Adv. Artif. Intell. Mach. Learn.最新文献_第2页

Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding 以室内场景理解为重点的6D物体姿态估计研究综述

Adv. Artif. Intell. Mach. Learn. Pub Date : 2022-12-04 DOI: 10.54364/AAIML.2022.1141

Negar Nejatishahidin, Pooya Fayyazsanavi

引用次数: 0

Evaluation of Explanation Methods of AI - CNNs in Image Classification Tasks with Reference-based and No-reference Metrics 基于参考和无参考指标的图像分类任务中AI - cnn的解释方法评价

Adv. Artif. Intell. Mach. Learn. Pub Date : 2022-12-02 DOI: 10.54364/AAIML.2023.1143

A. Zhukov, J. Benois-Pineau, R. Giot

{"title":"Evaluation of Explanation Methods of AI - CNNs in Image Classification Tasks with Reference-based and No-reference Metrics","authors":"A. Zhukov, J. Benois-Pineau, R. Giot","doi":"10.54364/AAIML.2023.1143","DOIUrl":"https://doi.org/10.54364/AAIML.2023.1143","url":null,"abstract":"The most popular methods in AI-machine learning paradigm are mainly black boxes. This is why explanation of AI decisions is of emergency. Although dedicated explanation tools have been massively developed, the evaluation of their quality remains an open research question. In this paper, we generalize the methodologies of evaluation of post-hoc explainers of CNNs’ decisions in visual classification tasks with reference and no-reference based metrics. We apply them on our previously developed explainers (FEM1 , MLFEM), and popular Grad-CAM. The reference-based metrics are Pearson correlation coefficient and Similarity computed between the explanation map and its ground truth represented by a Gaze Fixation Density Map obtained with a psycho-visual experiment. As a no-reference metric, we use stability metric, proposed by Alvarez-Melis and Jaakkola. We study its behaviour, consensus with reference-based metrics and show that in case of several kinds of degradation on input images, this metric is in agreement with reference-based ones. Therefore, it can be used for evaluation of the quality of explainers when the ground truth is not available.","PeriodicalId":373878,"journal":{"name":"Adv. Artif. Intell. Mach. Learn.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115064482","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

Parametric PDF for Goodness of Fit 参数PDF的拟合优度

Adv. Artif. Intell. Mach. Learn. Pub Date : 2022-10-25 DOI: 10.48550/arXiv.2210.14005

N. Katz, Uri Itai

引用次数: 0

The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system 光纤阵列激光发射系统自适应功率光束的人工智能自学习控制器

Adv. Artif. Intell. Mach. Learn. Pub Date : 2022-04-08 DOI: 10.54364/AAIML.2023.1148

A. Vorontsov, G. A. Filimonov

{"title":"The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system","authors":"A. Vorontsov, G. A. Filimonov","doi":"10.54364/AAIML.2023.1148","DOIUrl":"https://doi.org/10.54364/AAIML.2023.1148","url":null,"abstract":"In this study we consider adaptive power beaming with a fiber-array laser transmitter system in presence of atmospheric turbulence. For optimization of power transition through the atmosphere a fiber-array is traditionally controlled by stochastic parallel gradient descent (SPGD) algorithm where control feedback is provided via a radio frequency link by an optical-to-electrical power conversion sensor, attached to a cooperative target. The SPGD algorithm continuously and randomly perturbs voltages applied to fiber-array phase shifters and fiber tip positioners in order to maximize sensor signal, i.e. uses, the so-called, “blind” optimization principle. By contrast to this approach a prospective artificially intelligent (AI) control systems for synthesis of optimal control can utilize various pupil- or target-plane data available for the analysis including wavefront sensor data, photo-voltaic array (PVA) data, other optical or atmospheric parameters, and potentially can eliminate well-known drawbacks of SPGDbased controllers. In this study an optimal control is synthesized by a deep neural network (DNN) using target-plane PVA sensor data as its input. A DNN training is occurred online in sync with control system operation and is performed by applying of small perturbations to DNN’s outputs. This approach does not require initial DNN’s pre-training as well as guarantees optimization of system performance in time. All theoretical results are verified by numerical experiments.","PeriodicalId":373878,"journal":{"name":"Adv. Artif. Intell. Mach. Learn.","volume":"17 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131845307","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

Autonomous Vehicles: Open-Source Technologies, Considerations, and Development 自动驾驶汽车:开源技术、考虑和发展

Adv. Artif. Intell. Mach. Learn. Pub Date : 2022-01-25 DOI: 10.54364/aaiml.2023.1145

Oussama Saoudi, Ishwar Singh, H. Mahyar

{"title":"Autonomous Vehicles: Open-Source Technologies, Considerations, and Development","authors":"Oussama Saoudi, Ishwar Singh, H. Mahyar","doi":"10.54364/aaiml.2023.1145","DOIUrl":"https://doi.org/10.54364/aaiml.2023.1145","url":null,"abstract":"Autonomous vehicles are the culmination of advances in many areas such as sensor technologies, artificial intelligence (AI), networking, and more. This paper will introduce the reader to the technologies that build autonomous vehicles. It will focus on open-source tools and libraries for autonomous vehicle development, making it cheaper and easier for developers and researchers to participate in the field. The topics covered are as follows. First, we will discuss the sensors used in autonomous vehicles and summarize their performance in different environments, costs, and unique features. Then we will cover Simultaneous Localization and Mapping (SLAM) and algorithms for each modality. Third, we will review popular open-source driving simulators, a cost-effective way to train machine learning models and test vehicle software performance. We will then highlight embedded operating systems and the security and development considerations when choosing one. After that, we will discuss Vehicle-to-Vehicle (V2V) and Internet-of-Vehicle (IoV) communication, which are areas that fuse networking technologies with autonomous vehicles to extend their functionality. We will then review the five levels of vehicle automation, commercial and open-source Advanced Driving Assistance Systems, and their features. Finally, we will touch on the major manufacturing and software companies involved in the field, their investments, and their partnerships. These topics will give the reader an understanding of the industry, its technologies, active research, and the tools available for developers to build autonomous vehicles.","PeriodicalId":373878,"journal":{"name":"Adv. Artif. Intell. Mach. Learn.","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124667954","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

