E. Naess, Vajira Lasantha Thambawita, S. Hicks, M. Riegler, P. Halvorsen
Colorectal cancer is a severe health issue globally and a significant cause of cancer-related mortality, but it is treatable if found at an early stage. Early detection is usually done through a colonoscopy, where clinicians search for cancer precursors called polyps. Research has shown that clinicians miss between 14% and 30% of polyps during standard screenings of the gastrointestinal tract. Furthermore, once the polyps have been found, clinicians often overestimate the size of the polyps. In this respect, automatic analysis of medical images for detecting and locating polyps is a research area where machine learning has excelled in recent years. Still, current models have much room for improvement. In this paper, we propose a novel approach based on learning to segment within several grids, which we introduce to U-Net and Pix2Pix architectures. In short, we have experimented using several grid sizes, and using two open-source polyp segmentation datasets for cross-data training and testing. Our results suggest that segmentation at lower resolutions produces better results at the cost of less precision, which proved useful for the cases where higher precision segmentations gave limited results. Generally, compared to traditional U-Net and Pix2Pix, our grid-based approaches improve segmentation performance.
{"title":"Pyramidal Segmentation of Medical Images using Adversarial Training","authors":"E. Naess, Vajira Lasantha Thambawita, S. Hicks, M. Riegler, P. Halvorsen","doi":"10.1145/3463944.3469100","DOIUrl":"https://doi.org/10.1145/3463944.3469100","url":null,"abstract":"Colorectal cancer is a severe health issue globally and a significant cause of cancer-related mortality, but it is treatable if found at an early stage. Early detection is usually done through a colonoscopy, where clinicians search for cancer precursors called polyps. Research has shown that clinicians miss between 14% and 30% of polyps during standard screenings of the gastrointestinal tract. Furthermore, once the polyps have been found, clinicians often overestimate the size of the polyps. In this respect, automatic analysis of medical images for detecting and locating polyps is a research area where machine learning has excelled in recent years. Still, current models have much room for improvement. In this paper, we propose a novel approach based on learning to segment within several grids, which we introduce to U-Net and Pix2Pix architectures. In short, we have experimented using several grid sizes, and using two open-source polyp segmentation datasets for cross-data training and testing. Our results suggest that segmentation at lower resolutions produces better results at the cost of less precision, which proved useful for the cases where higher precision segmentations gave limited results. Generally, compared to traditional U-Net and Pix2Pix, our grid-based approaches improve segmentation performance.","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127132808","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}
The development of remote/wearable sensors enables more research in the health care area. Based on these kinds of sensors, the information of human's active level, health parameters can be collected to predict one's health status. Sleeping quality is an important factor to make a person feel healthy. In this work, we summarize the current models to predict sleeping quality. Inputs of those models could be environmental factors, activities, or time-series data from wearable sensors. The characteristic of the input data may lead to the choice of prediction models. The domain of data that was used to forecast sleeping quality will be considered carefully in parallel with the prediction model. Challenges and future work for this research direction will be discussed in this paper.
