2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第8页

Towards Intelligent Reading through Multimodal and Contextualized Word LookUp 通过多模态和语境化查词实现智能阅读

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00203

Swetha Govindu, Raviteja Vidya Guttula, Swati Kohli, Poonam Patil, Anagha Kulkarni, Ilmi Yoon

引用次数: 1

Improve Learner-based Recommender System with Learner’s Mood in Online Learning Platform 基于学习者情绪的在线学习平台学习者推荐系统的改进

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00271

Qing Tang, Marie-Hélène Abel, E. Negre

引用次数: 0

Data-Driven State of Charge Estimation of Li-ion Batteries using Supervised Machine Learning Methods 基于监督机器学习方法的锂离子电池充电状态估计

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00144

Yichun Li, Mina Maleki, Shadi Banitaan, Ming-Jie Chen

{"title":"Data-Driven State of Charge Estimation of Li-ion Batteries using Supervised Machine Learning Methods","authors":"Yichun Li, Mina Maleki, Shadi Banitaan, Ming-Jie Chen","doi":"10.1109/ICMLA52953.2021.00144","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00144","url":null,"abstract":"Recently, electrical vehicles (EVs) have attracted considerable attention from researchers due to the transition of the transportation industry and the increasing demand in the clean energy domain. State of charge (SOC) of Li-ion batteries has a significant role in improving the efficiency, performance, and reliability of EVs. Estimating the SOC of the Li-ion battery cannot be done directly from inner measurements due to the complex and dynamic nature of these kinds of batteries. Several data-driven approaches have recently been used to estimate the SOC of Li-ion batteries, benefiting from the availability of battery data and hardware computing capacity. However, selecting the discriminative features and best supervised machine learning (ML) models for accurate battery states estimation is still challenging. Thus, this paper investigates the effect of different ML models and extracted input features of Li-ion batteries, including Electrochemical Impedance Spectroscopy (EIS) and multi-channel feature set on the SOC prediction. The results on the public Panasonic dataset indicate that using EIS feature set as an input to the deep neural network (DNN) model is more efficient than the multi-channel feature set. Moreover, the DNN model outperforms the Gaussian process regression (GPR) model in terms of the mean squared error, mean absolute error, and root mean squared error rates for the SOC prediction.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"873-878"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86230746","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: 6

Purrai: A Deep Neural Network based Approach to Interpret Domestic Cat Language Purrai:一种基于深度神经网络的方法来解释家猫的语言

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00104

Weilin Sun, V. Lu, Aaron Truong, Hermione Bossolina, Yuan Lu

{"title":"Purrai: A Deep Neural Network based Approach to Interpret Domestic Cat Language","authors":"Weilin Sun, V. Lu, Aaron Truong, Hermione Bossolina, Yuan Lu","doi":"10.1109/ICMLA52953.2021.00104","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00104","url":null,"abstract":"Being able to understand and communicate with domestic cats has always been fascinating to humans, although it is considered a difficult task even for phonetics experts. In this paper, we present our approach to this problem: Purrai, a neural-network-based machine learning platform to interpret cat’s language. Our framework consists of two parts. First, we build a comprehensively constructed cat voice dataset that is 3.7x larger than any existing public available dataset [1]. To improve accuracy, we also use several techniques to ensure labeling quality, including rule-based labeling, cross validation, cosine distance, and outlier detection, etc. Second, we design a two-stage neural network structure to interpret what cats express in the context of multiple sounds called sentences. The first stage is a modification of Google’s Vggish architecture [2] [3], which is a Convolutional Neural Network (CNN) architecture that focuses on the classification of nine primary cat sounds. The second stage takes the probability outputs of a sequence of sound classifications from the first stage and determines the emotional meaning of a cat sentence. Our first stage architecture generates a top-l and top-2 accuracy of 74.1% and 92.1%, better than that of the state-of-the-art approach: 64.9% and 83.4% [4]. Our sentence-based AI model achieves an accuracy of 81.1% for emotion prediction.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"11 1","pages":"622-627"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82592225","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

Tiny Generative Image Compression for Bandwidth-Constrained Sensor Applications 用于带宽受限传感器应用的微小生成图像压缩

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00094

Nikolai Körber, A. Siebert, S. Hauke, Daniel Mueller-Gritschneder

{"title":"Tiny Generative Image Compression for Bandwidth-Constrained Sensor Applications","authors":"Nikolai Körber, A. Siebert, S. Hauke, Daniel Mueller-Gritschneder","doi":"10.1109/ICMLA52953.2021.00094","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00094","url":null,"abstract":"Deep image compression algorithms based on Generative Adversarial Networks (GANs) are a promising direction to address the strict communication bandwidth limitations commonly encountered in IoT sensor networks (e.g. Low Power Wide Area Networks). However, current methods do not consider that the sensor nodes, which perform the image encoding, usually only offer very limited computation and memory capabilities, e.g. a resource-constrained tiny device such as a micro-controller. In this paper, we propose the first tiny generative image compression method specifically designed for image compression on micro-controllers. We base our encoder on the well-known MobileNetV2 network architecture, while keeping the decoder side fixed. To cope with the resulting asymmetric design of the compression pipeline, we investigate the impact of different training strategies (end-to-end, knowledge distillation) and integer quantization techniques (post-training, quantization-aware training) on the GAN-training stability. On the Cityscapes dataset, we achieve a compression performance that is very close to the state-of-the-art, while requiring 99% less SRAM size, 97% smaller flash storage and 87% less multiply-add operations. Our findings suggest that tiny generative image compression is particularly well suited for application-specific domains.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"84 1","pages":"564-569"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89856831","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

