2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第9页

Towards Emotion Cause Generation in Natural Language Processing using Deep Learning 基于深度学习的自然语言处理中的情感原因生成

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00027

M. Riyadh, M. O. Shafiq

引用次数: 0

Clustering image data with a fixed embedding 用固定嵌入聚类图像数据

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00148

Yan-Bin Chen, Khong-Loon Tiong, Chen-Hsiang Yeang

引用次数: 0

COVID-19 Detection from Cough Recording by means of Explainable Deep Learning 基于可解释深度学习的咳嗽录音COVID-19检测

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00261

M. Cesarelli, Marcello Di Giammarco, Giacomo Iadarola, Fabio Martinelli, F. Mercaldo, A. Santone, Michele Tavone

引用次数: 0

Real-Time Change Detection At the Edge 边缘实时变化检测

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00130

K. Gadiraju, Zexi Chen, B. Ramachandra, Ranga Raju Vatsavai

{"title":"Real-Time Change Detection At the Edge","authors":"K. Gadiraju, Zexi Chen, B. Ramachandra, Ranga Raju Vatsavai","doi":"10.1109/ICMLA55696.2022.00130","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00130","url":null,"abstract":"Detecting changes in real-time using remote sensing data is of paramount importance in areas such as crop health monitoring, weed detection, and disaster management. However, real-time change detection using remote sensing imagery faces several challenges: a) it requires real-time data extraction which is a challenge for traditional satellite imagery sources such as MODIS and LANDSAT due to the latency associated with collecting and processing the data. Due to the advances made in the past decade in drone technology, Unmanned Aerial Vehicles (UAVs) can be used for real-time data collection. However, a large percentage of this data will be unlabeled which limits the use of well-known supervised machine learning methods; b) from an infrastructure perspective, the cloud-edge solution of processing the data collected from UAVs (edge) only on the cloud is also constrained by latency and bandwidth-related issues. Due to these limitations, transferring large amounts of data between cloud and edge, or storing large amounts of information regarding past time periods on an edge device is infeasible. We can limit the amount of data transferred between the cloud and edge by performing analyses on-the-fly at the edge using low-power devices (edge devices) that can be connected to UAVs. However, edge devices have computational and memory bottlenecks, which would limit the usage of complex machine learning algorithms. In this paper, we demonstrate how an unsupervised GMM-based real-time change detection method at the edge can be used to identify weeds in real-time. We evaluate the scalability of our method on edge computing and traditional devices such as NVIDIA Jetson TX2, RTX 2080, and traditional Intel CPUs. We perform a case study for weed detection on images collected from UAVs. Our results demonstrate both the efficacy and computational efficiency of our method.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114862593","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

Bayesian Rule Ontologies For XAI Classification and Regression 面向XAI分类与回归的贝叶斯规则本体

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00031

A. K. Panda, B. Kosko

{"title":"Bayesian Rule Ontologies For XAI Classification and Regression","authors":"A. K. Panda, B. Kosko","doi":"10.1109/ICMLA55696.2022.00031","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00031","url":null,"abstract":"A random foam defines a modular rule-based ontology after sampling from a neural or other input-output system. A random foam combines several rule-based systems and averages the systems. It gives a Bayesian posterior over the subsystems. It also gives separate Bayesian posteriors over the rules of each subsystem. The shape of the rules controls how well the random-foam ontology performs in classification and regression. We found that a heterogenous ontology that mixes different rule shapes can perform better than a homogenous ontology based on a single Gaussian or other rule type. Random foams are also universal function approximators. So they can train on a neural black box and act as its explainable proxy system. We prove this uniform approximation theorem for the important case of bump-function random foams with throughput combination. Random foams also measure their output’s uncertainty through the conditional variance. Bump function rules performed better than Cauchy rules at classification while Cauchy rules performed better at regression. Gaussian rules performed best in both classification and regression. A homogeneous Gaussian random foam that trained on a 96.7% accurate neural classifier was itself 95.96% accurate on the MNIST data set. A heterogeneous random foam with two-thirds Gaussian rules and one-third Laplacian rules did better than did the all-Gaussian foam ontology.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115176933","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

