Proceedings : Annual International Computer Software and Applications Conference. COMPSAC最新文献_第2页

Design Scheme of Perceptual Hashing based on Output of CNN for Digital Watermarking 基于CNN输出的感知哈希数字水印设计方案

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC Pub Date : 2021-01-01 DOI: 10.1109/COMPSAC51774.2021.00189

Zhaoxiong Meng, T. Morizumi, S. Miyata, H. Kinoshita

引用次数: 0

A reactive system for specifying and running flexible cloud service business processes based on machine learning 用于指定和运行基于机器学习的灵活云服务业务流程的响应式系统

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC Pub Date : 2021-01-01 DOI: 10.1109/COMPSAC51774.2021.00220

Imen Ben Fraj, Y. Hlaoui, Leila Jemni Ben Ayed

引用次数: 0

The Use of Metadata in Open Educational Resources Repositories: An Exploratory Study 元数据在开放教育资源库中的应用:一项探索性研究

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC Pub Date : 2020-07-13 DOI: 10.1109/COMPSAC48688.2020.00025

William Simão de Deus, E. Barbosa

{"title":"The Use of Metadata in Open Educational Resources Repositories: An Exploratory Study","authors":"William Simão de Deus, E. Barbosa","doi":"10.1109/COMPSAC48688.2020.00025","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00025","url":null,"abstract":"Context: Open Educational Resources (OERs) are free and open materials to support teaching and learning. Generally, OERs are stored in digital repositories and use metadata to describe their content. Because of factors such as dissemination and use of resources, the OER repository collections are increasing rapidly, along with your metadata. However, the metadata is not yet a correctly and frequently used by users, generating several problems finding and retrieving OER. Objective: Considering this scenario, we intend to identify how metadata are being used in the context of OER and what are its implications for finding resources in OER repositories. Method: For this, we conducted an exploratory study across a robust data set composed of 1,243,938 metadata. This data set was build with web crawlers that automatically extracted and organized the data into spreadsheets. Results: Through our research, we identified the main challenges of metadata used in OER context, the impact in the search engines, and identified the key standards and values adopted. Conclusion: Among the most common issues detected we highlight the problem of standardization of metadata values, the lack of presentation of relevant data, the low utilization of metadata during retrieve operation by search engines.","PeriodicalId":74502,"journal":{"name":"Proceedings : Annual International Computer Software and Applications Conference. COMPSAC","volume":"43 1","pages":"123-132"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90210791","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}

引用次数: 2

A Novel Gesture Detection Technique to Increase Security in NFC Contactless Smartcards 一种新的手势检测技术提高NFC非接触式智能卡的安全性

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC Pub Date : 2020-07-01 DOI: 10.1109/COMPSAC48688.2020.00051

Daniel Pérez Asensio, A. P. Yuste

引用次数: 1

Generating Region of Interests for Invasive Breast Cancer in Histopathological Whole-Slide-Image. 从组织病理全切片图像中生成浸润性乳腺癌的兴趣区

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC Pub Date : 2020-07-01 Epub Date: 2020-09-22 DOI: 10.1109/compsac48688.2020.0-174

