Background & contextInspired by the nature-cultures of belonging from Black hair care, we conducted a design experiment to bridge computerscience (CS) education, urban gardening, and cosmetology in a culturally r...
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Background & contextInspired by the nature-cultures of belonging from Black hair care, we conducted a design experiment to bridge computerscience (CS) education, urban gardening, and cosmetology in a culturally responsive computing (CRC) library *** design was oriented around a small-scale aquaponics system to grow mint and lavender for making natural cosmetic products. We hypothesized that this could inform the design and implementation of computational thinking and computerprogramming educational *** analyzed qualitative and quantitative data from the design experiment to theorize the processes of using the aquaponics system to enroll Black nature-cultures of belonging in the CRC *** that the program supported children's self-confidence in and knowledge of CS, nature-culture inspired CS education appears *** respectful engagement with the discourses and practices of Black hair care, we provide insight into how nature-cultures can contribute to more diverse, inclusive, and pluralistic forms of CS education.
Teaching computer architecture to students focused on high-level programming is a challenging task. Such students often struggle with the complexities of this subject, exhibiting difficulties in grasping connections b...
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In the wake of the so-called fourth industrial revolution, computerprogramming has become a foundational competency across engineering disciplines. Yet engineering students often resist the notion that computer progr...
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In the wake of the so-called fourth industrial revolution, computerprogramming has become a foundational competency across engineering disciplines. Yet engineering students often resist the notion that computerprogramming is a skill relevant to their future profession. Here are presented two activities aimed at supporting the early development of engineering students' attitudes and abilities regarding programming in a first-year engineering course. Both activities offer students insights into the way programs are constructed, which have been identified as a source of confusion that may negatively affect acceptance. In the first activity, a structured, language-independent way to approach programming problems through guided questions was introduced, which has previously been used successfully in introductory computerscience courses. The team hypothesized that guiding students through a structured reflection on how they construct programs for their class assignments might help reveal an understandable structure to them. Results showed that students in the intervention group scored nearly a full letter grade higher on the unit's final programming assessment than those in the control condition. The second activity aimed to help students recognize how their experience with MATLAB might help them interpret code in other programming languages. In the intervention group, students were asked to review and provide comments for code written in a variety of programming languages. A qualitative analysis of their reflections examined what skills students reported they used and, specifically, how prior MATLAB experience may have aided their ability to read and comment on the unfamiliar code. Overall, the ability to understand and recognize syntactic constructs was an essential skill in making sense of code written in unfamiliar programming languages. Syntactic constructs, lexical elements, and patterns were all recognized as essential landmarks used by students interpreting cod
Due to the development of industrial economy, it has caused serious damage to the ecological environment. Based on the industrial structure and production scale, rural industrial economic parks are planned to analyze ...
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Due to the development of industrial economy, it has caused serious damage to the ecological environment. Based on the industrial structure and production scale, rural industrial economic parks are planned to analyze the quantity and weight of pollutants emitted from the original industries. The results showed that the quantity and weight of hydrogen sulfide in the coking industry were 10kg/t and 94, respectively. The weight of smoke and carbon monoxide in the steelmaking industry was relatively high, with 54 and 34, respectively. Non-dominated sorting genetic algorithm and multi-objective programming model are used to optimize the comprehensive benefits and industrial structure of rural industrial ecological economy. According to the experimental results, when the scale of the coking industry was 135600 tons, the steelmaking industry was 314900 tons, the ironmaking industry was 148100 tons, and the underground coal gasification industry was 424.76 million Nm3. The comprehensive economic benefits of the industry reached the optimal level of 0.6415. The environmental and comprehensive benefits generated by the increased power generation industry were 64.98 and 40.87, respectively. Therefore, it indicates that the dual objective programming model combining non-dominated sorting genetic algorithm can improve the rural industrial ecological economy.
In March 2020, all elementary, middle, and high schools in Japan were temporary closed for approximately one month in response to the COVID-19 pandemic. During this interval, we initiated a programming education progr...
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Various first order approaches have been proposed in the literature to solve Linear programming (LP) problems, recently leading to practically efficient solvers for large-scale LPs. From a theoretical perspective, lin...
