教学课程资源库 更多>>

关键词： religion   race   public opinion   capital punishment   racial resentment  

摘要： How do racial attitudes affect the relationship between religion and support for capital punishment? Past research has clearly established important links between religion, racial attitudes, and capital punishment. Yet, it remains unclear how racial attitudes affect this relationship. We examine this question using a large sample of respondents from the General Social Survey from 1994 to 2018. We find evidence of a strong moderating effect where the relationship between religion and support for capital punishment varies considerably contingent on racial resentment. At high levels of racial resentment, support for capital punishment is uniformly high across all religious traditions while there are large disparities at low levels of racial resentment. Thus, strongly held, prejudicial racial attitudes overshadow the association between religion and support for capital punishment. Future research should more seriously consider the racial dynamics embedded religion and punishment.

关键词： Generative AI   Globalization   Fourth Industrial Revolution   Social Implications Of Technology   Technology Forecasting   Large Language Models   Chatbots   Video On Demand   Social Networking Online   Computer Science   Web Sites   Internet   Games   Educational Programs   Prompt Engineering   Computer Science   Cold War   Industrial Revolution   Fourth Industrial Revolution   Britannica   Machine Learning   Science And Technology   Students In Class   Syllabus   Security Management   Field Of Computer Science   Department Of Computer Science   Warsaw Pact   Tech Industry   University Of Southern California  

摘要： Generative artificial intelligence is starting a new industrial revolution that is global in scope. The global desire for that technology maybe has started Cold War 2.0.

关键词： UDL Redesign Cycle for Online Courses   Universal Design for Learning   Instructional design   Online course design   Higher education  

摘要： This article focuses on the practical implementation of Universal Design for Learning (UDL) in an online computer science course, articulating the collaborative efforts between the instructional designer (the first author) and the faculty member to redesign the course using UDL principles. Specific instances of redesigned learning modules and artifacts in adherence to UDL principles and results and insights gained from the implementation are reported. The article concludes with recommendations for higher education faculty and instructional designers to consider when applying UDL in their online courses.

关键词： Side-channel attacks   Timing   Security   Hardware   Software   Computer science   Behavioral sciences   Cache side-channel attack   eviction-hidden buffer   secure replacement policy  

摘要： Cache side-channel attacks remain a stubborn source of cross-core secret leakage. Such attacks exploit the timing difference between cache hits and misses. Most defenses thus choose to prevent cache evictions. Given that two possible types of evictions-flush-based and conflict-based-use different architectural features, these defenses have to integrate hybrid defense strategies, incur OS modification, and sacrifice performance to completely throttle cache side-channel attacks. In this article, we present TreasureCache against cache side-channel attacks without modifying OS or sacrificing performance. Instead of preventing cache evictions with various costs, we advocate to allow cache evictions as is and hide exploitable evictions in our specialized small eviction-hidden buffer. The buffer guarantees a fast hit time comparative to LLC hits. This instantly closes the timing gap between accessing exploitable blocks when they are in and out of the LLC. Moreover, with the help of our buffer, we no longer have to disable flush instructions or shared memory. A lightweight constant-time flush instruction can help TreasureCache to prevent both flush-based and conflict-based side-channel attacks. We validate TreasureCache security and performance through extensive experiments. With a hardware overhead of less than 0.5%, TreasureCache reduces the secret-leakage resolution by about 1,000 times without introducing any performance slowdown.

关键词： Prototypes   Computer science   Costs   Training data   Software   Usability   Chatbots   Commercialization   Digital systems  

关键词： Computer science education   Epistemology   Feminist standpoint theory   Inclusivity   Lived experiences   Positionality   Subjectivity  

摘要： In this article, we present a conceptual framework for teaching computer science (CS) to address the unique challenges faced by marginalized groups. The framework is grounded in feminist standpoint theory and describes three key practitioner-focused areas aimed at broadening participation and increasing participation in CS education (CSEd). These principles are 1) Creating dialogic and dialectical relationships with the CS community, 2) Embracing intersectionality in CSEd, and 3) Framing CS education as a lived experience. It also offers a distinct perspective in that it is concerned with how both sides of the dialogue (i.e., the oppressed and the oppressor) grow and change in relation to one another. The strategies, along with the framework detailed in this paper, provide scholars and educators with new avenues for unpacking underrepresentation in CSEd and potential methods to address it.

