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[. Inspired by this trend, some scholars proposed to use the computing power of convolutional neural networks to calculate the parameters that need to be estimated in the physical imaging model [, The emergence of the GAN (generative adversarial network) opened up another path for image enhancement issues. Pattern recognition is an essential tool in computational thinking in computer science as well as in everyday life. In software engineering and computer science, abstraction is a technique for arranging complexity of computer systems. Han, J.; Zhou, J.; Wang, L.; Wang, Y.; Ding, Z. FE-GAN: Fast and Efficient Underwater Image Enhancement Model Based on Conditional GAN. Video Technol. to better predict brain activity and behavior during lan-guage processing than static word embeddings, includ-ing during naturalistic story comprehension (Schrimpf et Introduction. %%EOF Conceptualization, J.H. Fast underwater image enhancement for improved visual perception. It works by establishing a level of complexity on which a person interacts with the system, suppressing the more complex details below the current level. The processing of underwater images can vastly ease the difficulty of underwater robots tasks and promote ocean exploration development. Examples of Pattern Recognition in Everyday Life. You are accessing a machine-readable page. EasyTech Wins Tech & Learning Awards of Excellence: Best of 2022, How One School District is Driving Digital Wellness in Students (& How to Join), What is Digital Literacy: Definition and Uses in Daily Life, Texas Technology Standards: Big Changes Need Big Solutions, Definition of Computer Science, Computational Thinking and Coding, Get Creative with Professional Development for Technology Integration. Computational thinking is the process of defining a step-by-step solution to a complex problem or to achieve a specific goal. those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). stream Abstraction in learning is the process of taking away or removing certain characteristics of a complex problem to reduce it to its most essential components. In this approach, we can also think of the Principles as the Strategy, the high level concepts needed to find a computational solution; the Ideas can then be seen as the particular Tactics, the patterns or methods that are known to work in many different settings; and, finally, the Techniques as the Tools that can be used in specific situations. enables us to navigate complex problems more effectively while helping to find relevance and clarity at scale. For example, if youre driving on the freeway and you notice cars bunching together in the left lane down the road, you might decide to change into the right lane. Electronics. Correspondence to Volume 12, Issue 1, pages 540549, ISSN 22178309, DOI: 10.18421/TEM12164, February 2023. Thats all you need to know. Cho, Y.; Jeong, J.; Kim, A. Model-assisted multiband fusion for single image enhancement and applications to robot vision. Learn more about abstraction in computational thinking by downloading our free guide for educators: The Ultimate Guide to Computational Thinking for Educators, How to Help Students Improve Pattern Recognition Skills, 3 Important Additions to Digital Literacy for Students in 2023. Abstraction is similar to the selective filtering function in our brains that gates the neural signals with which we are constantly bombarded so we can make sense of our world and focus on whats essential to us. 32773285. All authors have read and agreed to the published version of the manuscript. In this section, we chose a relatively complete set of real and artificial synthetic underwater images to test the enhancement effect of the proposed model. Experiments on different datasets show that the enhanced image can achieve higher PSNR and SSIM values, and the mAP value also achieved significant results in the object detection task. Copyright Learning.com 2023. This article proposed an underwater image enhancement model FE-GAN (fast and efficient generative adversarial network) to solve these problems. What's Next? Decision Sciences, 22(2), 219240. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2126 July 2017; pp. We conducted feature fusion experiments between the encoder and decoder utilizing concatenate and aggregation, respectively. [, In recent years, deep learning gradually occupied a leading position in the field of computer vision with its high plasticity and universality. These rules, in turn, can directly inform the final algorithm well use in the second step of constructing the computational solution. You ask your smart speaker what the weather will be that 2022 has been an exciting year at Learning.com! IEEE. Example 1: Can you spot the sequence in these numbers ? The processing of underwater images can vastly ease the difficulty of underwater robots' tasks and promote ocean exploration development. and J.Z. Packed with plugged and unplugged examples, this guide will give you a foundational understanding of computational thinking and the confidence to address this topic with students. In the case of the school register, the input will be a Character entered against the student name It could be / or P if the student is present, and N, \ or L if they are not present. Your alarm on your smart phone wakes you in the morningthats powered by computer science. (2012). Patterns exist between different problems and within a single problem. https://doi.org/10.3390/electronics12051227, Han J, Zhou J, Wang L, Wang Y, Ding Z. FE-GAN: Fast and Efficient Underwater Image Enhancement Model Based on Conditional GAN. IEEE Trans. To further improve the quality of the generated image, we introduce the pixel-level and image-level loss functions into the objective function formulation. In: Keane, T., Fluck, A.E. Abstracting Further As abstraction is a concept often explored in computer science, particularly with students learning to use object-oriented programming (OOP) languages, looking up . After defining the problem precisely, it involves these three steps: Computational problem solving thus involves finding an appropriate representation of, or context for, the data, and using that representation in an algorithmic, step-by-step procedure that solves the problem once the problem is clearly defined. 