Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00062
J. Schubert, U. W. Bolin
In this paper, we expand a methodology for horizon scanning of scientific literature to discover scientific trends. In this methodology, scientific articles are automatically clustered within a broadly defined field of research based on the topic. We develop a new method to allow an analyst to handle the large number of clusters that result from the automatic clustering of articles. The method is based on estimating an information-theoretical distance between all possible pairs of clusters. Each of the scientific articles has a probability distribution of affiliation over all possible clusters arising from the clustering process. Using these, we investigate possible pairwise mergers between all pairs of existing clusters and calculate the entropies of the probability distributions of all articles after each possible merger of two clusters. These entropies are visualized in a dendritic tree and a cluster graph. The merger with minimal total entropy is the proposed cluster pair to be merged.
{"title":"Cluster Management of Scientific Literature in HSTOOL","authors":"J. Schubert, U. W. Bolin","doi":"10.1109/ICMLA55696.2022.00062","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00062","url":null,"abstract":"In this paper, we expand a methodology for horizon scanning of scientific literature to discover scientific trends. In this methodology, scientific articles are automatically clustered within a broadly defined field of research based on the topic. We develop a new method to allow an analyst to handle the large number of clusters that result from the automatic clustering of articles. The method is based on estimating an information-theoretical distance between all possible pairs of clusters. Each of the scientific articles has a probability distribution of affiliation over all possible clusters arising from the clustering process. Using these, we investigate possible pairwise mergers between all pairs of existing clusters and calculate the entropies of the probability distributions of all articles after each possible merger of two clusters. These entropies are visualized in a dendritic tree and a cluster graph. The merger with minimal total entropy is the proposed cluster pair to be merged.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"11 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":"127851369","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}
Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00126
Hao Niu, H. Ung, Shinya Wada
Human activity recognition using ambient sensors has become particularly important due to social demands of applications in smart homes. To address the problem of labeling sensing data for every individual house, cross-house human activity recognition is proposed to use available labeled houses (source domains) to train recognition models for applying to unlabeled houses (target domains). In this paper, we propose a method of source domain selection for cross-house human activity recognition. We first improve the method for representing semantic relationships of sensors. To select the best similar source houses for a target house, we then propose a method for calculating similarity score between two houses. Using 19 houses of the CASAS dataset, we evaluate the recognition performance in target houses using models trained by several similar source houses, randomly selected houses, dissimilar source houses, and all source houses without selection. Experimental results illustrate that the average accuracy of models trained from the small number of the best similar houses achieve the best performance, and thus they confirm the effectiveness of our proposed method.
{"title":"Source Domain Selection for Cross-House Human Activity Recognition with Ambient Sensors","authors":"Hao Niu, H. Ung, Shinya Wada","doi":"10.1109/ICMLA55696.2022.00126","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00126","url":null,"abstract":"Human activity recognition using ambient sensors has become particularly important due to social demands of applications in smart homes. To address the problem of labeling sensing data for every individual house, cross-house human activity recognition is proposed to use available labeled houses (source domains) to train recognition models for applying to unlabeled houses (target domains). In this paper, we propose a method of source domain selection for cross-house human activity recognition. We first improve the method for representing semantic relationships of sensors. To select the best similar source houses for a target house, we then propose a method for calculating similarity score between two houses. Using 19 houses of the CASAS dataset, we evaluate the recognition performance in target houses using models trained by several similar source houses, randomly selected houses, dissimilar source houses, and all source houses without selection. Experimental results illustrate that the average accuracy of models trained from the small number of the best similar houses achieve the best performance, and thus they confirm the effectiveness of our proposed method.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"91 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":"124655825","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}
Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00248
Khensa Daoudi, Maroua Yamami, S. Benferhat, Lila Méziani
The representation and combination of imprecise information is an important topic present in many applications. This paper first deals with the representation of imprecise positions of objects detected from maps and images of urban networks. In particular, it deals with the question of the combination of uncertain information, from different sources, to address the problem of inaccuracies related to the geographical coordinates of the detected objects. To illustrate the representation and the combination modes presented in this paper, we focus on wastewater networks data. More precisely, we use the manhole detection problem as an example of object detection in our study. We will use two sources of data: i) the images obtained from the google street view utility and ii) the maps of the sanitation networks. As the geographical positions of the detected objects are imprecise, we will use possibility theory to represent this uncertainty. Possibility theory is particularly suitable for representing qualitative uncertainty, where only the plausibility relation (between the different geographical positions that are candidates to be the actual position of the manholes) is important. Finally, we propose to use two aggregation modes, conjunctive and disjunctive modes, to combine the possibility distributions associated with the detected objects.