Self-Attention Mechanism in GANs for Molecule Generation gan分子生成的自注意机制

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00017

S. Chinnareddy, Pranav Grandhi, Apurva Narayan

引用次数: 2

Con Connections: Detecting Fraud from Abstracts using Topological Data Analysis 连接:利用拓扑数据分析从摘要中检测欺诈

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00069

Sarah Tymochko, Julien Chaput, T. Doster, Emilie Purvine, Jackson Warley, T. Emerson

{"title":"Con Connections: Detecting Fraud from Abstracts using Topological Data Analysis","authors":"Sarah Tymochko, Julien Chaput, T. Doster, Emilie Purvine, Jackson Warley, T. Emerson","doi":"10.1109/ICMLA52953.2021.00069","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00069","url":null,"abstract":"In this paper we present a novel approach for identifying fraudulent papers from their titles and abstracts. The premise of the approach is that there are holes in the presentation of the approach and findings of fraudulent research papers. As an abstract is intended to highlight key features of the approach as well as important conclusions the authors seek to determine if the assumed existence of holes can be identified from analysis of abstracts alone. The data set considered is derived from papers sharing a single author with labels determined based on a formal linguistic analysis of the complete documents. To detect these logical and literary holes we utilize techniques from topological data analysis which summarizes data based on the presence of multi-dimensional, topological holes. We find that, in fact, topological features derived through a combination of techniques in natural language processing and time-series analysis allow for superior detection of the fraudulent papers than the natural language processing tools alone. Thus we conclude that the connections and holes present in the abstracts of research cons contributes to an ability to infer the scientific validity of the corresponding work.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"92 1","pages":"403-408"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74474223","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

Guided-Generative Network for noise detection in Monte-Carlo rendering 蒙特卡罗渲染中噪声检测的导向生成网络

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00018

Jérôme Buisine, F. Teytaud, S. Delepoulle, C. Renaud

{"title":"Guided-Generative Network for noise detection in Monte-Carlo rendering","authors":"Jérôme Buisine, F. Teytaud, S. Delepoulle, C. Renaud","doi":"10.1109/ICMLA52953.2021.00018","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00018","url":null,"abstract":"Estimating the features to be extracted from an image for classification tasks are sometimes difficult, especially if images are related to a particular kind of noise. The aim of this paper is to propose a neural network architecture named Guided-Generative Network (GGN) to extract refined information that allows to correctly quantify the noise present in a sliding window of images. GNN tends to find the desired features to address such a problem in order to emit a detection criterion of this noise. The proposed GGN is applied on photorealistic images which are rendered by Monte-Carlo methods by evaluating a large number of samples per pixel. An insufficient number of samples per pixel tends to result in residual noise which is very noticeable to humans. This noise can be reduced by increasing the number of samples, as proven by Monte-Carlo theory, but this involves considerable computational time. Finding the right number of samples needed for human observers to perceive no noise is still an open problem. The results obtained show that GGN can correctly solve the problem without prior knowledge of the noise while being competitive with existing methods.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"17 1","pages":"61-66"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79267064","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

Perceptually Constrained Fast Adversarial Audio Attacks 感知约束快速对抗性音频攻击

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00135

Jason Henry, Mehmet Ergezer, M. Orescanin

{"title":"Perceptually Constrained Fast Adversarial Audio Attacks","authors":"Jason Henry, Mehmet Ergezer, M. Orescanin","doi":"10.1109/ICMLA52953.2021.00135","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00135","url":null,"abstract":"Audio adversarial attacks on deep learning models are of great interest given the commercial success and proliferation of these technologies. These types of attacks have been successfully demonstrated, however, artifacts introduced in the adversarial audio are easily detectable by a human observer. In this work, an expansion of the fast audio adversarial perturbation framework is proposed that can produce an adversarial attack that is imperceptible to a human observer in near-real time using black-box attacks. This is achieved by proposing a perceptually motivated penalty function. We propose a perceptual fast audio adversarial perturbation generator (PFAPG) that employs a loudness constrained loss function, in lieu of a conventional L-2 norm, between the adversarial example and original audio signal. We compare the performance of PFAPG against the conventional constraint based on the MSE on three audio recognition datasets: speaker recognition, speech command, and the Ryerson audiovisual database of emotional speech and song. Our results indicate that, on average, PFAPG equipped with the loudness-constrained loss function yields a 11% higher success rate, while reducing the undesirable distortion artifacts in adversarial audio by 10% dB compared to the prevalent MSE constraints.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"3 1","pages":"819-824"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75289722","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

Argue to Learn: Accelerated Argumentation-Based Learning

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00183

H. Ayoobi, M. Cao, R. Verbrugge, B. Verheij

引用次数: 6