The world has been witnessing an increase in malware attacks in recent years. Specifically, ransomware attacks, where attackers lock or encrypt victims' files and ask for a ransom to unlock or decrypt the files and restore the device's state. Ransomware dark market has become very profitable, and its cybercriminals make millions of dollars in revenue. One of the most active ransomware attacks in recent years is Conti ransomware. It works under a ransomware-as-a-service (RaaS) business model. The first beta version of Conti ransomware was seen in October 2019, and its first known attack was reported in July 2020 and has been operational since then. In this paper, we track the development of Conti ransomware, categorize its samples, and compare their features to understand its success and efficiency, which made it top the charts in terms of revenue and the number of attacks. First, we collect many Conti ransomware samples from its beta version to the latest known release. Then we analyze them in an isolated environment and categorize them into seven versions based on their release date and feature similarities. Finally, for each version, we list its features and the previous version's addition, deletion, and/or modification with our reasoning for these changes. This research shows that although Conti started as a beta version with minimal ransomware features, it gradually added new features or modified existing ones through the adoption of continuous development and delivery. For example, API hashing, API run-time loading, and efficient encryption mechanism area are all features added over time and have yet to exist in their earlier releases.
{"title":"Conti Ransomware Development Evaluation","authors":"S. Alzahrani, Yang Xiao, S. Asiri","doi":"10.1145/3564746.3587004","DOIUrl":"https://doi.org/10.1145/3564746.3587004","url":null,"abstract":"The world has been witnessing an increase in malware attacks in recent years. Specifically, ransomware attacks, where attackers lock or encrypt victims' files and ask for a ransom to unlock or decrypt the files and restore the device's state. Ransomware dark market has become very profitable, and its cybercriminals make millions of dollars in revenue. One of the most active ransomware attacks in recent years is Conti ransomware. It works under a ransomware-as-a-service (RaaS) business model. The first beta version of Conti ransomware was seen in October 2019, and its first known attack was reported in July 2020 and has been operational since then. In this paper, we track the development of Conti ransomware, categorize its samples, and compare their features to understand its success and efficiency, which made it top the charts in terms of revenue and the number of attacks. First, we collect many Conti ransomware samples from its beta version to the latest known release. Then we analyze them in an isolated environment and categorize them into seven versions based on their release date and feature similarities. Finally, for each version, we list its features and the previous version's addition, deletion, and/or modification with our reasoning for these changes. This research shows that although Conti started as a beta version with minimal ransomware features, it gradually added new features or modified existing ones through the adoption of continuous development and delivery. For example, API hashing, API run-time loading, and efficient encryption mechanism area are all features added over time and have yet to exist in their earlier releases.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115715950","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}
Secure electronic voting (e-voting) schemes are an imperative topic in today's society as we transition to an increasingly online world. Conventional e-voting processes are centralized that heavily rely on a central aggregator which can raise privacy concerns. Hence, this paper proposes a novel decentralized e-voting scheme that uses the BGW protocol, secure multi-party computation (MPC), and secret sharing as part of a distributed computation mechanism among all the voters to generate a fair and safe e-voting system. Additionally, the proposed scheme provides fully decentralized vote counting, ensures anonymity to further enhance the security of the protocol, and delivers the results of the vote to users. The security of the proposed e-voting scheme is evaluated using prominent security theorems, where the analysis shows that the scheme satisfies several security attributes of secure e-voting, and is more secure and reliable than conventional e-voting protocols.
{"title":"Secure and Decentralized Anonymous E-Voting Scheme","authors":"Allan B. Pedin, N. Siasi","doi":"10.1145/3564746.3587107","DOIUrl":"https://doi.org/10.1145/3564746.3587107","url":null,"abstract":"Secure electronic voting (e-voting) schemes are an imperative topic in today's society as we transition to an increasingly online world. Conventional e-voting processes are centralized that heavily rely on a central aggregator which can raise privacy concerns. Hence, this paper proposes a novel decentralized e-voting scheme that uses the BGW protocol, secure multi-party computation (MPC), and secret sharing as part of a distributed computation mechanism among all the voters to generate a fair and safe e-voting system. Additionally, the proposed scheme provides fully decentralized vote counting, ensures anonymity to further enhance the security of the protocol, and delivers the results of the vote to users. The security of the proposed e-voting scheme is evaluated using prominent security theorems, where the analysis shows that the scheme satisfies several security attributes of secure e-voting, and is more secure and reliable than conventional e-voting protocols.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"53 65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123528208","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}
Colorectal polyps, which are associated with colorectal cancer, can be detected using a colonoscopy. Using the data from colonoscopy images to segment polyps is crucial in medical practice because it provides critical data for identification and surgery. However, precise segmentation of polyps is difficult due to the following factors: the polyp-bordering mucosa boundary is not sharp, and polyps of the same type differ in texture, size, and color. We propose to use the DeepLabV3+ architecture for image segmentation for medical purposes by examining its segmentation results on colonoscopy images from the datasets Kvasir and CVC-ClinicDB. DeepLabV3+ generates an F1-score of 0.865 for CVC-ClinicDB on an NVIDIA A100 class Cloud-Based GPU. The model is divided into the following parts: an encoder that performs separable convolution on the input map and a decoder that up-samples the data provided by the encoder using transpose convolution. Our approach significantly enhances segmentation accuracy and offers a number of benefits with respect to generality and real-time segmentation efficiency, according to evaluations done quantitatively and qualitatively on the two datasets.
