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

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MVAR and Causal Modeling of Relationship between Physiological Signals and Affective States 生理信号与情感状态关系的MVAR及因果模型
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00065
Behnaz Jafari, K. Lai, S. Yanushkevich
{"title":"MVAR and Causal Modeling of Relationship between Physiological Signals and Affective States","authors":"Behnaz Jafari, K. Lai, S. Yanushkevich","doi":"10.1109/CAI54212.2023.00065","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00065","url":null,"abstract":"This paper investigates the association and causality between multi-modal bio-signals measured using wearables, and the subjects’ affective state. The chosen methods are the multivariate auto-regressive model and Granger causality test. In particular, we focused on the state of stress and detected that respiratory, electrocardiogram, and accelerometer signals have the strongest associations with stress. These signals demonstrated the highest correlation values of 0.23, 0.2, and 0.18 respectively. The p-value for Granger causal F-test also shows a strong causal relationship between stress and the physiological signals.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"154 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131549130","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
Predictive Logistics Models for Autonomous Vehicles Deployment in Adversarial Environments 对抗环境下自动驾驶车辆部署的预测物流模型
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00047
R. Babiceanu
{"title":"Predictive Logistics Models for Autonomous Vehicles Deployment in Adversarial Environments","authors":"R. Babiceanu","doi":"10.1109/cai54212.2023.00047","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00047","url":null,"abstract":"Resilient logistics operations call for a holistic and crosscutting approach to proactively address both real-time and persistent adversarial events in several operational areas to outfit mobility platforms, networks, and Command and Control (C2) systems to support continued uninterrupted operations. This research proposes the development of robust mobility platforms for Unmanned Autonomous Vehicles deployment and remote maintenance in uncertain adversarial environment with predictive logistics guarantees, including platform reliability evaluation, and remote inspection. Artificial Intelligence/Machine Learning (AI/ML) predictive analytics are employed to select, deploy, monitor, and respond to mobility field mission events. An example use case of deployment with remote activities and maintenance requirements is provided.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"18 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":"133430678","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
Assessing Upper Limb Motor Function in the Immediate Post-Stroke Period Using Accelerometry 用加速度计评估中风后即刻上肢运动功能
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00064
Mackenzie Wallich, K. Lai, S. Yanushkevich
{"title":"Assessing Upper Limb Motor Function in the Immediate Post-Stroke Period Using Accelerometry","authors":"Mackenzie Wallich, K. Lai, S. Yanushkevich","doi":"10.1109/CAI54212.2023.00064","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00064","url":null,"abstract":"Accelerometry has been extensively studied as an objective means of measuring upper limb function in patients post-stroke. The objective of this paper is to determine whether the accelerometry-derived measurements frequently used in more long-term rehabilitation studies can also be used to monitor and rapidly detect sudden changes in upper limb motor function in more recently hospitalized stroke patients. Six binary classification models were created by training on variable data window times of paretic upper limb accelerometer feature data. The models were assessed on their effectiveness for differentiating new input data into two classes: severe or moderately severe motor function. The classification models yielded Area Under the Curve (AUC) scores that ranged from 0.72 to 0.82 for 15-minute data windows to 0.77 to 0.94 for 120-minute data windows. These results served as a preliminary assessment and a basis on which to further investigate the efficacy of using accelerometry and machine learning to alert healthcare professionals to rapid changes in motor function in the days immediately following a stroke.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"101 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":"132759988","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 New Method Using LLMs for Keypoints Generation in Qualitative Data Analysis 一种利用llm生成定性数据分析关键点的新方法
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00147
Fengxiang Zhao, Fan Yu, T. Trull, Yi Shang
{"title":"A New Method Using LLMs for Keypoints Generation in Qualitative Data Analysis","authors":"Fengxiang Zhao, Fan Yu, T. Trull, Yi Shang","doi":"10.1109/CAI54212.2023.00147","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00147","url":null,"abstract":"Qualitative data analysis (QDA) is useful for identifying patterns, themes, and relationships among data. In this paper, we propose a new method that uses large language models (LLMs), such as GPT-based Models, to improve QDA, in Ecological Momentary Assessment (EMA) studies as an example, by automating keypoints extraction and relevance evaluation. Experimental results on the IBM-ArgKP-2021 dataset show improved performance of the new method over existing work, achieving higher accuracy while reducing time and effort in the coding process of QDA, and demonstrate the effectiveness of our proposed method in various application settings.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"10 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":"120937579","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
Prediction of Length-of-stay at Intensive Care Unit (ICU) Using Machine Learning based on MIMIC-III Database 基于MIMIC-III数据库的机器学习预测重症监护病房(ICU)住院时间
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00142
M. Hasan, S. Hamdan, S. Poudel, J. Vargas, K. Poudel
{"title":"Prediction of Length-of-stay at Intensive Care Unit (ICU) Using Machine Learning based on MIMIC-III Database","authors":"M. Hasan, S. Hamdan, S. Poudel, J. Vargas, K. Poudel","doi":"10.1109/CAI54212.2023.00142","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00142","url":null,"abstract":"The length-of-stay (LOS) is critical for patient care and accommodation in the intensive care unit (ICU). In this work, we developed a framework to predict the LOS using the Medical Information Mart for Intensive Care (MIMIC-III) database. We extracted six features from individual patients and submitted them to the regressors model and examined how well these features could predict LOS. We considered four prediction regimes; extreme gradient boosting (XGBoost), support vector regressor, random forest, and voting regressor. Our analysis reveals that XGBoost yields the best result among other regressors with R2 0.86 and root mean square error (RMSE) 1.2. Remarkably, our results show that ICD9 (9th International classification of diseases code), saline intake per hour, and drug rates are the top three critical features for predicting the LOS.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"41 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":"121033622","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
