Der Luo;Hui Chen","doi":"10.23919/JSC.2023.0007","DOIUrl":"10.23919/JSC.2023.0007","url":null,"abstract":"Prior studies have shown the importance of classroom dialogue in academic performance, through which knowledge construction and social interaction among students take place. However, most of them were based on small scale or qualitative data, and few has explored the availability and potential of big data collected from online classrooms. To address this issue, this paper analyzes dialogues in live classrooms of a large online learning platform in China based on natural language processing techniques. The features of interactive types and emotional expression are extracted from classroom dialogues. We then develop neural network models based on these features to predict high- and low-academic performing students, and employ interpretable AI (artificial intelligence) techniques to determine the most important predictors in the prediction models. In both STEM (science, technology, engineering, mathematics) and non-STEM courses, it is found that high-performing students consistently exhibit more positive emotion, cognition and off-topic dialogues in all stages of the lesson than low-performing students. However, while the metacognitive dialogue illustrates its importance in non-STEM courses, this effect cannot be found in STEM courses. While high-performing students in non-STEM courses show negative emotion in the last stage of lessons, STEM students show positive emotion.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 1","pages":"12-29"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/10184064/10184065.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45221995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Video Games Localization into Arabic: Gamers' Reactions to Localizing PUBG and Free Fire 电子游戏阿拉伯语本地化：玩家对PUBG和Free Fire本地化的反应

社会计算(英文) Pub Date : 2023-03-01 DOI: 10.23919/JSC.2023.0004

Shatha Jarrah;Saleh Al-Salman;Ahmad S Haider

引用次数: 0