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/ICMLA55696.2022.00259
Mashrura Tasnim, Jekaterina Novikova
Depression is the most common psychological disorder and is considered as a leading cause of disability and suicide worldwide. An automated system capable of detecting signs of depression in human speech can contribute to ensuring timely and effective mental health care for individuals suffering from the disorder. Developing such automated system requires accurate machine learning models, capable of capturing signs of depression. However, state-of-the-art models based on deep acoustic representations require abundant data, meticulous selection of features, and rigorous training; the procedure involves enormous computational resources. In this work, we explore the effectiveness of two different acoustic feature groups-conventional hand-curated and deep representation features, for predicting the severity of depression from speech. We explore the relevance of possible contributing factors to the models’ performance, including gender of the individual, severity of the disorder, content and length of speech. Our findings suggest that models trained on conventional acoustic features perform equally well or better than the ones trained on deep representation features at significantly lower computational cost, irrespective of other factors, e.g. content and length of speech, gender of the speaker and severity of the disorder. This makes such models a better fit for deployment where availability of computational resources is restricted, such as real time depression monitoring applications in smart devices.
{"title":"Cost-effective Models for Detecting Depression from Speech","authors":"Mashrura Tasnim, Jekaterina Novikova","doi":"10.1109/ICMLA55696.2022.00259","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00259","url":null,"abstract":"Depression is the most common psychological disorder and is considered as a leading cause of disability and suicide worldwide. An automated system capable of detecting signs of depression in human speech can contribute to ensuring timely and effective mental health care for individuals suffering from the disorder. Developing such automated system requires accurate machine learning models, capable of capturing signs of depression. However, state-of-the-art models based on deep acoustic representations require abundant data, meticulous selection of features, and rigorous training; the procedure involves enormous computational resources. In this work, we explore the effectiveness of two different acoustic feature groups-conventional hand-curated and deep representation features, for predicting the severity of depression from speech. We explore the relevance of possible contributing factors to the models’ performance, including gender of the individual, severity of the disorder, content and length of speech. Our findings suggest that models trained on conventional acoustic features perform equally well or better than the ones trained on deep representation features at significantly lower computational cost, irrespective of other factors, e.g. content and length of speech, gender of the speaker and severity of the disorder. This makes such models a better fit for deployment where availability of computational resources is restricted, such as real time depression monitoring applications in smart devices.","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":"122888918","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/ICMLA55696.2022.00277
Mashael Al-Luhaybi, S. Swift, S. Counsell, A. Tucker
This paper addresses the problem that many machine learning classifiers make decisions based on data that are biased and can therefore result in prejudiced decisions. For example, in education (which this paper focuses on) a student may be rejected from a course based on historical decisions in the data that only exist due to historical biases in society or due to the skewed sampling of the data. Other approaches to dealing with bias in data include resampling methods (to counter imbalanced samples) and dimensionality reduction (to focus only on relevant features to the classification task). In this paper, we explore issues of modelling bias explicitly so that we can identify the types of bias and whether they are accounting for inflated predictive accuracies. In particular, we compare graphical model approaches to building classifiers, that are transparent in how they make decisions, with two forms of Deep Multi-label Convolutional Neural Networks to investigate if models can be built that maximise accuracy and minimise bias. We carry out this comparison on student entry and performance data from a higher educational institution.