{"title":"Models to Predict Sleeping Quality from Activities and Environment: Current Status, Challenges and Opportunities","authors":"Thi Phuoc Van Nguyen, D. Nguyen, K. Zettsu","doi":"10.1145/3463944.3469268","DOIUrl":"https://doi.org/10.1145/3463944.3469268","url":null,"abstract":"The development of remote/wearable sensors enables more research in the health care area. Based on these kinds of sensors, the information of human's active level, health parameters can be collected to predict one's health status. Sleeping quality is an important factor to make a person feel healthy. In this work, we summarize the current models to predict sleeping quality. Inputs of those models could be environmental factors, activities, or time-series data from wearable sensors. The characteristic of the input data may lead to the choice of prediction models. The domain of data that was used to forecast sleeping quality will be considered carefully in parallel with the prediction model. Challenges and future work for this research direction will be discussed in this paper.","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126121406","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}
{"title":"Session details: Keynote & Invited Talks","authors":"M. Dao","doi":"10.1145/3265987.3286584","DOIUrl":"https://doi.org/10.1145/3265987.3286584","url":null,"abstract":"","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130747892","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}
{"title":"Session details: Session 1: Full Papers","authors":"C. Gurrin","doi":"10.1145/3483311","DOIUrl":"https://doi.org/10.1145/3483311","url":null,"abstract":"","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123363693","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}
G. Baugerud, M. Johnson, Ragnhild Klingenberg Røed, M. Lamb, Martine B. Powell, Vajira Lasantha Thambawita, S. Hicks, Pegah Salehi, Syed Zohaib Hassan, P. Halvorsen, M. Riegler
In this article, we present our ongoing work in the field of training police officers who conduct interviews with abused children. The objectives in this context are to protect vulnerable children from abuse, facilitate prosecution of offenders, and ensure that innocent adults are not accused of criminal acts. There is therefore a need for more data that can be used for improved interviewer training to equip police with the skills to conduct high-quality interviews. To support this important task, we propose to research a training program that utilizes different system components and multimodal data from the field of artificial intelligence such as chatbots, generation of visual content, text-to-speech, and speech-to-text. This program will be able to generate an almost unlimited amount of interview and also training data. The goal of combining all these different technologies and datatypes is to create an immersive and interactive child avatar that responds in a realistic way, to help to support the training of police interviewers, but can also produce synthetic data of interview situations that can be used to solve different problems in the same domain.
{"title":"Multimodal Virtual Avatars for Investigative Interviews with Children","authors":"G. Baugerud, M. Johnson, Ragnhild Klingenberg Røed, M. Lamb, Martine B. Powell, Vajira Lasantha Thambawita, S. Hicks, Pegah Salehi, Syed Zohaib Hassan, P. Halvorsen, M. Riegler","doi":"10.1145/3463944.3469269","DOIUrl":"https://doi.org/10.1145/3463944.3469269","url":null,"abstract":"In this article, we present our ongoing work in the field of training police officers who conduct interviews with abused children. The objectives in this context are to protect vulnerable children from abuse, facilitate prosecution of offenders, and ensure that innocent adults are not accused of criminal acts. There is therefore a need for more data that can be used for improved interviewer training to equip police with the skills to conduct high-quality interviews. To support this important task, we propose to research a training program that utilizes different system components and multimodal data from the field of artificial intelligence such as chatbots, generation of visual content, text-to-speech, and speech-to-text. This program will be able to generate an almost unlimited amount of interview and also training data. The goal of combining all these different technologies and datatypes is to create an immersive and interactive child avatar that responds in a realistic way, to help to support the training of police interviewers, but can also produce synthetic data of interview situations that can be used to solve different problems in the same domain.","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132414126","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}
Siri S. Eide, M. Riegler, H. Hammer, J. B. Bremnes
In this paper, we present the tower network, a novel, computationally lightweight deep neural network for multimodal data analytics and video prediction. The tower network is especially useful when it comes to combining different types of input data, a problem not greatly explored within deep learning. The architecture is further applied to a real-world example, where information from historic meteorological observations and numerical weather predictions are combined to produce high-quality forecasts of temperature for 1 to 6 hours into the future. The performance of the proposed model is assessed in terms of root mean squared error (RMSE), and the tower network outperforms even state-of-the-art forecasts from the Norwegian weather forecasting app yr.no from 3 hours into the future. On average, the RMSE of the tower network is approximately 6% smaller than that of yr.no, and approximately 27% smaller than that of the raw numerical weather predictions.
