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关键词： Ethics   literature review   computing education   computer science education  

摘要： The computing education research community now has at least 40 years of published research on teaching ethics in higher education. To examine the state of our field, we present a systematic literature review of papers in the Association for Computing Machinery computing education venues that describe teaching ethics in higher-education computing courses. Our review spans all papers published to SIGCSE, ICER, ITiCSE, CompEd, Koli Calling, and TOCE venues through 2022, with 100 papers fulfilling our inclusion criteria. Overall, we found a wide variety in content, teaching strategies, challenges, and recommendations. The majority of the papers did not articulate a conception of "ethics," and those that did used many different conceptions, from broadly applicable ethical theories to social impact to specific computing application areas (e.g., data privacy and hacking). Instructors used many different pedagogical strategies (e.g., discussions, lectures, assignments) and formats (e.g., stand-alone courses, incorporated within a technical course). Many papers identified measuring student knowledge as a particular challenge, and 59% of papers included mention of assessments or grading. Of the 69% of papers that evaluated their ethics instruction, most used student self-report surveys, course evaluations, and instructor reflections. While many papers included calls for more ethics content in computing, specific recommendations were rarely broadly applicable, preventing a synthesis of guidelines. To continue building on the last 40 years of research and move toward a set of best practices for teaching ethics in computing, our community should delineate our varied conceptions of ethics, examine which teaching strategies are best suited for each, and explore how to measure student learning.

关键词： Task analysis   Crowdsourcing   Heuristic algorithms   Computer science   Approximation algorithms   Sun   Schedules   Opportunistic networks   ONs   mobile crowdsourcing   task assignment   mobile social network   priority-aware  

摘要： Mobile crowdsourcing technology is a special type of crowdsourcing that outsources location-based human tasks to workers with mobile devices. The user recruitment and task assignment strategies are critically important for successful mobile crowdsourcing. A number of task assignment algorithms for opportunistic network-based mobile crowdsourcing have been proposed for different design goals. However, none of them considers the priority of tasks. In this article, we introduce the priority-aware task assignment that maximizes the successful task rate of prioritized tasks by keeping with the overall makespan of the tasks as short as possible. To this end, the priority-aware offline task assignment (PFTA) and online task assignment (PNTA) are proposed. Since the task assignment problem is NP-hard, the proposed PFTA and PNTA rely on the greedy strategy based on the priority of tasks and/or the workload of tasks. In addition, the approximation ratio of PFTA and the competitive ratio of PNTA are analyzed. The performance of the proposed schemes are evaluated using real mobility traces, and the simulation results demonstrate that PFTA and PNTA achieve their design goals.

关键词： Program comprehension   source code   C plus   Python   bug fixing   new feature tasks   programming education   learning behavior   eye-tracking study  

摘要： Background and Context: Understanding how a student programmer solves different task types in different programming languages is essential to understanding how we can further improve teaching tools to support students to be industry-ready when they graduate. It also provides insight into students' thought processes in different task types and languages. Few (if any) studies investigate whether any differences exist between the reading and navigation behavior while completing different types of tasks in different programming languages. Objectives: We investigate whether the use of a certain programming language (C++ versus Python) and type of task (new feature versus bug fixing) has an impact on performance and eye movement behavior in students exposed to both languages and task types. Participants: Fourteen students were recruited from a Python course that taught Python as an introductory programming language. Study Method: An eye tracker was used to track how student programmers navigate and view source code in different programming languages for different types of tasks. The students worked in the Geany Integrated Development Environment (IDE, used also in their course) while eye-tracking data was collected behind the scenes making their working environment realistic compared to prior studies. Each task type had a Python and C++ version, albeit on different problems to avoid learning effects. Standard eye-tracking metrics of fixation count and fixation durations were calculated on various areas of the screen and on source code lines. Normalized versions of these metrics were used to compare across languages and tasks. Findings: We found that the participants had significantly longer average fixation duration and total fixation duration adjusted for source code length during bug fixing tasks than the feature addition tasks, indicating bug fixing is harder. Furthermore, participants looked at lines adjacent to the line containing the bug more often before looking at

摘要： Students' dropout of Computer Science (CS) education is a crucial issue. This study aims to investigate one of the aspects which can help to retain our students. It is vital to examine CS education on the challenge of competence transition within the BSc curriculum from faculty perspectives. Teachers' expectations about students' skills and knowledge are important to understand because they influence learning outcomes and teaching methodologies. Acquiring hard skills and professional skills has outstanding significance in preparing students for their future careers. This study uses Q-methodology to identify the different viewpoints on the skills necessary to obtain a CS degree. Teachers from CS bachelor's programmes at a large European university participated in the study and shared their opinions. The participants were asked to rank the statements along a spectrum of "the most important skill" to "unimportant skill" (containing hard skills as well as soft skills). Factor analysis revealed five factors that are key components to obtaining a degree in CS: 1. analytical and technical skills, 2. teamwork and self-study experience, 3. group programming experience, 4. communication and problem-solving skills, 5. mathematical foundations and process modelling ability. This exploratory study applied a new research instrument and approach to incorporate teachers' perspectives into research and practice. These findings could help administrators develop new curricula in order to increase students' retention. We confirmed the need for acquiring professional skills and highlighted the need for designing new programmes which can improve students' soft skills to prepare them for work in the IT field.

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.