{"title":"Exploring the Explicit Modelling of Bias in Machine Learning Classifiers: A Deep Multi-label ConvNet Approach *","authors":"Mashael Al-Luhaybi, S. Swift, S. Counsell, A. Tucker","doi":"10.1109/ICMLA55696.2022.00277","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00277","url":null,"abstract":"This paper addresses the problem that many machine learning classifiers make decisions based on data that are biased and can therefore result in prejudiced decisions. For example, in education (which this paper focuses on) a student may be rejected from a course based on historical decisions in the data that only exist due to historical biases in society or due to the skewed sampling of the data. Other approaches to dealing with bias in data include resampling methods (to counter imbalanced samples) and dimensionality reduction (to focus only on relevant features to the classification task). In this paper, we explore issues of modelling bias explicitly so that we can identify the types of bias and whether they are accounting for inflated predictive accuracies. In particular, we compare graphical model approaches to building classifiers, that are transparent in how they make decisions, with two forms of Deep Multi-label Convolutional Neural Networks to investigate if models can be built that maximise accuracy and minimise bias. We carry out this comparison on student entry and performance data from a higher educational institution.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"82 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":"129808166","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/ICMLA55696.2022.10102767
Samira Khorshidi, Bao Wang, G. Mohler
Temporal point processes have many applications, from crime forecasting to modeling earthquake aftershocks sequences. Due to the flexibility and expressiveness of deep learning, neural network-based approaches have recently shown promise for modeling point process intensities. However, there is a lack of research on the robustness of such models in regards to adversarial attacks and natural shocks to systems. Precisely, while neural point processes may outperform simpler parametric models on in-sample tests, how these models perform when encountering adversarial examples or sharp non-stationary trends remains unknown. Current work proposes several white-box and blackbox adversarial attacks against temporal point processes modeled by deep neural networks. Extensive experiments confirm that predictive performance and parametric modeling of neural point processes are vulnerable to adversarial attacks. Additionally, we evaluate the vulnerability and performance of these models in the presence of non-stationary abrupt changes, using the crimes dataset, during the Covid-19 pandemic, as an example.
{"title":"Adversarial Attacks on Deep Temporal Point Process","authors":"Samira Khorshidi, Bao Wang, G. Mohler","doi":"10.1109/ICMLA55696.2022.10102767","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.10102767","url":null,"abstract":"Temporal point processes have many applications, from crime forecasting to modeling earthquake aftershocks sequences. Due to the flexibility and expressiveness of deep learning, neural network-based approaches have recently shown promise for modeling point process intensities. However, there is a lack of research on the robustness of such models in regards to adversarial attacks and natural shocks to systems. Precisely, while neural point processes may outperform simpler parametric models on in-sample tests, how these models perform when encountering adversarial examples or sharp non-stationary trends remains unknown. Current work proposes several white-box and blackbox adversarial attacks against temporal point processes modeled by deep neural networks. Extensive experiments confirm that predictive performance and parametric modeling of neural point processes are vulnerable to adversarial attacks. Additionally, we evaluate the vulnerability and performance of these models in the presence of non-stationary abrupt changes, using the crimes dataset, during the Covid-19 pandemic, as an example.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"10 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":"129246937","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/ICMLA55696.2022.00127
Sheuli Paul, Michael Sintek, Veton Këpuska, M. Silaghi, Liam Robertson
Understanding the intent is an essential step for maintaining effective communications. This essential feature is used in communications for assembling, patrolling, and surveillance. A fused and interactive multimodal system for human-robot communication, used in assembly applications, is presented in this paper. Communication is multimodal. Having the options of multiple communication modes such as gestures, text, symbols, graphics, images, and speech increase the chance of effective communication. The intent is the main component that we are aiming to model, specifically in human machine dialogues. For this, we extract the intents from spoken dialogues and fuse the intent with any detected matching gesture that is used in interaction with the robot. The main components of the presented system are: (1) a speech recognizer system using Kaldi, (2) a deep-learning based Dual Intent and Entity Transformer (DIET) based classifier for intent and entity extraction, (3) a hand gesture recognition system, and (4) a dynamic fusion model for speech and gesture based communication. These are evaluated on contextual assembly situation using a simulated interactive robot.