{"title":"Temperature Forecasting using Tower Networks","authors":"Siri S. Eide, M. Riegler, H. Hammer, J. B. Bremnes","doi":"10.1145/3463944.3469099","DOIUrl":"https://doi.org/10.1145/3463944.3469099","url":null,"abstract":"In this paper, we present the tower network, a novel, computationally lightweight deep neural network for multimodal data analytics and video prediction. The tower network is especially useful when it comes to combining different types of input data, a problem not greatly explored within deep learning. The architecture is further applied to a real-world example, where information from historic meteorological observations and numerical weather predictions are combined to produce high-quality forecasts of temperature for 1 to 6 hours into the future. The performance of the proposed model is assessed in terms of root mean squared error (RMSE), and the tower network outperforms even state-of-the-art forecasts from the Norwegian weather forecasting app yr.no from 3 hours into the future. On average, the RMSE of the tower network is approximately 6% smaller than that of yr.no, and approximately 27% smaller than that of the raw numerical weather predictions.","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125509953","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}
The satellite imagery data naturally exists as raster data. Useful information that can empower the domain experts to improve their decision-making abilities lies hidden in this data. However, finding this hidden knowledge is non-trivial and challenging due to the lack of open source integrated software to discover knowledge from raster data. In particular, existing open-source general-purpose data mining libraries, such as Knime [1], Mahout [3], Weka [5], Sci-kit [4], and SPMF [2], are inadequate to find knowledge hidden in raster datasets. In this talk, we present rasterMiner an integrated open-source software to discover knowledge from raster imagery datasets. It currently provides unsupervised learning techniques, such as pattern mining and clustering, to discover knowledge hidden in raster data. The key features of our software are as follows: (i) provides four pattern mining algorithms and four clustering algorithms to discover knowledge from raster data, (ii) Our software also provides "elbow method" to choose an appropriate k value for k-mean and k-means++ algorithms, (iii) Our software presents an integrated GUI that can facilitate the domain experts to choose algorithm(s) of their choice, (iv) Our software can also be accessed as a python-library, (v) The knowledge discovered by our software can be stored in standard formats so that the generated knowledge can be visualized using any GIS software.
{"title":"Discovering Knowledge Hidden in Raster Images using RasterMiner","authors":"R. U. Kiran","doi":"10.1145/3463944.3472812","DOIUrl":"https://doi.org/10.1145/3463944.3472812","url":null,"abstract":"The satellite imagery data naturally exists as raster data. Useful information that can empower the domain experts to improve their decision-making abilities lies hidden in this data. However, finding this hidden knowledge is non-trivial and challenging due to the lack of open source integrated software to discover knowledge from raster data. In particular, existing open-source general-purpose data mining libraries, such as Knime [1], Mahout [3], Weka [5], Sci-kit [4], and SPMF [2], are inadequate to find knowledge hidden in raster datasets. In this talk, we present rasterMiner an integrated open-source software to discover knowledge from raster imagery datasets. It currently provides unsupervised learning techniques, such as pattern mining and clustering, to discover knowledge hidden in raster data. The key features of our software are as follows: (i) provides four pattern mining algorithms and four clustering algorithms to discover knowledge from raster data, (ii) Our software also provides \"elbow method\" to choose an appropriate k value for k-mean and k-means++ algorithms, (iii) Our software presents an integrated GUI that can facilitate the domain experts to choose algorithm(s) of their choice, (iv) Our software can also be accessed as a python-library, (v) The knowledge discovered by our software can be stored in standard formats so that the generated knowledge can be visualized using any GIS software.","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122345006","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}
T. Nordmo, A. B. Ovesen, H. Johansen, M. Riegler, P. Halvorsen, Dag Johansen
Fish crime is considered a global and serious problem for a healthy and sustainable development of one of mankind's important sources of food. Technological surveillance and control solutions are emerging as remedies to combat criminal activities, but such solutions might also come with impractical and negative side-effects and challenges. In this paper, we present the concept and design of a surveillance system in lieu of current surveillance trends striking a delicate balance between privacy of legal actors while simultaneously capturing evidence-based footage, sensory data, and forensic proofs of illicit activities. Our proposed novel approach is to assist human operators in the 24/7 surveillance loop of remote professional fishing activities with a privacy-preserving Artificial Intelligence (AI) surveillance system operating in the same proximity as the activities being surveyed. The system will primarily be using video surveillance data, but also other sensor data captured on the fishing vessel. Additionally, the system correlates with other sources such as reports from other fish catches in the approximate area and time, etc. Only upon true positive flagging of specific potentially illicit activities by the locally executing AI algorithms, can forensic evidence be accessed from this physical edge, the fishing vessel. Besides a more privacy-preserving solution, our edge-based AI system also benefits from much less data that has to be transferred over unreliable, low-bandwidth satellite-based networks.
