Jung-Ho Kang, Tatiana Keruzel, Uk-Jin Baek, Kyung-Chang Lee
{"title":"Detection of Fish Cage Net Damage Using Image Processing with Mesh-Hole Grouping","authors":"Jung-Ho Kang, Tatiana Keruzel, Uk-Jin Baek, Kyung-Chang Lee","doi":"10.1109/TENSYMP55890.2023.10223678","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223678","url":null,"abstract":"In this paper, we propose a Mesh-hole grouping algorithm that detects damaged areas by comparing the area between neighboring net holes in order to detect damaged parts of net that occurs in a wagging fish cage net. An image pre-processing is performed to extract the net shape from the underwater net image and convert it into a binary image. Each net hole in the binarized net image is assigned a number, and the net holes adjacent to the reference net hole are grouped together into one group. These grouped net holes are then arranged in ascending order based on their area size. Then, if the difference between the area of the first widest hole and the area of the second widest hole in the group is greater than the average hole area of the corresponding group, it is detected as damaged. The net damage detection algorithm was evaluated on a dataset of 600 images and achieved the following performance metrics: accuracy 0.86, precision 0.86, and recall 0.88.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116892986","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":"The Race to Robustness: Exploiting Fragile Models for Urban Camouflage and the Imperative for Machine Learning Security","authors":"Harriet Farlow, M. Garratt, G. Mount, T. Lynar","doi":"10.1109/TENSYMP55890.2023.10223620","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223620","url":null,"abstract":"Adversarial Machine Learning (AML) represents the ability to disrupt Machine Learning (ML) algorithms through a range of methods that broadly exploit the architecture of deep learning optimisation. This paper presents Distributed Adversarial Regions (DAR), a novel method that implements distributed instantiations of computer vision-based AML attack methods that may be used to disguise objects from image recognition in both white and black box settings. We consider the context of object detection models used in urban environments, and benchmark the MobileNetV2, NasNetMobile and DenseNet169 models against a subset of relevant images from the ImageNet dataset. We evaluate optimal parameters (size, number and perturbation method), and compare to state-of-the-art AML techniques that perturb the entire image. We find that DARs can cause a reduction in confidence of 40.4% on average, but with the benefit of not requiring the entire image, or the focal object, to be perturbed. The DAR method is a deliberately simple approach where the intention is to highlight how an adversary with very little skill could attack models that may already be productionised, and to emphasise the fragility of foundational object detection models. We present this as a contribution to the field of ML security as well as AML. This paper contributes a novel adversarial method, an original comparison between DARs and other AML methods, and frames it in a new context - that of urban camouflage and the necessity for ML security and model robustness.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123295829","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}
Harel Yadid, Almog Algranti, Mark Levin, Ayal Taitler
{"title":"A2D: Anywhere Anytime Drumming","authors":"Harel Yadid, Almog Algranti, Mark Levin, Ayal Taitler","doi":"10.1109/TENSYMP55890.2023.10223631","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223631","url":null,"abstract":"The drum kit, which has only been around for around 100 years, is a popular instrument in many music genres such as pop, rock, and jazz. However, the road to owning a kit is expensive, both financially and space-wise. Also, drums are more difficult to move around compared to other instruments, as they do not fit into a single bag. We propose a no-drums approach that uses only two sticks and a smartphone or a webcam to provide an air-drumming experience. The detection algorithm combines deep learning tools with tracking methods for an enhanced user experience. Based on both quantitative and qualitative testing with humans-in-the-loop, we show that our system has zero misses for beginner level play and negligible misses for advanced level play. Additionally, our limited human trials suggest potential directions for future research.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"295 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133038705","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":"Towards Mitigating ChatGPT's Negative Impact on Education: Optimizing Question Design Through Bloom's Taxonomy","authors":"S. Elsayed","doi":"10.1109/TENSYMP55890.2023.10223662","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223662","url":null,"abstract":"The popularity of generative text AI tools in answering questions has led to concerns regarding their potential negative impact on students' academic performance and the challenges that educators face in evaluating student learning. To address these concerns, this paper introduces an evolutionary approach that aims to identify the best set of Bloom's taxonomy keywords to generate questions that these tools have low confidence in answering. The effectiveness of this approach is evaluated through a case study that uses questions from a Data Structures and Representation course being taught at the University of New South Wales in Canberra, Australia. The results demonstrate that the optimization algorithm can find keywords from different cognitive levels to create questions that ChatGPT has low confidence to answer. This study is a step forward to offer valuable insights for educators seeking to create more effective questions that promote critical thinking among students.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130045012","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}