2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第7页

Knowledge Guided Two-player Reinforcement Learning for Cyber Attacks and Defenses 面向网络攻击与防御的知识引导双人强化学习

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00213

Aritran Piplai, M. Anoruo, Kayode Fasaye, A. Joshi, Timothy W. Finin, Ahmad Ridley

引用次数: 6

Multi-Learning Generalised Low-Rank Models 多学习广义低秩模型

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00142

Francois Buet-Golfouse, Parth Pahwa

引用次数: 0

Exploring Edge Machine Learning-based Stress Prediction using Wearable Devices 利用可穿戴设备探索基于边缘机器学习的应力预测

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00203

Sang-Hun Sim, Tara Paranjpe, Nicole Roberts, Ming Zhao

{"title":"Exploring Edge Machine Learning-based Stress Prediction using Wearable Devices","authors":"Sang-Hun Sim, Tara Paranjpe, Nicole Roberts, Ming Zhao","doi":"10.1109/ICMLA55696.2022.00203","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00203","url":null,"abstract":"Stress is a central factor in our daily lives, impacting performance, decisions, well-being, and our interactions with others. With the development of IoT technology, smart wearable devices can handle diverse operations, including networking and recording biometric signals. The enhanced data processing capability of wearables has also allowed for increased stress awareness among users. Edge computing on such devices enables real-time feedback which can provide an opportunity to prevent severe consequences that might result if stress is left unaddressed. Edge computing can also strengthen privacy by implementing stress prediction on local devices without transferring personal information to the public cloud.This paper presents a framework for real-time stress prediction, specifically for police training cadets, using wearable devices and machine learning with support from cloud computing. We developed an application for Fitbit and the user's accompanying smartphone to collect heart rate fluctuations and corresponding stress levels entered by users and a cloud backend for storing data and training models. Real-world data for this study was collected from police cadets during a police academy training program. Machine learning classifiers for stress prediction were built using this data through classic machine learning models and neural networks. To analyze efficiency across different environments, the models were optimized using model compression and other relevant techniques and tested on cloud and edge environments. Evaluation using real data and real devices showed that the highest accuracy came from XGBoost and Tensorflow neural network models, and on-edge stress prediction models produced lower latency results than in-cloud prediction.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"22 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":"121257224","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

Deeper Bidirectional Neural Networks with Generalized Non-Vanishing Hidden Neurons 具有广义不消失隐藏神经元的深层双向神经网络

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00017

Olaoluwa Adigun, B. Kosko

引用次数: 0

Data-Efficient Automatic Model Selection in Unsupervised Anomaly Detection 无监督异常检测中的数据高效自动模型选择

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00227

Gautham Krishna Gudur, Raaghul R, Adithya K, Shrihari Vasudevan

引用次数: 0

Simulating New and Old Twitter User Activity with XGBoost and Probabilistic Hybrid Models 用XGBoost和概率混合模型模拟新旧Twitter用户活动

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00026

Frederick Mubang, Lawrence O. Hall

{"title":"Simulating New and Old Twitter User Activity with XGBoost and Probabilistic Hybrid Models","authors":"Frederick Mubang, Lawrence O. Hall","doi":"10.1109/ICMLA55696.2022.00026","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00026","url":null,"abstract":"The Volume Audience Match Simulator is an end-to-end approach for predicting user-to-user interactions on a given social media platform. It is comprised of 2 components: firstly, an XGBoost-driven volume prediction module that predicts the number of: (1) total activities, (2) active old users, and (3) newly active users over the span of 24 hours from the start time of prediction. Secondly, VAM contains a User-Assignment Module that takes as input the volume predictions and predicts the user-to-user interactions of the old and new users.In previous work, VAM has been used to predict Twitter discussions related to political crises. In this work, VAM was used to predict future activity on Twitter related to international economic affairs. We include more experiments and analyses than previous work performed with VAM. In this work, VAM is used to predict all types of retweets, including quotes and replies, unlike previous work, which only focused on regular retweets. Furthermore, we show that YouTube features, in addition to Reddit features can improve prediction performance. We examine the importance of the time series features used in VAM’s Volume Prediction module. Lastly, we show that VAM’s performance is significantly more accurate than other approaches when predicting highly-skewed, lowly-skewed, highly-sparse, and lowly-sparse time series.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"17 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":"116804837","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

