2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)最新文献

Exploring the Acceptance of Rumor Rebuttals: The Mediating Influence of Utilitarian and Hedonic Values 探索谣言反驳的接受度：功利主义和享乐主义价值观的中介影响

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2024-01-03 DOI: 10.1109/IMCOM60618.2024.10418300

Anjan Pal, Alton Y. K. Chua, D. Goh

{"title":"Exploring the Acceptance of Rumor Rebuttals: The Mediating Influence of Utilitarian and Hedonic Values","authors":"Anjan Pal, Alton Y. K. Chua, D. Goh","doi":"10.1109/IMCOM60618.2024.10418300","DOIUrl":"https://doi.org/10.1109/IMCOM60618.2024.10418300","url":null,"abstract":"This research examines the mediating roles of perceived utilitarian and hedonic values in the relations between users’ perceptions of rebuttals and their decisions to accept the debunking messages in combatting online rumors. For the purpose of this investigation, four mediation models, each comprised of two hypotheses, were proposed and tested with data collected from 305 participants. Parallel mediation analysis was performed on each model to test the proposed hypotheses. Results show that users’ perceptions of rebuttals were significantly associated with their decisions to accept the debunking messages in combatting online rumors. While both perceived utilitarian and hedonic values mediated the relations between users’ perceptions of rebuttals and their decisionmaking, the mediating role of the former was stronger than the latter. This research offers insights into the underlying process of how both values impact users’ decisions to accept rebuttals as the truth. It helps practitioners understand how users perceive and process rebuttals when dealing with online rumors.","PeriodicalId":518057,"journal":{"name":"2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"15 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532741","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

Aggressive Driver Behavior Detection Using Multi-Label Classification 利用多标签分类检测侵略性驾驶员行为

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2024-01-03 DOI: 10.1109/IMCOM60618.2024.10418298

Amira A. Amer, Dina Elreedy

{"title":"Aggressive Driver Behavior Detection Using Multi-Label Classification","authors":"Amira A. Amer, Dina Elreedy","doi":"10.1109/IMCOM60618.2024.10418298","DOIUrl":"https://doi.org/10.1109/IMCOM60618.2024.10418298","url":null,"abstract":"Autonomous driving and advanced driver assistance systems aim to add comfort and safety to transportation. One major challenge facing advanced driver assistance systems is detecting aggressive driving. Aggressive driving behavior is a radical reason for fatal accidents. The driving environment is one compelling aspect affecting aggressive driving behavior. However, driving environment data are expensive and not easy to get. Thus, this work proposes a novel approach for aggressive driving detection that considers the driving environment by predicting it as a target class and considers the relationship between the driving behavior and the driving environment. Specifically, the proposed approach formulates the problem as a multi-label classification problem where the predicted classes are the driver behavior style and driving environment. We adopt several multi-label algorithms, including binary relevance, classifier chains, label powerset, and RAkEL. Moreover, we apply two classifiers: Random forest and Support vector machines. Furthermore, we investigate the impact of feature selection on classification performance. We performed the experiments on a real-world dataset. The accomplished results illustrate the superiority of the multi-label approach in aggressive driving behavior detection. In addition, feature selection significantly enhances the classification performance.","PeriodicalId":518057,"journal":{"name":"2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"17 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532748","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

NoSimple: Data Bias Evaluation Metrics NoSimple：数据偏差评估指标

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2024-01-03 DOI: 10.1109/IMCOM60618.2024.10418419

S. Rahardja, P. Fränti

引用次数: 0

IRS-Assisted Data and Energy Transfer MAC Protocol IRS 辅助数据和能量转移 MAC 协议

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2024-01-03 DOI: 10.1109/IMCOM60618.2024.10418282

Weiyue Xing, Yijun Piao, Tae-Jin Lee

引用次数: 0

A Time-Sensitive Networking Traffic Scheduling Method Based on Q-Learning Routing Optimization 基于 Q-Learning 路由优化的时敏网络流量调度方法

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2024-01-03 DOI: 10.1109/IMCOM60618.2024.10418305

Jin Li, Min Wei, Chengjie Huo, Keecheon Kim

引用次数: 0

Content Analysis Of Social Media Platform Instagram Binus Tv (Period September 2022 - December 2022) 社交媒体平台 Instagram Binus Tv 的内容分析（2022 年 9 月至 2022 年 12 月）

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2024-01-03 DOI: 10.1109/imcom60618.2024.10418345

Joshua Immanuel Siahaan, F. M. Gasa

{"title":"Content Analysis Of Social Media Platform Instagram Binus Tv (Period September 2022 - December 2022)","authors":"Joshua Immanuel Siahaan, F. M. Gasa","doi":"10.1109/imcom60618.2024.10418345","DOIUrl":"https://doi.org/10.1109/imcom60618.2024.10418345","url":null,"abstract":"The shift in technology from conventional to digital is a great opportunity for people to maximize social media's utility in all fields, one of which is marketing with digital content through the Instagram platform. Through the approach of content marketing theory and conceptual types of social media posting categories, this research aims to reveal what types of content categories are applied to Instagram posts, as well as what forms of categories and types of content dominate BINUS TV's Instagram. Conducted with a quantitative method in the form of descriptive content analysis of 235 content posts on BINUS TV's Instagram for the period September 2022-December 2022, the results of this study describe the data description of the categories and types of posts applied to BINUS TV's social media accounts. Through content analysis and reliability test results by two coders with a reliability value of 85%, it was found that informative content became the most popular post category with a percentage of 55.7% among other post categories. Meanwhile, the most common type of content posted on the BINUS TV account is video, with a percentage of 53.2%. This number makes video posts the type of content that dominates BINUS TV account posts according to the period mentioned.","PeriodicalId":518057,"journal":{"name":"2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"58 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532577","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