{"title":"Intent based Multimodal Speech and Gesture Fusion for Human-Robot Communication in Assembly Situation","authors":"Sheuli Paul, Michael Sintek, Veton Këpuska, M. Silaghi, Liam Robertson","doi":"10.1109/ICMLA55696.2022.00127","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00127","url":null,"abstract":"Understanding the intent is an essential step for maintaining effective communications. This essential feature is used in communications for assembling, patrolling, and surveillance. A fused and interactive multimodal system for human-robot communication, used in assembly applications, is presented in this paper. Communication is multimodal. Having the options of multiple communication modes such as gestures, text, symbols, graphics, images, and speech increase the chance of effective communication. The intent is the main component that we are aiming to model, specifically in human machine dialogues. For this, we extract the intents from spoken dialogues and fuse the intent with any detected matching gesture that is used in interaction with the robot. The main components of the presented system are: (1) a speech recognizer system using Kaldi, (2) a deep-learning based Dual Intent and Entity Transformer (DIET) based classifier for intent and entity extraction, (3) a hand gesture recognition system, and (4) a dynamic fusion model for speech and gesture based communication. These are evaluated on contextual assembly situation using a simulated interactive robot.","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":"128508919","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/ICMLA55696.2022.00136
Dana Oshri Zalman, S. Fine
Variational inference provides a way to approximate probability densities. It does so by optimizing an upper or a lower bound on the likelihood of the observed data (the evidence). The classic variational inference approach suggests to maximize the Evidence Lower BOund (ELBO). Recent proposals suggest to optimize the variational Rényi bound (VR) and χ upper bound. However, these estimates are either biased or difficult to approximate, due to a high variance.In this paper we introduce a new upper bound (termed VRLU) which is based on the existing variational Rényi bound. In contrast to the existing VR bound, the Monte Carlo (MC) approximation of the VRLU bound is unbiased. Furthermore, we devise a (sandwiched) upper-lower bound variational inference method (termed VRS) to jointly optimize the upper and lower bounds. We present a set of experiments, designed to evaluate the new VRLU bound, and to compare the VRS method with the classic VAE and the VR methods over a set of digit recognition tasks. The experiments and results demonstrate the VRLU bound advantage, and the wide applicability of the VRS method.
{"title":"Variational Inference via Rényi Upper-Lower Bound Optimization","authors":"Dana Oshri Zalman, S. Fine","doi":"10.1109/ICMLA55696.2022.00136","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00136","url":null,"abstract":"Variational inference provides a way to approximate probability densities. It does so by optimizing an upper or a lower bound on the likelihood of the observed data (the evidence). The classic variational inference approach suggests to maximize the Evidence Lower BOund (ELBO). Recent proposals suggest to optimize the variational Rényi bound (VR) and χ upper bound. However, these estimates are either biased or difficult to approximate, due to a high variance.In this paper we introduce a new upper bound (termed VRLU) which is based on the existing variational Rényi bound. In contrast to the existing VR bound, the Monte Carlo (MC) approximation of the VRLU bound is unbiased. Furthermore, we devise a (sandwiched) upper-lower bound variational inference method (termed VRS) to jointly optimize the upper and lower bounds. We present a set of experiments, designed to evaluate the new VRLU bound, and to compare the VRS method with the classic VAE and the VR methods over a set of digit recognition tasks. The experiments and results demonstrate the VRLU bound advantage, and the wide applicability of the VRS method.","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":"131105412","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/ICMLA55696.2022.00046
Wen-Hao Chiang, G. Mohler
We propose a novel framework for integrating Hawkes processes with multi-armed bandit algorithms to solve spatio-temporal event forecasting and detection problems when data may be undersampled or spatially biased. In particular, we introduce an upper confidence bound algorithm using Bayesian spatial Hawkes process estimation for balancing the trade-off between exploiting geographic regions where data has been collected and exploring geographic regions where data is unobserved. We first validate our model using simulated data. We then apply it to the problem of disaster search and rescue using calls for service data from hurricane Harvey in 2017 and the problem of detection and clearance of improvised explosive devices (IEDs) using IED attack records in Iraq. Our model outperforms state-of-the-art baseline spatial MAB algorithms in terms of cumulative reward and several other ranking evaluation metrics.
