Pub Date : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106633
Alec Hager, Tariq Goland, Nicholas Sapio, Isaiah Hurt
As device integration continues to expand across the global market of the healthcare industry, the threats of data loss, exploitation and device control by nefarious actors are negative outcomes we are seeking to avoid. Medical device manufacturers in the U.S. market are required to comply with federal regulations like quality system regulations (QSRs) however, the U.S. Food and Drug Administration (FDA) does not conduct premarket testing or evaluations of implemented software on said devices, and is even less concerned with it once the hardware has been officially launched into the marketplace. The responsibility for validation of software design changes, penetration testing, and simulations regarding IoT security falls upon the manufacturer. Smart healthcare is the integration of digital solutions to improve patient outcomes and the operational efficiency of the facility. Privacy in the healthcare sector is the practice of ensuring the security and confidentiality of patient records, dependent upon the discretion of healthcare providers and the communication methods that are employed. Complex problems are those in which solutions can be derived from differing perspectives to achieve multiple results applicable to the scenario.We approach this subject from a systems perspective and emphasize taking actionable steps to achieve long term change in the policy, technical and social aspects of this complex problem. Due to the scale of the industry, individual scrutiny for each revision and update of all healthcare sector products by a third party is an unrealistic expectation. The keystone of our solution is to encourage the instantiation of a new NIST standard that would encompass the healthcare sector and the medical devices that are used within it. This categorization change positively influences device manufacturers’ access to the standards that are currently relevant within their domain.
{"title":"Securing private medical data, and influencing medical device design to prioritize privacy: A Systems Analysis Approach","authors":"Alec Hager, Tariq Goland, Nicholas Sapio, Isaiah Hurt","doi":"10.1109/SIEDS49339.2020.9106633","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106633","url":null,"abstract":"As device integration continues to expand across the global market of the healthcare industry, the threats of data loss, exploitation and device control by nefarious actors are negative outcomes we are seeking to avoid. Medical device manufacturers in the U.S. market are required to comply with federal regulations like quality system regulations (QSRs) however, the U.S. Food and Drug Administration (FDA) does not conduct premarket testing or evaluations of implemented software on said devices, and is even less concerned with it once the hardware has been officially launched into the marketplace. The responsibility for validation of software design changes, penetration testing, and simulations regarding IoT security falls upon the manufacturer. Smart healthcare is the integration of digital solutions to improve patient outcomes and the operational efficiency of the facility. Privacy in the healthcare sector is the practice of ensuring the security and confidentiality of patient records, dependent upon the discretion of healthcare providers and the communication methods that are employed. Complex problems are those in which solutions can be derived from differing perspectives to achieve multiple results applicable to the scenario.We approach this subject from a systems perspective and emphasize taking actionable steps to achieve long term change in the policy, technical and social aspects of this complex problem. Due to the scale of the industry, individual scrutiny for each revision and update of all healthcare sector products by a third party is an unrealistic expectation. The keystone of our solution is to encourage the instantiation of a new NIST standard that would encompass the healthcare sector and the medical devices that are used within it. This categorization change positively influences device manufacturers’ access to the standards that are currently relevant within their domain.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126722591","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 : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106662
Emma Kuttler, K. Barker, Jonas Johansson
The identification of important components with the potential for the most disruption is vital in network planning and analysis. Critical infrastructure systems are vulnerable to a variety of failures, whether natural (e.g., space weather, earthquakes) or intentional (e.g., malevolent acts). These systems are increasingly interconnected, which increases the risk of the propagation of disruptions. Prior research has focused largely on component importance measures that evaluate the disruption of one-at-a-time failures. However, the focus on single elements often ignores the functional and informational interdependencies between components, which can become dangerous with larger disruptions. We extend the problem of single-component disruption to multiple-component disruption using the technique for order preference by similarity to ideal solution (TOPSIS), a popular multi-criteria decision-making method. With this framework, the question becomes how to calculate the contribution of a single component to a disruption when there are multiple (n) components involved. We propose a method to calculate this contribution using a recursive formula. The technique uses lower-order disruptions to calculate higherorder disruptions, making the TOPSIS criteria dependent on one another. Ranking of the similarity scores follows the standard TOPSIS procedure to produce an ordered list of the most critical components. The methodology developed in this work is illustrated with a case study dealing with the Swedish power and telecommunications system, using loss of power and loss of flow as two impact measures. In this network, the proposed approach produces very little variability in the rankings of the nodes and edges. This is to be expected, given that the criteria and formula for calculating impact are not independent. This is also likely a result of the network itself – for n=1, very few components had any impact. To better visualize the variability in ranking for the nodes, we produced a heatmap. This work can be applied to a variety of network types, as the total number of disruption scenarios and the evaluation measures are left to the decision-maker.
