Bowling is a target sport that is popular among all age groups with professionals and amateur players. Delivering an accurate and consistent bowling throw into the lane requires the incorporation of motion techniques. Consequently, this research presents a novel IoT-Cloud based system for providing real-time monitoring and coaching services to bowling athletes. The system includes two inertial measurement units (IMUs) sensors for capturing motion data, a mobile application and a cloud server for processing the data. First, the quality of each phase of a throw is assessed using a Dynamic Time Wrapping (DTW) based algorithm. Second, an on device-level technique is proposed to identify common bowling errors. Finally, an SVM classification model is employed for assessing the skill level of bowler athletes. We recruited nine right-handed bowlers to perform 50 throws wearing the two sensors and using the proposed system. The results of our experiments suggest that the proposed system can effectively and efficiently assess the quality of the throw, detect common bowling errors and classify the skill level of the bowler.
{"title":"A Novel IoT-Based System for Ten Pin Bowling","authors":"Ilias Zosimadis, Ioannis Stamelos","doi":"arxiv-2301.10523","DOIUrl":"https://doi.org/arxiv-2301.10523","url":null,"abstract":"Bowling is a target sport that is popular among all age groups with\u0000professionals and amateur players. Delivering an accurate and consistent\u0000bowling throw into the lane requires the incorporation of motion techniques.\u0000Consequently, this research presents a novel IoT-Cloud based system for\u0000providing real-time monitoring and coaching services to bowling athletes. The\u0000system includes two inertial measurement units (IMUs) sensors for capturing\u0000motion data, a mobile application and a cloud server for processing the data.\u0000First, the quality of each phase of a throw is assessed using a Dynamic Time\u0000Wrapping (DTW) based algorithm. Second, an on device-level technique is\u0000proposed to identify common bowling errors. Finally, an SVM classification\u0000model is employed for assessing the skill level of bowler athletes. We\u0000recruited nine right-handed bowlers to perform 50 throws wearing the two\u0000sensors and using the proposed system. The results of our experiments suggest\u0000that the proposed system can effectively and efficiently assess the quality of\u0000the throw, detect common bowling errors and classify the skill level of the\u0000bowler.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522022","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}
Process analytics approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support decision-making across the organization. Process execution data once collected will contain hidden insights and actionable knowledge that are of considerable business value enabling firms to take a data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of 'current deployed process' (whose execution trace is often available in these logs), is critical to understanding the variation across the process instances, root-causes of inefficiencies and determining the areas for investing improvement efforts. In this survey, we discuss various methods that allow organizations to understand the behaviour of their processes, monitor currently running process instances, predict the future behavior of those instances and provide better support for operational decision-making across the organization.
{"title":"A Survey of Process-Oriented Data Science and Analytics for supporting Business Process Management","authors":"Asjad Khan, Aditya Ghose, Hoa Dam, Arsal Syed","doi":"arxiv-2301.10398","DOIUrl":"https://doi.org/arxiv-2301.10398","url":null,"abstract":"Process analytics approaches allow organizations to support the practice of\u0000Business Process Management and continuous improvement by leveraging all\u0000process-related data to extract knowledge, improve process performance and\u0000support decision-making across the organization. Process execution data once\u0000collected will contain hidden insights and actionable knowledge that are of\u0000considerable business value enabling firms to take a data-driven approach for\u0000identifying performance bottlenecks, reducing costs, extracting insights and\u0000optimizing the utilization of available resources. Understanding the properties\u0000of 'current deployed process' (whose execution trace is often available in\u0000these logs), is critical to understanding the variation across the process\u0000instances, root-causes of inefficiencies and determining the areas for\u0000investing improvement efforts. In this survey, we discuss various methods that\u0000allow organizations to understand the behaviour of their processes, monitor\u0000currently running process instances, predict the future behavior of those\u0000instances and provide better support for operational decision-making across the\u0000organization.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522109","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}
Agriculture is a huge domain where an enormous landscape of systems interact to support agricultural processes, which are becoming increasingly digital. From the perspective of agricultural service providers, a prominent challenge is interoperability. In the Fraunhofer lighthouse project Cognitive Agriculture (COGNAC), we investigated how the usage of Industry 4.0 digital twins (I4.0 DTs) can help overcome this challenge. This paper contributes architecture drivers and a solution concept using I4.0 DTs in the agricultural domain. Furthermore, we discuss the opportunities and limitations offered by I4.0 DTs for the agricultural domain.
