Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00118
Rommel Fernandes, Lei Huang, G. Vejarano
Research advancement of human-computer interaction (HCI) has recently been made to help post-stroke victims dealing with physiological problems such as speech impediments due to aphasia. This paper investigates different deep learning approaches used for non-audible speech recognition using electromyography (EMG) signals with a novel approach employing continuous wavelet transforms (CWT) and convolutional neural networks (CNNs). To compare its performance with other popular deep learning approaches, we collected facial surface EMG bio-signals from subjects with binary and multi-class labels, trained and tested four models, including a long-short term memory(LSTM) model, a bi-directional LSTM model, a 1-D CNN model, and our proposed CWT-CNN model. Experimental results show that our proposed approach performs better than the LSTM models, but is less efficient than the 1-D CNN model on our collected data set. In comparison with previous research, we gained insights on how to improve the performance of the model for binary and multi-class silent speech recognition.
{"title":"Non-Audible Speech Classification Using Deep Learning Approaches","authors":"Rommel Fernandes, Lei Huang, G. Vejarano","doi":"10.1109/CSCI49370.2019.00118","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00118","url":null,"abstract":"Research advancement of human-computer interaction (HCI) has recently been made to help post-stroke victims dealing with physiological problems such as speech impediments due to aphasia. This paper investigates different deep learning approaches used for non-audible speech recognition using electromyography (EMG) signals with a novel approach employing continuous wavelet transforms (CWT) and convolutional neural networks (CNNs). To compare its performance with other popular deep learning approaches, we collected facial surface EMG bio-signals from subjects with binary and multi-class labels, trained and tested four models, including a long-short term memory(LSTM) model, a bi-directional LSTM model, a 1-D CNN model, and our proposed CWT-CNN model. Experimental results show that our proposed approach performs better than the LSTM models, but is less efficient than the 1-D CNN model on our collected data set. In comparison with previous research, we gained insights on how to improve the performance of the model for binary and multi-class silent speech recognition.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126021081","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 : 2019-12-01DOI: 10.1109/csci49370.2019.00084
Seungchul Lee, Daeyoung Kim
In manufacturing industry, significant productivity losses arise due to equipment failures. Therefore, it is an important task to prevent the equipment from failure by monitoring each machine's sensor data in advance. However, most of the current developed systems have been only focused on monitoring the sensor data and have a difficulty in applying advanced algorithms to the real-time stream data. To address issues, we implemented an intelligent system that employs real-time streaming engine loaded with the machine learning libraries for predictive maintenance analysis. By applying a deep-learning based model to the real-time streaming data, we can provide not only trends of raw sensor data but also give an indicator representing an equipment's status in real-time. We anticipate that our system contributes to recognize the equipment's status by monitoring the indicator for productivity improvement in manufacturing industry in real-time.
{"title":"A Real-Time Based Intelligent System for Predicting Equipment Status","authors":"Seungchul Lee, Daeyoung Kim","doi":"10.1109/csci49370.2019.00084","DOIUrl":"https://doi.org/10.1109/csci49370.2019.00084","url":null,"abstract":"In manufacturing industry, significant productivity losses arise due to equipment failures. Therefore, it is an important task to prevent the equipment from failure by monitoring each machine's sensor data in advance. However, most of the current developed systems have been only focused on monitoring the sensor data and have a difficulty in applying advanced algorithms to the real-time stream data. To address issues, we implemented an intelligent system that employs real-time streaming engine loaded with the machine learning libraries for predictive maintenance analysis. By applying a deep-learning based model to the real-time streaming data, we can provide not only trends of raw sensor data but also give an indicator representing an equipment's status in real-time. We anticipate that our system contributes to recognize the equipment's status by monitoring the indicator for productivity improvement in manufacturing industry in real-time.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126338065","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00139
R. Hasanah, Rakhmat Ramadhan, H. Suyono, T. Taufik
This paper presents a comparative study on the performance of PID and Voltage Mode Control (VMC) in a step-down voltage or buck DC-DC converter. The converter is being used in a smart wall plug for powering electrical devices in future smart house or building. Computer simulations using Simulink were performed to model the controllers in the converter and to investigate their performance. Results indicate that longer time is required by the VMC to reach a similar steady state condition as that acquired by the PID on the output voltage of the converter. Additionally, the steady state error on the output voltage from the PID was observed to be less than 1%, which is better than percent error obtained from the VMC.