{"title":"Network Importance Measures for Multi-Component Disruptions","authors":"Emma Kuttler, K. Barker, Jonas Johansson","doi":"10.1109/SIEDS49339.2020.9106662","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106662","url":null,"abstract":"The identification of important components with the potential for the most disruption is vital in network planning and analysis. Critical infrastructure systems are vulnerable to a variety of failures, whether natural (e.g., space weather, earthquakes) or intentional (e.g., malevolent acts). These systems are increasingly interconnected, which increases the risk of the propagation of disruptions. Prior research has focused largely on component importance measures that evaluate the disruption of one-at-a-time failures. However, the focus on single elements often ignores the functional and informational interdependencies between components, which can become dangerous with larger disruptions. We extend the problem of single-component disruption to multiple-component disruption using the technique for order preference by similarity to ideal solution (TOPSIS), a popular multi-criteria decision-making method. With this framework, the question becomes how to calculate the contribution of a single component to a disruption when there are multiple (n) components involved. We propose a method to calculate this contribution using a recursive formula. The technique uses lower-order disruptions to calculate higherorder disruptions, making the TOPSIS criteria dependent on one another. Ranking of the similarity scores follows the standard TOPSIS procedure to produce an ordered list of the most critical components. The methodology developed in this work is illustrated with a case study dealing with the Swedish power and telecommunications system, using loss of power and loss of flow as two impact measures. In this network, the proposed approach produces very little variability in the rankings of the nodes and edges. This is to be expected, given that the criteria and formula for calculating impact are not independent. This is also likely a result of the network itself – for n=1, very few components had any impact. To better visualize the variability in ranking for the nodes, we produced a heatmap. This work can be applied to a variety of network types, as the total number of disruption scenarios and the evaluation measures are left to the decision-maker.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126731824","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 : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106677
Rylee Runyon, Isabel Joyner, Karlynn McCarthy
The Rotating Hot Exhaust Nozzles of the F402 Turbofan engine are two of four Exhaust Nozzles that produce thrust, allowing the AV-8B Harrier Jet to perform vertical and short take-off and landing. Handling damage and weld repairs during maintenance and transport have affected the Nozzle’s throat area. A deformed throat area has caused a production of thrust outside of tolerances required for normal engine performance. A metal trimmer or compensator may be fitted to an oversized nozzle to control thrust by reducing the throat area. The trimmer’s size may be changed or removed in the case of an undersized nozzle. Precise measurement of the Nozzle’s throat area is critical to adjusting with trimmers as necessary. One of Sweet Briar College’s Senior Capstone teams has been tasked with addressing a technical need of the U.S. Navy. The goal is to design a Throat Area Measurement System for the F402 Rotating Hot Exhaust Nozzle. Requirements defined by the client and the Sweet Briar College team led to the evaluation of concepts and selection of an image processing method to obtain the throat area value. This technique involves capturing an image of the Nozzle at a calibrated camera position, then uploading it to a MATLAB program written by the team. The program runs necessary functions to binarize the image, then calculate a throat area value in square inches. Image processing requires high contrast for accuracy; therefore, a background has been designed to accommodate this specification. The team has included code to mitigate unwanted image noise and output a value. Construction of a physical prototype has allowed for an analysis of the design process and an understanding of where alterations must be made. With this code and prototype, the team has taken the first steps to automating and improving the process to obtain area values for the Hot Exhaust Nozzle.