On Label Quality in Class Imbalance Setting -A Case Study 论班级失衡设置中的标签质量——以实例为例

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00256

Jumanah Alshehri, Marija Stanojevic, E. Dragut, Z. Obradovic

{"title":"On Label Quality in Class Imbalance Setting -A Case Study","authors":"Jumanah Alshehri, Marija Stanojevic, E. Dragut, Z. Obradovic","doi":"10.1109/ICMLA55696.2022.00256","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00256","url":null,"abstract":"Producing high-quality labeled data is a challenge in any supervised learning problem, where in many cases, human involvement is necessary to ensure the label quality. However, human annotations are not flawless, especially in the case of a challenging problem. In nontrivial problems, the high disagreement among annotators results in noisy labels, which affect the performance of any machine learning model. In this work, we consider three noise reduction strategies to improve the label quality in the Article-Comment Alignment Problem, where the main task is to classify article-comment pairs according to their relevancy level. The first considered labeling disagreement reduction strategy utilizes annotators’ background knowledge during the label aggregation step. The second strategy utilizes user disagreement during the training process. In the third and final strategy, we ask annotators to perform corrections and relabel the examples with noisy labels. We deploy these strategies and compare them to a resampling strategy for addressing the class imbalance, another common supervised learning challenge. These alternatives were evaluated on ACAP, a multiclass text pairs classification problem with highly imbalanced data, where one of the classes represents at most 15% of the dataset’s entire population. Our results provide evidence that considered strategies can reduce disagreement between annotators. However, data quality improvement is insufficient to enhance classification accuracy in the article-comment alignment problem, which exhibits a high-class imbalance. The model performance is enhanced for the same problem by addressing the imbalance issue with a weight loss-based class distribution resampling. We show that allowing the model to pay more attention to the minority class during the training process with the presence of noisy examples improves the test accuracy by 3%.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"336 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116353038","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

Predicting COVID-19 Case Counts using Twitter Image Data 使用Twitter图像数据预测COVID-19病例数

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00260

Seth Ockerman, Erin Carrier

{"title":"Predicting COVID-19 Case Counts using Twitter Image Data","authors":"Seth Ockerman, Erin Carrier","doi":"10.1109/ICMLA55696.2022.00260","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00260","url":null,"abstract":"A crucial task with diseases, such as COVID-19, is accurate forecasting of cases for early detection of spikes, which allows policymakers to adjust local restrictions. The use of face masks to prevent disease spread among the general population has become widespread due to the COVID-19 pandemic. While predictive models for COVID-19 case counts exist, capturing localized information about mask usage has the potential to improve prediction accuracy. In this paper, we develop time series models that utilize Twitter image data for COVID-19 case count prediction. A crucial part of such a model is the accurate detection of face mask presence in Twitter images, which we train a convolutional neural network (CNN) to perform. While multiple datasets exist to train CNNs for face mask detection, existing datasets do not adequately represent the complexity nor the diversity in social media images. To address this and create a sufficiently accurate CNN for use with social media images, we also present a new social media face mask image dataset designed for the training of CNNs to detect the presence of face masks in complex real-world images, such as social media images. The presented dataset consists of approximately 120k images and attempts to more adequately account for diversity in ethnicity, mask type, and physical orientation of individuals in images than existing datasets. We demonstrate the effectiveness of both the CNN model for face mask detection and the resulting time series model trained on data obtained from applying the CNN model to historical twitter data, illustrating that data on the presence of masks in social media images can increase predictive accuracy of time series models for COVID-19 case counts.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114433546","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

Secure Federated Training: Detecting Compromised Nodes and Identifying the Type of Attacks 安全联合训练:检测受损节点和识别攻击类型

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00183

Pretom Roy Ovi, A. Gangopadhyay, R. Erbacher, Carl E. Busart

{"title":"Secure Federated Training: Detecting Compromised Nodes and Identifying the Type of Attacks","authors":"Pretom Roy Ovi, A. Gangopadhyay, R. Erbacher, Carl E. Busart","doi":"10.1109/ICMLA55696.2022.00183","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00183","url":null,"abstract":"Federated learning (FL) allows a set of clients to collaboratively train a model without sharing private data. As a result, FL has limited control over the local data and corresponding training process. Therefore, it is susceptible to poisoning attacks in which malicious clients use malicious training data or local updates to poison the global model. In this work, we first studied the data level and model level poisoning attacks. We simulated model poisoning attacks by tampering the local model updates during each round of communication and data poisoning attacks by training a few clients on malicious data. And clients under such attacks carry faulty information to the server, poison the global model, and restrict it from convergence. Therefore, detecting clients under attacks as well as identifying the type of attacks are required to recover the clients from their malicious status. To address these issues, we proposed a way under federated framework that enables the detection of malicious clients and attack types while ensuring data privacy. Our clustering-based approach utilizes the neuron’s activations from the local models to identify the type of poisoning attacks. We also proposed to check the weight distribution of local model updates among the participating clients to detect malicious clients. Our experimental results validated the robustness of the proposed framework against the attacks mentioned above by successfully detecting the compromised clients and the attack types. Moreover, the global model trained on MNIST data couldn’t reach the optimal point even after 75 rounds because of malicious clients, whereas the proposed approach by detecting the malicious clients ensured convergence within only 30 rounds and 40 rounds in independent and identically distributed (IID) and non- independent and identically distributed (non-IID) setup respectively.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114805634","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