{"title":"Managing imprecise map and image data in a possibility theory framework","authors":"Khensa Daoudi, Maroua Yamami, S. Benferhat, Lila Méziani","doi":"10.1109/ICMLA55696.2022.00248","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00248","url":null,"abstract":"The representation and combination of imprecise information is an important topic present in many applications. This paper first deals with the representation of imprecise positions of objects detected from maps and images of urban networks. In particular, it deals with the question of the combination of uncertain information, from different sources, to address the problem of inaccuracies related to the geographical coordinates of the detected objects. To illustrate the representation and the combination modes presented in this paper, we focus on wastewater networks data. More precisely, we use the manhole detection problem as an example of object detection in our study. We will use two sources of data: i) the images obtained from the google street view utility and ii) the maps of the sanitation networks. As the geographical positions of the detected objects are imprecise, we will use possibility theory to represent this uncertainty. Possibility theory is particularly suitable for representing qualitative uncertainty, where only the plausibility relation (between the different geographical positions that are candidates to be the actual position of the manholes) is important. Finally, we propose to use two aggregation modes, conjunctive and disjunctive modes, to combine the possibility distributions associated with the detected objects.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"62 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":"132472996","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}
Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00236
Mohammed Terry-Jack, N. Rozanov
We introduce a novel zero-shot learning (ZSL) method, known as ‘self-alignment training’, and use it to train a vanilla autoencoder which is then evaluated on four prominent ZSL Tasks CUB, SUN, AWA1&2. Despite being a far simpler model than the competition, our method achieved results on par with SOTA. In addition, we also present a novel ‘contrastive-loss’ objective to allow autoencoders to learn from negative samples. In particular, we achieve new SOTA of 64.5 on AWA2 for Generalised ZSL and a new SOTA for standard ZSL of 47.7 on SUN. The code is publicly accessible on https://github.com/Wluper/satae.
{"title":"Connecting the Semantic Dots: Zero-shot Learning with Self-Aligning Autoencoders and a New Contrastive-Loss for Negative Sampling","authors":"Mohammed Terry-Jack, N. Rozanov","doi":"10.1109/ICMLA55696.2022.00236","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00236","url":null,"abstract":"We introduce a novel zero-shot learning (ZSL) method, known as ‘self-alignment training’, and use it to train a vanilla autoencoder which is then evaluated on four prominent ZSL Tasks CUB, SUN, AWA1&2. Despite being a far simpler model than the competition, our method achieved results on par with SOTA. In addition, we also present a novel ‘contrastive-loss’ objective to allow autoencoders to learn from negative samples. In particular, we achieve new SOTA of 64.5 on AWA2 for Generalised ZSL and a new SOTA for standard ZSL of 47.7 on SUN. The code is publicly accessible on https://github.com/Wluper/satae.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"168 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":"132526147","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}
Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00167
Florian Meissl, F. Eibensteiner, P. Petz, J. Langer
The trend toward the Internet of Things has led to a rapid increase in the amount of data that needs to be processed. Artificial intelligence (AI) can serve as a very helpful tool to extract or compress essential information of data. However, AI places high demands on a system’s hardware. This is not exactly in line with the strengths of embedded systems.This paper combines AI on embedded systems with the not-yet fully explored subject of online handwriting recognition (HWR). The main contribution is the deployment and real-time operation of AI on a microcontroller (MCU). Model architectures using long short-term memory (LSTM) cells and 1D convolutional neural networks (CNNs) are used to process live data from inertial measurement units (IMUs) sensors. The dataset used for training the AI models was recorded with a self-developed prototype. After training, the models are converted and deployed on a MCU. The conversion process includes quantization from a 32-bit floating-point to an 8-bit fixed-point datatype. The TensorFlow Lite Micro (TFLM) framework is used to run inference on the MCU. For predictions in real-time optimizations are applied to the framework, which results in running inference approx. 827 times faster. The optimized AI model implementation is then used to classify handwritten characters using the live data from the IMU sensors. This first approach has shown, that the separation of the symbols is necessary to be able to classify characters from live sensor data with high accuracy.