{"title":"Image Processing for Measurement Analysis of the AV-8B F-402 Hot Nozzle","authors":"Rylee Runyon, Isabel Joyner, Karlynn McCarthy","doi":"10.1109/SIEDS49339.2020.9106677","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106677","url":null,"abstract":"The Rotating Hot Exhaust Nozzles of the F402 Turbofan engine are two of four Exhaust Nozzles that produce thrust, allowing the AV-8B Harrier Jet to perform vertical and short take-off and landing. Handling damage and weld repairs during maintenance and transport have affected the Nozzle’s throat area. A deformed throat area has caused a production of thrust outside of tolerances required for normal engine performance. A metal trimmer or compensator may be fitted to an oversized nozzle to control thrust by reducing the throat area. The trimmer’s size may be changed or removed in the case of an undersized nozzle. Precise measurement of the Nozzle’s throat area is critical to adjusting with trimmers as necessary. One of Sweet Briar College’s Senior Capstone teams has been tasked with addressing a technical need of the U.S. Navy. The goal is to design a Throat Area Measurement System for the F402 Rotating Hot Exhaust Nozzle. Requirements defined by the client and the Sweet Briar College team led to the evaluation of concepts and selection of an image processing method to obtain the throat area value. This technique involves capturing an image of the Nozzle at a calibrated camera position, then uploading it to a MATLAB program written by the team. The program runs necessary functions to binarize the image, then calculate a throat area value in square inches. Image processing requires high contrast for accuracy; therefore, a background has been designed to accommodate this specification. The team has included code to mitigate unwanted image noise and output a value. Construction of a physical prototype has allowed for an analysis of the design process and an understanding of where alterations must be made. With this code and prototype, the team has taken the first steps to automating and improving the process to obtain area values for the Hot Exhaust Nozzle.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126880909","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 : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106680
Alara Bedir, Rahi Desai, N. Kulkarni, Kayla Wallet, Ryan Wells, Michael Smith
Institutions of higher education are vast interconnected networks of departments, programs, majors, and courses, whose complexity is only increased by the rapid growth and availability of technology-based learning in recent years. In today’s data-driven world, it is critical for college students in all academic disciplines to understand the basic concepts of “systems thinking” and how systems thinking strategies can be applied to nearly any problem they encounter in their careers. While making this information available to students online is an easy way to disseminate the content within the complex network of higher education, the decision to do so may be at the expense of students’ understanding of the material. Therefore, this paper aims to assess the effectiveness of an online module in introducing systems thinking concepts to both engineers and non-engineers. To conduct the study, a gap analysis was performed among existing online education platforms, resulting in the selection of Thinkific as the most effective massive open online course (MOOC) platform through which to disseminate our online module content. Thinkific has open access and allows for interactive participation through the Internet. A short online module was developed and validated in Thinkific using human design principles and user testing. Upon completion of the module design, groups of students in the College of Arts and Sciences (CLAS) and in the School of Engineering and Applied Science (SEAS) at the University of Virginia completed a pre-test, the online module, and a post-test. The qualitative and quantitative results of the pre- and post-tests were analyzed to determine the effectiveness of the module and to learn how understanding varies by intended major. Through this analysis, two findings were elicited: online learning increases learning and understanding concerning key systems thinking concepts, and this learning and understanding is not significantly different between CLAS and SEAS participants. These results inform educators about the degree of emphasis that should be placed on continued development and scalability of online learning programs to enhance understanding of systems thinking concepts. More broadly, this study contributes to the growing body of literature which seeks to understand the impact of technology on the spread of information not only within the field of higher education, but within other large systems as well.
