2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)最新文献_第2页

Trajectory-Based Dynamic Handwriting Recognition Using Fusion Neural Network 基于轨迹的融合神经网络动态手写识别

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/TAAI54685.2021.00011

Tzu-An Huang, Sai-Keung Wong, Lan-Da Van

引用次数: 0

Finding the Key Factors of Successful Personal Brand of Internet Celebrities 网络红人成功打造个人品牌的关键因素

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00060

Kuei-Chien Chiu, Chih-Sung Lai, Roman Sokorevskiy, Hsing-Hui Chu, R. Chen

{"title":"Finding the Key Factors of Successful Personal Brand of Internet Celebrities","authors":"Kuei-Chien Chiu, Chih-Sung Lai, Roman Sokorevskiy, Hsing-Hui Chu, R. Chen","doi":"10.1109/taai54685.2021.00060","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00060","url":null,"abstract":"Although social media marketing is a pretty fledgling marvel, it develops into comprehensive and independent commerce. It fulfills the possibility of marketing much easier and faster than traditional methods. With the development of social media, internet celebrities emerge as an essential role in social media marketing. The research purpose of this study is to explore and find the critical factors of the successful personal brand of internet celebrities by taking the Russian social media market as an example. This study employed a survey method to conduct quantitative research through an online questionnaire. According to the analysis of the 203 responses from Russian-speaking people, the research results show that gender, age, education, and occupation will influence fewer factors than other demographic features. In addition, attitude and brand will also be affected by fewer demographic features than other factors be. This implies that internet celebrities should perform segment marketing to people in different geographies by their concerns. Furthermore, according to the weighted importance, the brand will be affected by attitude, personality, and value in ascending order. It reveals that perceived value is the most critical factor among the others and earns widespread respect. That means internet celebrities have to pay more attention to audiences' perceived value and formulate appropriate strategies to increase their value to the public.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128849554","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

Multi-class Sentiment Analysis 多类情感分析

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00045

Show-Jane Yen, Yue-Shi Lee, Chung-Ken Lee

引用次数: 3

[Copyright notice] (版权)

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00003

引用次数: 0

A Hybrid Deep Learning Network for Long-Term Travel Time Prediction in Freeways 高速公路长期出行时间预测的混合深度学习网络

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00023

Ming-Chu Ho, Yu-Cing Chen, Chih-Chieh Hung, Hsien-Chu Wu

{"title":"A Hybrid Deep Learning Network for Long-Term Travel Time Prediction in Freeways","authors":"Ming-Chu Ho, Yu-Cing Chen, Chih-Chieh Hung, Hsien-Chu Wu","doi":"10.1109/taai54685.2021.00023","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00023","url":null,"abstract":"Travel time plays a vital role in people’s daily lives. It can help them not merely avoid traffic congestion but save time as well. When people need to drive to different cities by taking highways, travel time become more and more important now that they can check it to arrange better routes. Moreover, because COVID-19 are epidemic across Taiwan, people prefer to drive rather than taking public transportation. Therefore, accurate predictions of travel time is of great significance. In order to obtain precise predictions and correspond to situations in real life, we divide data into long and short sequences and create three types of dataset, including the whole year, only national holidays, and non-holidays. Additionally, on account of the interactive influence of time in different segments of the freeway, we exploit data to predict next-hour travel time instead of next 5 minutes. We introduce a deep learning model which hybrids tendency from XGBoost and recency embeddings from a fully-connected neural network, respectively. It can capture crucial features of both long and short sequences and observe implicit correlations between XGBoost and a fully-connected neural network. Extensive experiments on the dataset illustrate that our model achieves eminent performances and outperforms other state-of-the-art models.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115167280","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

Return-of-Interest Conscious Truth Inference for Crowdsourcing Queries 众包查询的利益回报意识真相推理

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00020

L. Leung, Po-An Yang, Kun-Ta Chuang

{"title":"Return-of-Interest Conscious Truth Inference for Crowdsourcing Queries","authors":"L. Leung, Po-An Yang, Kun-Ta Chuang","doi":"10.1109/taai54685.2021.00020","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00020","url":null,"abstract":"In the big data era, the flourishing development of Internet services brings a lot of user generated data, in which most new information cannot be systematically retrieved by current knowledge bases. For example, a dramatic number of new hashtags appear in the social media every day, resulting in much unknown but valuable knowledge that requires reliable category/attribute labeling strategies. The crowdsourcing platform provides an effective tool to leverage opinions from the Internet crowd. In this paper, we propose incorporating varied task importance, called Return of Interest (RoI), into resource allocation in crowdsourcing. The awareness of RoI is important in the business sense, but it introduces new challenges. In this paper, we propose a two-phase framework, called Macro-Assignment and Micro-Optimization (MAMO), to simultaneously consider the issue of budget allocation and the chance of iteratively obtaining RoI. With the fixed budget, we prove that worker allocation to diverse pools for the best expectation of RoI in return is a NPhard challenge. We propose a Dynamic-Programming strategy to resolve the issue effectively. As shown in our experimental results, we demonstrate that the DP-based strategy can significantly outperform the baseline greedy approaches, also indicating its feasibility to be deployed as the standard component for budget allocation in crowdsourcing.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133553895","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

