Pub Date : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-173
Ricardo Buettner, Kai Wannenwetsch, Daniel Loskan
Computer-assisted surgical procedures have become a major part of the development of robotics and medicine. These treatments can offer many benefits, such as a shorter recovery time, and improved quality and accuracy of diagnoses. We reviewed computer support literature for surgical interventions included in top peer-reviewed journals and conferences. Based on the review, we identify areas which are ready for computer support in surgical interventions and show future research needs-
{"title":"A Systematic Literature Review of Computer Support for Surgical Interventions","authors":"Ricardo Buettner, Kai Wannenwetsch, Daniel Loskan","doi":"10.1109/COMPSAC48688.2020.0-173","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-173","url":null,"abstract":"Computer-assisted surgical procedures have become a major part of the development of robotics and medicine. These treatments can offer many benefits, such as a shorter recovery time, and improved quality and accuracy of diagnoses. We reviewed computer support literature for surgical interventions included in top peer-reviewed journals and conferences. Based on the review, we identify areas which are ready for computer support in surgical interventions and show future research needs-","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115572031","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-205
R. Harkanson, Carter Chiu, Yoohwan Kim, Ju-Yeon Jo
The transparency and immutability properties offered by the burgeoning field of blockchain technology make it an increasingly popular choice for applications across many domains. One such application is online gaming. Blockchain offers participants the ability to verify the fairness of games in a manner previously unattainable by the classical centralized approach. However, the introduction of blockchain incurs additional overhead and poses unique challenges necessary to overcome in order to be a viable alternative. The transparency blockchain provides opens potential avenues for collusion, particularly in multiplayer games, which must be addressed. Furthermore, the emulation of random state, a core component of online gaming, is rendered difficult in a decentralized context. No approach exists which manages random state for multiplayer gaming in a completely decentralized manner. We propose a novel approach toward this end, significantly extending the utility of blockchain technology to online gaming.
{"title":"A Framework for Decentralized Private Random State Generation and Maintenance for Multiplayer Gaming Over Blockchain","authors":"R. Harkanson, Carter Chiu, Yoohwan Kim, Ju-Yeon Jo","doi":"10.1109/COMPSAC48688.2020.0-205","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-205","url":null,"abstract":"The transparency and immutability properties offered by the burgeoning field of blockchain technology make it an increasingly popular choice for applications across many domains. One such application is online gaming. Blockchain offers participants the ability to verify the fairness of games in a manner previously unattainable by the classical centralized approach. However, the introduction of blockchain incurs additional overhead and poses unique challenges necessary to overcome in order to be a viable alternative. The transparency blockchain provides opens potential avenues for collusion, particularly in multiplayer games, which must be addressed. Furthermore, the emulation of random state, a core component of online gaming, is rendered difficult in a decentralized context. No approach exists which manages random state for multiplayer gaming in a completely decentralized manner. We propose a novel approach toward this end, significantly extending the utility of blockchain technology to online gaming.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122619929","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.000-4
V. Aistov, Benedikt Kirpes, Micha Roon
Recent advances in distributed ledger technologies enable new types of decentralized governance and financing for technology platforms. In this paper, we analyze the current state-of-the-art in platform financing and propose a novel way to sustainably finance decentralized technology platforms using a blockchain-based token economy. We design and develop a token model and demonstrate its usefulness for financing the Open Charging Network, an electric vehicle charging platform governed by the Share&Charge foundation. Based on a multi-method simulation approach, we evaluate our token economy model and show, that it can provide sustainable financing for a technology platform with decentralized governance.
{"title":"A Blockchain Token Economy Model for Financing a Decentralized Electric Vehicle Charging Platform","authors":"V. Aistov, Benedikt Kirpes, Micha Roon","doi":"10.1109/COMPSAC48688.2020.000-4","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.000-4","url":null,"abstract":"Recent advances in distributed ledger technologies enable new types of decentralized governance and financing for technology platforms. In this paper, we analyze the current state-of-the-art in platform financing and propose a novel way to sustainably finance decentralized technology platforms using a blockchain-based token economy. We design and develop a token model and demonstrate its usefulness for financing the Open Charging Network, an electric vehicle charging platform governed by the Share&Charge foundation. Based on a multi-method simulation approach, we evaluate our token economy model and show, that it can provide sustainable financing for a technology platform with decentralized governance.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114484597","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00033
Caroline D. Hardin, Jennifer Dalsen
To address a gap in digital privacy education, the authors created a theoretically informed interactive game to help middle and high school students gain a systems thinking perspective of the sociocultural aspect of negotiating digital privacy. The game has been run at three technology conferences, from which pilot data shows students gaining new skills in conceptualizing how (in addition to discretion and technology literacy) they can perform digital privacy socioculturally to resolve the tensions between their figured worlds. In addition, a lesson plan and a website have been created to help teachers access and utilize this game. This paper discusses the theoretical framework, design decisions, pilot data, and future work planned for Digital Privacy Detectives.
