Pub Date : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00126
John T. Hancock, T. Khoshgoftaar, Joffrey L. Leevy
We present a simple approach for detecting brute force attacks in the CSE-CIC-IDS2018 Big Data dataset. We show our approach is preferable to more complex approaches since it is simpler, and yields stronger classification performance. Our contribution is to show that it is possible to train and test simple Decision Tree models with two independent variables to classify CSE-CIC-IDS2018 data with better results than reported in previous research, where more complex Deep Learning models are employed. Moreover, we show that Decision Tree models trained on data with two independent variables perform similarly to Decision Tree models trained on a larger number independent variables. Our experiments reveal that simple models, with AUC and AUPRC scores greater than 0.99, are capable of detecting brute force attacks in CSE-CIC-IDS2018. To the best of our knowledge, these are the strongest performance metrics published for the machine learning task of detecting these types of attacks. Furthermore, the simplicity of our approach, combined with its strong performance, makes it an appealing technique.
{"title":"Detecting SSH and FTP Brute Force Attacks in Big Data","authors":"John T. Hancock, T. Khoshgoftaar, Joffrey L. Leevy","doi":"10.1109/ICMLA52953.2021.00126","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00126","url":null,"abstract":"We present a simple approach for detecting brute force attacks in the CSE-CIC-IDS2018 Big Data dataset. We show our approach is preferable to more complex approaches since it is simpler, and yields stronger classification performance. Our contribution is to show that it is possible to train and test simple Decision Tree models with two independent variables to classify CSE-CIC-IDS2018 data with better results than reported in previous research, where more complex Deep Learning models are employed. Moreover, we show that Decision Tree models trained on data with two independent variables perform similarly to Decision Tree models trained on a larger number independent variables. Our experiments reveal that simple models, with AUC and AUPRC scores greater than 0.99, are capable of detecting brute force attacks in CSE-CIC-IDS2018. To the best of our knowledge, these are the strongest performance metrics published for the machine learning task of detecting these types of attacks. Furthermore, the simplicity of our approach, combined with its strong performance, makes it an appealing technique.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"25 1","pages":"760-765"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81309974","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}
In club football, scouting is a crucial aspect of player recruitment, with elite football clubs investing millions of dollars in scouting and signing the best player for their team every year. Scouting requires great analytical and observational skills from the scout, to find the best player for any position in the team. A scout needs to analyze the player by watching his in-game actions, physical attributes and make a judgement on how the player might fit into the team. Every team has a formation, a style of play and a specific profile of player is required for a given position depending on the aforementioned factors. But scouts only watch a player play a few matches in person, and prepare their scouting report based on a player’s performance in those matches. This process is flawed as the scout is expected to watch a few games and make estimates of the player’s performance in a new team. The player statistics can help the scout in making better data-driven decisions. A player’s career statistics can provide a picture of how the player performs individually, but they fail to predict player chemistry alongside a team. Misjudgement in scouting can lead to losses of millions of dollars to a club. We propose to solve this problem by utilising vast amounts of quantitative and qualitative player statistics (from 3+ sources), and by incorporating data science and machine learning algorithms to simulate real world performances of the team after the addition of the newly scouted player. We take into account specific player requirements and classify a player into one of our specific 15 player types, and use the team’s formation and style of play to predict the players that will have the best chemistry with any given lineup, thereby facilitating scouts in making better decisions.
