2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)最新文献_第6页

The Security Method in MQTT Protocol for Internet of Things 物联网MQTT协议中的安全方法

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00061

Chia-Fen Hsieh, Chih-Kai Chang

{"title":"The Security Method in MQTT Protocol for Internet of Things","authors":"Chia-Fen Hsieh, Chih-Kai Chang","doi":"10.1109/taai54685.2021.00061","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00061","url":null,"abstract":"One of the most extensive protocols on the Internet of Things is Message Queuing Telemetry Transport (MQTT). However, there is no complete security method for the security of this protocol. The confidentiality and integrity of the message cannot be ensured. In the Industrial Internet of Things, there is more and more confidential or sensitive information. Therefore, it is important to deliver the message correctly. The issue of information security has gradually received attention. MQTT, which only relies on TCP/IP, does not have encryption protection. It may become the target of a man-in-the-middle attack. This paper uses a new architecture to protect MQTT in two stages. First, it uses a one-time-password as the first-stage authentication mechanism. It is an OTP-based identity verification method and an effective algorithm to protect the device from improper use. It can eliminate the risk of unauthorized users gaining access rights. The second stage is to use the simple restriction of black and white lists. It realizes the second identity verification. Finally, to prevent sensitive information from being stolen or modified after being cracked. It encrypts the payload with Advanced Encryption Standard (AES). Ensure that confidential or sensitive information will not leak out due to attacks. In this way, the confidentiality and integrity of the data can be ensured.","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":"133908650","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: 2

Housing Price Prediction by Using Generative Adversarial Networks 基于生成对抗网络的房价预测

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00018

Chia-Fen Hsieh, Tzu-Chieh Lin

引用次数: 0

Identifying Non-Intentional Ad Traffic on the Demand-Side in Display Advertising 在展示广告的需求方识别非故意的广告流量

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00021

Duy-An Ha, Thi-Thanh-An Nguyen, Wen-Yuan Zhu, S. Yuan

{"title":"Identifying Non-Intentional Ad Traffic on the Demand-Side in Display Advertising","authors":"Duy-An Ha, Thi-Thanh-An Nguyen, Wen-Yuan Zhu, S. Yuan","doi":"10.1109/taai54685.2021.00021","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00021","url":null,"abstract":"The ad traffic from fraudulent or invalid activities costs advertisers a significant proportion of their ad spending. For advertisers, the ad traffic from fraudulent or invalid activities is non-intentional, and this non-intentional ad traffic should not be considered for ad delivery. In this paper, we would like to safeguard the interests of advertisers by identifying the non-intentional ad traffic from the perspective of the Demand-Side Platform (DSP), which serves the advertisers by managing their advertising budget and delivering ads to the right audience in display advertising. Then, DSPs could filter out the identified non-intentional ad traffic to avoid ad spending on and ad delivery of this traffic. To identify the non-intentional ad traffic, our approach is based on Positive-Unlabeled (PU) learning. In particular, we first extract the features which represent the corresponding access behavior, and label the partial non-intentional ad traffic instances we confirmed. Then, given the labeled non-intentional ad traffic instances and the unlabeled ad traffic instances, we build a model to infer the degree of non-intention for each incoming ad request based on our feature space. Our experimental results show that our approach outperforms the baselines on various metrics on one real dataset.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"42 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":"124117904","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

Analyze influence factors in customer’s insurance transaction by decision tree model 运用决策树模型分析客户保险交易的影响因素

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00051

Che-Nan Kuo, Yu-Da Lin, Yu-Huei Cheng

{"title":"Analyze influence factors in customer’s insurance transaction by decision tree model","authors":"Che-Nan Kuo, Yu-Da Lin, Yu-Huei Cheng","doi":"10.1109/taai54685.2021.00051","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00051","url":null,"abstract":"In recent years, the development of the digital is rapidly in the world. In a variety of technologies gradually mature, the Internet and mobile device popularization, the IOT and cloud computing services, driving the growth of all kinds of data, so that the data greatly increased and diversified. The value of these data can be used to predict the consumer’s behavior, difference the user groups to study out efficient marketing strategies, and create differentiated competitiveness.In order to predict the consumer’s behavior of buying insurance products, the research collected 4474 insurance transactions from a bank in Taiwan Tainan. After the data pre-processing, the available transaction number is 3430. In these organized transactions, we let the classification of insurance products as the dependent variable, and the attributes of customers as independent variables. Then, using the correlation analysis by chi-squared test to carry out un-relevant factors. Analyzing the influence factors by decision tree machine learning model. According to the analysis result of the decision tree model, the accuracy rate almost close to 70%, and the most important influence factors are the actual insurance fee and currency. These two influence factors can be used as a reference for the bank in Taiwan Tainan to precise the marketing.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"179 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":"131515797","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

Research on Teaching Effectiveness of Computational Thinking Based on Service Learning 基于服务学习的计算思维教学效果研究

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00037

Bing-Hong Chen, Tsui-Feng Huang, Sheng-Chieh Chou

{"title":"Research on Teaching Effectiveness of Computational Thinking Based on Service Learning","authors":"Bing-Hong Chen, Tsui-Feng Huang, Sheng-Chieh Chou","doi":"10.1109/taai54685.2021.00037","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00037","url":null,"abstract":"Computational thinking has been widely regarded as an important ability to adapt to the future. College students use the knowledge they have learned to help elementary students in the community learn computational thinking, thereby enhancing the motivation and achievement of the course. Use Scratch software tools to support the learning of computational thinking. In addition to cultivating students’ basic knowledge and abilities, it also assists the lack of learning resources in community elementary schools through practical actions of service learning, so that students can focus their learning on task-base purposes. Comprehensive research and analysis are conducted based on the evaluation of the students' completed works, the learning satisfaction scale, and the data of the key indicators of self-evaluation of computational thinking, plus the questionnaire survey of the primary school students receiving assistance. The results show that: it helps to stimulate students' desire to learn, thereby significantly improving academic performance and learning motivation. At the same time, it makes students have self-confidence and a sense of accomplishment, and makes learners aware of the inadequacy of self-learning, and promotes their willingness to learn from passive to active.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"136 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":"132052413","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