{"title":"Assessing Student Learning of Systems Thinking Concepts in an Online Education Module","authors":"Alara Bedir, Rahi Desai, N. Kulkarni, Kayla Wallet, Ryan Wells, Michael Smith","doi":"10.1109/SIEDS49339.2020.9106680","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106680","url":null,"abstract":"Institutions of higher education are vast interconnected networks of departments, programs, majors, and courses, whose complexity is only increased by the rapid growth and availability of technology-based learning in recent years. In today’s data-driven world, it is critical for college students in all academic disciplines to understand the basic concepts of “systems thinking” and how systems thinking strategies can be applied to nearly any problem they encounter in their careers. While making this information available to students online is an easy way to disseminate the content within the complex network of higher education, the decision to do so may be at the expense of students’ understanding of the material. Therefore, this paper aims to assess the effectiveness of an online module in introducing systems thinking concepts to both engineers and non-engineers. To conduct the study, a gap analysis was performed among existing online education platforms, resulting in the selection of Thinkific as the most effective massive open online course (MOOC) platform through which to disseminate our online module content. Thinkific has open access and allows for interactive participation through the Internet. A short online module was developed and validated in Thinkific using human design principles and user testing. Upon completion of the module design, groups of students in the College of Arts and Sciences (CLAS) and in the School of Engineering and Applied Science (SEAS) at the University of Virginia completed a pre-test, the online module, and a post-test. The qualitative and quantitative results of the pre- and post-tests were analyzed to determine the effectiveness of the module and to learn how understanding varies by intended major. Through this analysis, two findings were elicited: online learning increases learning and understanding concerning key systems thinking concepts, and this learning and understanding is not significantly different between CLAS and SEAS participants. These results inform educators about the degree of emphasis that should be placed on continued development and scalability of online learning programs to enhance understanding of systems thinking concepts. More broadly, this study contributes to the growing body of literature which seeks to understand the impact of technology on the spread of information not only within the field of higher education, but within other large systems as well.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122291565","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 : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106674
M. Schenkel, Tiffany Nguyen, Cem Kutay, Emily Chen, Brendan Vachris, Nicholas Kim, R. Dobson
The Virginia Department of Transportation (VDOT) has identified the West Water Street corridor in downtown Charlottesville as an area of focus for bicyclist safety due to a high rate of pedestrian crashes between 2012 and 2016. Water Street hosts one of the main bicycle routes in the city; however, there is a high level of traffic stress for bicyclists. Therefore, it is critical to determine pedestrian and bicyclist safety countermeasures. Ideally, Water Street would be able to accommodate vehicles, pedestrians, and bicyclists in a safe and efficient manner. The focus of this project is to research, create, and test alternative roadway designs to improve bicyclist and pedestrian safety in the Water Street corridor. The design team analyzes best practices from other bicycle-and pedestrian-friendly cities to inspire design ideas for the specific Water Street corridor. Multi-criteria decision analysis is used to choose the best design concept. This one concept is then extrapolated to other similar designs in which one aspect of the main design alternative is changed. The team tests those designs using a virtual reality (VR) environment and biometric data collection. The team is currently (February 2020) starting to conduct experiments in the VR environment. A user testing plan is being created and reviewed that will then be executed in March 2020 to conduct experiments. User comfort and safety will be evaluated across four different design alternatives. From this evaluation, a recommendation will be proposed to the City of Charlottesville as to what design should be implemented.