{"title":"DCNN-based Polyps Segmentation using Colonoscopy images","authors":"Ishita Paul, Divya Bhaskaracharya","doi":"10.1145/3564746.3587020","DOIUrl":"https://doi.org/10.1145/3564746.3587020","url":null,"abstract":"Colorectal polyps, which are associated with colorectal cancer, can be detected using a colonoscopy. Using the data from colonoscopy images to segment polyps is crucial in medical practice because it provides critical data for identification and surgery. However, precise segmentation of polyps is difficult due to the following factors: the polyp-bordering mucosa boundary is not sharp, and polyps of the same type differ in texture, size, and color. We propose to use the DeepLabV3+ architecture for image segmentation for medical purposes by examining its segmentation results on colonoscopy images from the datasets Kvasir and CVC-ClinicDB. DeepLabV3+ generates an F1-score of 0.865 for CVC-ClinicDB on an NVIDIA A100 class Cloud-Based GPU. The model is divided into the following parts: an encoder that performs separable convolution on the input map and a decoder that up-samples the data provided by the encoder using transpose convolution. Our approach significantly enhances segmentation accuracy and offers a number of benefits with respect to generality and real-time segmentation efficiency, according to evaluations done quantitatively and qualitatively on the two datasets.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125585985","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}
Samuel Hekman, Meghan Brock, Md Abdullah Al Hafiz Khan, Xinyue Zhang
The use of AI to detect human emotion is growing rapidly with the need for human-like customer service without constant human interaction, conducting market research, monitoring opinions on social media, and so on. One model that is often used for this sentiment detection is a recurrent neural network with long short-term memory. Another, newer model that can be used is a temporal convolutional network, but the differences between these two models are under-researched. The goal of this project is to apply these models and compare their performance when detecting sentiment. This will provide some guidance to programmers, allowing them to be more aware of the implications of implementing one model over the other. We find that overall, a temporal convolutional network outperforms the recurrent neural network with long short-term memory for sentiment analysis; our temporal convolutional network achieves 72% accuracy when detecting sentiment.
{"title":"Comparison of Long Short-Term Memory Networks and Temporal Convolutional Networks for Sentiment Analysis","authors":"Samuel Hekman, Meghan Brock, Md Abdullah Al Hafiz Khan, Xinyue Zhang","doi":"10.1145/3564746.3587000","DOIUrl":"https://doi.org/10.1145/3564746.3587000","url":null,"abstract":"The use of AI to detect human emotion is growing rapidly with the need for human-like customer service without constant human interaction, conducting market research, monitoring opinions on social media, and so on. One model that is often used for this sentiment detection is a recurrent neural network with long short-term memory. Another, newer model that can be used is a temporal convolutional network, but the differences between these two models are under-researched. The goal of this project is to apply these models and compare their performance when detecting sentiment. This will provide some guidance to programmers, allowing them to be more aware of the implications of implementing one model over the other. We find that overall, a temporal convolutional network outperforms the recurrent neural network with long short-term memory for sentiment analysis; our temporal convolutional network achieves 72% accuracy when detecting sentiment.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131600805","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}
Eric C Moses, Chris S. Crawford, E. Sazonov, M. McCrory, J. G. Thomas, J. Higgins
Obesity and being overweight is an ongoing issue and a pervasive concern in the United States. There are many programs available that try to assist with weight loss in a variety of forms which are sometimes reactive in their philosophies and methods. This paper discusses insights on the design of SMS messages that seek to provide users just-in-time feedback regarding energy intake (J1) and eating rate (J2). This was accomplished by surveying participants' responses to SMS messages via a study to ascertain whether they would comply with the direction provided via SMS to further their weight loss journey. Results from this study suggest that participants overall felt that the SMS messages designed to aid them in their weight loss journey are helpful and would result in compliance. Results also suggest that participants had higher levels of comprehension with numeric messages.