{"title":"Performance Study of PID and Voltage Mode Controllers in Voltage Regulator for Smart DC Wall-Plug","authors":"R. Hasanah, Rakhmat Ramadhan, H. Suyono, T. Taufik","doi":"10.1109/CSCI49370.2019.00139","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00139","url":null,"abstract":"This paper presents a comparative study on the performance of PID and Voltage Mode Control (VMC) in a step-down voltage or buck DC-DC converter. The converter is being used in a smart wall plug for powering electrical devices in future smart house or building. Computer simulations using Simulink were performed to model the controllers in the converter and to investigate their performance. Results indicate that longer time is required by the VMC to reach a similar steady state condition as that acquired by the PID on the output voltage of the converter. Additionally, the steady state error on the output voltage from the PID was observed to be less than 1%, which is better than percent error obtained from the VMC.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123430062","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00113
Adam Hennad, P. Cockett, L. McLauchlan, M. Mehrubeoglu
Volume computations are important for the characterization of three-dimensional (3D) objects. In the case of irregularly-shaped objects, volumetric analysis remains challenging due to the missing symmetry in the geometry. 3D scanners provide a solution for digitizing the shape of objects for 3D visualization; however, typical scanners do not provide detailed quantitative information which offers significant advantage in both research and development applications. In this work, tools and operations that utilize digital 3D data captured via a 3D structured-light scanner are investigated to develop algorithms that accurately model and compute the volume of non-uniform objects. Specifically, limpet seashells are utilized to develop the models for volumetric analysis and characterization using MATLAB programming toolboxes after the 3D scans are completed.
{"title":"Characterization of Irregularly-Shaped Objects Using 3D Structured Light Scanning","authors":"Adam Hennad, P. Cockett, L. McLauchlan, M. Mehrubeoglu","doi":"10.1109/CSCI49370.2019.00113","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00113","url":null,"abstract":"Volume computations are important for the characterization of three-dimensional (3D) objects. In the case of irregularly-shaped objects, volumetric analysis remains challenging due to the missing symmetry in the geometry. 3D scanners provide a solution for digitizing the shape of objects for 3D visualization; however, typical scanners do not provide detailed quantitative information which offers significant advantage in both research and development applications. In this work, tools and operations that utilize digital 3D data captured via a 3D structured-light scanner are investigated to develop algorithms that accurately model and compute the volume of non-uniform objects. Specifically, limpet seashells are utilized to develop the models for volumetric analysis and characterization using MATLAB programming toolboxes after the 3D scans are completed.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126521971","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00061
Samuel Dixon, Raleigh Hansen, Wesley Deneke
We present a method of representing human activities as Probabilistic Context Free Grammars(PCFGs). Our method will allow these grammars to be learned from any source of data that describe sequences of human actions. We describe how representing human activities as PCFGs will allow them to be used for multiple proposed applications. The method proposed is interpretable such that the representation of an activity can be edited by a human annotator for further increase in performance. We also introduce a method of simulating realistic sequences of human actions, and describe how realistic noise is injected into this data. We propose methods of inducting grammars from this synthetic data and experiments to evaluate both the data and the ability of PCFGs to represent human activities.
{"title":"Probabilistic Grammar Induction for Long Term Human Activity Parsing","authors":"Samuel Dixon, Raleigh Hansen, Wesley Deneke","doi":"10.1109/CSCI49370.2019.00061","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00061","url":null,"abstract":"We present a method of representing human activities as Probabilistic Context Free Grammars(PCFGs). Our method will allow these grammars to be learned from any source of data that describe sequences of human actions. We describe how representing human activities as PCFGs will allow them to be used for multiple proposed applications. The method proposed is interpretable such that the representation of an activity can be edited by a human annotator for further increase in performance. We also introduce a method of simulating realistic sequences of human actions, and describe how realistic noise is injected into this data. We propose methods of inducting grammars from this synthetic data and experiments to evaluate both the data and the ability of PCFGs to represent human activities.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129203228","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00168
C. Beaton
Distance learning has brought phenomenal changes to the educational playing field. In higher education, variances of distance learning can mean blended learning, flipped classrooms, or video modules/components. While distance learning results in no physical in-person interaction, online supplements physical interpersonal interactions. This paper will focus on distance learning in relation to people with disabilities, demonstrating the challenges that are faced with providing access to learners.