{"title":"Dutkat: A Multimedia System for Catching Illegal Catchers in a Privacy-Preserving Manner","authors":"T. Nordmo, A. B. Ovesen, H. Johansen, M. Riegler, P. Halvorsen, Dag Johansen","doi":"10.1145/3463944.3469102","DOIUrl":"https://doi.org/10.1145/3463944.3469102","url":null,"abstract":"Fish crime is considered a global and serious problem for a healthy and sustainable development of one of mankind's important sources of food. Technological surveillance and control solutions are emerging as remedies to combat criminal activities, but such solutions might also come with impractical and negative side-effects and challenges. In this paper, we present the concept and design of a surveillance system in lieu of current surveillance trends striking a delicate balance between privacy of legal actors while simultaneously capturing evidence-based footage, sensory data, and forensic proofs of illicit activities. Our proposed novel approach is to assist human operators in the 24/7 surveillance loop of remote professional fishing activities with a privacy-preserving Artificial Intelligence (AI) surveillance system operating in the same proximity as the activities being surveyed. The system will primarily be using video surveillance data, but also other sensor data captured on the fishing vessel. Additionally, the system correlates with other sources such as reports from other fish catches in the approximate area and time, etc. Only upon true positive flagging of specific potentially illicit activities by the locally executing AI algorithms, can forensic evidence be accessed from this physical edge, the fishing vessel. Besides a more privacy-preserving solution, our edge-based AI system also benefits from much less data that has to be transferred over unreliable, low-bandwidth satellite-based networks.","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129253800","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}
{"title":"Session details: Session 2: Short Papers","authors":"Thanh-Binh Nguyen","doi":"10.1145/3483312","DOIUrl":"https://doi.org/10.1145/3483312","url":null,"abstract":"","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"299 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131958127","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}
Privacy protection technology has become a crucial part of almost every existing cross-data analysis application. The privacy-preserving technique allows sharing sensitive personal information and preserves the users' privacy. This new trend influences data collection results by improving the analytical accuracy, increasing the number of participants, and better understand the participants' environments. Herein, collecting these personal data is significant to many advantageous applications such as health monitoring. Nevertheless, these applications encounter real privacy threats and concerns about handling personal information. This paper aims to determine privacy-preserving personal data mining technologies and analyze these technologies' advantages and shortcomings. Our purpose is to provide an in-depth understanding of personal data privacy and highlight important viewpoints, existing challenges, and future research directions.
{"title":"Investigation on Privacy-Preserving Techniques For Personal Data","authors":"Rafik Hamza, K. Zettsu","doi":"10.1145/3463944.3469267","DOIUrl":"https://doi.org/10.1145/3463944.3469267","url":null,"abstract":"Privacy protection technology has become a crucial part of almost every existing cross-data analysis application. The privacy-preserving technique allows sharing sensitive personal information and preserves the users' privacy. This new trend influences data collection results by improving the analytical accuracy, increasing the number of participants, and better understand the participants' environments. Herein, collecting these personal data is significant to many advantageous applications such as health monitoring. Nevertheless, these applications encounter real privacy threats and concerns about handling personal information. This paper aims to determine privacy-preserving personal data mining technologies and analyze these technologies' advantages and shortcomings. Our purpose is to provide an in-depth understanding of personal data privacy and highlight important viewpoints, existing challenges, and future research directions.","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123081270","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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