RZUD: A Novel Hybrid Model for Small Sized Handgun Detection RZUD：用于小型手枪检测的新型混合模型

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2024-01-03 DOI: 10.1109/IMCOM60618.2024.10418397

Arif Warsi, Munaisyah Abdullah, Nasreen Jawaid, Sheroz Khan, Muhammad Yahya

{"title":"RZUD: A Novel Hybrid Model for Small Sized Handgun Detection","authors":"Arif Warsi, Munaisyah Abdullah, Nasreen Jawaid, Sheroz Khan, Muhammad Yahya","doi":"10.1109/IMCOM60618.2024.10418397","DOIUrl":"https://doi.org/10.1109/IMCOM60618.2024.10418397","url":null,"abstract":"Closed-circuit television (CCTV) cameras have become ubiquitous tools for security, supplemented by an active system that can automatically detect firearms, a measure intended to discourage criminal activities like gun violence. However, accurately identifying small handguns poses a unique challenge due to their lack of distinguishing features. This deficiency leads many existing algorithms to produce false positives and negatives. To address this issue, a novel hybrid model named RZUD (RoI-ZOOM-UNBLUR-DETECT) has been developed. RZUD operates in four stages: selecting regions of interest, zooming in on selected regions, unblurring the resized regions, and ultimately performing detection. This comprehensive approach significantly improves detection accuracy. In empirical evaluations, RZUD outperformed state-of-the-art object detection algorithms including YOLOv3 and YOLOv7. When tested on a small-sized handgun dataset, YOLOv3 registered a 56% F1 score, but when combined with RZUD, this figure improved to 76%, marking a 20% improvement. Similarly, YOLOv7's F1 score rose from 56% to 77% when coupled with RZUD, a remarkable 21% gain. In essence, RZUD's novel methodology effectively elevates small handgun detection accuracy.","PeriodicalId":518057,"journal":{"name":"2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"165 3","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532591","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

Evaluation of Road Network Effectiveness with Rank-Based Random Walk 用基于等级的随机漫步评估路网效果

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2024-01-03 DOI: 10.1109/IMCOM60618.2024.10418378

Da-Young Lee, Hwan-Gue Cho

引用次数: 0

The Design of Independent-Uniform Knowledge Sources of Blackboard Architecture in Timber Harvesting Decision-Making 木材采伐决策中黑板架构的独立-统一知识源设计

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2024-01-03 DOI: 10.1109/IMCOM60618.2024.10418421

Hana Munira Muhd Mukhtar, Roslan Ismail

引用次数: 0

Ensemble Learning based on CNN and Transformer Models for Leaf Diseases Classification 基于 CNN 和变压器模型的集合学习用于叶病分类

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2024-01-03 DOI: 10.1109/IMCOM60618.2024.10418393

Li-Hua Li, Radius Tanone

{"title":"Ensemble Learning based on CNN and Transformer Models for Leaf Diseases Classification","authors":"Li-Hua Li, Radius Tanone","doi":"10.1109/IMCOM60618.2024.10418393","DOIUrl":"https://doi.org/10.1109/IMCOM60618.2024.10418393","url":null,"abstract":"Symptoms on the leaves are often the first indication of a plant disease. In order not to affect the process of crop production, farmers need to identify plant diseases on their leaves as quickly as possible. This problem has long been addressed by a variety of computational techniques, such as deep learning models. Today, many specialized deep learning models are built using Transformer or Convolution Neural Networks (CNN). However, the accuracy and performance of individual deep learning models depends on many factors, such as the number of parameters, training time, and the dataset used. Often a single model is not well suited to solving problems such as image classification of leaf diseases. This study proposes an ensemble learning based on CNN and Transformer models. The models used in this study are MobileNetV3, DenseNet201, ResNext50, Vision Transformer and Swin Transformer. The purpose of ensemble learning with these five models is to achieve accuracy and good performance through weighted voting such as hard voting and soft voting. The experimental findings indicate that the utilization of ensemble learning, employing a combination of five models, yields enhanced accuracy and performance in the classification of three distinct types of datasets: corn leaf diseases, grape leaf diseases, and potato leaf diseases. Our experiment also showed that the Vision Transformer model has higher accuracy compared to other models. To perform a detailed analysis, we use the Grad-CAM technique to visualize how all models use the gradient to create a classification score. The results of this experiment can be a recommendation for the agricultural sector so that they can be implemented as early as possible to address the problem of leaf diseases classification.","PeriodicalId":518057,"journal":{"name":"2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"64 2","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532594","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