67236732. Mao, X.; Li, Q.; Xie, H.; Lau, R.Y. All cats have similar characteristics. As technology advances and adapts faster and Computational thinking is problem-solving. Underwater cable detection in the images using edge classification based on texture information. IGI Global. Anna is equips managing editor, though she also likes to dabble in writing from time to time. Founded in 1999, Learning.com provides educators with solutions to prepare their students with critical digital skills. ; software, J.H. These essential principles are also the buzzwords you can put on your rsum or CV so lets first delve into an intuitive understanding of the more important ones, especially decomposition, pattern recognition, and abstraction, as well as its cousin, generalization. British Machine Vision Conference (BMVC), London, UK, 47 September 2017; Volume 1. Read more about Shannons Information Theory and Computational Thinking in my new book, also publicly viewable on ResearchGate. With the research and application of AUVs (autonomous underwater vehicles) and ROVs (remote operated vehicles), ocean exploration has achieved many breakthrough results. As shown in. Zhang, H.; Zhang, S.; Wang, Y.; Liu, Y.; Yang, Y.; Zhou, T.; Bian, H. Subsea pipeline leak inspection by autonomous underwater vehicle. Extensive experiments were carried out on real and artificially synthesized benchmark underwater image datasets, and qualitative and quantitative comparisons with state-of-the-art methods were implemented. UIQM is expressed as a linear combination of these three indexes. Disclaimer/Publishers Note: The statements, opinions and data contained in all publications are solely Abstraction is an essential part of computational thinking. In addition, being able to identify the general principles that underly the patterns weve identified allows us to generalize patterns and trends into rules. After Jeanette Wing in 2006 described computational thinking (CT) as a fundamental skill for everyone just like reading or arithmetic, it has become a widely discussed topic all over the world. Information is the result of processing data by putting it in a particular context to reveal its meaning. This helps to simplify or break down the problem to make it easier to resolve. In the case of insufficient natural light, the image obtained with the artificial light source itself is extremely distorted. Now from this general knowledge of patterns in cats, we could draw the general outline of a cat. Electronics 2023, 12, 1227. Computational Thinking is a set of techniques for solving complex problems that can be classified into three steps: Problem Specification, Algorithmic Expression, and Solution Implementation & Evaluation.The principles involved in each step of the Computational Thinking approach are listed above and discussed in detail below. Next, we will try to optimize more network modules with structural reparameterization to improve the enhancement effect of the model on images with insufficient brightness, and focus on the practical application in underwater object detection and scene analysis. Let's take a brief look at the periodic table and how we frequently we see many other topics represented (abstraction) today in periodic table fashion. A, Algorithmic Expression: We then need to find an algorithm, a precise sequence of steps, that solves the problem using appropriate data representations. Liu, P.; Wang, G.; Qi, H.; Zhang, C.; Zheng, H.; Yu, Z. To do this you would need to use a searching algorithm, like a Binary Search or a Linear Search. Islam, M.J.; Xia, Y.; Sattar, J. ; methodology, J.H. Compare Google Maps to a physical map vs GPs systems. This pattern can then be applied to any systems that tracks and monitors student data, including attendance, punctuality and recording homework marks. The first line is the unprocessed original distorted images, and the second line is the FE-GAN processed images. Scientific Reports, 10(1), 110. ; data curation, L.W. QT%^[g5XM.GTFySXX;S$[+?D@_[6E[jmYWNM~jxIoVx2I#UP$0mq'J"e'i[t4B/vdZciYh;'@3B$u$Wq|"60(puvCU It was proposed by Ref. Cognitive Science, 12(2), 257285. Nayar, S.K. This research was funded by Key R&D plan of Shandong Province (2020JMRH0101), National Deep Sea Center. Article metric data becomes available approximately 24 hours after publication online. In this process, pattern recognition is Digital literacy refers to the knowledge and ability to use technology effectively and responsibly. Problem Specification: We start by analyzing the problem, stating it precisely, and establishing the criteria for the solution. permission is required to reuse all or part of the article published by MDPI, including figures and tables. Its a drawing of a pipe. [. The information needed will be surname only. Relating natural language aptitude to individual differences in learning programming languages. In Early childhood development: Concepts, methodologies, tools, and applications (pp. Due to the limitation of memory, all pictures were resized to. Uoi|^;KAzMe}_-wmF~8|7osQw{SW"hog+`9T*#AcIiHm#H!7Ix./2N)##%i}>.J4gnFQte < PSNR is an index used in the image field to measure the quality of reconstructed images, which is defined by taking the logarithm of MSE (mean squared error). 