{"title":"Distance Learning as a Levelling Tool for People with Disabilities","authors":"C. Beaton","doi":"10.1109/CSCI49370.2019.00168","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00168","url":null,"abstract":"Distance learning has brought phenomenal changes to the educational playing field. In higher education, variances of distance learning can mean blended learning, flipped classrooms, or video modules/components. While distance learning results in no physical in-person interaction, online supplements physical interpersonal interactions. This paper will focus on distance learning in relation to people with disabilities, demonstrating the challenges that are faced with providing access to learners.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130166774","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00224
Ahlam Mallak, Akash Sonnad, M. Fathi
A Mobile Wireless Sensor Network (MWSN) is a network of mobile sensor nodes that are spatially separated in an open or closed space, which work altogether to sense various system environmental and physical parameters. The state-of-the art is full of approaches for modelling WSNs and MWSNs using different simulation tools and programming languages. Such models require the system expert interference to change the simulated model itself whenever any change is required. Without this interference or having knowledge of the simulated system, these models tend to generate fixed-case sensor data and lack the dynamicity and the ability for further user-specific changes at run-time. In this paper, a two-phase dynamic simulation toolbox -so-called 'SenGen'- is presented and tested, where a full simulation of an indoor MWSN system is established using Simulink. Then a Graphical User Interface (GUI) is created with MATLAB, to overall perform as a dynamic toolbox for sensor data generation in MWSNs.
{"title":"SenGen: A Two-Phase Dynamic Simulation and Toolbox of an Indoor Mobile Wireless Sensor Network for Sensor Monitoring and Dataset Generation","authors":"Ahlam Mallak, Akash Sonnad, M. Fathi","doi":"10.1109/CSCI49370.2019.00224","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00224","url":null,"abstract":"A Mobile Wireless Sensor Network (MWSN) is a network of mobile sensor nodes that are spatially separated in an open or closed space, which work altogether to sense various system environmental and physical parameters. The state-of-the art is full of approaches for modelling WSNs and MWSNs using different simulation tools and programming languages. Such models require the system expert interference to change the simulated model itself whenever any change is required. Without this interference or having knowledge of the simulated system, these models tend to generate fixed-case sensor data and lack the dynamicity and the ability for further user-specific changes at run-time. In this paper, a two-phase dynamic simulation toolbox -so-called 'SenGen'- is presented and tested, where a full simulation of an indoor MWSN system is established using Simulink. Then a Graphical User Interface (GUI) is created with MATLAB, to overall perform as a dynamic toolbox for sensor data generation in MWSNs.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133957499","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00185
Raksha Pahlad, B. Gatsheni
Leaders at company AB within different functional areas needed to effectively facilitate the integration of BI initiatives into business operations. Semi-structured interviews were used to extract key concepts and attributes relevant to business functional areas, from business leaders and these were related to BI techniques. Thematic analysis on collected data was used to identify critical success factors (CSFs). A conceptual framework was developed which comprises business CSFs that are related to opportunities for value derivation from BI activities. This framework can be used as a guideline by Company AB for opportunity assessment and BI implementation, thereby enabling Company AB to leverage the value of BI. A decision tree predictive analytics model whose business rules potentially assist in proactive churn management for companies that have customer transaction volumes as a feature, was developed. This analytics model shows that claims that are not submitted to a client's historically most frequently used medical aids and variances in transactional claim volumes of more than 20%, are good indicators of a client churn. Companies that provide value to the private healthcare industry via the facilitation and management of transactional data flows between healthcare providers and medical aid administrators will benefit from the insights derived from this model.
{"title":"A Framework for Leveraging Business Intelligence to Manage Transactional Data Flows between Private Healthcare Providers and Medical Aid Administrators","authors":"Raksha Pahlad, B. Gatsheni","doi":"10.1109/CSCI49370.2019.00185","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00185","url":null,"abstract":"Leaders at company AB within different functional areas needed to effectively facilitate the integration of BI initiatives into business operations. Semi-structured interviews were used to extract key concepts and attributes relevant to business functional areas, from business leaders and these were related to BI techniques. Thematic analysis on collected data was used to identify critical success factors (CSFs). A conceptual framework was developed which comprises business CSFs that are related to opportunities for value derivation from BI activities. This framework can be used as a guideline by Company AB for opportunity assessment and BI implementation, thereby enabling Company AB to leverage the value of BI. A decision tree predictive analytics model whose business rules potentially assist in proactive churn management for companies that have customer transaction volumes as a feature, was developed. This analytics model shows that claims that are not submitted to a client's historically most frequently used medical aids and variances in transactional claim volumes of more than 20%, are good indicators of a client churn. Companies that provide value to the private healthcare industry via the facilitation and management of transactional data flows between healthcare providers and medical aid administrators will benefit from the insights derived from this model.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114076837","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00141
Samuel Ndueso John, Etinosa Noma-Osaghae, K. Okokpujie, Chinonso Okereke, Joshua Ananaba, O. Omoruyi
Road crashes account for over a million deaths around the world every year. It is one of the leading causes of death for young people between the ages of fifteen and twenty-nine. Road accidents cause a whooping loss of up to three percent of the many nations' Gross Domestic Product (GDP) and ninety percent of these accidents occur in low to middle income countries with a sizable fifty-four percent share of the world's vehicular population. One of the Sustainable Development Goals (SDGs) is the reduction of road accidents around the world by half of its current value by 2020. This goal becomes a hit if low to medium-income nations get safer roads. This paper proposes a collision avoidance system that provides drivers with an automated preemptive response to impending car accidents with the aid of distance predictive analysis via sensors connected to the braking system of the vehicle, which in turn slows down the speed of the vehicle or completely stops it from moving altogether. The proposed collision avoidance system makes use of ultrasonic sensors and a unique localization algorithm to deliver a largely user-based vehicular protection from collision.