{"title":"Using I4.0 digital twins in agriculture","authors":"Rodrigo Falcão, Raghad Matar, Bernd Rauch","doi":"arxiv-2301.09682","DOIUrl":"https://doi.org/arxiv-2301.09682","url":null,"abstract":"Agriculture is a huge domain where an enormous landscape of systems interact\u0000to support agricultural processes, which are becoming increasingly digital.\u0000From the perspective of agricultural service providers, a prominent challenge\u0000is interoperability. In the Fraunhofer lighthouse project Cognitive Agriculture\u0000(COGNAC), we investigated how the usage of Industry 4.0 digital twins (I4.0\u0000DTs) can help overcome this challenge. This paper contributes architecture\u0000drivers and a solution concept using I4.0 DTs in the agricultural domain.\u0000Furthermore, we discuss the opportunities and limitations offered by I4.0 DTs\u0000for the agricultural domain.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"328 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523257","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}
Dynamic digital timing analysis is a promising alternative to analog simulations for verifying particularly timing-critical parts of a circuit. A necessary prerequisite is a digital delay model, which allows to accurately predict the input-to-output delay of a given transition in the input signal(s) of a gate. Since all existing digital delay models for dynamic digital timing analysis are deterministic, however, they cannot cover delay fluctuations caused by PVT variations, aging and analog signal noise. The only exception known to us is the $eta$-IDM introduced by F"ugger et al. at DATE'18, which allows to add (very) small adversarially chosen delay variations to the deterministic involution delay model, without endangering its faithfulness. In this paper, we show that it is possible to extend the range of allowed delay variations so significantly that realistic PVT variations and aging are covered by the resulting extended $eta$-IDM.
{"title":"A Digital Delay Model Supporting Large Adversarial Delay Variations","authors":"Daniel Öhlinger, Ulrich Schmid","doi":"arxiv-2301.09588","DOIUrl":"https://doi.org/arxiv-2301.09588","url":null,"abstract":"Dynamic digital timing analysis is a promising alternative to analog\u0000simulations for verifying particularly timing-critical parts of a circuit. A\u0000necessary prerequisite is a digital delay model, which allows to accurately\u0000predict the input-to-output delay of a given transition in the input signal(s)\u0000of a gate. Since all existing digital delay models for dynamic digital timing\u0000analysis are deterministic, however, they cannot cover delay fluctuations\u0000caused by PVT variations, aging and analog signal noise. The only exception\u0000known to us is the $eta$-IDM introduced by F\"ugger et al. at DATE'18, which\u0000allows to add (very) small adversarially chosen delay variations to the\u0000deterministic involution delay model, without endangering its faithfulness. In\u0000this paper, we show that it is possible to extend the range of allowed delay\u0000variations so significantly that realistic PVT variations and aging are covered\u0000by the resulting extended $eta$-IDM.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"33 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523252","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}
Dhaivat Joshi, Suhas Diggavi, Mark J. P. Chaisson, Sreeram Kannan
Motivation: Detection of structural variants (SV) from the alignment of sample DNA reads to the reference genome is an important problem in understanding human diseases. Long reads that can span repeat regions, along with an accurate alignment of these long reads play an important role in identifying novel SVs. Long read sequencers such as nanopore sequencing can address this problem by providing very long reads but with high error rates, making accurate alignment challenging. Many errors induced by nanopore sequencing have a bias because of the physics of the sequencing process and proper utilization of these error characteristics can play an important role in designing a robust aligner for SV detection problems. In this paper, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using basecalled nanopore reads along with the nanopore physics to improve alignments for SVs (ii) incorporating SV specific changes to the alignment pipeline (iii) adapting these into existing state-of-the-art long read aligner pipeline, minimap2 (v2.24), for efficient alignments. Results: We show that HQAlign captures about 4%-6% complementary SVs across different datasets which are missed by minimap2 alignments while having a standalone performance at par with minimap2 for real nanopore reads data. For the common SV calls between HQAlign and minimap2, HQAlign improves the start and the end breakpoint accuracy for about 10%-50% of SVs across different datasets. Moreover, HQAlign improves the alignment rate to 89.35% from minimap2 85.64% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86.65% from 83.48% for nanopore reads alignment to GRCh37 human genome.