Predicting anxiety treatment outcomes with machine learning 用机器学习预测焦虑治疗结果

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00160

Marija Stanojevic, Lesley A. Norris, P. Kendall, Z. Obradovic

{"title":"Predicting anxiety treatment outcomes with machine learning","authors":"Marija Stanojevic, Lesley A. Norris, P. Kendall, Z. Obradovic","doi":"10.1109/ICMLA55696.2022.00160","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00160","url":null,"abstract":"Youth anxiety disorders are highly prevalent and associated with considerable concurrent functional impairments. According to the State of the World’s Children report, 13% of youth between 10 and 19 years old have a diagnosed mental health disorder, 40% of which are anxious and depressive disorders. In a typical longitudinal anxiety clinical study, many explanatory variables are observed in a few patients. As patients drop or miss appointments, collected data has a high missing rate in explanatory and predicted variables. We suggest using machine learning methods to improve understanding of treatments and prediction of outcomes in such studies. We propose machine learning-based imputation for understanding youth anxiety data containing features with high missing rates. In the dataset used, the missing rate of features is up to 80%, making them impossible to use in traditional analysis. Our results show that the proposed iterative imputation with a bag of elastic net regressions imputes missing data better than traditional imputation methods and allow for the best prediction result. We investigate imputation and prediction performance change when using jointly data from multiple studies, where each study has a different bias and missing rate. Leveraging joint dataset allows for predicting the therapy outcome in longitudinal studies with few patients. Additionally, we can now impute or predict features and diagnoses not reported by the clinical study. In conducted experiments, pooling data from nine different studies resulted in 9.3% smaller imputation and 33% lower prediction errors, respectively. Results have higher confidence than when studies are considered separately. We also explored the performance of imputation and prediction in the domain adaptation case of withdrawn patients, in which 50% improvement is obtained when data from all studies are used to impute and train the model.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134638302","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

Predicting MXene Properties via Machine Learning 通过机器学习预测MXene属性

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00278

Eric W. Vertina, N. Deskins, Emily Sutherland, Oren Mangoubi

{"title":"Predicting MXene Properties via Machine Learning","authors":"Eric W. Vertina, N. Deskins, Emily Sutherland, Oren Mangoubi","doi":"10.1109/ICMLA55696.2022.00278","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00278","url":null,"abstract":"MXenes are a recently discovered class of 2-D materials which possess a diverse set of electrical, chemical, and physical properties, and have a wide range of applications, including batteries, photovoltaics, and chemical sensors. The properties of a given MXene are determined by its chemical composition, and there are likely an infinite number of possible MXenes. Unfortunately, each MXene is costly and time-consuming to synthesize, and there is a need for machine learning (ML) models which can accurately predict MXene properties and guide synthesis of MXenes with desirable properties. To address this issue, we created interpretable ML models that accurately predict the following MXene properties which have not been previously predicted with ML: Work Function, Fermi Level, Heat of Formation, Density of States at Fermi Level (Density of States), and whether a MXene is magnetic. Our model predicts these properties for novel MXenes which have yet to be synthesized in the lab, and does so using only standard elemental information of the constituent atoms of a given MXene material as input. To train our model, we used experimental data from MXenes synthesized in the lab in previous works and data computed using Density Functional Theory (DFT). To create our model, we first applied Sparse Principal Components Analysis (SPCA) to reduce model dimension while preserving the interpretability of features. Then, Random Forest and XGBoost models were created to predict the specified MXene target properties and to output a feature importance score for input features. XGBoost models had the lowest root-mean-squared-error (RMSE) for each target property, with RMSE values as follows: Work Function, 0.308 J; Heat of Formation, 0.128 eV/atom; Fermi Level, 0.46 eV; Density of States, 1.984 eV−1.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134100349","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