{"title":"Hawkes Process Multi-armed Bandits for Search and Rescue","authors":"Wen-Hao Chiang, G. Mohler","doi":"10.1109/ICMLA55696.2022.00046","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00046","url":null,"abstract":"We propose a novel framework for integrating Hawkes processes with multi-armed bandit algorithms to solve spatio-temporal event forecasting and detection problems when data may be undersampled or spatially biased. In particular, we introduce an upper confidence bound algorithm using Bayesian spatial Hawkes process estimation for balancing the trade-off between exploiting geographic regions where data has been collected and exploring geographic regions where data is unobserved. We first validate our model using simulated data. We then apply it to the problem of disaster search and rescue using calls for service data from hurricane Harvey in 2017 and the problem of detection and clearance of improvised explosive devices (IEDs) using IED attack records in Iraq. Our model outperforms state-of-the-art baseline spatial MAB algorithms in terms of cumulative reward and several other ranking evaluation metrics.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"4 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":"128672750","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/ICMLA55696.2022.00056
Hao Xin, M. Zhu
Image-to-image regression is an important computer vision task. In this paper, we propose a novel image-to-image regression model following the recent trend in generative modeling that employs Stochastic Differential Equations (SDEs) and score matching. We first apply diffusion processes to regression data using designed SDEs, and then perform inference by gradually reversing the processes. In particular, our method uses synchronized diffusion, which simultaneously applies diffusion to both input and response images to stabilize diffusion and subsequent parameter learning. Furthermore, based on the Expectation-Maximization (EM) algorithm, we develop an effective algorithm for prediction. We implement a conditional U-Net architecture with pre-trained DenseNet encoder for our proposed model and refer to it as DenseSocre. Our new model is able to generate diverse outcomes for image colorization, and the proposed prediction algorithm is able to achieve close to state-of-art performance on high-resolution monocular depth estimation.
{"title":"Score-based Image-to-Image Regression with Synchronized Diffusion","authors":"Hao Xin, M. Zhu","doi":"10.1109/ICMLA55696.2022.00056","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00056","url":null,"abstract":"Image-to-image regression is an important computer vision task. In this paper, we propose a novel image-to-image regression model following the recent trend in generative modeling that employs Stochastic Differential Equations (SDEs) and score matching. We first apply diffusion processes to regression data using designed SDEs, and then perform inference by gradually reversing the processes. In particular, our method uses synchronized diffusion, which simultaneously applies diffusion to both input and response images to stabilize diffusion and subsequent parameter learning. Furthermore, based on the Expectation-Maximization (EM) algorithm, we develop an effective algorithm for prediction. We implement a conditional U-Net architecture with pre-trained DenseNet encoder for our proposed model and refer to it as DenseSocre. Our new model is able to generate diverse outcomes for image colorization, and the proposed prediction algorithm is able to achieve close to state-of-art performance on high-resolution monocular depth estimation.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"219 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":"122447426","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/ICMLA55696.2022.00211
Adam Lehavi, S. Kim
In the realm of cybersecurity, intrusion detection systems (IDS) detect and prevent attacks based on collected computer and network data. In recent research, IDS models have been constructed using machine learning (ML) and deep learning (DL) methods such as Random Forest (RF) and deep neural networks (DNN). Feature selection (FS) can be used to construct faster, more interpretable, and more accurate models. We look at three different FS techniques; RF information gain (RF-IG), correlation feature selection using the Bat Algorithm (CFS-BA), and CFS using the Aquila Optimizer (CFS-AO). Our results show CFS-BA to be the most efficient of the FS methods, building in 55% of the time of the best RF-IG model while achieving 99.99% of its accuracy. This reinforces prior contributions attesting to CFS-BA’s accuracy while building upon the relationship between subset size, CFS score, and RF-IG score in final results.
{"title":"Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems","authors":"Adam Lehavi, S. Kim","doi":"10.1109/ICMLA55696.2022.00211","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00211","url":null,"abstract":"In the realm of cybersecurity, intrusion detection systems (IDS) detect and prevent attacks based on collected computer and network data. In recent research, IDS models have been constructed using machine learning (ML) and deep learning (DL) methods such as Random Forest (RF) and deep neural networks (DNN). Feature selection (FS) can be used to construct faster, more interpretable, and more accurate models. We look at three different FS techniques; RF information gain (RF-IG), correlation feature selection using the Bat Algorithm (CFS-BA), and CFS using the Aquila Optimizer (CFS-AO). Our results show CFS-BA to be the most efficient of the FS methods, building in 55% of the time of the best RF-IG model while achieving 99.99% of its accuracy. This reinforces prior contributions attesting to CFS-BA’s accuracy while building upon the relationship between subset size, CFS score, and RF-IG score in final results.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"25 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":"126637594","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：