[Title page iii] [标题页iii]

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00002

引用次数: 0

HAEE: Question Classification Using Hierarchical Intra-Attention Enhancement Encoder 使用分层注意内增强编码器的问题分类

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00031

Jen-Wei Wang, Kai-Hsiang Chen, Jen-Wei Huang

{"title":"HAEE: Question Classification Using Hierarchical Intra-Attention Enhancement Encoder","authors":"Jen-Wei Wang, Kai-Hsiang Chen, Jen-Wei Huang","doi":"10.1109/taai54685.2021.00031","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00031","url":null,"abstract":"With the development of E-commerce, an Automated Question-Answering system takes a crucial part in customer service. Question classification, which assigns labels to questions according to the answer types, is one of the tasks in question answering. Previous methods usually used handcraft features like named entity recognition, but it needs the predefined dictionary or tools. The machine learning approaches are recently applied to this task and achieve high accuracy. In this paper, we proposed HAEE, a Hierarchical intra-Attention Enhancement Encoder which composed of bidirectional GRUs and intra-attentions. In addition, we adopt the character input to address the issue of the OOV (Out-Of-Vocabulary) problem and create multiple intra-attentions to simulate the certain relationships between characters (Chinese) or words (English) to enhance the influence of tokens on the sentence. We evaluate the HAEE model in an actual corporate setting and several datasets. As shown in the experimental results, our HAEE model outperforms the existing state-of-the-art models on question classification tasks, especially for the Chinese corpus.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125037533","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 Machine-Learning-based Approach for Parameter Control in Bee Colony Optimization for Traveling Salesman Problem 基于机器学习的旅行商问题蜂群优化参数控制方法

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00019

Chong Gee Tan, Shin Siang Choong, L. Wong

{"title":"A Machine-Learning-based Approach for Parameter Control in Bee Colony Optimization for Traveling Salesman Problem","authors":"Chong Gee Tan, Shin Siang Choong, L. Wong","doi":"10.1109/taai54685.2021.00019","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00019","url":null,"abstract":"Metaheuristics are a set of algorithms which is capable of solving Combinatorial Optimization Problems (COPs). When a metaheuristic is used to solve a COP, one of the major aspects is to determine an appropriate parameter setting. Poor practice of determining parameter values may lead to inability to find optimal solutions or getting invalid conclusions from the experimental results. This research proposes a machine-learning-based parameter control mechanism for a metaheuristic, i.e. the Bee Colony Optimization (BCO) algorithm. The proposed mechanism consists of three main phases: Data Collection, Model Training, and Deployment. In order to examine the performance of the BCO algorithm with the parameter control mechanism, a set of 16 TSP instances is used as test bed. The experimental results show that it is significantly better than the BCO implementation using the parameter values that are determined via a manual tuning process. The proposed parameter control mechanism overcomes the shortcomings of manual parameter tuning and dynamically adjust the parameter values throughout the BCO optimization process.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126435289","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

Secured Steganographic Scheme Utilizing Fuzzy Threshold with Weighted Matrix 基于加权矩阵模糊阈值的安全隐写方案

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00027

Sharmistha Jana, Biswapati Jana, T. Lu

{"title":"Secured Steganographic Scheme Utilizing Fuzzy Threshold with Weighted Matrix","authors":"Sharmistha Jana, Biswapati Jana, T. Lu","doi":"10.1109/taai54685.2021.00027","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00027","url":null,"abstract":"The idea of similarity measure, also known as entropy measurement, is used to discriminate between dissimilar objects. In many cases, mathematical, psychological, and fuzzy approaches have been utilized to explore it. Taking these principles into consideration, a new fuzzy threshold-based steganographic system is designed to choose pixel blocks for data embedding based on the degree of the pixel category. Then the secret data has been embedded through sum-of-entry-wise multiplication operation using predefined weighted matrix and selected pixel block. The secret data has been successfully recovered during the extraction phase using the required pixel range, fuzzy threshold, and weighted matrix, which are responsible for shared secret key to improve security and robustness. Finally, the proposed technique is tested using steganographic attacks and several types of analysis to determine its imperceptibility and robustness. Moreover, various experimental tests have been carried out to demonstrate the efficacy and effectiveness of the proposed method. From a security standpoint, it has been observed that unknown weighted matrix, threshold value and reference table have failed to retrieve the secret from watermarked image. The anticipated consequence highlighted certain outstanding magnificent aspects in the fields of image authentication, tamper detection, and digital forgery detection, all of which are essential to the technological life. This system benefits a wide range of government and business sectors, including health care, commercial security, defense, and intellectual property rights.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130121125","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