FakeCLIP: Multimodal Fake Caption Detection with Mixed Languages for Explainable Visualization FakeCLIP:用于可解释可视化的混合语言的多模态假标题检测

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00010

Christian Nathaniel Purwanto, Joan Santoso, Po-Ruey Lei, Hui-Kuo Yang, Wen-Chih Peng

{"title":"FakeCLIP: Multimodal Fake Caption Detection with Mixed Languages for Explainable Visualization","authors":"Christian Nathaniel Purwanto, Joan Santoso, Po-Ruey Lei, Hui-Kuo Yang, Wen-Chih Peng","doi":"10.1109/taai54685.2021.00010","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00010","url":null,"abstract":"Existing fake news research relies on news propagation or news metadata. Waiting for propagation structure to be enough is a waste of time. Hoping for reliable metadata information is also a waste because all data can be forged. The most natural way for human when verifying a news is through the content itself. In social media, most of the circulating news are in minimal content which consist of image and its text caption. We propose FakeCLIP to examine whether a caption truly describes the corresponding image or not. As far as we know, we are the first one to tackle fake news using fake caption approach. We found mixed languages problem where one single text can consist of many different languages mixed together. We provide explainable visualization for intuitive reasoning of which part contains fake information. Moreover, we also consider alignment of what happens in the image that being discussed in the text caption while showing the fake signal over them. Our proposed method performs better than the current state-of-the-art on Twitter datasets by 11.1%.","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":"130316298","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

Interpolation based reversible hiding scheme by using center folding strategy and adjusting hiding operator 采用中心折叠策略和调节隐藏算子的插值可逆隐藏方案

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00025

T. Vo, T. Lu, Somya Agrawal, Biswapati Jana

引用次数: 1

Improving Multi-Scale Models with A Comparative Framework for Semantic Segmentation 基于语义分割比较框架的多尺度模型改进

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-11-01 DOI: 10.1109/taai54685.2021.00065

Ting-Chen Hsu, Bor-Shen Lin

{"title":"Improving Multi-Scale Models with A Comparative Framework for Semantic Segmentation","authors":"Ting-Chen Hsu, Bor-Shen Lin","doi":"10.1109/taai54685.2021.00065","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00065","url":null,"abstract":"State-of-the-art models of semantic segmentation, based on global convolutional network, ASPP, self-attention, and so on, focus on capturing context information through the fusion of multi-scale features and integrating local features with large kernels. However, these models have not been compared yet in parallel to interpret their relative efficacies. This makes it difficult to further combine or improve these models due to their complicated network structures. In this paper, a general multi-scale framework of semantic image segmentation was proposed to investigate and compare the network structures of the models in parallel. Three alternative modules were proposed to improve these methods, and the experiments show the proposed modules can give superior segmentation results and achieve outstanding performance on Pascal VOC2012 images segmentation datasets. Additionally, this framework was shown to be flexible for integrating the multi-scale features and operations from different levels. Experimental results show that the low-level operation can extract local details and the high-level operation the overall contour, so the output features from different levels complement each other to improve the performance effectively.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"73-74 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":"128631366","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

New Pruning Method Based on DenseNet Network for Image Classification 基于DenseNet网络的图像分类剪枝方法

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-08-28 DOI: 10.1109/taai54685.2021.00028

Ruikang Ju, Ting-Yu Lin, Jen-Shiun Chiang

引用次数: 3

Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection 局部可解释的一类信用卡欺诈检测异常

2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2021-08-05 DOI: 10.1109/taai54685.2021.00014

Tungyu Wu, Youting Wang

{"title":"Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection","authors":"Tungyu Wu, Youting Wang","doi":"10.1109/taai54685.2021.00014","DOIUrl":"https://doi.org/10.1109/taai54685.2021.00014","url":null,"abstract":"For the highly imbalanced credit card fraud detection problem, most existing methods either use data augmentation methods or conventional machine learning models, while anomaly-detection-based approaches are not sufficient. Furthermore, few studies have employed AI interpretability tools to investigate the feature importance of transaction data, which is crucial for the black-box fraud detection module. Considering these two points together, we propose a novel anomaly detection framework for credit card fraud detection as well as a model-explaining module responsible for prediction explanations. The fraud detection model is composed of two deep neural networks, which are trained in an unsupervised and adversarial manner. Precisely, the generator is an AutoEncoder aiming to reconstruct genuine transaction data, while the discriminator is a fully-connected network for fraud detection. The explanation module has three white-box explainers in charge of interpretations of the AutoEncoder, discriminator, and the whole detection model, respectively. Experimental results show the state-of-the-art performances of our fraud detection model on the benchmark dataset compared with baselines. In addition, prediction analyses by three explainers are presented, offering a clear perspective on how each feature of an instance of interest contributes to the final model output. Our code is available at https://github.com/tony10101105/Locally-Interpretable-One-Class-Anomaly-Detection-for-Credit-Card-Fraud-Detection.","PeriodicalId":343821,"journal":{"name":"2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123331484","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