{"title":"HQAlign: Aligning nanopore reads for SV detection using current-level modeling","authors":"Dhaivat Joshi, Suhas Diggavi, Mark J. P. Chaisson, Sreeram Kannan","doi":"arxiv-2301.03834","DOIUrl":"https://doi.org/arxiv-2301.03834","url":null,"abstract":"Motivation: Detection of structural variants (SV) from the alignment of\u0000sample DNA reads to the reference genome is an important problem in\u0000understanding human diseases. Long reads that can span repeat regions, along\u0000with an accurate alignment of these long reads play an important role in\u0000identifying novel SVs. Long read sequencers such as nanopore sequencing can\u0000address this problem by providing very long reads but with high error rates,\u0000making accurate alignment challenging. Many errors induced by nanopore\u0000sequencing have a bias because of the physics of the sequencing process and\u0000proper utilization of these error characteristics can play an important role in\u0000designing a robust aligner for SV detection problems. In this paper, we design\u0000and evaluate HQAlign, an aligner for SV detection using nanopore sequenced\u0000reads. The key ideas of HQAlign include (i) using basecalled nanopore reads\u0000along with the nanopore physics to improve alignments for SVs (ii)\u0000incorporating SV specific changes to the alignment pipeline (iii) adapting\u0000these into existing state-of-the-art long read aligner pipeline, minimap2\u0000(v2.24), for efficient alignments. Results: We show that HQAlign captures about 4%-6% complementary SVs across\u0000different datasets which are missed by minimap2 alignments while having a\u0000standalone performance at par with minimap2 for real nanopore reads data. For\u0000the common SV calls between HQAlign and minimap2, HQAlign improves the start\u0000and the end breakpoint accuracy for about 10%-50% of SVs across different\u0000datasets. Moreover, HQAlign improves the alignment rate to 89.35% from minimap2\u000085.64% for nanopore reads alignment to recent telomere-to-telomere CHM13\u0000assembly, and it improves to 86.65% from 83.48% for nanopore reads alignment to\u0000GRCh37 human genome.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"4 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523319","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}
Real-time monitoring of nervous system function with immediate communication of relevant information to the surgeon enables prevention and/or mitigation of iatrogenic injury in many surgical procedures. The hardware and software infrastructure and demonstrated usefulness of telemedicine in support of IONM originated in a busy university health center environment and then spread widely as comparable functional capabilities were added by commercial equipment manufacturers. The earliest implementations included primitive data archival and case documentation capabilities and relied primarily on deidentification for security. They emphasized full-featured control of the real-time data display by remote observers. Today, remote IONM is routinely utilized in more than 200,000 high-risk surgical procedures/year in the United States. For many cases, remote observers rely on screen capture to view the data as it is displayed in the remote operating room while providing sophisticated security capabilities and data archival and standardized metadata and case documentation.
{"title":"The Evolution of Real-time Remote Intraoperative Neurophysiological Monitoring (IONM)","authors":"Jeffrey Balzer, Julia Caviness, Don Krieger","doi":"arxiv-2301.10225","DOIUrl":"https://doi.org/arxiv-2301.10225","url":null,"abstract":"Real-time monitoring of nervous system function with immediate communication\u0000of relevant information to the surgeon enables prevention and/or mitigation of\u0000iatrogenic injury in many surgical procedures. The hardware and software\u0000infrastructure and demonstrated usefulness of telemedicine in support of IONM\u0000originated in a busy university health center environment and then spread\u0000widely as comparable functional capabilities were added by commercial equipment\u0000manufacturers. The earliest implementations included primitive data archival\u0000and case documentation capabilities and relied primarily on deidentification\u0000for security. They emphasized full-featured control of the real-time data\u0000display by remote observers. Today, remote IONM is routinely utilized in more\u0000than 200,000 high-risk surgical procedures/year in the United States. For many\u0000cases, remote observers rely on screen capture to view the data as it is\u0000displayed in the remote operating room while providing sophisticated security\u0000capabilities and data archival and standardized metadata and case\u0000documentation.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523254","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}
Algorithms are becoming more capable, and with that comes hic sunt dracones (here be dragons). The term symbolizes areas beyond our known maps. We use this term since we are stepping into an exciting, potentially dangerous, and unknown area with algorithms. Our curiosity to understand the natural world drives our search for new methods. For this reason, it is crucial to explore this subject. The project's objective is to overlay the information obtained, in conjunction with the state of hardware today, to see if we can determine the likely directions for future algorithms'. Even though we slightly cover non-classical computing in this paper, our primary focus is on classical computing (i.e., digital computers). It is worth noting that non-classical quantum computing requires classical computers to operate; they are not mutually exclusive.