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ISBN:
(纸本)9783031598340;9783031598357
Various first order approaches have been proposed in the literature to solve Linear programming (LP) problems, recently leading to practically efficient solvers for large-scale LPs. From a theoretical perspective, linear convergence rates have been established for first order LP algorithms, despite the fact that the underlying formulations are not strongly convex. However, the convergence rate typically depends on the Hoffman constant of a large matrix that contains the constraint matrix, as well as the right hand side, cost, and capacity vectors. We introduce a first order approach for LP optimization with a convergence rate depending polynomially on the circuit imbalance measure, which is a geometric parameter of the constraint matrix, and depending logarithmically on the right hand side, capacity, and cost vectors. This provides much stronger convergence guarantees. For example, if the constraint matrix is totally unimodular, we obtain polynomial-time algorithms, whereas the convergence guarantees for approaches based on primal-dual formulations may have arbitrarily slow convergence rates for this class. Our approach is based on a fast gradient method due to Necoara, Nesterov, and Glineur (Math. Prog. 2019);this algorithm is called repeatedly in a framework that gradually fixes variables to the boundary. This technique is based on a new approximate version of Tardos's method, that was used to obtain a strongly polynomial algorithm for combinatorial LPs (Oper. Res. 1986).
The beamforming optimization in continuous aperture array (CAPA)-based multi-user communications is studied. In contrast to conventional spatially discrete antenna arrays, CAPAs can exploit the full spatial degrees of...
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Evolutionary algorithms are increasingly recognised as a viable computational approach for the automated optimisation of deep neural networks (DNNs) within artificial intelligence. This method extends to the training ...
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ISBN:
(纸本)9783031779404;9783031779411
Evolutionary algorithms are increasingly recognised as a viable computational approach for the automated optimisation of deep neural networks (DNNs) within artificial intelligence. This method extends to the training of DNNs, an approach known as neuroevolution. However, neuroevolution is an inherently resource-intensive process, with certain studies reporting the consumption of thousands of GPU days for refining and training a single DNN network. To address the computational challenges associated with neuroevolution while still attaining good DNN accuracy, surrogate models emerge as a pragmatic solution. Despite their potential, the integration of surrogate models into neuroevolution is still in its early stages, hindered by factors such as the effective use of high-dimensional data and the representation employed in neuroevolution. In this context, we address these challenges by employing a suitable representation based on Linear Genetic programming, denoted as NeuroLGP, and leveraging Kriging Partial Least Squares. The amalgamation of these two techniques culminates in our proposed methodology known as the NeuroLGP-Surrogate Model (NeuroLGP-SM). For comparison purposes, we also code and use a baseline approach incorporating a repair mechanism, a common practice in neuroevolution. Notably, the baseline approach surpasses the renowned VGG-16 model in accuracy. Given the computational intensity inherent in DNN operations, a singular run is typically the norm. To evaluate the efficacy of our proposed approach, we conducted 96 independent runs spanning a duration of 4weeks. Significantly, our methodologies consistently outperform the baseline, with the SM model demonstrating superior accuracy or comparable results to the NeuroLGP approach. Noteworthy is the additional advantage that the SM approach exhibits a 25% reduction in computational requirements, further emphasising its efficiency for neuroevolution.
High-resolution simulations of particle-laden flows are computationally limited to a scale of thousands of particles due to the complex interactions between particles and fluid. Some approaches to increase the number ...
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ISBN:
(数字)9783031295737
ISBN:
(纸本)9783031295720;9783031295737
High-resolution simulations of particle-laden flows are computationally limited to a scale of thousands of particles due to the complex interactions between particles and fluid. Some approaches to increase the number of particles in such simulations require information about the fluid-induced force on a particle, which is a major challenge in this research area. In this paper, we present an approach to develop symbolic models for the fluid-induced force. We use a graph network as inductive bias to model the underlying pairwise particle interactions. The internal parts of the network are then replaced by symbolic models using a genetic programming algorithm. We include prior problem knowledge in our algorithm. The resulting equations show an accuracy in the same order of magnitude as state-of-the-art approaches for different benchmark datasets. They are interpretable and deliver important building blocks. Our approach is a promising alternative to "black-box" models from the literature.
Task planning for autonomous agents has typically been done using deep learning models and simulation- based reinforcement learning. This research proposes combining inductive learning techniques with goal-directed an...
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Task planning for autonomous agents has typically been done using deep learning models and simulation- based reinforcement learning. This research proposes combining inductive learning techniques with goal-directed answer set programming to increase the explainability and reliability of systems for task breakdown and completion. Preliminary research has led to the creation of a Python harness that utilizes s(CASP) to solve task problems in a computationally efficient way. Although this research is in the early stages, we are exploring solutions to complex problems in simulated task completion.
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