Pub Date : 2024-01-22DOI: 10.1109/mitp.2023.3333074
George Strawn
Herman Hollerith was an American inventor and engineer who played a significant role in the development of computing and information processing systems. He is best known for inventing the punched card tabulating machine, which was used for processing data and performing calculations. This invention laid the foundation for modern data processing and greatly influenced the development of early computers. Hollerith’s work eventually led to the formation of the company that would later become IBM. (Courtesy of ChatGPT.) John Shaw Billings was a prominent American librarian, physician, and the first director of the New York Public Library. He is best known for his significant contributions to the field of medical librarianship and the organization of medical information. Billings was instrumental in the development of the Library of the Surgeon General’s Office, which later became the National Library of Medicine. He played a key role in expanding its collection and making it a comprehensive resource for medical literature. (ChatGPT does not mention his contributions to the census or punched card data processing.)
赫尔曼-霍莱里斯是美国发明家和工程师，在计算机和信息处理系统的发展中发挥了重要作用。他最著名的成就是发明了用于处理数据和进行计算的打卡制表机。这项发明为现代数据处理奠定了基础，并极大地影响了早期计算机的发展。霍乐思的工作最终促成了后来 IBM 公司的成立。(约翰-肖-比林斯（John Shaw Billings）是美国著名的图书管理员、医生和纽约公共图书馆的第一任馆长。他因在医学图书馆学和医学信息组织领域的重大贡献而闻名于世。比林斯在外科医生办公室图书馆（后来成为国家医学图书馆）的发展过程中发挥了重要作用。他在扩大图书馆馆藏并使其成为医学文献综合资源方面发挥了关键作用。(ChatGPT 没有提到他对人口普查或打孔卡数据处理的贡献）。
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Pub Date : 2024-01-22DOI: 10.1109/mitp.2023.3339449
Susanna Kaiser, Stephan Sand, Magdalena Linkiewicz, Henry Meißner, Dirk Baumbach, Ralf Berger
For professional use cases like police or fire brigade operations, a reliable self-localization is advantageous for the coordination of first responders (FRs). Self-localization is especially difficult in indoor scenarios where neither global navigation satellite systems’ signals may be received nor additional signals of opportunity exist for enabling reliable positioning. In this article, we propose an overall system that combines self-localization, communication of the FRs’ locations, 3-D building reconstruction or floor plans (if available), and visualization. The indoor navigation technique is based solely on inertial sensors and builds on a simultaneous localization and mapping technique. It is capable of using any information about the building layout as prior information for enhancing indoor positioning, georeferencing the positions, and finally, visualizing the results in a suitable visualization tool.
{"title":"An Overall First Responder Tracking and Coordination Framework","authors":"Susanna Kaiser, Stephan Sand, Magdalena Linkiewicz, Henry Meißner, Dirk Baumbach, Ralf Berger","doi":"10.1109/mitp.2023.3339449","DOIUrl":"https://doi.org/10.1109/mitp.2023.3339449","url":null,"abstract":"For professional use cases like police or fire brigade operations, a reliable self-localization is advantageous for the coordination of first responders (FRs). Self-localization is especially difficult in indoor scenarios where neither global navigation satellite systems’ signals may be received nor additional signals of opportunity exist for enabling reliable positioning. In this article, we propose an overall system that combines self-localization, communication of the FRs’ locations, 3-D building reconstruction or floor plans (if available), and visualization. The indoor navigation technique is based solely on inertial sensors and builds on a simultaneous localization and mapping technique. It is capable of using any information about the building layout as prior information for enhancing indoor positioning, georeferencing the positions, and finally, visualizing the results in a suitable visualization tool.","PeriodicalId":49045,"journal":{"name":"IT Professional","volume":"3 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139678687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/mitp.2023.3322410
Abdul Majeed, Seong Oun Hwang
The artificial intelligence (AI) field is going through a dramatic revolution in terms of new horizons for research and real-world applications, but some research trajectories in AI are becoming detrimental over time. Recently, there has been a growing call in the AI community to combat a dominant research trend named model-centric AI (MC-AI), which only fiddles with complex AI codes/algorithms. MC-AI may not yield desirable results when applied to real-life problems like predictive maintenance due to limited or poor-quality data. In contrast, a relatively new paradigm named data-centric (DC-AI) is becoming more popular in the AI community. In this article, we discuss and compare MC-AI and DC-AI in terms of basic concepts, working mechanisms, and technical differences. Then, we highlight the potential benefits of the DC-AI approach to foster further research on this recent paradigm. This pioneering work on DC-AI and MC-AI can pave the way to understand the fundamentals and significance of these two paradigms from a broader perspective.
{"title":"Technical Analysis of Data-Centric and Model-Centric Artificial Intelligence","authors":"Abdul Majeed, Seong Oun Hwang","doi":"10.1109/mitp.2023.3322410","DOIUrl":"https://doi.org/10.1109/mitp.2023.3322410","url":null,"abstract":"The artificial intelligence (AI) field is going through a dramatic revolution in terms of new horizons for research and real-world applications, but some research trajectories in AI are becoming detrimental over time. Recently, there has been a growing call in the AI community to combat a dominant research trend named model-centric AI (MC-AI), which only fiddles with complex AI codes/algorithms. MC-AI may not yield desirable results when applied to real-life problems like predictive maintenance due to limited or poor-quality data. In contrast, a relatively new paradigm named data-centric (DC-AI) is becoming more popular in the AI community. In this article, we discuss and compare MC-AI and DC-AI in terms of basic concepts, working mechanisms, and technical differences. Then, we highlight the potential benefits of the DC-AI approach to foster further research on this recent paradigm. This pioneering work on DC-AI and MC-AI can pave the way to understand the fundamentals and significance of these two paradigms from a broader perspective.","PeriodicalId":49045,"journal":{"name":"IT Professional","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139678797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual homing is a lightweight approach to visual navigation. Given the stored information of an initial “home” location, the navigation task back to this location is achieved from any other location by comparing the stored home information to the current image and extracting a motion vector. A challenge that constrains the applicability of visual homing is that the home location must be within the robot’s field of view to initiate the homing process. Thus, we propose a blockchain approach to visual navigation for a heterogeneous robot team over a wide area of visual navigation. Because it does not require map data structures, the approach is useful for robot platforms with a small computational footprint, and, because it leverages current visual information, it supports a resilient and adaptive path selection. Further, we present a lightweight proof-of-work mechanism for reaching consensus in the untrustworthy visual homing network.
{"title":"A Decentralized Cooperative Navigation Approach for Visual Homing Networks","authors":"Mohamed Rahouti, Damian Lyons, Senthil Kumar Jagatheesaperumal, Kaiqi Xiong","doi":"10.1109/mitp.2023.3323865","DOIUrl":"https://doi.org/10.1109/mitp.2023.3323865","url":null,"abstract":"Visual homing is a lightweight approach to visual navigation. Given the stored information of an initial “home” location, the navigation task back to this location is achieved from any other location by comparing the stored home information to the current image and extracting a motion vector. A challenge that constrains the applicability of visual homing is that the home location must be within the robot’s field of view to initiate the homing process. Thus, we propose a blockchain approach to visual navigation for a heterogeneous robot team over a wide area of visual navigation. Because it does not require map data structures, the approach is useful for robot platforms with a small computational footprint, and, because it leverages current visual information, it supports a resilient and adaptive path selection. Further, we present a lightweight proof-of-work mechanism for reaching consensus in the untrustworthy visual homing network.","PeriodicalId":49045,"journal":{"name":"IT Professional","volume":"28 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139678810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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