{"title":"Lost in Algorithms","authors":"Andrew N. Sloss","doi":"arxiv-2301.10333","DOIUrl":"https://doi.org/arxiv-2301.10333","url":null,"abstract":"Algorithms are becoming more capable, and with that comes hic sunt dracones\u0000(here be dragons). The term symbolizes areas beyond our known maps. We use this\u0000term since we are stepping into an exciting, potentially dangerous, and unknown\u0000area with algorithms. Our curiosity to understand the natural world drives our\u0000search for new methods. For this reason, it is crucial to explore this subject. The project's objective is to overlay the information obtained, in\u0000conjunction with the state of hardware today, to see if we can determine the\u0000likely directions for future algorithms'. Even though we slightly cover\u0000non-classical computing in this paper, our primary focus is on classical\u0000computing (i.e., digital computers). It is worth noting that non-classical\u0000quantum computing requires classical computers to operate; they are not\u0000mutually exclusive.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"139 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523249","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}
Everybody knows very well about the COVID-19 pandemic, lockdown, and its impacts and effects on every field of life, from childhood to senior citizens, from local to global. The underlying research study focuses on students' involvement in online classes. This paper assesses the effect of the COVID-19 pandemic on the students' participation and involvement during online classes compared to the physical classes, cheating behavior, health effects, and study styles of the students of diverse degrees and age groups. This research study contributes to the real problems and challenges that students faced during online classes during the COVID-19 pandemic. The percentages of the students' responses with different color schemes shown in Fig. 1, Fig. 2, Fig.3(a), Fig.3(b) and Fig.4 are conveying powerful and meaningful insight. These figures and the results given in Table I and Table II indicate that most students are not fully involved during online classes due to technical issues, remote distance, etc. We applied the Test here because we do not have exact population means. We used ttest_1samp with default value 0 to compute the variables' statistics and p-value. These values are minimal in favor of rejecting the null or H0 (hypothesis) and accepting the alternate or H1 (hypothesis). It further means that students' involvement during online classes is severely affected.
{"title":"Predicting the Students Involvements and its Impacts on Learning Outcomes Through Online Education During Covid-19","authors":"Muhammad Nadeem, Faisal Bukhari, Ali Hussain","doi":"arxiv-2301.00031","DOIUrl":"https://doi.org/arxiv-2301.00031","url":null,"abstract":"Everybody knows very well about the COVID-19 pandemic, lockdown, and its\u0000impacts and effects on every field of life, from childhood to senior citizens,\u0000from local to global. The underlying research study focuses on students'\u0000involvement in online classes. This paper assesses the effect of the COVID-19\u0000pandemic on the students' participation and involvement during online classes\u0000compared to the physical classes, cheating behavior, health effects, and study\u0000styles of the students of diverse degrees and age groups. This research study\u0000contributes to the real problems and challenges that students faced during\u0000online classes during the COVID-19 pandemic. The percentages of the students'\u0000responses with different color schemes shown in Fig. 1, Fig. 2, Fig.3(a),\u0000Fig.3(b) and Fig.4 are conveying powerful and meaningful insight. These figures\u0000and the results given in Table I and Table II indicate that most students are\u0000not fully involved during online classes due to technical issues, remote\u0000distance, etc. We applied the Test here because we do not have exact population\u0000means. We used ttest_1samp with default value 0 to compute the variables'\u0000statistics and p-value. These values are minimal in favor of rejecting the null\u0000or H0 (hypothesis) and accepting the alternate or H1 (hypothesis). It further\u0000means that students' involvement during online classes is severely affected.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"327 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523258","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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