542 TEM Journal - Volume 12 / Number 1 / 2023. Behind the scenes, a process will occur to add up the number of times the student was present for a lesson. [, Johnson, J.; Alahi, A.; Fei-Fei, L. Perceptual losses for real-time style transfer and super-resolution. a student will typically study a 2-year course. Diagram and history of programming languages. HIGHLIGHTS who: Kay-Dennis Boom and colleagues from the (UNIVERSITY) have published the research work: Education and Information Technologies (2022) 27:8289-8310 Relationships between computational thinking and the quality of computer programs, in the Journal: (JOURNAL) what: This study examines the relationship between different forms of computational thinking and two different measures of . Languages: Students create a personal guide that dictates when to use the formal and informal you in Spanish class or the two to know verbs in French, which, mind you, always confounded me. All of these required the people behind them to think about big, broad, and complex concepts; to break down the problem and to experiment; and to find patterns amongst the experimentations; and to eventually abstract this concrete knowledge to package it into these sterile statements that shelter us from the complexity and difficulty waded through to arrive at this law. Although each of the problems are different you should see a pattern in the problem types. While the phrase computational thinking contains the word computational, it has applications far outside computer science. 0 https://doi.org/10.3390/electronics12051227, Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. (1992). All of these are needed to come up with the eventual computational solution to the problem. Computational problems, in general, require a certain mode of approach or way of thinking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2730 June 2016; pp. Can you think of any abstraction in each one? Consider the student search system, it can be represented using the following terms: Think back to your student planner program from Lesson 1. We can represent parts of a system in general terms, including Variables, Constants, Key Processes, repeated Processes, Inputs and Outputs. It might be a new pattern that occurs several times in your own program, or it might exist elsewhere in other programs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 1823 June 2018; pp. 172179). In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 2229 October 2017; pp. 16821691. Li, J.; Liang, X.; Wei, Y.; Xu, T.; Feng, J.; Yan, S. Perceptual generative adversarial networks for small object detection. Although there is an algorithm where one method may be faster than another, pattern matching is a key to com posing the solution. Like the other elements of computational thinking, abstraction occurs inherently and can be addressed throughout curriculum with students. Results on different datasets prove that the model also has good generalization ability. Abstraction is actually similar to the selective filtering function in our brains that gates the neural signals with which we are constantly bombarded so we can make sense of our world and focus on whats essential to us. Abstraction helps students return to the larger problem that prompted this whole computational thinking adventure and identify the most important details from the earlier phases. 1996-2023 MDPI (Basel, Switzerland) unless otherwise stated. Seeing is understanding: The effect of visualisation in understanding programming concepts. hko 2023; 12(5):1227. Different loss functions based on texture and content are combined with weights to constrain the generator and discriminator. Understanding abstraction enables students to make sense of problems they encounter, helping them to not be overwhelmed in the face of something complex and to persist, compute, iterate, and ideate. Generalization can help us to organize ideas or components, as we do when we classify some animals as vertebrates and others as invertebrates. ; Key Processes - these are the things that are critical to the system - for . The results show that our model produces better images, and has good generalization ability and real-time performance, which is more conducive to the practical application of underwater robot tasks. Let's examine some other common problems. Students conduct a survey of peers and analyze the data to note the key findings, create visualizations, present the findings. Zeng, L.; Sun, B.; Zhu, D. Underwater target detection based on Faster R-CNN and adversarial occlusion network. "A$n1D2ldfH e/X,r,fAd5Xl>}A`0Y"XMX"Sn)2L@_\8Lw_ O Of course not, your computer just turns itself on. CrossRef Cognitive Influences on Learning Programming. 