2020 Second International Conference on Transdisciplinary AI (TransAI)最新文献_第2页

NPDSLINKS: Nexus-PORTAL-DOORS-Scribe Learning Intelligence aNd Knowledge System Nexus-PORTAL-DOORS-Scribe学习智能和知识系统

2020 Second International Conference on Transdisciplinary AI (TransAI) Pub Date : 2020-09-01 DOI: 10.1109/TransAI49837.2020.00027

Shreya Choksi, Peter Hong, Sohyb Mashkoor, C. Taswell

{"title":"NPDSLINKS: Nexus-PORTAL-DOORS-Scribe Learning Intelligence aNd Knowledge System","authors":"Shreya Choksi, Peter Hong, Sohyb Mashkoor, C. Taswell","doi":"10.1109/TransAI49837.2020.00027","DOIUrl":"https://doi.org/10.1109/TransAI49837.2020.00027","url":null,"abstract":"With the continuing growth in use of large complex data sets for artificial intelligence applications (AIA), unbiased methods should be established for assuring the validity and reliability of both input data and output results. Advancing such standards will help to reduce problems described with the aphorism ‘Garbage In, Garbage Out’ (GIGO). This concern remains especially important for AIA tools that execute within the environment of interoperable systems which share, exchange, convert, and/or interchange data and metadata such as the Nexus-PORTAL-DOORS-Scribe (NPDS) cyberinfrastructure and its associated Learning Intelligence aNd Knowledge System (LINKS) applications. The PORTAL-DOORS Project (PDP) has developed the NPDS cyberinfrastructure with lexical PORTAL registries, semantic DOORS directories, hybrid Nexus diristries, and Scribe registrars. As a self-referencing and self-describing system, the NPDS cyberinfrastructure has been designed to operate as a pervasive distributed network of data repositories compliant with the Hierarchically Distributed Mobile Metadata (HDMM) architectural style. Building on the foundation of the NPDS cyberinfrastructure with its focus on data, PDP has now introduced LINKS applications with their focus on algorithms and analysis of the data. In addition, PDP has launched a pair of new websites at NPDSLINKS.net and NPDSLINKS.org which will serve respectively as the root of the NPDS cyberinfrastructure and the home for definitions and standards on quality descriptors and quantitative measures to evaluate the data contained within NPDS records. Prototypes of these descriptors and measures for use with NPDS and LINKS are introduced in this report. PDP envisions building better AIA and preventing the unwanted phenomenon of GIGO by using the combination of metrics to detect and reduce bias from data, the NPDS cyberinfrastructure for the data, and LINKS applications for the algorithms.","PeriodicalId":151527,"journal":{"name":"2020 Second International Conference on Transdisciplinary AI (TransAI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124405575","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: 3

Towards Passive Authentication using Inertia Variations: An Experimental Study on Smartphones 利用惯性变化实现被动认证:智能手机的实验研究

2020 Second International Conference on Transdisciplinary AI (TransAI) Pub Date : 2020-09-01 DOI: 10.1109/TransAI49837.2020.00019

James Brown, Aaditya Raval, Mohd Anwar

引用次数: 1

Applications of AI in cybersecurity 人工智能在网络安全中的应用

2020 Second International Conference on Transdisciplinary AI (TransAI) Pub Date : 2020-09-01 DOI: 10.1109/TransAI49837.2020.00031

Matthias Hofstetter, R. Riedl, Thomas Gees, A. Koumpis, Thomas Schaberreiter

引用次数: 4

Learning and Intelligence in Human-Cyber-Physical Systems: Framework and Perspective 人-信息-物理系统中的学习与智能:框架与视角

2020 Second International Conference on Transdisciplinary AI (TransAI) Pub Date : 2020-09-01 DOI: 10.1109/TransAI49837.2020.00032

Baicun Wang, Xingyu Li, T. Freiheit, B. Epureanu

引用次数: 8

A unified framework on node classification using graph convolutional networks 基于图卷积网络的节点分类统一框架

2020 Second International Conference on Transdisciplinary AI (TransAI) Pub Date : 2020-09-01 DOI: 10.1109/TransAI49837.2020.00015

Saurabh Mithe, Katerina Potika

{"title":"A unified framework on node classification using graph convolutional networks","authors":"Saurabh Mithe, Katerina Potika","doi":"10.1109/TransAI49837.2020.00015","DOIUrl":"https://doi.org/10.1109/TransAI49837.2020.00015","url":null,"abstract":"Graphs contain a plethora of valuable information about the underlying data which can be extracted, analyzed, and visualized using Machine Learning (ML). The challenge is that graphs are non-Euclidean structures, and cannot be directly used with ML techniques. In order to overcome this challenge, one way is to encode nodes into an equivalent Euclidean representation in the form of a low-dimensional vector, also called an embedding vector, and the encoding process is called node embedding. During the recent years, various ML techniques have been developed that learn the encoding of the nodes automatically. Some of these techniques, called Graph Convolutional Networks (GCN), use variants of the convolutional neural networks adapted for graphs. The focus of this paper is two-fold. Firstly, to develop a unified framework focusing on three major GCN techniques in order to analyze, evaluate, and compare their performance on select benchmark datasets for the task of node classification. And secondly, to implement a new attention aggregator for GraphSAGE, and compare the performance of the aggregator with the existing GCN methods as well as the other aggregators provided by GraphSAGE.","PeriodicalId":151527,"journal":{"name":"2020 Second International Conference on Transdisciplinary AI (TransAI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123546925","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

Object Detection and Segmentation in Chest X-rays for Tuberculosis Screening 胸片结核筛查中的目标检测与分割