{"title":"Bicyclist and Pedestrian Safety Improvements on Water Street Corridor","authors":"M. Schenkel, Tiffany Nguyen, Cem Kutay, Emily Chen, Brendan Vachris, Nicholas Kim, R. Dobson","doi":"10.1109/SIEDS49339.2020.9106674","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106674","url":null,"abstract":"The Virginia Department of Transportation (VDOT) has identified the West Water Street corridor in downtown Charlottesville as an area of focus for bicyclist safety due to a high rate of pedestrian crashes between 2012 and 2016. Water Street hosts one of the main bicycle routes in the city; however, there is a high level of traffic stress for bicyclists. Therefore, it is critical to determine pedestrian and bicyclist safety countermeasures. Ideally, Water Street would be able to accommodate vehicles, pedestrians, and bicyclists in a safe and efficient manner. The focus of this project is to research, create, and test alternative roadway designs to improve bicyclist and pedestrian safety in the Water Street corridor. The design team analyzes best practices from other bicycle-and pedestrian-friendly cities to inspire design ideas for the specific Water Street corridor. Multi-criteria decision analysis is used to choose the best design concept. This one concept is then extrapolated to other similar designs in which one aspect of the main design alternative is changed. The team tests those designs using a virtual reality (VR) environment and biometric data collection. The team is currently (February 2020) starting to conduct experiments in the VR environment. A user testing plan is being created and reviewed that will then be executed in March 2020 to conduct experiments. User comfort and safety will be evaluated across four different design alternatives. From this evaluation, a recommendation will be proposed to the City of Charlottesville as to what design should be implemented.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134210050","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 : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106661
W. Gies, James F. Overby, Nick Saraceno, Jordan Frome, Emily York, A. Salman
Mass Data Collection through Facial Recognition Technology poses a threat to the personal privacy of millions, with big data companies selling and analyzing user data points without appropriate consent and having unrestricted access to said data. Personal privacy should be a right that requires protection in our interconnected society that is becoming ever more reliant on technology. We believe that as this technology progresses without proper restriction, its implementation will effectively eliminate personal privacy, effectively placing a constraint on user autonomy. This is due in part to the vagueness of current policies centered around privacy. If this does not change, personal privacy could be eliminated. FRT has many features, some of which are controversial and impede its effectiveness. Through a systems analysis approach that examines the integration of the social and technical dimensions of these privacy problems, our preliminary research examines several approaches that will be most effective in addressing this complex problem while supporting FRT development. By proposing the following solutions, we hope to prevent personal privacy infringement via FRT: Propose and pass policy similar to the Commercial Facial Recognition Privacy Act, improve data encryption for databases with sensitive personal information (such as Facial Recognition data), and change the way privacy policy is delivered to the consumer by setting maximum length standards on the policy that is written and using less legal jargon to promote the reading and understanding of these privacy policies and what they require of the user. Based on analysis of these proposed solutions, we believe that there will be an increase in security and the feeling of personal privacy across the board.
{"title":"Restricting Data Sharing and Collection of Facial Recognition Data by the Consent of the User: A Systems Analysis","authors":"W. Gies, James F. Overby, Nick Saraceno, Jordan Frome, Emily York, A. Salman","doi":"10.1109/SIEDS49339.2020.9106661","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106661","url":null,"abstract":"Mass Data Collection through Facial Recognition Technology poses a threat to the personal privacy of millions, with big data companies selling and analyzing user data points without appropriate consent and having unrestricted access to said data. Personal privacy should be a right that requires protection in our interconnected society that is becoming ever more reliant on technology. We believe that as this technology progresses without proper restriction, its implementation will effectively eliminate personal privacy, effectively placing a constraint on user autonomy. This is due in part to the vagueness of current policies centered around privacy. If this does not change, personal privacy could be eliminated. FRT has many features, some of which are controversial and impede its effectiveness. Through a systems analysis approach that examines the integration of the social and technical dimensions of these privacy problems, our preliminary research examines several approaches that will be most effective in addressing this complex problem while supporting FRT development. By proposing the following solutions, we hope to prevent personal privacy infringement via FRT: Propose and pass policy similar to the Commercial Facial Recognition Privacy Act, improve data encryption for databases with sensitive personal information (such as Facial Recognition data), and change the way privacy policy is delivered to the consumer by setting maximum length standards on the policy that is written and using less legal jargon to promote the reading and understanding of these privacy policies and what they require of the user. Based on analysis of these proposed solutions, we believe that there will be an increase in security and the feeling of personal privacy across the board.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128713254","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 : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106659
Andrew Koch, Jiahao Tian, Michael D. Porter
Crime linkage is the process of grouping together crime events that share a common offender. It is the first step in investigation, profiling, and being able to predict the characteristics of future crimes. In the absence of DNA evidence, crime linkage is carried out by considering the characteristics and features of the crime, crime scene, or offender as recorded by police investigators. The ability of police to link the crimes from an offender depends on two aspects: how consistently the offender carries out their crimes and how distinctive their crimes are from the crimes committed by the other offenders operating in a region. The more consistent and distinctive an offender behaves, the easier it is for police to link their offenses. Likewise, the crime features (e.g., location, crime type, point of entry) that have the most consistency and distinctiveness across all offenders will be the most useful for linkage models. This paper develops two metrics for measuring the consistency and distinctiveness of a crime series; the consistency score based on Simpson’s index and the distinctiveness score based on the Kullback-Leibler Divergence. A Monte Carlo method is also developed to evaluate the statistical significance of the scores. We calculate the scores for the offenders in a mid-sized US county, identify the most important linkage features, and analyze the distribution of consistency and distinctiveness score for all offenders. These results can help police understand which crime features are most useful for linkage, measure the potential for linkage success in their jurisdiction, and identify the type of offenders that will be most difficult to apprehend.
