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2023 IEEE Conference on Artificial Intelligence (CAI)最新文献

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Neural Network Based Prediction of Terrorist Attacks Using Explainable Artificial Intelligence 利用可解释的人工智能进行基于神经网络的恐怖袭击预测
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00089
Anna Rösner, A. Gegov, D. Ouelhadj, A. Hopgood, Serge Da Deppo
{"title":"Neural Network Based Prediction of Terrorist Attacks Using Explainable Artificial Intelligence","authors":"Anna Rösner, A. Gegov, D. Ouelhadj, A. Hopgood, Serge Da Deppo","doi":"10.1109/CAI54212.2023.00089","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00089","url":null,"abstract":"Al has transformed the field of terrorism prediction, allowing law enforcement agencies to identify potential threats much more quickly and accurately. This paper proposes a first-time application of a neural network to predict the \"success\" of a terrorist attack. The neural network attains an accuracy of 91.66% and an F1 score of 0.954. This accuracy and F1 score are higher than those achieved with alternative benchmark models. However, using Al for predictions in highstakes decisions also has limitations, including possible biases and ethical concerns. Therefore, the explainable Al (XAI) tool LIME is used to provide more insights into the algorithm's inner workings.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116254247","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
IEEE CAI 2023 Organizing Committee IEEE CAI 2023组委会
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00007
{"title":"IEEE CAI 2023 Organizing Committee","authors":"","doi":"10.1109/cai54212.2023.00007","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00007","url":null,"abstract":"","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131957322","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
How Well Can Language Models Understand Politeness? 语言模型如何理解礼貌?
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00106
Can Li, Bin Pang, Wenbo Wang, Lingshu Hu, Matthew Gordon, Detelina Marinova, Bitty Balducci, Yi Shang
{"title":"How Well Can Language Models Understand Politeness?","authors":"Can Li, Bin Pang, Wenbo Wang, Lingshu Hu, Matthew Gordon, Detelina Marinova, Bitty Balducci, Yi Shang","doi":"10.1109/cai54212.2023.00106","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00106","url":null,"abstract":"Politeness plays a key role in social communications. Previous work proposed an SVM-based computational method for predicting politeness using linguistic features on a corpus that contains Wikipedia and Stack Exchange requests data. To extend this prior work, we focus on evaluating the performance of state-of-the-art language models on politeness prediction using the same dataset. Two models are applied in this study. First, we fine-tune BERT on politeness data and then use the fine-tuned model for politeness prediction. Second, we use ChatGPT to predict politeness. The results show that both fine-tuned BERT and ChatGPT achieved better results than the state-of-the-art results on both Wikipedia and Stack Exchange data. Fine-tuned BERT outperforms zero shot ChatGPT, but ChatGPT can provide explanations for its prediction. Moreover, fine-tuned BERT outperforms human-level performance by 2.28% on Wikipedia corpus.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128512789","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
Fast and Robust Wind Speed Prediction Under Impulsive Noise via Adaptive Graph-Sign Diffusion 基于自适应图符号扩散的脉冲噪声下快速鲁棒风速预测
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00135
Yi Yan, E. Kuruoğlu
{"title":"Fast and Robust Wind Speed Prediction Under Impulsive Noise via Adaptive Graph-Sign Diffusion","authors":"Yi Yan, E. Kuruoğlu","doi":"10.1109/CAI54212.2023.00135","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00135","url":null,"abstract":"Online estimation of time-varying wind speed across various locations is a crucial task for applications such as renewable energy generation, weather prediction, and environmental science. In this paper, we propose an adaptive Graph-Sign Diffusion (GSD) algorithm to predict the time-varying wind speed in real time. Leveraging the expressiveness power of Graph Signal Processing, our proposed GSD algorithm is formulated on a combination of adaptive graph filtering, graph diffusion, and l1-norm optimization. The GSD algorithm outputs a fast and robust prediction of time-varying graph signals under impulsive noise in an online manner. Experimenting with real-world data shows that the GSD algorithm accurately predicts the time-varying wind speed at multiple sensor locations.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134092962","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
Learning Collaborative Multi-Target Search for A Visual Drone Swarm 视觉无人机群的学习协同多目标搜索
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00012
Jiaping Xiao, Yi Xuan Marcus Tan, Xin-qiu Zhou, M. Feroskhan
{"title":"Learning Collaborative Multi-Target Search for A Visual Drone Swarm","authors":"Jiaping Xiao, Yi Xuan Marcus Tan, Xin-qiu Zhou, M. Feroskhan","doi":"10.1109/CAI54212.2023.00012","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00012","url":null,"abstract":"In this paper, we propose a multi-agent reinforcement learning approach, POCA-Mix, to achieve collaborative multi-target search with a visual drone swarm. The proposed approach leverages the benefits of curriculum learning and mixed credit assignment to guide the drone swarm in per-forming the search task with only local visual perception in a constrained 3D environment. To validate the performance of the proposed approach, we conducted simulation experiments with various combinations regarding the number of drones and targets. The results demonstrate that the proposed approach outperforms other baseline methods such as PPO and MA-POCA with a higher success rate in different scenarios.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121841729","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
IEEE CAI 2023 Reviewers IEEE CAI 2023审稿人
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00009
