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关键词： Computer science   artificial intelligence  

ISBN: (数字)9781119847656 ISBN: (纸本)9781119847465

摘要： With the advent of advanced technologies in AI, driverless vehicles have elevated curiosity among various sectors of society.  The automotive industry is in a technological boom with autonomous vehicle concepts. Autonomous driving is one of the crucial application areas of Artificial Intelligence (AI). Autonomous vehicles are armed with sensors, radars, and cameras. This made driverless technology possible in many parts of the world. In short, our traditional vehicle driving may swing to driverless technology. Many researchers are trying to come out with novel AI algorithms that are capable of handling driverless technology. The current existing algorithms are not able to support and elevate the concept of autonomous vehicles. This addresses the necessity of novel methods and tools focused to design and develop frameworks for autonomous vehicles. There is a great demand for energy-efficient solutions for managing the data collected with the help of sensors. These operations are exclusively focused on non-traditional programming approaches and depend on machine learning techniques, which are part of AI. There are multiple issues that AI needs to resolve for us to achieve a reliable and safe driverless technology. The purpose of this book is to find effective solutions to make autonomous vehicles a reality, presenting their challenges and endeavors. The major contribution of this book is to provide a bundle of AI solutions for driverless technology that can offer a safe, clean, and more convenient riskless mode of transportation.

摘要： Copyright and computer science continue to intersect and clash, but they can coexist. The advent of new technologies such as digitization of visual and aural creations, sharing technologies, search engines, social media offerings, and more challenge copyright-based industries and reopen questions about the reach of copyright law. Breakthroughs in artificial intelligence research, especially Large Language Models that leverage copyrighted material as part of training models, are the latest examples of the ongoing tension between copyright and computer science. The exuberance, rush-to-market, and edge problem cases created by a few misguided companies now raises challenges to core legal doctrines and may shift Open Internet practices for the worse. That result does not have to be, and should not be, the outcome. This Article shows that, contrary to some scholars’ views, fair use law does not bless all ways that someone can gain access to copyrighted material even when the purpose is fair use. Nonetheless, the scientific need for more data to advance AI research means access to large book corpora and the Open Internet is vital for the future of that research. The copyright industry claims, however, that almost all uses of copyrighted material must be compensated, even for non-expressive uses. The Article’s solution accepts that both sides need to change. It is one that forces the computer science world to discipline its behaviors and, in some cases, pay for copyrighted material. It also requires the copyright industry to abandon its belief that all uses must be compensated or restricted to uses sanctioned by the copyright industry. As part of this re-balancing, the Article addresses a problem that has grown out of this clash and is under theorized. © 2024, CC BY.

关键词： Aerospace engineering   Computer science  

摘要： Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone's intention is important to assigning risk and executing countermeasures. Intentions are often intangible and unobservable, and a variety of tangible intention classes are often inferred as a proxy. However, inference of drone intention classes using observational data alone is inherently unreliable due to observational and learning bias. Here, we developed a control-physics informed machine learning (CPhy-ML) that can robustly infer across intention classes. The CPhy-ML couples the representation power of deep learning with the conservation laws of aerospace models to reduce bias and instability. The CPhy-ML achieves a 48.28% performance improvement over traditional trajectory prediction methods. The reward inference results outperforms conventional inverse reinforcement learning approaches, decreasing the root mean squared spectral norm error from 3.3747 to 0.3229.

关键词： Machine learning   Computer science   Technology   Policy  

摘要： First-of-its-kind US bill would address the environmental costs of the technology, but there's a long way to go.

关键词： Students  

摘要： Computer Science education in sub-Saharan Africa has evolved over the past decades. The number of institutions offering distinct undergraduate programs has grown, thus increasing the number of students enrolling in the computer science discipline. Several computer science degree programs have emerged with one of the objectives being to satisfy the growing demand for local talent and skills. In this paper, we provide a snapshot of the evolution of undergraduate computer science education in selected countries in Sub-Saharan Africa over the past 20+ years and an overview of the developments in computer science education and observed trends. The setup of educational institutions in Africa and the operational context requires unique modalities for the design and delivery of computer science education that meets the demands of the industry, amongst others. This paper provides insights into the best practices in the computer science curricula in the selected countries, as well as an overview of the pedagogical and delivery approaches to computer science education. The paper highlights case studies from institutions in the selected countries, namely Uganda, South Africa, Ghana, Tanzania, and Kenya with a consolidated summary of the current and emerging challenges and opportunities in all these countries. The paper concludes by providing persectives on the future landscape of computer science in Sub-Saharan Africa. © 2024 Association for Computing Machinery. All rights reserved.

摘要： No abstract available.