{"title":"Data Driven football scouting assistance with simulated player performance extrapolation","authors":"Shantanu Ghar, Sayali Patil, Venkhatesh Arunachalam","doi":"10.1109/ICMLA52953.2021.00189","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00189","url":null,"abstract":"In club football, scouting is a crucial aspect of player recruitment, with elite football clubs investing millions of dollars in scouting and signing the best player for their team every year. Scouting requires great analytical and observational skills from the scout, to find the best player for any position in the team. A scout needs to analyze the player by watching his in-game actions, physical attributes and make a judgement on how the player might fit into the team. Every team has a formation, a style of play and a specific profile of player is required for a given position depending on the aforementioned factors. But scouts only watch a player play a few matches in person, and prepare their scouting report based on a player’s performance in those matches. This process is flawed as the scout is expected to watch a few games and make estimates of the player’s performance in a new team. The player statistics can help the scout in making better data-driven decisions. A player’s career statistics can provide a picture of how the player performs individually, but they fail to predict player chemistry alongside a team. Misjudgement in scouting can lead to losses of millions of dollars to a club. We propose to solve this problem by utilising vast amounts of quantitative and qualitative player statistics (from 3+ sources), and by incorporating data science and machine learning algorithms to simulate real world performances of the team after the addition of the newly scouted player. We take into account specific player requirements and classify a player into one of our specific 15 player types, and use the team’s formation and style of play to predict the players that will have the best chemistry with any given lineup, thereby facilitating scouts in making better decisions.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"19 1","pages":"1160-1167"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84331322","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00053
Narges Manouchehri, N. Bouguila, Wentao Fan
In this paper, we propose hierarchical Pitman-Yor process mixtures of multivariate Beta distributions and learn this novel clustering method by online variational inference. The flexibility of this mixture model and its non-parametric hierarchical structure help in fitting our data. Also, the model complexity and model parameters are estimated simultaneously. We apply our proposed model to real medical applications. Our motivation is that labelling healthcare data is sensitive and expensive. Also, interpretability and evidence-based decision-making are some basic needs of medicine. These conditions led us to focus on clustering as it doesn’t need labelling. Another driving reason is that the amount of publicly available data in medicine is less compared to other fields due to the confidential regulations. To evaluate our proposed model, we compare its performance with other similar alternatives. The experimental results indicate the potential of our proposed model.
{"title":"Batch and Online Variational Learning of Hierarchical Pitman-Yor Mixtures of Multivariate Beta Distributions","authors":"Narges Manouchehri, N. Bouguila, Wentao Fan","doi":"10.1109/ICMLA52953.2021.00053","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00053","url":null,"abstract":"In this paper, we propose hierarchical Pitman-Yor process mixtures of multivariate Beta distributions and learn this novel clustering method by online variational inference. The flexibility of this mixture model and its non-parametric hierarchical structure help in fitting our data. Also, the model complexity and model parameters are estimated simultaneously. We apply our proposed model to real medical applications. Our motivation is that labelling healthcare data is sensitive and expensive. Also, interpretability and evidence-based decision-making are some basic needs of medicine. These conditions led us to focus on clustering as it doesn’t need labelling. Another driving reason is that the amount of publicly available data in medicine is less compared to other fields due to the confidential regulations. To evaluate our proposed model, we compare its performance with other similar alternatives. The experimental results indicate the potential of our proposed model.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"6 1","pages":"298-303"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87435668","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00098
Patrick Bujok, Maria Jensen, Steffen M. Larsen, R. A. Alphinas
Machine Learning applications provide a promising method to support clinical practitioners in Breast Cancer (BC) detection. Currently, Fine Needle Aspiration (FNA) is a commonly applied diagnostic method for BC tumors, which, however, is associated with ominous false negative misclassifications. For this purpose, the present study explores Artificial Neural Networks (ANNs) with the aim of outperforming clinical practices via FNA in classifying benign or malignant BC cases with regard to an improved accuracy and reduced False Negative Rate (FNR) using the Breast Cancer Wisconsin (Diagnostic) Dataset (WDBC). The findings reveal that a dense ANN with a single hidden layer including 15 neurons can reach a testing accuracy of 98.60% and a FNR of 0% on a scaled dataset. In combination with several introduced improvement measures, a high degree of generalizability is associated with the model under the consideration of the relatively small dataset. As a result, this model outperforms not only clinical practitioners but also 72 classifiers from the recent literature.