{"title":"Criminal Consistency and Distinctiveness","authors":"Andrew Koch, Jiahao Tian, Michael D. Porter","doi":"10.1109/SIEDS49339.2020.9106659","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106659","url":null,"abstract":"Crime linkage is the process of grouping together crime events that share a common offender. It is the first step in investigation, profiling, and being able to predict the characteristics of future crimes. In the absence of DNA evidence, crime linkage is carried out by considering the characteristics and features of the crime, crime scene, or offender as recorded by police investigators. The ability of police to link the crimes from an offender depends on two aspects: how consistently the offender carries out their crimes and how distinctive their crimes are from the crimes committed by the other offenders operating in a region. The more consistent and distinctive an offender behaves, the easier it is for police to link their offenses. Likewise, the crime features (e.g., location, crime type, point of entry) that have the most consistency and distinctiveness across all offenders will be the most useful for linkage models. This paper develops two metrics for measuring the consistency and distinctiveness of a crime series; the consistency score based on Simpson’s index and the distinctiveness score based on the Kullback-Leibler Divergence. A Monte Carlo method is also developed to evaluate the statistical significance of the scores. We calculate the scores for the offenders in a mid-sized US county, identify the most important linkage features, and analyze the distribution of consistency and distinctiveness score for all offenders. These results can help police understand which crime features are most useful for linkage, measure the potential for linkage success in their jurisdiction, and identify the type of offenders that will be most difficult to apprehend.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114105491","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 : 2020-04-01DOI: 10.1109/sieds49339.2020.9106672
Ryan Ferrera, John Mark Pittman, M. Zapryanov, Oliver Schaer, Stephen Adams
Companies face many challenges when it comes to increasing revenue, but one of them is how to turn low or no-revenue customers into high revenue customers. When surveying their opportunities to do so, companies often turn to marketing. However, when deciding which among the many options to market to an existing customer, with only finite resources to do so, companies must make a choice rooted in the expected value that reflects the customer’s interest in the offer and the business value of that product or feature. This paper explores techniques to identify customers and study product allocations that allow to increase revenue by nudging customers from lower-revenue groups to higher-revenue groups by recommending the next product to market. Our approach utilizes k-means clustering to identify customer segments based on the recency, frequency, and monetary value (RFM) of their purchases. Further, we demonstrate that an association analysis technique called Market Basket Analysis (MBA) can be extended to not only identify products commonly purchased with the products a specific customer already has, but also to identify which products are associated with higher-revenue customer behavior. We close with a discussion on how these two techniques (clustering and association analysis) can be combined to optimally nudge customers from low-revenue groups to high-revenue groups by incrementally marketing products that more-closely align with the purchasing behavior of higher-revenue customers.