物联网的趋势导致需要处理的数据量迅速增加。人工智能(AI)可以作为一种非常有用的工具来提取或压缩数据中的重要信息。然而，人工智能对系统的硬件要求很高。这并不完全符合嵌入式系统的优势。本文将嵌入式系统上的人工智能与尚未完全探索的在线手写识别(HWR)相结合。主要贡献是在微控制器(MCU)上部署和实时操作人工智能。使用长短期记忆(LSTM)单元和一维卷积神经网络(cnn)的模型架构来处理来自惯性测量单元(imu)传感器的实时数据。用于训练人工智能模型的数据集是用自主开发的原型记录的。经过训练后，将模型转换并部署在单片机上。转换过程包括从32位浮点到8位定点数据类型的量化。使用TensorFlow Lite Micro (TFLM)框架在MCU上运行推理。对于预测中的实时优化应用于框架，这导致运行推理近似。快了827倍。然后使用优化的AI模型实现使用来自IMU传感器的实时数据对手写字符进行分类。第一种方法表明，符号的分离对于能够从实时传感器数据中对字符进行高精度分类是必要的。
{"title":"Online Handwriting Recognition using LSTM on Microcontroller and IMU Sensors","authors":"Florian Meissl, F. Eibensteiner, P. Petz, J. Langer","doi":"10.1109/ICMLA55696.2022.00167","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00167","url":null,"abstract":"The trend toward the Internet of Things has led to a rapid increase in the amount of data that needs to be processed. Artificial intelligence (AI) can serve as a very helpful tool to extract or compress essential information of data. However, AI places high demands on a system’s hardware. This is not exactly in line with the strengths of embedded systems.This paper combines AI on embedded systems with the not-yet fully explored subject of online handwriting recognition (HWR). The main contribution is the deployment and real-time operation of AI on a microcontroller (MCU). Model architectures using long short-term memory (LSTM) cells and 1D convolutional neural networks (CNNs) are used to process live data from inertial measurement units (IMUs) sensors. The dataset used for training the AI models was recorded with a self-developed prototype. After training, the models are converted and deployed on a MCU. The conversion process includes quantization from a 32-bit floating-point to an 8-bit fixed-point datatype. The TensorFlow Lite Micro (TFLM) framework is used to run inference on the MCU. For predictions in real-time optimizations are applied to the framework, which results in running inference approx. 827 times faster. The optimized AI model implementation is then used to classify handwritten characters using the live data from the IMU sensors. This first approach has shown, that the separation of the symbols is necessary to be able to classify characters from live sensor data with high accuracy.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"20 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":"132043978","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}
Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00113
Chenwei Sun, Martin Trat, Jane Bender, J. Ovtcharova, George Jeppesen, Jan Bär
We propose an unsupervised-learning-based method for anomaly detection and root cause analysis for an industrial press machine. A skip-connected autoencoder with 55% performance improvement measured by reconstruction root mean square error to vanilla variant in average is used to train the collected multivariant time series data in different schemes. We then conduct a stacked evaluation method for both machine- level anomalies with the root cause localization and anomaly on specific cylinder tracks. Both real-world and synthetic anomalies embedded in real data are used for evaluation. The result shows that the multi-models training scheme and the relatively short window length can gain better performance, i.e., fewer anomaly false alarms and misses.