{"title":"IEEE CAI 2023 Reviewers","authors":"","doi":"10.1109/cai54212.2023.00009","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00009","url":null,"abstract":"","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125043370","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
DeepSnow/Rain: Light Weather Recognition 深雪/雨:轻天气识别
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00048
Hidetomo Sakaino, Natnapat Gaviphat, Louie Zamora, Dwi Fetiria Ningrum, Alivanh Insisiengmay
{"title":"DeepSnow/Rain: Light Weather Recognition","authors":"Hidetomo Sakaino, Natnapat Gaviphat, Louie Zamora, Dwi Fetiria Ningrum, Alivanh Insisiengmay","doi":"10.1109/CAI54212.2023.00048","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00048","url":null,"abstract":"Weather conditions impact our daily life and transportation. Various sensors, i.e., rain gauges, have been used to monitor weather conditions. However, their implementations are limited, capturing heavier rainfall and snowfall amounts. In contrast, camera image-based sensing is another option, but lighter rainfall and snowfall patterns are hard to be recognized even by state-of-the-art Deep Learning (DL) models despite the indications of heavier events that follow. A single DL is known to deal with limited single tasks for high accuracy. Therefore, this paper proposes DeepSnow/Rain: an integrated DL model consisting of DeepSnow, DeepScene, and DeepRoad. DeepScene is panoptic segmentation of scenes with umbrella and pedestrian recognition. Since it is hard to classify rain or snow with only two objects, road conditions are recognized by implementing DeepRoad. Experimental results in cities show promising results to monitor lighter weather condition changes over time during rainfall or snowfall.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125671498","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
Continuous Inference of Time Recurrent Neural Networks for Field Oriented Control 面向场控制的时间递归神经网络连续推理
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00119
Felix Kreutz, Daniel Scholz, Julian Hille, Huang Jiaxin, Florian Hauer, Klaus Knobloch, C. Mayr
{"title":"Continuous Inference of Time Recurrent Neural Networks for Field Oriented Control","authors":"Felix Kreutz, Daniel Scholz, Julian Hille, Huang Jiaxin, Florian Hauer, Klaus Knobloch, C. Mayr","doi":"10.1109/CAI54212.2023.00119","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00119","url":null,"abstract":"Deep recurrent networks can be computed as an unrolled computation graph in a defined time window. In theory, the unrolled network and a continuous time recurrent computation are equivalent. However, we encountered a shift in accuracy for models based on LSTM-/GRU- and SNN-cells during the switch from unrolled computation during training towards a continuous stateful inference without state resets. In this work, we evaluate these time recurrent neural network approaches based on the error created by using a time continuous inference. This error would be small in case of good time domain generalization and we can show that some training setups are favourable for that with the chosen example use case. A real time critical motor position prediction use case is chosen as a reference. This task can be phrased as a time series regression problem. A time continuous stateful inference for time recurrent neural networks benefits an embedded systems by reduced need of compute resources.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129048553","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
Artificial Intelligence Designer of Materials and Processes for Advanced Power Generation 先进发电材料与工艺人工智能设计师
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00084
Vyacheslav Romanov
{"title":"Artificial Intelligence Designer of Materials and Processes for Advanced Power Generation","authors":"Vyacheslav Romanov","doi":"10.1109/cai54212.2023.00084","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00084","url":null,"abstract":"Motivation for this research is the need to accelerate design of high-performance materials and processes to be used in advanced fossil energy power plants and, by doing so, bridge the gap between the shortening technological cycles and the long qualification testing of new alloys for energy applications. The artificial intelligence can exploit causal graph neural networks and other advanced network architectures to represent domain knowledge and engineering constraints. In this presentation, ‘deep-freeze’ graphs, ‘convoluted filtering’ networks, ‘mirror-image’ graphs, and adversarial ensemble methods are utilized to support inversion modeling for optimization of the complex compositions and complex processes in design of high-performing alloys, with their properties tailored to the energy application specifications.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122484010","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
Multimodal and multi-view predictive maintenance: A case study in the oil industry 多模式和多视角预测性维护:石油行业的案例研究
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00104
Tomas Souper, Duarte Oliper, Vitor Rolla
{"title":"Multimodal and multi-view predictive maintenance: A case study in the oil industry","authors":"Tomas Souper, Duarte Oliper, Vitor Rolla","doi":"10.1109/CAI54212.2023.00104","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00104","url":null,"abstract":"This paper explores comprehensive prognostics and health management in the oil and gas industry. It provides a proactive approach to equipment maintenance by detecting potential problems and predicting faults before they occur. Distillers are crucial oil and gas industry components, requiring regular maintenance and monitoring to maintain optimal performance and prevent unplanned maintenance. Deep learning models have shown promising results for predictive maintenance, and multimodality can bring generalization capabilities to these models. This study proposes a multimodal (and multi-view) approach for predictive maintenance in an oil and gas industry distiller dataset. The goal is to demonstrate that this approach can achieve more liable and generalizable models for predictive maintenance than state-of-the-art neural networks when training on a medium-scale dataset.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115846080","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
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