Shreyas Malakarjun Patil, Li Tong, May D Wang

{"title":"Generating Region of Interests for Invasive Breast Cancer in Histopathological Whole-Slide-Image.","authors":"Shreyas Malakarjun Patil, Li Tong, May D Wang","doi":"10.1109/compsac48688.2020.0-174","DOIUrl":"10.1109/compsac48688.2020.0-174","url":null,"abstract":"The detection of the region of interests (ROIs) on Whole Slide Images (WSIs) is one of the primary steps in computer-aided cancer diagnosis and grading. Early and accurate identification of invasive cancer regions in WSI is critical in the improvement of breast cancer diagnosis and further improvements in patient survival rates. However, invasive cancer ROI segmentation is a challenging task on WSI because of the low contrast of invasive cancer cells and their high similarity in terms of appearance, to non-invasive regions. In this paper, we propose a CNN based architecture for generating ROIs through segmentation. The network tackles the constraints of data-driven learning and working with very low-resolution WSI data in the detection of invasive breast cancer. Our proposed approach is based on transfer learning and the use of dilated convolutions. We propose a highly modified version of U-Net based auto-encoder, which takes as input an entire WSI with a resolution of 320×320. The network was trained on low-resolution WSI from four different data cohorts and has been tested for inter as well as intra- dataset variance. The proposed architecture shows significant improvements in terms of accuracy for the detection of invasive breast cancer regions.","PeriodicalId":74502,"journal":{"name":"Proceedings : Annual International Computer Software and Applications Conference. COMPSAC","volume":"2020 ","pages":"723-728"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7537355/pdf/nihms-1602234.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38567544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving the Behavior of Evasive Targets in Cooperative Target Observation 改进协同目标观测中回避目标的行为

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC Pub Date : 2020-07-01 DOI: 10.1109/COMPSAC48688.2020.00015

T. Silva, M. Araújo, R. J. F. Junior, L. Costa, J. Andrade, G. Campos, J. Celestino

{"title":"Improving the Behavior of Evasive Targets in Cooperative Target Observation","authors":"T. Silva, M. Araújo, R. J. F. Junior, L. Costa, J. Andrade, G. Campos, J. Celestino","doi":"10.1109/COMPSAC48688.2020.00015","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00015","url":null,"abstract":"The Cooperative Targets Observation (CTO) problem consists of two groups of agents: observers and targets. The observer agents aim to maximize the Average Number of Observed Targets (ANOT) in environments where there are more targets than observers. In most of the approaches to this problem, the behavior of the target agents is very simple, out of reality in competitive multiagent environments. More recently, two strategies improved the behavior of the targets when under observation, i.e., the straight-line strategy and controlled randomization. However, in a surveillance scenario, it is reasonable to assume that targets can be modeled as an organization, with rules, structures of authorities and relationships, and rationality to try to predict the behavior of observers. The objective of this work is to propose and validate four strategies for the team of target agents in the CTO problem, three involving clustering algorithms and two organizational paradigms and one using neural networks. The approaches were implemented and tested on the NetLogo agent-based simulation platform. The results showed that target team performance increased considerably when these were modeled as rational agents in an organization and able to try to predict the behavior of their observers.","PeriodicalId":74502,"journal":{"name":"Proceedings : Annual International Computer Software and Applications Conference. COMPSAC","volume":"202 1","pages":"36-41"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77001795","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}

引用次数: 1

Regularization of Deep Neural Networks for EEG Seizure Detection to Mitigate Overfitting. 深度神经网络的正则化用于脑电癫痫检测以缓解过度拟合。

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC Pub Date : 2020-07-01 Epub Date: 2020-09-22 DOI: 10.1109/COMPSAC48688.2020.0-182