{"title":"Investigating the Design of Just-in-Time Adaptive Interventions (JITAIs) Messages Targeting Eating Behaviors","authors":"Eric C Moses, Chris S. Crawford, E. Sazonov, M. McCrory, J. G. Thomas, J. Higgins","doi":"10.1145/3564746.3587028","DOIUrl":"https://doi.org/10.1145/3564746.3587028","url":null,"abstract":"Obesity and being overweight is an ongoing issue and a pervasive concern in the United States. There are many programs available that try to assist with weight loss in a variety of forms which are sometimes reactive in their philosophies and methods. This paper discusses insights on the design of SMS messages that seek to provide users just-in-time feedback regarding energy intake (J1) and eating rate (J2). This was accomplished by surveying participants' responses to SMS messages via a study to ascertain whether they would comply with the direction provided via SMS to further their weight loss journey. Results from this study suggest that participants overall felt that the SMS messages designed to aid them in their weight loss journey are helpful and would result in compliance. Results also suggest that participants had higher levels of comprehension with numeric messages.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125130921","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}
In recent years, machine learning (ML) has achieved huge success in healthcare and medicine areas. However, recent work has demonstrated that ML is vulnerable to privacy leakage since it exhibits to overfit the training datasets. Especially, in healthcare and medical communities, there are concerns that medical images and electronic health records containing protected health information (PHI) are vulnerable to inference attacks. These PHI might be unwittingly leaked when the aforementioned data is used for training ML models to address necessary healthcare concerns. Given access to the trained ML model, the attacker is able to adopt membership inference attacks (MIA) to determine whether a specific data sample is used in the corresponding medical training dataset. In this paper, we concentrate on MIA and propose a new method to determine whether a sample was used to train the given ML model or not. Our method is based on the observation that a trained machine learning model usually is lesser sensitive to the feature value perturbations on its training samples compared with the non-training samples. The key idea of our method is to perturb a training sample's feature value in the corresponding feature space and then compute the relationship between each feature's perturbation and the corresponding prediction's change as features to train the attack model. We used publicly available medical datasets such as diabetes and heartbeat categorization data to evaluate our method. Our evaluation shows that the proposed attack can perform better than the existing membership inference attack method.
{"title":"Evaluation of Query-Based Membership Inference Attack on the Medical Data","authors":"Lakshmi Prasanna Pedarla, Xinyue Zhang, Liang Zhao, Hafiz Khan","doi":"10.1145/3564746.3587027","DOIUrl":"https://doi.org/10.1145/3564746.3587027","url":null,"abstract":"In recent years, machine learning (ML) has achieved huge success in healthcare and medicine areas. However, recent work has demonstrated that ML is vulnerable to privacy leakage since it exhibits to overfit the training datasets. Especially, in healthcare and medical communities, there are concerns that medical images and electronic health records containing protected health information (PHI) are vulnerable to inference attacks. These PHI might be unwittingly leaked when the aforementioned data is used for training ML models to address necessary healthcare concerns. Given access to the trained ML model, the attacker is able to adopt membership inference attacks (MIA) to determine whether a specific data sample is used in the corresponding medical training dataset. In this paper, we concentrate on MIA and propose a new method to determine whether a sample was used to train the given ML model or not. Our method is based on the observation that a trained machine learning model usually is lesser sensitive to the feature value perturbations on its training samples compared with the non-training samples. The key idea of our method is to perturb a training sample's feature value in the corresponding feature space and then compute the relationship between each feature's perturbation and the corresponding prediction's change as features to train the attack model. We used publicly available medical datasets such as diabetes and heartbeat categorization data to evaluate our method. Our evaluation shows that the proposed attack can perform better than the existing membership inference attack method.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127764644","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}
K. Kyei, Yasmin Eady, A. Esterline, Joseph Shelton
We investigate how trust can be defined and exploited in the problem of accepting a candidate for enrollment in a group in a distributed setting. A simulation in Python using the NetworkX package has the servers (nodes) implement the WedID protocol. Each node has a profile document with a FOAF graph connecting the server to its neighbors via the foaf:knows property. We assume that the FOAF graphs have been assembled into a network tying together the group. Enrollment relies heavily on two novel notions of trust: one is the trust one node has in another, which we take to come from several kinds of sources, and the other is the trustworthiness a node enjoys because of its centrality. If someone objects to the candidate, there is a vote, a novel procedure that weights a node's votes by its trustworthiness and allows votes based on reference weighted by trust in the referrer. Our main contributions are the procedure for accepting a candidate and the two notions of trust. Results to date include the simulation mentioned; thirty-five test runs provided reasonable results. Work in progress is an implementation of the procedures on a group of servers implementing the WebID protocol.