{"title":"Digital Privacy Detectives: An Interactive Game for Classrooms","authors":"Caroline D. Hardin, Jennifer Dalsen","doi":"10.1109/COMPSAC48688.2020.00033","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00033","url":null,"abstract":"To address a gap in digital privacy education, the authors created a theoretically informed interactive game to help middle and high school students gain a systems thinking perspective of the sociocultural aspect of negotiating digital privacy. The game has been run at three technology conferences, from which pilot data shows students gaining new skills in conceptualizing how (in addition to discretion and technology literacy) they can perform digital privacy socioculturally to resolve the tensions between their figured worlds. In addition, a lesson plan and a website have been created to help teachers access and utilize this game. This paper discusses the theoretical framework, design decisions, pilot data, and future work planned for Digital Privacy Detectives.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128467353","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00041
Xiang Li, Jingsha He, Nafei Zhu, Ziqiang Hou
Collaborative filtering based on single domains has become widely used in today's recommendation system. Nevertheless, it has two problems that need to be solved, i.e., the cold start problem and the data sparseness problem. As the result, cross-domain recommendation technology has emerged, which aims at integrating user preference characteristics from different domains. This paper proposes a collaborative filtering recommendation method based on multi-domain semantic fusion (CF-MDS). CF-MDS achieves cross-domain item similarity calculation through semantic analysis and ontology and integrates data from different domains iteratively based on domain relevance to rate users on target domain items and to produce a cross-domain user-item rating matrix. Collaborative filtering technology is then combined with multi-domain fusion recommendation algorithm. Experimental results show that the proposed method can deal effectively with the cold start problem and data sparsity problem that exist in traditional recommendation systems as well as can improve the diversity of recommendation. Compared to other cross-domain recommendation methods, the proposed method can better meet personal needs of users and also improve the accuracy of recommendation.
{"title":"Collaborative Filtering Recommendation Based on Multi-Domain Semantic Fusion","authors":"Xiang Li, Jingsha He, Nafei Zhu, Ziqiang Hou","doi":"10.1109/COMPSAC48688.2020.00041","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00041","url":null,"abstract":"Collaborative filtering based on single domains has become widely used in today's recommendation system. Nevertheless, it has two problems that need to be solved, i.e., the cold start problem and the data sparseness problem. As the result, cross-domain recommendation technology has emerged, which aims at integrating user preference characteristics from different domains. This paper proposes a collaborative filtering recommendation method based on multi-domain semantic fusion (CF-MDS). CF-MDS achieves cross-domain item similarity calculation through semantic analysis and ontology and integrates data from different domains iteratively based on domain relevance to rate users on target domain items and to produce a cross-domain user-item rating matrix. Collaborative filtering technology is then combined with multi-domain fusion recommendation algorithm. Experimental results show that the proposed method can deal effectively with the cold start problem and data sparsity problem that exist in traditional recommendation systems as well as can improve the diversity of recommendation. Compared to other cross-domain recommendation methods, the proposed method can better meet personal needs of users and also improve the accuracy of recommendation.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129035538","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-76
B. Banitalebi, S. S. Appadoo, A. Thavaneswaran, Md. Erfanul Hoque
Electricity is a special commodity that has to be kept available at all times. In fact, power plants need to have accurate forecast of electricity demand in order to provide enough electricity for customers. Final customers are able to establish their own power plants to decrease their dependency on the grid. For example rooftop photovoltaic panels are getting more popular among residential customers. It seems that meteorological variables such as solar irradiance play an important role in load forecasting. Moreover, temperature is also a main determinant of electricity demand. In this paper, we propose a model for shortterm load forecasting which consists of hourly weather data (including seasonal variation as well) and historical load data. Machine learning algorithms such as support vector regression (SVR), least absolute shrinkage and selection operator (LASSO) regression and a multilayer neural network (NN) are used for short-term load forecasting. In order to improve the forecast accuracy (smaller mean absolute error) of NN, we propose a dual phase forecasting method. In the first phase, data driven double exponential smoothing (DDDES) is used to generate electricity load forecasts. In the second phase, the results of first phase forecasting are fed into a multilayer NN to have more accurate forecasts of electricity demand. It is shown that NN outperforms the other two methods. Our data analysis shows a significant improvement in terms of performance where maximum mean absolute error (MAE) decreases from 367.26 to 115.30.