Explainable Learning-Based Intrusion Detection Supported by Memristors 忆阻器支持的可解释的基于学习的入侵检测
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00092
Jing Chen, G. Adam
{"title":"Explainable Learning-Based Intrusion Detection Supported by Memristors","authors":"Jing Chen, G. Adam","doi":"10.1109/CAI54212.2023.00092","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00092","url":null,"abstract":"Deep learning based methods have demonstrated great success in network intrusion detection. However, the use of Deep Neural Networks (DNNs) makes it difficult to support real-time, packet-level detections in communication networks that handle high-speed traffic with low latency and energy. To this end, this paper proposes a novel approach to efficiently realize a DNN-based classifier by converting it into a pruned, explainable decision tree and evaluating its hardware implementation using an emerging architecture based on memristor devices, in order to support network intrusion detections on the fly. Preliminary experiments on real-world datasets show that the proposed method achieves nearly four orders of magnitude speed up while retaining the desired accuracy.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"5 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":"127225982","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
Robust AI-enabled Simulation of Treatment Paths with Markov Decision Process for Breast Cancer Patients 基于马尔可夫决策过程的乳腺癌患者治疗路径鲁棒人工智能模拟
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00053
S. Hossain, Mohammadreza Reza Ebrahimi, B. Padmanabhan, I. E. El Naqa, Paul C. Kuo, Abigail Beard, Sarah Merkel
{"title":"Robust AI-enabled Simulation of Treatment Paths with Markov Decision Process for Breast Cancer Patients","authors":"S. Hossain, Mohammadreza Reza Ebrahimi, B. Padmanabhan, I. E. El Naqa, Paul C. Kuo, Abigail Beard, Sarah Merkel","doi":"10.1109/CAI54212.2023.00053","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00053","url":null,"abstract":"Development in AI/ML-based methodologies has facilitated improvement in clinical decision making at various stages of treatment in breast cancer care. While this addresses patient needs at specific stages of treatment, the overall treatment path of a patient from a holistic standpoint has remained understudied due to challenges in accessing the relevant data. In this study, we propose to develop an AI-enabled treatment path simulation for breast cancer patients while characterizing the treatment paths as a Markov decision process (MDP). In order to avoid the limitations of healthcare records, which are often incomplete and subject to misinformation, we have leveraged clinical practice guidelines and expertise from physicians at Moffitt Cancer Center to develop the MDP. Our study of developing such an MDP, leveraging domain knowledge, contributes to improving research on treatment path simulation for breast cancer patients.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"70 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":"127365444","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
Measuring Modality Utilization in Multi-Modal Neural Networks 测量多模态神经网络的模态利用
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00014
Saurav Singh, Panos P. Markopoulos, E. Saber, Jesse D. Lew, Jamison Heard
{"title":"Measuring Modality Utilization in Multi-Modal Neural Networks","authors":"Saurav Singh, Panos P. Markopoulos, E. Saber, Jesse D. Lew, Jamison Heard","doi":"10.1109/CAI54212.2023.00014","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00014","url":null,"abstract":"Multimodal data provides information from different sensor types about the same underlying phenomenon and enhances machine learning performance. However, neural networks trained end-to-end on all the modalities tend to rely mostly on one of the most dominant modalities. The black box nature of neural networks makes it difficult to assess the reliance of the network on various modalities. This work presents a novel modality utilization metric that quantifies the network reliance on different modalities. The proposed metric is validated on NTIRE-21 (classification problem) and MCubeS (image segmentation problem) datasets. The modality utilization metric contributes towards the explainability of multimodal neural networks and offers great utility in the field of multimodal data fusion.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"19 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":"114865640","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
Shedding Light on Darkness: Enhancing Object Detection Robustness with Synthetic Perturbations for Real-world Challenges 照亮黑暗:用合成扰动增强现实世界挑战的目标检测鲁棒性
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00023
N. Premakumara, Brian Jalaian, N. Suri
{"title":"Shedding Light on Darkness: Enhancing Object Detection Robustness with Synthetic Perturbations for Real-world Challenges","authors":"N. Premakumara, Brian Jalaian, N. Suri","doi":"10.1109/CAI54212.2023.00023","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00023","url":null,"abstract":"Robustness against distribution shifts is crucial for object detection models in real-world applications. In this study, we evaluate the performance of four state-of-the-art models against natural perturbations, retrain them with synthetic perturbations using the AugLy augmentation package, and assess their improved performance against natural perturbations. Our empirical ablation study focuses on the brightness perturbation modality using the COCO 2017 and ExDARK datasets. Our findings suggest that synthetic perturbations can effectively improve model robustness against real-world distribution shifts, providing valuable insights for deploying robust object detection models in real-world scenarios.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"37 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":"131118940","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
The Global Workspace Theory: A Step Towards Artificial General Intelligence 全球工作空间理论:迈向通用人工智能的一步
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00125
Mohamed Abdelwahab, P. Aarabi
{"title":"The Global Workspace Theory: A Step Towards Artificial General Intelligence","authors":"Mohamed Abdelwahab, P. Aarabi","doi":"10.1109/CAI54212.2023.00125","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00125","url":null,"abstract":"Global Workspace Theory (GWT) and Artificial General Intelligence (AGI) are two concepts in cognitive science and Artificial Intelligence, respectively. This paper discusses the possibility of achieving AGI using a deep learning implementation of GWT. The shared latent space for GWT is trained using the latent spaces of the connected deep learning modules. This implementation aims to enhance the performance of specialized models in their specified tasks and achieve more general functions from single-task/specialized modules. The paper also discusses the possible applications of this implementation in healthcare.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"2 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":"131138939","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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