27942802. For the ImageNet dataset, we randomly selected 628 pairs of real underwater images for testing. IEEE Transactions on Software Engineering, 18(5), 368. A Feature The Singapore 2103 primary curriculum uses the term "algorithm" 26 times, and every single time it is in explicit reference to learning or practising the standard arithmetic algorithms. White, G. L. (2001). Abstraction in computational thinking enables us to navigate complexity and find relevance and clarity at scale. Beaver neighbourhoods consist of rivers running between ponds. [, Akkaynak, D.; Treibitz, T. Sea-thru: A method for removing water from underwater images. Information not needed is gender, age and date of birth as all this will be obtained from the student search. Recognising patterns things that are common between problems or programs is one of the key aspects of computational thinking. [, Fabbri, C.; Islam, M.J.; Sattar, J. In computational thinking, one of the integral steps to the problem-solving process is pattern recognition. https://www.mdpi.com/openaccess. Prat, C., Madhyastha, T., Mottarella, M., & Kuo, C. (2020). Two different Student IMS systems might have different ways of taking a register. Here, we chose YOLOv5 as the object detector. The results in the second, fifth, and last columns show that the fuzzy target can be detected in the processed image. Refs. Founded in 1999, Learning.com provides educators with solutions to prepare their students with critical digital skills. "K/S-M?8 dy"pq!mrb";IRPO^~/.O8`b[8rdjt`` FQ%lf0) SL ]($q_i9 V101gc`M`8*bZA`oae97fL>,v@S2p2BLH3qk3pt)@R y c_ Data are the raw facts or observations of nature and computation is the manipulation of data by some systematic procedure carried out by some computing agent. Jaffe, J.S. 820827. We can look for distinguishing attributes ( colour, shape, size), extract features or matching patterns. Cognitive load theory and the format of instruction. Students generalize chord progressions for common musical genres into a set of general principles they can communicate. We dont care HOW they do them only that they work. [. Help us to further improve by taking part in this short 5 minute survey, A Fast and Efficient Semi-Unsupervised Segmentation and Feature-Extraction Methodology for Artificial Intelligence and Radiomics Applications: A Preliminary Study Applied to Glioblastoma, Attention-Oriented Deep Multi-Task Hash Learning, https://irvlab.cs.umn.edu/resources/euvp-dataset, https://creativecommons.org/licenses/by/4.0/. Formulas were created after patterns were identified and applied to create a common solution. We see this in compression of text files, photos and videos, and often the computers will compress when doing backups. The details of the hierarchical attention encoder (HAE) are shown in, For the discriminator, we use a Markov discriminator [, The conditional generative adversarial network introduces additional auxiliary information and can learn the mapping. When a patient discusses symptoms with a doctor or undergoes a series of tests, the results are compared against known patterns to quickly identify types of infections or injuries that may be causing the symptoms and to apply corresponding solutions to the diagnoses. TEM Journal. To summarise abstraction is the gathering of the general characteristics we need and the filtering out of the details and characteristics that we do not need.. A website providing comprehensive coverage of computer programming. Based on HAE and DRB, we construct a fast and efficient underwater image enhancement network. positive feedback from the reviewers. Deep generative adversarial compression artifact removal. Cycle-GAN [. You can even think of it as an alternative definition of critical thinking or evidence-based reasoning where your solutions result from the data and how you think about that data: Data + How to Think about that Data = Computational Thinking. For example, when you press the power button on your computer, do you know what is going on? To further verify the generalization ability of FE-GAN, we selected 990 images from the artificially synthesized dataset for testing and compared them with the corresponding ground truth images. It hides the underlying complexity in a programming language, which makes it simpler to implement algorithms and communicate with digital tools. >/)gU)FOW_s U}Bgw5]\0QOo, \rz0gx1Ato{C -T/~3IjdzjXM'l2%50TpY?.G/-SYrUT5Af7. For them to use technology responsibly, safely and effectively, they need to understand the Digital literacy encompasses the skills required to use technology safely, effectively and responsibly. This helps the programmer to save time reinventing the wheel when a solution to a given problem may already exist. 770778. They constitute a way of reasoning or thinking logically and methodically about solving any problem in any area! More specifically, it is a set of skills and processes that enable individuals to navigate complex Were excited to share that Learning.coms EasyTech has won in this years Tech & Learning Awards of Excellence: Best of 2022 in the Primary Technology is undoubtedly a fixture in students lives. In Proceeding 2000 IEEE international symposium on visual languages (pp. It can also expand the difference between the features of different objects in the image, improve the image quality, enrich the amount of information, and strengthen the recognition effect. In recent years, many learning-based methods used, Structural reparameterization is used in our encoder to speed up inference. Download the Ultimate Guide to Computational Thinking for Educators. UIQM expresses as follows: In the ImageNet dataset, we randomly selected 5500 pairs of images for training and the remaining 628 pairs for testing. Computational thinking is a problem-solving skill set that is used to tackle problems in computer science. Computational thinking is a problem-solving skill that develops an algorithm, or series of steps to perform a task or solve a problem. Computational thinking is the process of defining a step-by-step solution to a complex problem or to achieve a specific goal. We automatically process this pattern and can reasonably predict how much time we have before the light will turn green. We also know that an algorithm is an effective procedure, a sequence of step-by-step instructions for solving a specific kind of problem using particular data structures, which designate specific data representations.