{"title":"Modeling of Short-Term Electricity Demand and Comparison of Machine Learning Approaches for Load Forecasting","authors":"B. Banitalebi, S. S. Appadoo, A. Thavaneswaran, Md. Erfanul Hoque","doi":"10.1109/COMPSAC48688.2020.00-76","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-76","url":null,"abstract":"Electricity is a special commodity that has to be kept available at all times. In fact, power plants need to have accurate forecast of electricity demand in order to provide enough electricity for customers. Final customers are able to establish their own power plants to decrease their dependency on the grid. For example rooftop photovoltaic panels are getting more popular among residential customers. It seems that meteorological variables such as solar irradiance play an important role in load forecasting. Moreover, temperature is also a main determinant of electricity demand. In this paper, we propose a model for shortterm load forecasting which consists of hourly weather data (including seasonal variation as well) and historical load data. Machine learning algorithms such as support vector regression (SVR), least absolute shrinkage and selection operator (LASSO) regression and a multilayer neural network (NN) are used for short-term load forecasting. In order to improve the forecast accuracy (smaller mean absolute error) of NN, we propose a dual phase forecasting method. In the first phase, data driven double exponential smoothing (DDDES) is used to generate electricity load forecasts. In the second phase, the results of first phase forecasting are fed into a multilayer NN to have more accurate forecasts of electricity demand. It is shown that NN outperforms the other two methods. Our data analysis shows a significant improvement in terms of performance where maximum mean absolute error (MAE) decreases from 367.26 to 115.30.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129616625","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-130
Zhimin Chen, Xingang Wang, Heng Li, Hu Wang
With the proliferation of positioning mobile devices, people’s trajectory data are posted on the net including spatial locations and semantic contexts such as in the form of text like twitter posted text. How to organize or fuse the raw spatial trajectories and context semantic data into a structured whole for analysis further is a problem, the focus of which is mostly how to annotate episodes in raw trajectories. In this paper we examine a structured and partially self-describing way for semantic organization and fusion of trajectory data. We annotate episodes with structured sentiments, events, or topic words, where sentiments given in a self-describing way and events are represented using the form from the natural language processing literature. Besides, all the data in the whole model are represented with JSON.
{"title":"On Semantic Organization and Fusion of Trajectory Data","authors":"Zhimin Chen, Xingang Wang, Heng Li, Hu Wang","doi":"10.1109/COMPSAC48688.2020.0-130","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-130","url":null,"abstract":"With the proliferation of positioning mobile devices, people’s trajectory data are posted on the net including spatial locations and semantic contexts such as in the form of text like twitter posted text. How to organize or fuse the raw spatial trajectories and context semantic data into a structured whole for analysis further is a problem, the focus of which is mostly how to annotate episodes in raw trajectories. In this paper we examine a structured and partially self-describing way for semantic organization and fusion of trajectory data. We annotate episodes with structured sentiments, events, or topic words, where sentiments given in a self-describing way and events are represented using the form from the natural language processing literature. Besides, all the data in the whole model are represented with JSON.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126995837","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00287
J. So, Ho Wai Tung, Ada P. L. Chan, Simon C. K. Wong, Adam Wong, Henry C. B. Chan
Smart Learning Environment (SLE) aims at promoting personalized education in various form with different settings fitting the learners’ needs. Many works have done to realise the environment for academic studies. However, the development of the generic competencies is another key element of education. The engagement of students in the developmental activities of generic competencies (GDA) is a main concern of the organizers in tertiary education institutions, particularly the student affair offices. They want to have some predictors to reflect the participation of students with some identifiable factors so that the provision can planned correspondingly. In this work, we attempted to the evaluation on a set of attributes of students to their participation on the GDA by means of the correlation and the classification through logical regression. We studied the records of 1649 graduates in a tertiary education institution across two academic years and found that some single factors are reliable in predicting the tendency of students in taking part in the GDA.
