The arrival of cloud computing has also subverted users’ usage concept: instead of buying hardware and software, users buy cloud services. Users are no longer directly faced with complex software and hardware resourc...
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Nowadays, deep learning has been regarded as a key factor to determine education by researchers. Numerous studies have revealed that, when a student does deep learn rather than learning superficially, they tended to r...
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The omnipresence of software systems across all aspects of society has necessitated that future technology professionals are aware of ethical concerns raised by the design and development of software and are trained t...
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ISBN:
(纸本)9798350378986;9798350378979
The omnipresence of software systems across all aspects of society has necessitated that future technology professionals are aware of ethical concerns raised by the design and development of software and are trained to minimize harm by undertaking responsible engineering. This need has become even more urgent with artificial intelligence (AI) driven software deployment. In this paper we present a study of an interactive pedagogical intervention - role-play case studies - designed to teach undergraduate technology students about ethics with a focus on software systems. Drawing on the situated learning perspective from the learning Sciences, we created case studies, associated stakeholder roles, discussion scripts, and pre and post discussion assignments to guide students' learning. Open-ended data was collected from thirty-nine students and analyzed qualitatively. Findings from the study show that by taking on different perspectives on a problem, students were able to identify a range of ethical issues and understand the role of the software system process holistically, taking context, complexity, and trade-offs into account. In their discussion and reflections, students deliberated the role of software in society and the role of humans in automation. The curricula, including case studies, are publicly available for implementation.
teaching is a process that requires permanent observation and improvement. With the rapid development of e-learning, there was a need to review, improve and optimize the process of evaluating students’ performance. T...
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PK-12 district and school leaders play an important role in translating equitable computer science (CS) educational reform into practice. This study unpacks U.S. PK-12 superintendents' and high school principals&#...
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ISBN:
(纸本)9798400706264
PK-12 district and school leaders play an important role in translating equitable computer science (CS) educational reform into practice. This study unpacks U.S. PK-12 superintendents' and high school principals' perceptions toward CS. Results reveal that both leaders express positive support for CS education but do not feel that other stakeholders in their school districts, like school board members, parents/guardians, and teachers, are equally supportive of CS. Perceptions were lowest for leaders in the Western U.S. and for leaders who oversee majority low-wealth students. The implications of these findings on CS education reform are discussed.
Sparse code book multiple access (SCMA), due to its good link performance, adapt to large-scale access scenarios, helps to cope with the surge of 5G and next generation mobile communication equipment and the challenge...
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In response to the problems of false environment construction, poor teaching effectiveness, and low student learning enthusiasm in the current virtual reality environment construction and teaching simulation research ...
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Depth information in images is crucial for applications in autonomous driving, 3D reconstruction, and robot navigation. Self-supervised depth estimation methods have gained considerable attention because they rely sol...
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ISBN:
(纸本)9798350391961;9798350391954
Depth information in images is crucial for applications in autonomous driving, 3D reconstruction, and robot navigation. Self-supervised depth estimation methods have gained considerable attention because they rely solely on unlabeled video inputs. Traditional sensing equipment, such as LiDAR, incurs high costs and has limited effectiveness in adverse driving conditions, making image-based depth estimation a more economical and versatile alternative. However, real-world applications often involve diverse and noisy environmental conditions, whereas most existing models are trained on clean, ideal datasets. Our paper presents an advanced deep-learning method for robust self-supervised depth estimation, specifically designed to operate effectively under various dynamic scenes and noise conditions, which is called DSG. Our approach takes into account the noise and disturbances that can occur in 3 driving scenarios. Results on standard datasets demonstrate the model's effectiveness, showing excellent robustness in handling noisy and diverse conditions. These strategies enhance the decision-making and safety evaluation capabilities of autonomous vehicles, facilitate the widespread adoption of low-cost sensing devices, and significantly improve model robustness in variable environments. The proposed self-supervised depth estimation method substantially enhances the reliability and accuracy of deep learning models, especially in dynamically changing application scenes.
Now that the world is changing, people should take control of their own learning and adopt self-regulated inquiry as a lifelong priority. People learn lots of things from different sources which influence to the way o...
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The emergence of the sixth generation (6G) wireless networks brings new challenges and opportunities for efficient computing offloading and resource allocation. This paper proposes a novel Deep Reinforcement learning-...
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ISBN:
(纸本)9798350349795;9798350349788
The emergence of the sixth generation (6G) wireless networks brings new challenges and opportunities for efficient computing offloading and resource allocation. This paper proposes a novel Deep Reinforcement learning-based computing Offloading and Resource Allocation (DRL-CORA) algorithm for 6G networks. The algorithm leverages the power of deep reinforcement learning to dynamically determine the optimal computing offloading decisions and resource allocation strategies. The Deep Reinforcement learning-based DCORA algorithm for computation offloading and resource allocation is effective, as demonstrated by our simulations. When compared directly, the suggested DCORA algorithm performs 15% better than other baseline systems.
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