In recent years, the authors have been developing the Virtual-Physical Space (ViPS) reference architecture that can be adapted to various CPSS-like applications. ViPS supports the virtualization of physical 'thing...
详细信息
international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from t...
international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of ieee ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.
As more and more domains are incorporating cognitive computing tools to develop models to process and understand data in a cohesive, yet effective manner, the medical domain is also seeking advancements aided by artif...
详细信息
ISBN:
(纸本)9781728114194
As more and more domains are incorporating cognitive computing tools to develop models to process and understand data in a cohesive, yet effective manner, the medical domain is also seeking advancements aided by artificial intelligence. While the amount of research available to any individual increases regularly, the ability to keep up with new information becomes a challenge due to the sheer quantity of information. The use of artificial intelligence to help process large amounts of information can overcome those barriers. However, progress in this field is hindered by several challenges including: incomplete medical data sets, the confidential nature of data as it holds private information of individuals, the complexity and nuances of natural language (within medicine), and even the unwillingness of health-care providers to adopt newer techniques. Though the data may be specialized, the models and techniques designed and discussed in this paper can help provide a framework, or starting point for those interested in effectively developing, maintaining, and using these models to help improve the quality of health-care. The purpose of this paper is to serve as resource which can be used to start developing similar models and put them to use in everyday practice in the medical domain.
progress in computational methods has been stimulated by the widespread availability of cheap computational power leading to the improved efficiency of simulation software. Simulation tools become indispensable tools ...
详细信息
ISBN:
(纸本)9781728131795
progress in computational methods has been stimulated by the widespread availability of cheap computational power leading to the improved efficiency of simulation software. Simulation tools become indispensable tools for engineers who are interested in attacking increasingly more significant problems or are interested in searching larger phase space of process and system variables to find the optimal design. In this paper, we show and introduce a new approach to modelling of a real physical process (binary alloy solidification) which involves time-stepping technique and allows to decrease computational cost. Implementation of our algorithm does not require a parallel computing environment but can use it after making some minor adjustments in source code. Our strategy divides domains of a dynamically changing physical phenomena. We are the first (to our best knowledge) to show that it is possible to use such a sequential organisation of calculations during the simulation of the solidification process. Our method is independent of domains considered, because of the natural separation of domain types (a cast, a core, a mould, etc.). Finally, we performed numerical experiments and demonstrate that our approach allows reducing computational time against traditional sequential computations and gives physically appropriate results.
Because of the quantity, complexity and speed measurements of big data, access to the data needed for big data applications becomes ever more challenging for both end-users and IT-experts. access. This has however led...
详细信息
Measuring student achievement and competencies in mathematics is important for the teacher and the educational system, as well as in view of improving the motivation to learn among students. In this study we aim to de...
详细信息
progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources tha...
详细信息
ISBN:
(纸本)9781728125190
progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources that follow a cycle composed of pre-processing steps for data curation and preparation for subsequent computing steps, and later analysis and analytics steps applied to the results. However, scientific workflows are currently fragmented in multiple components, with different processes for computing and data management, and with gaps in the viewpoints of the user profiles involved. Our vision is that future workflow environments and tools for the development of scientific workflows should follow a holistic approach, where both data and computing are integrated in a single flow built on simple, high-level interfaces. The topics of research that we propose involve novel ways to express the workflows that integrate the different data and compute processes, dynamic runtimes to support the execution of the workflows in complex and heterogeneous computing infrastructures in an efficient way, both in terms of performance and energy. These infrastructures include highly distributed resources, from sensors and instruments, and devices in the edge, to High-Performance computing and Cloud computing resources. This paper presents our vision to develop these workflow environments and also the steps we are currently following to achieve it.
The work is aimed at supporting the decision-making of ground station operators who monitor and diagnose spacecraft subsystems. The problem of automatic construction of color-bright cognitive symbols that contribute t...
详细信息
ISBN:
(数字)9781728170862
ISBN:
(纸本)9781728170879
The work is aimed at supporting the decision-making of ground station operators who monitor and diagnose spacecraft subsystems. The problem of automatic construction of color-bright cognitive symbols that contribute to an operational understanding of the current situation is solved. For the first time, a comprehensive formal assessment of the quality of cognitive symbols designed to observe the state of a complex real-time dynamic system was given.
The use of camera-equipped Unmanned Aerial Vehicles (UAVs, or "drones") for a wide range of aerial video capturing applications, including media production, surveillance, search and rescue operations, etc., ...
详细信息
In the field of multimodal communication, sign language is and continues to be, one of the most understudied areas. Thanks to the recent advances in the field of deep learning, there are far-reaching implications and ...
详细信息
ISBN:
(纸本)9781728141251
In the field of multimodal communication, sign language is and continues to be, one of the most understudied areas. Thanks to the recent advances in the field of deep learning, there are far-reaching implications and applications that neural networks can have for sign language mastering. This paper describes a method for ASL alphabet recognition using Convolutional Neural Networks (CNN), which allows to monitor user's learning progress. American Sign Language (ASL) alphabet recognition by computer vision is a challenging task due to the complexity in ASL signs, high interclass similarities, large intraclass variations, and constant occlusions. We produced a robust model that classifies letters correctly in a majority of cases. The experimental results encouraged us to investigate the adoption of AI techniques to support learning of a sign language, as a natural language with its own syntax and lexicon. The challenge was to deliver a mobile sign language training solution that users may adopt during their everyday life. To satisfy the indispensable additional computational resources to the locally connected enduser devices, we propose the adoption of a Fog-computing Architecture.
暂无评论