{"title":"Trust-based Enrollment in a Group in a Distributed Setting","authors":"K. Kyei, Yasmin Eady, A. Esterline, Joseph Shelton","doi":"10.1145/3564746.3587014","DOIUrl":"https://doi.org/10.1145/3564746.3587014","url":null,"abstract":"We investigate how trust can be defined and exploited in the problem of accepting a candidate for enrollment in a group in a distributed setting. A simulation in Python using the NetworkX package has the servers (nodes) implement the WedID protocol. Each node has a profile document with a FOAF graph connecting the server to its neighbors via the foaf:knows property. We assume that the FOAF graphs have been assembled into a network tying together the group. Enrollment relies heavily on two novel notions of trust: one is the trust one node has in another, which we take to come from several kinds of sources, and the other is the trustworthiness a node enjoys because of its centrality. If someone objects to the candidate, there is a vote, a novel procedure that weights a node's votes by its trustworthiness and allows votes based on reference weighted by trust in the referrer. Our main contributions are the procedure for accepting a candidate and the two notions of trust. Results to date include the simulation mentioned; thirty-five test runs provided reasonable results. Work in progress is an implementation of the procedures on a group of servers implementing the WebID protocol.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116272650","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}
J. Jenkins, Cynthia L. Stenger, Jessica E. Stovall, Andrea D. Beesley, Jay L. Jackson, Mark G. Terwilliger
It is important to introduce computer programming concepts to K-12 students in preparation and motivation for college-level programming courses. Furthermore, generalization and abstraction are skills needed in computer science and other STEM majors. The Collaborative Partnership to teach mathematical Reasoning through Computer PRogramming (CPR2) Instructional Model (IM) was developed to use Python programming as a vehicle for explicit instruction in mathematical generalization and abstraction. Students are introduced to programming for the purpose of exploring mathematical concepts. They write code embedded with mathematical general expressions, observe the behavior of those expressions at scale in the execution of their programs, make conjectures about emerging patterns, and write convincing arguments to support these conjectures. During a two-week summer institute in 2020, middle school math teachers participated in professional development using the CPR2 instructional model. Teachers were provided with an introduction to programming and then shown how to apply the instructional model to math concepts while addressing computer science and math standards in the process. During the following fall and spring, the CPR2-trained teachers piloted lessons using Python programming to explore math concepts in their own classrooms. In this paper, details about the instructional model, the collaborative design research process used to refine professional development sessions, the key role of experienced teacher mentors in supporting new participants, and the incorporation of programming into the teachers' math classrooms are detailed. The results of design sessions and the pilot implementation are also discussed.
{"title":"Connecting Programming to Mathematical Generalization: A Pilot Study of Professional Development and Instructional Materials","authors":"J. Jenkins, Cynthia L. Stenger, Jessica E. Stovall, Andrea D. Beesley, Jay L. Jackson, Mark G. Terwilliger","doi":"10.1145/3564746.3587021","DOIUrl":"https://doi.org/10.1145/3564746.3587021","url":null,"abstract":"It is important to introduce computer programming concepts to K-12 students in preparation and motivation for college-level programming courses. Furthermore, generalization and abstraction are skills needed in computer science and other STEM majors. The Collaborative Partnership to teach mathematical Reasoning through Computer PRogramming (CPR2) Instructional Model (IM) was developed to use Python programming as a vehicle for explicit instruction in mathematical generalization and abstraction. Students are introduced to programming for the purpose of exploring mathematical concepts. They write code embedded with mathematical general expressions, observe the behavior of those expressions at scale in the execution of their programs, make conjectures about emerging patterns, and write convincing arguments to support these conjectures. During a two-week summer institute in 2020, middle school math teachers participated in professional development using the CPR2 instructional model. Teachers were provided with an introduction to programming and then shown how to apply the instructional model to math concepts while addressing computer science and math standards in the process. During the following fall and spring, the CPR2-trained teachers piloted lessons using Python programming to explore math concepts in their own classrooms. In this paper, details about the instructional model, the collaborative design research process used to refine professional development sessions, the key role of experienced teacher mentors in supporting new participants, and the incorporation of programming into the teachers' math classrooms are detailed. The results of design sessions and the pilot implementation are also discussed.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124239824","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}
Concurrent data structures play a critical role in the overall performance of GPGPU applications. Stack is one of the basic data structures and finds numerous applications where data is processed in a Last In First Out (LIFO) fashion. Although concurrent stack is well researched for multi-core CPUs, there is little research pointing to the conversion of CPU stacks into a GPU-friendly form. In this paper, we propose a concurrent search-based GPU stack named Scan Stack. The proposed stack is designed to take advantage of GPU memory access patterns, memory coalescence, and thread structures (i.e., warps) to increase throughput. Our experiments on an NVIDIA RTX 3090 show that our proposed scan stack significantly improves the throughput and scalability for all benchmarks when reducing the search area. However, the greatest improvements are shown when elimination is possible, and this improvement reaches nearly 39 times what a non-optimized structure is capable of.