{"title":"Outperforming Clinical Practices in Breast Cancer Detection: A Superior Dense Neural Network in Classification and False Negative Reduction","authors":"Patrick Bujok, Maria Jensen, Steffen M. Larsen, R. A. Alphinas","doi":"10.1109/ICMLA52953.2021.00098","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00098","url":null,"abstract":"Machine Learning applications provide a promising method to support clinical practitioners in Breast Cancer (BC) detection. Currently, Fine Needle Aspiration (FNA) is a commonly applied diagnostic method for BC tumors, which, however, is associated with ominous false negative misclassifications. For this purpose, the present study explores Artificial Neural Networks (ANNs) with the aim of outperforming clinical practices via FNA in classifying benign or malignant BC cases with regard to an improved accuracy and reduced False Negative Rate (FNR) using the Breast Cancer Wisconsin (Diagnostic) Dataset (WDBC). The findings reveal that a dense ANN with a single hidden layer including 15 neurons can reach a testing accuracy of 98.60% and a FNR of 0% on a scaled dataset. In combination with several introduced improvement measures, a high degree of generalizability is associated with the model under the consideration of the relatively small dataset. As a result, this model outperforms not only clinical practitioners but also 72 classifiers from the recent literature.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"25 1","pages":"589-594"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87677746","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00273
Maya Kapoor, Joshua Melton, Michael Ridenhour, S. Krishnan, Thomas Moyer
Data provenance graphs, detailed traces of system behavior, are a popular construct to analyze and forecast malicious cyber activity like advanced persistent threats (APT). A critical limitation of existing analysis techniques is the lack of an automated analytic framework to predict APTs. In this work, we address that limitation by augmenting efficient capture and storage mechanisms to include automated analysis. Specifically, we propose PROV-GEM, a deep graph learning framework to identify malicious anomalous behavior from provenance data. Since data provenance graphs are complex datasets often expressed as heterogeneous attributed multiplex networks, we use a unified relation-aware embedding framework to capture the necessary contexts and associated interactions between the various entities manifest in the data. Furthermore, provenance graphs by nature are rich detailed structures that are heavily attributed compared to other complex systems that have been used traditionally in graph machine learning applications. Towards that end, our framework uniquely captures “multi-embeddings” that can represent varied contexts of nodes and their multi-faceted nature. We demonstrate the efficacy of our embeddings by applying PROV-GEM to two publicly available APT provenance graph datasets from StreamSpot and Unicorn. PROV-GEM achieves strong performance on both datasets with a 99% accuracy and 97% F1-score on the StreamSpot dataset, and a 97% accuracy and 89% F1-score on the Unicorn dataset, equaling or outperforming comparable state-of-the-art APT threat detection models. Unlike other frameworks, PROV-GEM utilizes an efficient graph convolutional approach coupled with relational self-attention to generate rich graph embeddings that capture the complex topology of data provenance graphs, providing an effective automated analytic framework for APT detection.