{"title":"Vehicle Collision Avoidance System Using Localization Algorithm and Predictive Analysis","authors":"Samuel Ndueso John, Etinosa Noma-Osaghae, K. Okokpujie, Chinonso Okereke, Joshua Ananaba, O. Omoruyi","doi":"10.1109/CSCI49370.2019.00141","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00141","url":null,"abstract":"Road crashes account for over a million deaths around the world every year. It is one of the leading causes of death for young people between the ages of fifteen and twenty-nine. Road accidents cause a whooping loss of up to three percent of the many nations' Gross Domestic Product (GDP) and ninety percent of these accidents occur in low to middle income countries with a sizable fifty-four percent share of the world's vehicular population. One of the Sustainable Development Goals (SDGs) is the reduction of road accidents around the world by half of its current value by 2020. This goal becomes a hit if low to medium-income nations get safer roads. This paper proposes a collision avoidance system that provides drivers with an automated preemptive response to impending car accidents with the aid of distance predictive analysis via sensors connected to the braking system of the vehicle, which in turn slows down the speed of the vehicle or completely stops it from moving altogether. The proposed collision avoidance system makes use of ultrasonic sensors and a unique localization algorithm to deliver a largely user-based vehicular protection from collision.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122361852","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00034
Zackary Foreman, Thomas Bekman, T. Augustine, H. Jafarian
Currently, the guidelines for business entities to collect and use consumer information from online sources is guided by the Fair Information Practice Principles set forth by the Federal Trade Commission in the United States. These guidelines are inadequate, outdated, and provide little protection for consumers. Moreover, there are many techniques to anonymize the stored data that was collected by large companies and governments. However, what does not exist is a framework that is capable of evaluating and scoring the effects of this information in the event of a data breach. In this work, a framework for scoring and evaluating the vulnerability of private data is presented. This framework is created to be used in parallel with currently adopted frameworks that are used to score and evaluate other areas of deficiencies within the software, including CVSS and CWSS. It is dubbed the Privacy Assessment Vulnerability Scoring System (PAVSS) and quantifies the privacy-breach vulnerability an individual takes on when using an online platform. This framework is based on a set of hypotheses about user behavior, inherent properties of an online platform, and the usefulness of available data in performing a cyber attack. The weight each of these metrics has within our model is determined by surveying cybersecurity experts. Finally, we test the validity of our user-behavior based hypotheses, and indirectly our model by analyzing user posts from a large twitter data set.
{"title":"PAVSS: Privacy Assessment Vulnerability Scoring System","authors":"Zackary Foreman, Thomas Bekman, T. Augustine, H. Jafarian","doi":"10.1109/CSCI49370.2019.00034","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00034","url":null,"abstract":"Currently, the guidelines for business entities to collect and use consumer information from online sources is guided by the Fair Information Practice Principles set forth by the Federal Trade Commission in the United States. These guidelines are inadequate, outdated, and provide little protection for consumers. Moreover, there are many techniques to anonymize the stored data that was collected by large companies and governments. However, what does not exist is a framework that is capable of evaluating and scoring the effects of this information in the event of a data breach. In this work, a framework for scoring and evaluating the vulnerability of private data is presented. This framework is created to be used in parallel with currently adopted frameworks that are used to score and evaluate other areas of deficiencies within the software, including CVSS and CWSS. It is dubbed the Privacy Assessment Vulnerability Scoring System (PAVSS) and quantifies the privacy-breach vulnerability an individual takes on when using an online platform. This framework is based on a set of hypotheses about user behavior, inherent properties of an online platform, and the usefulness of available data in performing a cyber attack. The weight each of these metrics has within our model is determined by surveying cybersecurity experts. Finally, we test the validity of our user-behavior based hypotheses, and indirectly our model by analyzing user posts from a large twitter data set.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124087577","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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