{"title":"Retailer’s Dilemma: Personalized Product Marketing to Maximize Revenue","authors":"Ryan Ferrera, John Mark Pittman, M. Zapryanov, Oliver Schaer, Stephen Adams","doi":"10.1109/sieds49339.2020.9106672","DOIUrl":"https://doi.org/10.1109/sieds49339.2020.9106672","url":null,"abstract":"Companies face many challenges when it comes to increasing revenue, but one of them is how to turn low or no-revenue customers into high revenue customers. When surveying their opportunities to do so, companies often turn to marketing. However, when deciding which among the many options to market to an existing customer, with only finite resources to do so, companies must make a choice rooted in the expected value that reflects the customer’s interest in the offer and the business value of that product or feature. This paper explores techniques to identify customers and study product allocations that allow to increase revenue by nudging customers from lower-revenue groups to higher-revenue groups by recommending the next product to market. Our approach utilizes k-means clustering to identify customer segments based on the recency, frequency, and monetary value (RFM) of their purchases. Further, we demonstrate that an association analysis technique called Market Basket Analysis (MBA) can be extended to not only identify products commonly purchased with the products a specific customer already has, but also to identify which products are associated with higher-revenue customer behavior. We close with a discussion on how these two techniques (clustering and association analysis) can be combined to optimally nudge customers from low-revenue groups to high-revenue groups by incrementally marketing products that more-closely align with the purchasing behavior of higher-revenue customers.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115392157","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 : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106654
Shirley Wang, Nicholas Anselmo, Miller Garrett, Ryan Remias, Matthew Trivett, Anders Christoffersen, N. Bezzo
Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular thanks to the multiplicity of operations in which they can be deployed such as surveillance, search and rescue, mapping, transportation, hobby and recreational activities. Although sensors like LIDARs and cameras are often present on such systems for motion planning to avoid obstacles, collisions can still occur in very dense and unstructured environments, especially if disturbances are present. In this work, we research techniques to recover UAVs after a collision has occurred. We note that the on-board sensors, especially the inertial sensor used to stabilize the UAV, run at a high frequencies obtaining hundreds of data points every second. At run-time, this can be leveraged at the moment of a collision to quickly detect and recover the system. Our approach considers knowledge of UAV system dynamics to predict the expected behavior of the vehicle under safe flight conditions and leverage such expectations together with inertial data to detect collisions rapidly (on the order of milliseconds). We also propose a potential field-based approach to map the collision and create the correct reactive maneuver to avoid the collided object and bring the system back to a stable and safe configuration. Experiments are executed using ROS on two micro-quadrotor UAV platforms having different dynamics and performances, while colliding with poles and walls positioned in different configurations. In our results, we are able to show that the UAVs are successfully able to detect and avoid a collision, while also providing a rigorous analysis of the conditions in which the system can recover from imminent collisions.
{"title":"Fly-Crash-Recover: A Sensor-based Reactive Framework for Online Collision Recovery of UAVs","authors":"Shirley Wang, Nicholas Anselmo, Miller Garrett, Ryan Remias, Matthew Trivett, Anders Christoffersen, N. Bezzo","doi":"10.1109/SIEDS49339.2020.9106654","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106654","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular thanks to the multiplicity of operations in which they can be deployed such as surveillance, search and rescue, mapping, transportation, hobby and recreational activities. Although sensors like LIDARs and cameras are often present on such systems for motion planning to avoid obstacles, collisions can still occur in very dense and unstructured environments, especially if disturbances are present. In this work, we research techniques to recover UAVs after a collision has occurred. We note that the on-board sensors, especially the inertial sensor used to stabilize the UAV, run at a high frequencies obtaining hundreds of data points every second. At run-time, this can be leveraged at the moment of a collision to quickly detect and recover the system. Our approach considers knowledge of UAV system dynamics to predict the expected behavior of the vehicle under safe flight conditions and leverage such expectations together with inertial data to detect collisions rapidly (on the order of milliseconds). We also propose a potential field-based approach to map the collision and create the correct reactive maneuver to avoid the collided object and bring the system back to a stable and safe configuration. Experiments are executed using ROS on two micro-quadrotor UAV platforms having different dynamics and performances, while colliding with poles and walls positioned in different configurations. In our results, we are able to show that the UAVs are successfully able to detect and avoid a collision, while also providing a rigorous analysis of the conditions in which the system can recover from imminent collisions.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124611315","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 : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106651