{"title":"PROV-GEM: Automated Provenance Analysis Framework using Graph Embeddings","authors":"Maya Kapoor, Joshua Melton, Michael Ridenhour, S. Krishnan, Thomas Moyer","doi":"10.1109/ICMLA52953.2021.00273","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00273","url":null,"abstract":"Data provenance graphs, detailed traces of system behavior, are a popular construct to analyze and forecast malicious cyber activity like advanced persistent threats (APT). A critical limitation of existing analysis techniques is the lack of an automated analytic framework to predict APTs. In this work, we address that limitation by augmenting efficient capture and storage mechanisms to include automated analysis. Specifically, we propose PROV-GEM, a deep graph learning framework to identify malicious anomalous behavior from provenance data. Since data provenance graphs are complex datasets often expressed as heterogeneous attributed multiplex networks, we use a unified relation-aware embedding framework to capture the necessary contexts and associated interactions between the various entities manifest in the data. Furthermore, provenance graphs by nature are rich detailed structures that are heavily attributed compared to other complex systems that have been used traditionally in graph machine learning applications. Towards that end, our framework uniquely captures “multi-embeddings” that can represent varied contexts of nodes and their multi-faceted nature. We demonstrate the efficacy of our embeddings by applying PROV-GEM to two publicly available APT provenance graph datasets from StreamSpot and Unicorn. PROV-GEM achieves strong performance on both datasets with a 99% accuracy and 97% F1-score on the StreamSpot dataset, and a 97% accuracy and 89% F1-score on the Unicorn dataset, equaling or outperforming comparable state-of-the-art APT threat detection models. Unlike other frameworks, PROV-GEM utilizes an efficient graph convolutional approach coupled with relational self-attention to generate rich graph embeddings that capture the complex topology of data provenance graphs, providing an effective automated analytic framework for APT detection.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"115 1","pages":"1720-1727"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77194918","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00207
Louis Jensen, Jayme Fosa, Ben Teitelbaum, Peter Chin
Time series data has become ubiquitous in the modern era of data collection. With the increase of these time series data streams, the demand for automatic time series anomaly detection has also increased. Automatic monitoring of data allows engineers to investigate only unusual behavior in their data streams. Despite this increase in demand for automatic time series anomaly detection, many popular methods fail to offer a general purpose solution. Some demand expensive labelling of anomalies, others require the data to follow certain assumed patterns, some have long and unstable training, and many suffer from high rates of false alarms. In this paper we demonstrate that simpler is often better, showing that a fully unsupervised multilayer perceptron autoencoder is able to outperform much more complicated models with only a few critical improvements. We offer improvements to help distinguish anomalous subsequences near to each other, and to distinguish anomalies even in the midst of changing distributions of data. We compare our model with state-of-the-art competitors on benchmark datasets sourced from NASA, Yahoo, and Numenta, achieving improvements beyond competitive models in all three datasets.
{"title":"How Dense Autoencoders can still Achieve the State-of-the-art in Time-Series Anomaly Detection","authors":"Louis Jensen, Jayme Fosa, Ben Teitelbaum, Peter Chin","doi":"10.1109/ICMLA52953.2021.00207","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00207","url":null,"abstract":"Time series data has become ubiquitous in the modern era of data collection. With the increase of these time series data streams, the demand for automatic time series anomaly detection has also increased. Automatic monitoring of data allows engineers to investigate only unusual behavior in their data streams. Despite this increase in demand for automatic time series anomaly detection, many popular methods fail to offer a general purpose solution. Some demand expensive labelling of anomalies, others require the data to follow certain assumed patterns, some have long and unstable training, and many suffer from high rates of false alarms. In this paper we demonstrate that simpler is often better, showing that a fully unsupervised multilayer perceptron autoencoder is able to outperform much more complicated models with only a few critical improvements. We offer improvements to help distinguish anomalous subsequences near to each other, and to distinguish anomalies even in the midst of changing distributions of data. We compare our model with state-of-the-art competitors on benchmark datasets sourced from NASA, Yahoo, and Numenta, achieving improvements beyond competitive models in all three datasets.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"1272-1277"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82949939","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00092
Marzieh Mousavian, Jianhua Chen, S. Greening
Automatic detection of Major Depression Disorder (MDD) from brain MRI images with machine learning has been an active area of study. In this paper several methods are explored for MDD detection by combining features from structural and functional brain MRI images, and combining Atlas-based and spatial cube-based features. Experiments demonstrate good classification performance on an imbalanced dataset. The paper also presents a visualization that captures the spatial overlapping between the top discriminating spatial cube pairs and the regions of interests in the Harvard Atlas.