Biased Hypothesis Formation From Projection Pursuit 从投射追踪中形成有偏差的假设

Adv. Artif. Intell. Mach. Learn. Pub Date : 2022-01-03 DOI: 10.54364/aaiml.2021.1114

John Patterson, Chris S. Avery, Tyler Grear, D. Jacobs

{"title":"Biased Hypothesis Formation From Projection Pursuit","authors":"John Patterson, Chris S. Avery, Tyler Grear, D. Jacobs","doi":"10.54364/aaiml.2021.1114","DOIUrl":"https://doi.org/10.54364/aaiml.2021.1114","url":null,"abstract":"The effect of bias on hypothesis formation is characterized for an automated data-driven projection pursuit neural network to extract and select features for binary classification of data streams. This intelligent exploratory process partitions a complete vector state space into disjoint subspaces to create working hypotheses quantified by similarities and differences observed between two groups of labeled data streams. Data streams are typically time sequenced, and may exhibit complex spatio-temporal patterns. For example, given atomic trajectories from molecular dynamics simulation, the machine's task is to quantify dynamical mechanisms that promote function by comparing protein mutants, some known to function while others are nonfunctional. Utilizing synthetic two-dimensional molecules that mimic the dynamics of functional and nonfunctional proteins, biases are identified and controlled in both the machine learning model and selected training data under different contexts. The refinement of a working hypothesis converges to a statistically robust multivariate perception of the data based on a context-dependent perspective. Including diverse perspectives during data exploration enhances interpretability of the multivariate characterization of similarities and differences.","PeriodicalId":373878,"journal":{"name":"Adv. Artif. Intell. Mach. Learn.","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123288648","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

Using Machine Learning to Identify At-risk Students in an Introductory Programming Course at a Two-year Public College 在两年制公立大学的入门编程课程中，使用机器学习来识别有风险的学生

Adv. Artif. Intell. Mach. Learn. Pub Date : 2021-11-19 DOI: 10.21203/rs.3.rs-1096817/v1

C. Cooper

引用次数: 3

Competency Model Approach to AI Literacy: Research-based Path from Initial Framework to Model 人工智能素养的胜任力模型方法:从初始框架到模型的研究路径

Adv. Artif. Intell. Mach. Learn. Pub Date : 2021-08-12 DOI: 10.54364/aaiml.2022.1140

Farhana Faruqe, Ryan Watkins, L. Medsker

引用次数: 5

A Survey on Trust Metrics for Autonomous Robotic Systems 自主机器人系统信任指标研究

Adv. Artif. Intell. Mach. Learn. Pub Date : 2021-06-28 DOI: 10.54364/AAIML.2023.1155

Vincenzo DiLuoffo, W. Michalson

引用次数: 0

Exoskeleton-Based Multimodal Action and Movement Recognition: Identifying and Developing the Optimal Boosted Learning Approach 基于外骨骼的多模态动作和运动识别:识别和开发最佳的增强学习方法

Adv. Artif. Intell. Mach. Learn. Pub Date : 2021-06-18 DOI: 10.54364/aaiml.2021.1104

Nirmalya Thakur, C. Han

{"title":"Exoskeleton-Based Multimodal Action and Movement Recognition: Identifying and Developing the Optimal Boosted Learning Approach","authors":"Nirmalya Thakur, C. Han","doi":"10.54364/aaiml.2021.1104","DOIUrl":"https://doi.org/10.54364/aaiml.2021.1104","url":null,"abstract":"This paper makes two scientific contributions to the field of exoskeleton-based action and movement recognition. First, it presents a novel machine learning and pattern recognition-based framework that can detect a wide range of actions and movements - walking, walking upstairs, walking downstairs, sitting, standing, lying, stand to sit, sit to stand, sit to lie, lie to sit, stand to lie, and lie to stand, with an overall accuracy of 82.63%. Second, it presents a comprehensive comparative study of different learning approaches - Random Forest, Artificial Neural Network, Decision Tree, Multiway Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Decision Stump, AutoMLP, Linear Regression, Vector Linear Regression, Random Tree, Naïve Bayes, Naïve Bayes (Kernel), Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Deep Learning applied to this framework. The performance of each of these learning approaches was boosted by using the AdaBoost algorithm, and the Cross Validation approach was used for training and testing. The results show that in boosted form, the k-NN classifier outperforms all the other boosted learning approaches and is, therefore, the optimal learning method for this purpose. The results presented and discussed uphold the importance of this work to contribute towards augmenting the abilities of exoskeleton-based assisted and independent living of the elderly in the future of Internet of Things-based living environments, such as Smart Homes. As a specific use case, we also discuss how the findings of our work are relevant for augmenting the capabilities of the Hybrid Assistive Limb exoskeleton, a highly functional lower limb exoskeleton.","PeriodicalId":373878,"journal":{"name":"Adv. Artif. Intell. Mach. Learn.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127440875","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: 4