{"title":"Scan Stack: A Search-based Concurrent Stack for GPU","authors":"Noah South, B. Jang","doi":"10.1145/3564746.3587018","DOIUrl":"https://doi.org/10.1145/3564746.3587018","url":null,"abstract":"Concurrent data structures play a critical role in the overall performance of GPGPU applications. Stack is one of the basic data structures and finds numerous applications where data is processed in a Last In First Out (LIFO) fashion. Although concurrent stack is well researched for multi-core CPUs, there is little research pointing to the conversion of CPU stacks into a GPU-friendly form. In this paper, we propose a concurrent search-based GPU stack named Scan Stack. The proposed stack is designed to take advantage of GPU memory access patterns, memory coalescence, and thread structures (i.e., warps) to increase throughput. Our experiments on an NVIDIA RTX 3090 show that our proposed scan stack significantly improves the throughput and scalability for all benchmarks when reducing the search area. However, the greatest improvements are shown when elimination is possible, and this improvement reaches nearly 39 times what a non-optimized structure is capable of.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115099879","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}
This research explores the emergence of community oriented technology supported ecosystems of value creation and co-creation interactions, to devise and utilise opportunities for safe physical activity during lockdown periods. Qualitative data coding and analysis methods were deployed to investigate, from a value co-creation perspective, customer experiences in using online fitness classes organised by community fitness instructors. The adoption of the technological solutions to deliver online fitness classes was explored by applying the Technology-Organization-Environment (TOE) framework. The analysis of the data gathered from social media sites showed that most of the participants in the online classes found the service very valuable, and actively contributed to the development of the fitness service delivery model with suggestions and comments. The main causes of value destruction were poor Internet connectivity and technical problems with computer devices. Fitness providers adopted live stream and video conferencing tools, with Zoom emerging as the preferred platform for online session support. The study found that the global pandemic crisis had accelerated the adoption of already existing technologies to promote physical activity to the larger community when face-to-face participation was hampered or untenable. It also highlighted the importance of offering a service experience comparable to face-to-face fitness sessions through more advanced technology solutions.
{"title":"Co-Creating Well-Being and Health Options Using Internet Technologies","authors":"M. E. Villapol, K. Petrova","doi":"10.1145/3564746.3587010","DOIUrl":"https://doi.org/10.1145/3564746.3587010","url":null,"abstract":"This research explores the emergence of community oriented technology supported ecosystems of value creation and co-creation interactions, to devise and utilise opportunities for safe physical activity during lockdown periods. Qualitative data coding and analysis methods were deployed to investigate, from a value co-creation perspective, customer experiences in using online fitness classes organised by community fitness instructors. The adoption of the technological solutions to deliver online fitness classes was explored by applying the Technology-Organization-Environment (TOE) framework. The analysis of the data gathered from social media sites showed that most of the participants in the online classes found the service very valuable, and actively contributed to the development of the fitness service delivery model with suggestions and comments. The main causes of value destruction were poor Internet connectivity and technical problems with computer devices. Fitness providers adopted live stream and video conferencing tools, with Zoom emerging as the preferred platform for online session support. The study found that the global pandemic crisis had accelerated the adoption of already existing technologies to promote physical activity to the larger community when face-to-face participation was hampered or untenable. It also highlighted the importance of offering a service experience comparable to face-to-face fitness sessions through more advanced technology solutions.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126347075","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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