{"title":"Depression Detection Using Combination of sMRI and fMRI Image Features","authors":"Marzieh Mousavian, Jianhua Chen, S. Greening","doi":"10.1109/ICMLA52953.2021.00092","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00092","url":null,"abstract":"Automatic detection of Major Depression Disorder (MDD) from brain MRI images with machine learning has been an active area of study. In this paper several methods are explored for MDD detection by combining features from structural and functional brain MRI images, and combining Atlas-based and spatial cube-based features. Experiments demonstrate good classification performance on an imbalanced dataset. The paper also presents a visualization that captures the spatial overlapping between the top discriminating spatial cube pairs and the regions of interests in the Harvard Atlas.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"125 5 1","pages":"552-557"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80502286","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00170
D. Schwartz, Jonathan Michael Gomes Selman, P. Wrege, A. Paepcke
Artificial intelligence is increasingly used in ecological contexts to monitor animal and insect populations. Species of interest are those in danger of extinction, and those that play pivotal roles in agriculture. Noticing population declines or geographical shifts early enough for intervention can prevent local famine and disruption to the global food chain. Traditionally, data are collected in the field using human labor or sensors. Applicable classification models then analyze the data on central servers. The most expensive, and sometimes dangerous part of the remote sensing solution is the human labor of visiting the sensors, retrieving data, and changing batteries. Constantly sending all readings by radio is expensive in power. Instead, having AI in the sensors process readings, and only transmitting results could lead to an indefinitely autonomous, renewably powered solution. We implemented an elephant vocalization detector on a small processor board, and demonstrate that such a device can be operated at low enough power levels with considerable freedom of choice among AI technologies. We achieved a mean of 1.6W, in the best case staying within 75% of memory limits. Measurements covered three inference models, two batch sizes, and two floating point word width settings.
{"title":"Deployment of Embedded Edge-AI for Wildlife Monitoring in Remote Regions","authors":"D. Schwartz, Jonathan Michael Gomes Selman, P. Wrege, A. Paepcke","doi":"10.1109/ICMLA52953.2021.00170","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00170","url":null,"abstract":"Artificial intelligence is increasingly used in ecological contexts to monitor animal and insect populations. Species of interest are those in danger of extinction, and those that play pivotal roles in agriculture. Noticing population declines or geographical shifts early enough for intervention can prevent local famine and disruption to the global food chain. Traditionally, data are collected in the field using human labor or sensors. Applicable classification models then analyze the data on central servers. The most expensive, and sometimes dangerous part of the remote sensing solution is the human labor of visiting the sensors, retrieving data, and changing batteries. Constantly sending all readings by radio is expensive in power. Instead, having AI in the sensors process readings, and only transmitting results could lead to an indefinitely autonomous, renewably powered solution. We implemented an elephant vocalization detector on a small processor board, and demonstrate that such a device can be operated at low enough power levels with considerable freedom of choice among AI technologies. We achieved a mean of 1.6W, in the best case staying within 75% of memory limits. Measurements covered three inference models, two batch sizes, and two floating point word width settings.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"1035-1042"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89243102","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00121
Wenjun Zeng, Bakhtiar Amen
Automated flower detection and control is important to crop production and precision agriculture. Some computer vision methods have been proposed for flower detection, but their performances are not satisfactory on platforms with limited computing ability such as mobile and embedded devices, and thus not suitable for field applications. Herein we demonstrate two de novo approaches that can precisely detect the flowers of two bioenergy crops (potatoes and sweet potatoes) and can distinguish them from similar flowers of relative species (eggplants and Ipomoea triloba) on mobile devices. In this work, a custom dataset containing 495 manually labelled images is constructed for training and testing, and the latest state-of-the-art object detection model, YOLOv4, as well as its lightweight version, YOLOv4-tiny, are selected as the flower detection models. Some other milestone object detection models including YOLOv3, YOLOv3-tiny, SSD and Faster-RCNN are chosen as benchmarks for performance comparison. The comparative experiment results indicate that the retrained YOLOv4 model achieves a considerable high mean average precision (mAP= 91%;) but a slower inference speed (FPS) on a mobile device, while the retrained YOLOv4-tiny has a lower mAP of 87%; but reach a higher FPS of 9 on a mobile device. Two mobile applications are then developed by directly deploying YOLOv4-tiny model on a mobile app and by deploying YOLOv4 on a web API, respectively. The testing experiments indicate that both applications can not only achieve real-time and accurate detection, but also reduce computation burdens on mobile devices.