{"title":"Developing Predictors for Student Involvement in Generic Competency Development Activities in Smart Learning Environment","authors":"J. So, Ho Wai Tung, Ada P. L. Chan, Simon C. K. Wong, Adam Wong, Henry C. B. Chan","doi":"10.1109/COMPSAC48688.2020.00287","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00287","url":null,"abstract":"Smart Learning Environment (SLE) aims at promoting personalized education in various form with different settings fitting the learners’ needs. Many works have done to realise the environment for academic studies. However, the development of the generic competencies is another key element of education. The engagement of students in the developmental activities of generic competencies (GDA) is a main concern of the organizers in tertiary education institutions, particularly the student affair offices. They want to have some predictors to reflect the participation of students with some identifiable factors so that the provision can planned correspondingly. In this work, we attempted to the evaluation on a set of attributes of students to their participation on the GDA by means of the correlation and the classification through logical regression. We studied the records of 1649 graduates in a tertiary education institution across two academic years and found that some single factors are reliable in predicting the tendency of students in taking part in the GDA.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"139 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120869308","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-83
Ranran Wang, G. Hu, Chi Jiang, Huimin Lu, Yin Zhang
With the spread of COVID-19 worldwide, people¡¯s production and life have been significantly affected. Artificial intelligence and big data technologies have been vigorously developed in recent years. It is very significant to use data science and technology to help humans in a timely and accurate manner to prevent and control the development of the epidemic, maintain social stability and assess the impact of the epidemic. This paper explores how data science can play a role from the perspectives of epidemiology, social networking, and economics. In particular, for the existing epidemic model SIR, we present a parameter learning method using particle swarm optimization (PSO) and the least squares method, and use it to predict the trend of the epidemic. Aiming at the social network data, we provide a specific method to realize sentiment analysis during the epidemic and propose an explainable fake news detection technique based on a variety of data mining methods.
{"title":"Data Analytics for the COVID-19 Epidemic","authors":"Ranran Wang, G. Hu, Chi Jiang, Huimin Lu, Yin Zhang","doi":"10.1109/COMPSAC48688.2020.00-83","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-83","url":null,"abstract":"With the spread of COVID-19 worldwide, people¡¯s production and life have been significantly affected. Artificial intelligence and big data technologies have been vigorously developed in recent years. It is very significant to use data science and technology to help humans in a timely and accurate manner to prevent and control the development of the epidemic, maintain social stability and assess the impact of the epidemic. This paper explores how data science can play a role from the perspectives of epidemiology, social networking, and economics. In particular, for the existing epidemic model SIR, we present a parameter learning method using particle swarm optimization (PSO) and the least squares method, and use it to predict the trend of the epidemic. Aiming at the social network data, we provide a specific method to realize sentiment analysis during the epidemic and propose an explainable fake news detection technique based on a variety of data mining methods.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116263973","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-52
Jamesa V. Hogges, H. Shahriar, S. Sneha, Sheikh Iqbal Ahamed
Alzheimer's disease, the most common type of dementia, is ranked sixth amongst the leading causes of death in the United States. As the disease progresses, individuals affected will experience challenges with memory loss, vision impairment, word-finding, and reasoning. When riddled with such symptoms, password memorization can pose a problem. Even though there are several authentication systems in play, none considers all signs and symptoms Alzheimer's disease can cause. We propose a two-step password authentication system that would utilize geolocation and fingerprint biometric screening to assist this specific population by providing a more secure way to access their information.
{"title":"A Two-Step Password Authentication System for Alzheimer Patients","authors":"Jamesa V. Hogges, H. Shahriar, S. Sneha, Sheikh Iqbal Ahamed","doi":"10.1109/COMPSAC48688.2020.00-52","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-52","url":null,"abstract":"Alzheimer's disease, the most common type of dementia, is ranked sixth amongst the leading causes of death in the United States. As the disease progresses, individuals affected will experience challenges with memory loss, vision impairment, word-finding, and reasoning. When riddled with such symptoms, password memorization can pose a problem. Even though there are several authentication systems in play, none considers all signs and symptoms Alzheimer's disease can cause. We propose a two-step password authentication system that would utilize geolocation and fingerprint biometric screening to assist this specific population by providing a more secure way to access their information.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121528130","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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