{"title":"Unsupervised Anomaly Detection and Root Cause Analysis for an Industrial Press Machine based on Skip-Connected Autoencoder","authors":"Chenwei Sun, Martin Trat, Jane Bender, J. Ovtcharova, George Jeppesen, Jan Bär","doi":"10.1109/ICMLA55696.2022.00113","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00113","url":null,"abstract":"We propose an unsupervised-learning-based method for anomaly detection and root cause analysis for an industrial press machine. A skip-connected autoencoder with 55% performance improvement measured by reconstruction root mean square error to vanilla variant in average is used to train the collected multivariant time series data in different schemes. We then conduct a stacked evaluation method for both machine- level anomalies with the root cause localization and anomaly on specific cylinder tracks. Both real-world and synthetic anomalies embedded in real data are used for evaluation. The result shows that the multi-models training scheme and the relatively short window length can gain better performance, i.e., fewer anomaly false alarms and misses.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"15 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":"133362627","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}
Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00213
Aritran Piplai, M. Anoruo, Kayode Fasaye, A. Joshi, Timothy W. Finin, Ahmad Ridley
Cyber defense exercises are an important avenue to understand the technical capacity of organizations when faced with cyber-threats. Information derived from these exercises often leads to finding unseen methods to exploit vulnerabilities in an organization. These often lead to better defense mechanisms that can counter previously unknown exploits. With recent developments in cyber battle simulation platforms, we can generate a defense exercise environment and train reinforcement learning (RL) based autonomous agents to attack the system described by the simulated environment. In this paper, we describe a two-player game-based RL environment that simultaneously improves the performance of both the attacker and defender agents. We further accelerate the convergence of the RL agents by guiding them with expert knowledge from Cybersecurity Knowledge Graphs on attack and mitigation steps. We have implemented and integrated our proposed approaches into the CyberBattleSim system.
{"title":"Knowledge Guided Two-player Reinforcement Learning for Cyber Attacks and Defenses","authors":"Aritran Piplai, M. Anoruo, Kayode Fasaye, A. Joshi, Timothy W. Finin, Ahmad Ridley","doi":"10.1109/ICMLA55696.2022.00213","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00213","url":null,"abstract":"Cyber defense exercises are an important avenue to understand the technical capacity of organizations when faced with cyber-threats. Information derived from these exercises often leads to finding unseen methods to exploit vulnerabilities in an organization. These often lead to better defense mechanisms that can counter previously unknown exploits. With recent developments in cyber battle simulation platforms, we can generate a defense exercise environment and train reinforcement learning (RL) based autonomous agents to attack the system described by the simulated environment. In this paper, we describe a two-player game-based RL environment that simultaneously improves the performance of both the attacker and defender agents. We further accelerate the convergence of the RL agents by guiding them with expert knowledge from Cybersecurity Knowledge Graphs on attack and mitigation steps. We have implemented and integrated our proposed approaches into the CyberBattleSim system.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"72 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":"133948510","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}
Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00235
Kavitha Karimbi Mahesh, A. Nishmitha, Gowda Karthik Balgopal, Kausalya K Naik, Mranali Gourish Gaonkar
We present a lexicon-based approach for classifying opinionated social media texts in English and Hindi. The effect of conjunctions, degree modifiers, negations, emojis and emoticons in scoring the intensity of opinion expressed is further explored. Using a manually built Hindi polarity lexicon, we achieve an accuracy of 86.45% in classifying 2,717 Hindi reviews. A real-time analysis on YouTube reviews showed 86% accuracy for English review classification task.
{"title":"Aspect-based Sentiment Analysis of English and Hindi Opinionated Social Media Texts","authors":"Kavitha Karimbi Mahesh, A. Nishmitha, Gowda Karthik Balgopal, Kausalya K Naik, Mranali Gourish Gaonkar","doi":"10.1109/ICMLA55696.2022.00235","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00235","url":null,"abstract":"We present a lexicon-based approach for classifying opinionated social media texts in English and Hindi. The effect of conjunctions, degree modifiers, negations, emojis and emoticons in scoring the intensity of opinion expressed is further explored. Using a manually built Hindi polarity lexicon, we achieve an accuracy of 86.45% in classifying 2,717 Hindi reviews. A real-time analysis on YouTube reviews showed 86% accuracy for English review classification task.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"16 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":"132894863","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}
Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00155
Muhammad Arsalan, Tao Zheng, Avik Santra, V. Issakov
Contactless detection of hand gestures with radar has gained a lot of attention as an intuitive form of human-computer interface. In this paper, we propose an air-writing system, writing of linguistic characters or words in free space by hand gesture movements using a network of milli-meter wave radars. Most of the works reported in the literature are based on deep learning approaches, which in some cases can involve prohibitively large computational/energy costs making them undesirable for edge IoT devices, where energy efficiency is the prime concern. We propose a highly energy-efficient air-writing system using spiking neural networks, where the trajectory of the character created by fine range estimates together with trilateration from a network of radars are recognized and classified by a spiking neural network (SNN). The proposed system achieves a similar level of classification accuracy (98.6%) compared to the state-of-the-art deep learning methods for 15 characters containing 10 alphabets (A to J) and 5 numerals (1 to 5). Additionally, the proposed SNN model is of 3.7 MB in size making it memory efficient in terms of storage. We demonstrated the proposed method in real-time using a network of 60-GHz frequency-modulated continuous wave radar chipset.