Gabriela Barber, M. Cote, Finley Wetmore, Alec Yerkovich
A United States (US) government agency is charged with delivering US assistance to foreign countries in the aftermath of sudden onset disasters. A major element of this mission is the strategic storage of six critical commodities located at warehouses across the globe. A rapid needs assessment is necessary for determining the commodity types and amounts, which the agency then transports to the disaster location to be distributed to the affected population by partner organizations on the ground. Currently, the entire commodity shipment is sent via a chartered aircraft, obtained through an emergency bid process, to a target airfield for transfer to the distributing organization. Incremental commodity delivery is a potential strategy that would support decision efficiency. Based on the demand on the ground, shipments can be scheduled and sent when they are truly needed, using a variety of transport modes. The agency can reduce financial cost (transportation expenses) and carbon cost (environmental impact) while decreasing port inventory saturation that occurs when the number of commodities delivered exceeds the partner organizations’ distribution capacity. The incremental approach requires complex decision-making to assess available transport options and their financial and carbon efficiencies while meeting target arrival dates/times for each shipment. This project produced a decision support tool that uses historical and GIS data to project sequences of commodity increments and shipment alternatives that meet target arrival times. Users can then conduct informed tradeoff and scenario analyses during their decision process for specific disaster relief operations. The tool presents alternatives based upon four categories of constraints: delivery timing, cost efficiency, carbon efficiency, and the inventory capacity of the arrival port. User inputs include the commodity types and amounts to be delivered, the timeline within which they must arrive, and the target arrival port. The model utilizes multi-objective network optimization to present the potential tradeoffs between the current delivery strategy and the method of incremental shipments timed to meet commodity distribution rates. The tool may identify options that are more functionally and financially beneficial to the agency, its beneficiaries (i.e., more commodities can be provided if transportation costs decrease), and the distributing partners. It can also support an increase in environmentally conscious decisions, which is a growing priority in the humanitarian emergency community. The tool can also be adapted to meet the needs of similar organizations to support their decision-making pertaining to disaster supply chain management.
{"title":"Decision Support Tool for Enhancing Supply Chain Management in Disaster Relief Operations","authors":"Gabriela Barber, M. Cote, Finley Wetmore, Alec Yerkovich","doi":"10.1109/SIEDS49339.2020.9106651","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106651","url":null,"abstract":"A United States (US) government agency is charged with delivering US assistance to foreign countries in the aftermath of sudden onset disasters. A major element of this mission is the strategic storage of six critical commodities located at warehouses across the globe. A rapid needs assessment is necessary for determining the commodity types and amounts, which the agency then transports to the disaster location to be distributed to the affected population by partner organizations on the ground. Currently, the entire commodity shipment is sent via a chartered aircraft, obtained through an emergency bid process, to a target airfield for transfer to the distributing organization. Incremental commodity delivery is a potential strategy that would support decision efficiency. Based on the demand on the ground, shipments can be scheduled and sent when they are truly needed, using a variety of transport modes. The agency can reduce financial cost (transportation expenses) and carbon cost (environmental impact) while decreasing port inventory saturation that occurs when the number of commodities delivered exceeds the partner organizations’ distribution capacity. The incremental approach requires complex decision-making to assess available transport options and their financial and carbon efficiencies while meeting target arrival dates/times for each shipment. This project produced a decision support tool that uses historical and GIS data to project sequences of commodity increments and shipment alternatives that meet target arrival times. Users can then conduct informed tradeoff and scenario analyses during their decision process for specific disaster relief operations. The tool presents alternatives based upon four categories of constraints: delivery timing, cost efficiency, carbon efficiency, and the inventory capacity of the arrival port. User inputs include the commodity types and amounts to be delivered, the timeline within which they must arrive, and the target arrival port. The model utilizes multi-objective network optimization to present the potential tradeoffs between the current delivery strategy and the method of incremental shipments timed to meet commodity distribution rates. The tool may identify options that are more functionally and financially beneficial to the agency, its beneficiaries (i.e., more commodities can be provided if transportation costs decrease), and the distributing partners. It can also support an increase in environmentally conscious decisions, which is a growing priority in the humanitarian emergency community. The tool can also be adapted to meet the needs of similar organizations to support their decision-making pertaining to disaster supply chain management.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128589160","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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