2020 Second International Conference on Transdisciplinary AI (TransAI) Pub Date : 2020-09-01 DOI: 10.1109/TransAI49837.2020.00011

Terence Griffin, Yu Cao, Benyuan Liu, M. Brunette

{"title":"Object Detection and Segmentation in Chest X-rays for Tuberculosis Screening","authors":"Terence Griffin, Yu Cao, Benyuan Liu, M. Brunette","doi":"10.1109/TransAI49837.2020.00011","DOIUrl":"https://doi.org/10.1109/TransAI49837.2020.00011","url":null,"abstract":"Tuberculosis (TB) is a contagious disease leading to the deaths of approximately 2 million people annually. Providing healthcare professionals with better information, at a faster pace, is essential for combating this disease, especially in Low and Middle Income Countries with resource-constrained health systems. In this paper we describe how using convolution neural networks (CNNs) with an object level annotated dataset of chest X-rays (CXRs) allows us to identify the location of pulmonary issues indicative of TB. We compare the performance of Faster R-CNN, Mask R-CNN, and Cascade versions of each, demonstrating reasonable results with a small dataset. We present a method to reduce the false positive rate by comparing the location of a detected object with the known location of areas where the detected class is likely to occur in the lung. Our results show that with a dataset of high-quality, object level annotations, object detection and segmentation of CXRs is possible and could be used as part of an automated TB screening process. This work has the potential to improve the speed of TB diagnosis, if implemented within the corresponding health care system and adapted to existing clinical worktlows.","PeriodicalId":151527,"journal":{"name":"2020 Second International Conference on Transdisciplinary AI (TransAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129748032","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

Regions Discovery Algorithm for Pathfinding in Grid Based Maps 网格地图中寻路的区域发现算法

2020 Second International Conference on Transdisciplinary AI (TransAI) Pub Date : 2020-09-01 DOI: 10.1109/TransAI49837.2020.00018

Ying Fung Yiu, R. Mahapatra

{"title":"Regions Discovery Algorithm for Pathfinding in Grid Based Maps","authors":"Ying Fung Yiu, R. Mahapatra","doi":"10.1109/TransAI49837.2020.00018","DOIUrl":"https://doi.org/10.1109/TransAI49837.2020.00018","url":null,"abstract":"Pathfinding problems often have to be solved under many constraints including limited processing time, memory, and computational power. The challenges become bigger as the size and complexity of the search space increase. Therefore, pathfinding on large and complex maps can result in performance bottlenecks. Researchers proposed to reduce the search space using preprocessing techniques such as hierarchical pathfinding to overcome the bottlenecks. In this paper we present a novel graph partition technique to boost the speed of pathfinding and preserve optimality for grid based environments. To overcome the weaknesses of clustering methods that are used in traditional hierarchical pathfinding algorithms, we propose to develop a graph decomposition algorithm that abstracts regions based on local features. The objective of our approach is to maintain the pathfinding optimality by only eliminating the regions that are obsolete. Thus, any possible solution path will not be eliminated during the search. Our experiment results show that a search space can be reduced as much as 47%, leading to much faster execution and less memory consumption.","PeriodicalId":151527,"journal":{"name":"2020 Second International Conference on Transdisciplinary AI (TransAI)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125655149","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: 3

Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook 使用图形计算的金融犯罪和欺诈检测:应用考虑与展望

2020 Second International Conference on Transdisciplinary AI (TransAI) Pub Date : 2020-09-01 DOI: 10.1109/TransAI49837.2020.00029

Eren Kurshan, Honda Shen, Haojie Yu

引用次数: 13

Return to Bali 返回巴厘岛

2020 Second International Conference on Transdisciplinary AI (TransAI) Pub Date : 2020-09-01 DOI: 10.1109/TransAI49837.2020.00020

M. Böhlen, W. Sujarwo

引用次数: 1

Design and Analysis of Mobile App for Large-Scale Cyber-Argumentation 大型网络辩论移动App的设计与分析

2020 Second International Conference on Transdisciplinary AI (TransAI) Pub Date : 2020-09-01 DOI: 10.1109/TransAI49837.2020.00013

Najla Althuniyan, J. Sirrianni, Md Mahfuzer Rahman, X. Liu

{"title":"Design and Analysis of Mobile App for Large-Scale Cyber-Argumentation","authors":"Najla Althuniyan, J. Sirrianni, Md Mahfuzer Rahman, X. Liu","doi":"10.1109/TransAI49837.2020.00013","DOIUrl":"https://doi.org/10.1109/TransAI49837.2020.00013","url":null,"abstract":"People from different backgrounds share opinions about various issues over the Internet. The resulted discussions contain substantial information, from which we can derive the collective intelligence and the crowd wisdom. Several argumentation platforms have been developed to enable online deliberations with large-scale in-depth argumentation for effective online discussions. These platforms host structured argumentation networks that allow complex analytical models to mine the argumentation for collective intelligence. However, not all of those argumentation platforms were developed mobile applications. In this paper, we contribute with the design of a mobile application for cyber-argumentation. This mobile application supports intelligent cyber-argumentation and large-scale discussions and provides meaningful analytics on mobile devices. The platform has incorporated several analytical models to capture collective opinions, detect opinion polarization, and predict missing user opinions. An example is used to illustrate our design and models, and a system usability study of our application is presented. This application is an initial step to bring the multi-sided argumentation and deliberation into handheld devices and shows the potential in bringing multi-sided large-scale cyber-argumentation into the limited screen sizes platforms.","PeriodicalId":151527,"journal":{"name":"2020 Second International Conference on Transdisciplinary AI (TransAI)","volume":"36 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131575395","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: 0