{"title":"Contactless Low Power Air-Writing Based on FMCW Radar Networks Using Spiking Neural Networks","authors":"Muhammad Arsalan, Tao Zheng, Avik Santra, V. Issakov","doi":"10.1109/ICMLA55696.2022.00155","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00155","url":null,"abstract":"Contactless detection of hand gestures with radar has gained a lot of attention as an intuitive form of human-computer interface. In this paper, we propose an air-writing system, writing of linguistic characters or words in free space by hand gesture movements using a network of milli-meter wave radars. Most of the works reported in the literature are based on deep learning approaches, which in some cases can involve prohibitively large computational/energy costs making them undesirable for edge IoT devices, where energy efficiency is the prime concern. We propose a highly energy-efficient air-writing system using spiking neural networks, where the trajectory of the character created by fine range estimates together with trilateration from a network of radars are recognized and classified by a spiking neural network (SNN). The proposed system achieves a similar level of classification accuracy (98.6%) compared to the state-of-the-art deep learning methods for 15 characters containing 10 alphabets (A to J) and 5 numerals (1 to 5). Additionally, the proposed SNN model is of 3.7 MB in size making it memory efficient in terms of storage. We demonstrated the proposed method in real-time using a network of 60-GHz frequency-modulated continuous wave radar chipset.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"3 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":"121791344","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}
Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00082
Sandra Wilfling, M. Ebrahimi, Qamar Alfalouji, G. Schweiger, Mina Basirat
Many applications in energy systems require models that represent the non-linear dynamics of the underlying systems. Black-box models with non-linear architecture are suitable candidates for modeling these systems; however, they are computationally expensive and lack interpretability. An inexpensive white-box linear combination learned over a suitable polynomial feature set can result in a high-performing non-linear model that is easier to interpret, validate, and verify against reference models created by the domain experts. This paper proposes a workflow to learn a linear combination of non-linear terms for an engineered polynomial feature set. We firstly detect non-linear dependencies and then attempt to reconstruct them using feature expansion. Afterwards, we select possible predictors with the highest correlation coefficients for predictive regression analysis. We demonstrate how to learn inexpensive yet comprehensible linear combinations of non-linear terms from four datasets. Experimental evaluations show our workflow yields improvements in the metrics R2, CV-RMSE and MAPE in all datasets. Further evaluation of the learned models’ goodness of fit using prediction error plots also confirms that the proposed workflow results in models that can more accurately capture the nature of the underlying physical systems.
{"title":"Learning Non-linear White-box Predictors: A Use Case in Energy Systems","authors":"Sandra Wilfling, M. Ebrahimi, Qamar Alfalouji, G. Schweiger, Mina Basirat","doi":"10.1109/ICMLA55696.2022.00082","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00082","url":null,"abstract":"Many applications in energy systems require models that represent the non-linear dynamics of the underlying systems. Black-box models with non-linear architecture are suitable candidates for modeling these systems; however, they are computationally expensive and lack interpretability. An inexpensive white-box linear combination learned over a suitable polynomial feature set can result in a high-performing non-linear model that is easier to interpret, validate, and verify against reference models created by the domain experts. This paper proposes a workflow to learn a linear combination of non-linear terms for an engineered polynomial feature set. We firstly detect non-linear dependencies and then attempt to reconstruct them using feature expansion. Afterwards, we select possible predictors with the highest correlation coefficients for predictive regression analysis. We demonstrate how to learn inexpensive yet comprehensible linear combinations of non-linear terms from four datasets. Experimental evaluations show our workflow yields improvements in the metrics R2, CV-RMSE and MAPE in all datasets. Further evaluation of the learned models’ goodness of fit using prediction error plots also confirms that the proposed workflow results in models that can more accurately capture the nature of the underlying physical systems.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"23 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":"121702473","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}
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