{"title":"Applications of Mobile Machine Learning for Detecting Bio-energy Crops Flowers","authors":"Wenjun Zeng, Bakhtiar Amen","doi":"10.1109/ICMLA52953.2021.00121","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00121","url":null,"abstract":"Automated flower detection and control is important to crop production and precision agriculture. Some computer vision methods have been proposed for flower detection, but their performances are not satisfactory on platforms with limited computing ability such as mobile and embedded devices, and thus not suitable for field applications. Herein we demonstrate two de novo approaches that can precisely detect the flowers of two bioenergy crops (potatoes and sweet potatoes) and can distinguish them from similar flowers of relative species (eggplants and Ipomoea triloba) on mobile devices. In this work, a custom dataset containing 495 manually labelled images is constructed for training and testing, and the latest state-of-the-art object detection model, YOLOv4, as well as its lightweight version, YOLOv4-tiny, are selected as the flower detection models. Some other milestone object detection models including YOLOv3, YOLOv3-tiny, SSD and Faster-RCNN are chosen as benchmarks for performance comparison. The comparative experiment results indicate that the retrained YOLOv4 model achieves a considerable high mean average precision (mAP= 91%;) but a slower inference speed (FPS) on a mobile device, while the retrained YOLOv4-tiny has a lower mAP of 87%; but reach a higher FPS of 9 on a mobile device. Two mobile applications are then developed by directly deploying YOLOv4-tiny model on a mobile app and by deploying YOLOv4 on a web API, respectively. The testing experiments indicate that both applications can not only achieve real-time and accurate detection, but also reduce computation burdens on mobile devices.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"41 1","pages":"724-729"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91011208","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00014
Aashish Rai, Clara Ducher, J. Cooperstock
With the recent popularization of generative frameworks for producing photorealistic face images, we now have the ability to create a convincing graphical match for any particular individual. It is unrealistic, however, to rely solely on such generative methods to randomly produce the facial characteristics we are seeking. Instead, manipulation of facial attributes in the latent space, enabled by the InterFaceGAN framework, allows us to “tweak” these characteristics in the desired direction to improve the quality of the match. The challenge in this process is that attribute entanglement leads to a change of one feature having an undesirable impact on others. We explore several strategies to improve the results of these manipulations, and demonstrate how the automatic conditioning of attributes can be used to minimize the impact of such entanglement, and further, allow for improved control over complex (non-binary) attributes such as race or face shape.
{"title":"Improved Attribute Manipulation in the Latent Space of StyleGAN for Semantic Face Editing","authors":"Aashish Rai, Clara Ducher, J. Cooperstock","doi":"10.1109/ICMLA52953.2021.00014","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00014","url":null,"abstract":"With the recent popularization of generative frameworks for producing photorealistic face images, we now have the ability to create a convincing graphical match for any particular individual. It is unrealistic, however, to rely solely on such generative methods to randomly produce the facial characteristics we are seeking. Instead, manipulation of facial attributes in the latent space, enabled by the InterFaceGAN framework, allows us to “tweak” these characteristics in the desired direction to improve the quality of the match. The challenge in this process is that attribute entanglement leads to a change of one feature having an undesirable impact on others. We explore several strategies to improve the results of these manipulations, and demonstrate how the automatic conditioning of attributes can be used to minimize the impact of such entanglement, and further, allow for improved control over complex (non-binary) attributes such as race or face shape.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"21 39","pages":"38-43"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91505996","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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