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00029

Ana Carolina Rodrigues, R. Marcacini

{"title":"Sentence Similarity Recognition in Portuguese from Multiple Embedding Models","authors":"Ana Carolina Rodrigues, R. Marcacini","doi":"10.1109/ICMLA55696.2022.00029","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00029","url":null,"abstract":"Distinct pre-trained embedding models perform differently in sentence similarity recognition tasks. The current assumption is that they encode different features due to differences in algorithm design and characteristics of the datasets employed in the pre-trained process. The perspective of benefiting from different encoded features to generate more suitable representations motivated the assembly of multiple embedding models, so-called meta-embedding. Meta-embedding methods combine different pre-trained embedding models to perform a task. Recently, multiple pre-trained language representations derived from Transformers architecture-based systems have been shown to be effective in many downstream tasks. This paper introduces a supervised meta-embedding neural network to combine contextualized pre-trained models for sentence similarity recognition in Portuguese. Our results show that combining multiple sentence pre-trained embedding models outperforms single models and can be a promising alternative to improve performance sentence similarity. Moreover, we also discuss the results considering our simple extension of a model explainability method to the meta-embedding context, allowing the visual identification of the impact of each token on the sentence similarity score.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"37 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":"115512781","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 Task-independent Joint Control for Robotic Manipulators with Reinforcement Learning and Curriculum Learning 基于强化学习和课程学习的机械臂学习任务独立联合控制

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00201

Lars Væhrens, D. D. Álvarez, U. Berger, Simon Boegh

{"title":"Learning Task-independent Joint Control for Robotic Manipulators with Reinforcement Learning and Curriculum Learning","authors":"Lars Væhrens, D. D. Álvarez, U. Berger, Simon Boegh","doi":"10.1109/ICMLA55696.2022.00201","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00201","url":null,"abstract":"We present a deep reinforcement learning-based approach to control robotic manipulators and construct task-independent trajectories for point-to-point motions. The research objective in this work is to learn control in the joint action space, which can be generalized to various industrial manipulators. The approach necessitates that the neural network learns a mapping from joint movements to the reward landscape determined by the distance to the goal and nearby obstacles. In addition, curriculum learning is embedded in this approach to facilitate learning by reducing the complexity of the environment. Conducted experiments demonstrate how the reinforcement learning-based approach can be applied to three different industrial manipulators in simulation with minimal configuration changes. The results of our contribution demonstrate that a model can be trained in a simulation environment, transferred to the real world, and used in complex environments. Furthermore, the Sim2Real transfer, augmented by curriculum learning, highlights that the robots behave in the same way in the real world as in the simulation and that the operations in the real world are safe from a control and trajectory point-of-view.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"19 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":"116249232","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

Interpretability of ReLU for Inversion ReLU在反演中的可解释性

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00192

Boaz Ilan, A. Ranganath, Jacqueline Alvarez, Shilpa Khatri, Roummel F. Marcia

引用次数: 0

Are Post-Hoc Explanation Methods for Prostate Lesion Detection Effective for Radiology End Use? 前列腺病变检测的事后解释方法对放射学最终用途有效吗?

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00191

Mehmet Akif Gulum, Christopher M. Trombley, M. Ozen, M. Kantardzic

{"title":"Are Post-Hoc Explanation Methods for Prostate Lesion Detection Effective for Radiology End Use?","authors":"Mehmet Akif Gulum, Christopher M. Trombley, M. Ozen, M. Kantardzic","doi":"10.1109/ICMLA55696.2022.00191","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00191","url":null,"abstract":"Deep learning has demonstrated impressive performance for medical tasks such as cancer classification and lesion detection. While it has achieved impressive performance, it is a black-box algorithm and therefore is difficult to interpret. Interpretation is especially important in fields that are high-risk in nature such as the medical field. There recently has been various methods proposed to interpret deep learning algorithms. However, there are limited studies evaluating these explanation methods in clinical settings such as radiology. To that end, we conduct a pilot study that evaluates the effectiveness of explanation methods for radiology end use. We evaluate if explanation methods improve diagnosis performance and what method is preferred by radiologists. We also glean insight into what characteristics radiologists deem explainable. We found that explanation methods increase diagnosis performance however it is dependent on the individual method. We also find that the radiology cohort deem the themes insight, visualization, and accuracy to be the most sought after explainable characteristics. The insights garnered in this study have the potential to guide future developments and studies of explanation methods for clinical use.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"156 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":"126718294","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