Mohammed Saqib, Yuanda Zhu, May Dongmei Wang, Brett Beaulieu-Jones

{"title":"Regularization of Deep Neural Networks for EEG Seizure Detection to Mitigate Overfitting.","authors":"Mohammed Saqib, Yuanda Zhu, May Dongmei Wang, Brett Beaulieu-Jones","doi":"10.1109/COMPSAC48688.2020.0-182","DOIUrl":"10.1109/COMPSAC48688.2020.0-182","url":null,"abstract":"Seizure detection is a major goal for simplifying the workflow of clinicians working on EEG records. Current algorithms can only detect seizures effectively for patients already presented to the classifier. These algorithms are hard to generalize outside the initial training set without proper regularization and fail to capture seizures from the larger population. We proposed a data processing pipeline for seizure detection on an intra-patient dataset from the world's largest public EEG seizure corpus. We created spatially and session invariant features by forcing our networks to rely less on exact combinations of channels and signal amplitudes, but instead to learn dependencies towards seizure detection. For comparison, the baseline results without any additional regularization on a deep learning model achieved an F1 score of 0.544. By using random rearrangements of channels on each minibatch to force the network to generalize to other combinations of channels, we increased the F1 score to 0.629. By using random rescale of the data within a small range, we further increased the F1 score to 0.651 for our best model. Additionally, we applied adversarial multi-task learning and achieved similar results. We observed that session and patient specific dependencies were causing overfitting of deep neural networks, and the most overfitting models learnt features specific only to the EEG data presented. Thus, we created networks with regularization that the deep learning did not learn patient and session-specific features. We are the first to use random rearrangement, random rescale, and adversarial multitask learning to regularize intra-patient seizure detection and have increased sensitivity to 0.86 comparing to baseline study.","PeriodicalId":74502,"journal":{"name":"Proceedings : Annual International Computer Software and Applications Conference. COMPSAC","volume":"2020 ","pages":"664-673"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/COMPSAC48688.2020.0-182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38502651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Resource-Saving Approach for Adding Redundancy to a Network-on-Chip System 一种为片上网络系统增加冗余的资源节约方法

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC Pub Date : 2020-01-01 DOI: 10.1109/COMPSAC48688.2020.00-57

A. Osadchuk, B. Däne, W. Fengler

引用次数: 0

STPSA 2019 Welcome Message STPSA 2019欢迎辞

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC Pub Date : 2019-07-01 DOI: 10.1109/COMPSAC.2019.10267

Sheikh Iqbal Ahamed, Mohammad Zulkernine, H. Shahriar, H. Chi

引用次数: 0

Improving Classification of Breast Cancer by Utilizing the Image Pyramids of Whole-Slide Imaging and Multi-Scale Convolutional Neural Networks. 利用整体滑动成像的图像金字塔和多尺度卷积神经网络改进乳腺癌分类。

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC Pub Date : 2019-07-01 Epub Date: 2019-07-09 DOI: 10.1109/compsac.2019.00105

Li Tong, Ying Sha, May D Wang

{"title":"Improving Classification of Breast Cancer by Utilizing the Image Pyramids of Whole-Slide Imaging and Multi-Scale Convolutional Neural Networks.","authors":"Li Tong, Ying Sha, May D Wang","doi":"10.1109/compsac.2019.00105","DOIUrl":"10.1109/compsac.2019.00105","url":null,"abstract":"Whole-slide imaging (WSI) is the digitization of conventional glass slides. Automatic computer-aided diagnosis (CAD) based on WSI enables digital pathology and the integration of pathology with other data like genomic biomarkers. Numerous computational algorithms have been developed for WSI, with most of them taking the image patches cropped from the highest resolution as the input. However, these models exploit only the local information within each patch and lost the connections between the neighboring patches, which may contain important context information. In this paper, we propose a novel multi-scale convolutional network (ConvNet) to utilize the built-in image pyramids of WSI. For the concentric image patches cropped at the same location of different resolution levels, we hypothesize the extra input images from lower magnifications will provide context information to enhance the prediction of patch images. We build corresponding ConvNets for feature representation and then combine the extracted features by 1) late fusion: concatenation or averaging the feature vectors before performing classification, 2) early fusion: merge the ConvNet feature maps. We have applied the multi-scale networks to a benchmark breast cancer WSI dataset. Extensive experiments have demonstrated that our multiscale networks utilizing the WSI image pyramids can achieve higher accuracy for the classification of breast cancer. The late fusion method by taking the average of feature vectors reaches the highest accuracy (81.50%), which is promising for the application of multi-scale analysis of WSI.","PeriodicalId":74502,"journal":{"name":"Proceedings : Annual International Computer Software and Applications Conference. COMPSAC","volume":"2019 ","pages":"696-703"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302109/pdf/nihms-1595604.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38067062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0