关键词： Short-term trajectories   Expectancy -value -cost   Chemistry   Computer science   University students  

摘要： University students studying science, technology, engineering, and mathematics (STEM) tend to lose motivation, on average, over time. However, detailed understanding of when and how students are most likely to lose motivation, as well as whether motivational trajectories differ across contexts, is lacking. Accordingly, we investigated trajectories of expectancy, task values, and perceived costs in four introductory STEM courses (N = 2,104). Latent change score models revealed nuanced patterns of change, indicating average motivational declines in expectancy and task values and average increases in cost perceptions observed in prior research tend to occur primarily at the beginning of the semester. Motivational trajectories also differed in the shape and timing of mean changes across the four STEM courses, suggesting motivational loss trends may not be universal. Variation in trajectories also related to course performance and major persistence, with different motivational beliefs playing different roles at various times in the semester. Findings highlighted the importance of early course performance in predicting motivational change, as well as the importance of motivational changes for short- and long-term outcomes. Findings underscore the contextualized nature of motivational constructs, as both change patterns and relations to correlates differed across settings, indicating a need for researchers to carefully consider how samples are aggregated and further uncover contextual features that shape motivation. Overall, our study findings add essential understanding of motivational strengths and needs in real-world classroom contexts, with implications for the design and timing of intervention for instructors by examining what developmental patterns are common across settings and which are more particular to specific settings.

关键词： Reinforcement learning   Throughput   Vehicle dynamics   Schedules   Dynamic scheduling   Data augmentation   Computer science   Traffic signal control   reinforcement learning   fairness-aware scheduling   sample efficiency   multi-intersections  

摘要： Traffic congestion is becoming an increasingly prominent problem, and intelligent traffic signal control methods can effectively alleviate it. Recently, there has been a growing trend of applying reinforcement learning to traffic signal control for adaptive signal scheduling. However, most existing methods focus on improving traffic performance while neglecting the issue of scheduling fairness, resulting in long waiting time for some vehicles. Some works attempt to address fairness issues but often sacrifice transport performance. Furthermore, existing methods overlook the challenge of sample efficiency, especially when dealing with diversity-limited traffic data. Therefore, we propose a Fairness-aware and sample-Efficient traffic signal control method called FELight. Specifically, we first design a novel fairness metric and integrate it into decision process to penalize cases with high latency by setting a threshold for activating the fairness mechanism. Theoretical comparison with other fairness works proves why and when our fairness could bring advantages. Moreover, counterfactual data augmentation is employed to enrich interaction data, enhancing the sample efficiency of FELight. Self-supervised state representation is introduced to extract informative features from raw states, further improving sample efficiency. Experiments on real traffic datasets demonstrate that FELight provides relatively fairer traffic signal control without compromising performance compared to state-of-the-art approaches.

关键词： Student voice   equity   ethics and social responsibility with technology  

摘要： Culturally responsive computing (CRC), that centers sociopolitical issues and transformational uses of technology, has been described as valuable for increasing engagement with computing, especially for historically underrepresented minoritized students. But what do high school students think? Through a sociocultural lens prioritizing student voices recorded in 56 interviews over a period of 2 years (1-3 years after students' first experience with computer science (CS) education through Exploring Computer Science or Advanced Placement CS Principles in high school), this study centers the perspectives of 39 primarily low-income, Latine and Black youth from urban California and rural Mississippi public schools to understand what they perceive as the role of technology in our world and what they subsequently desire of their computing education. While none have studied CRC before, the majority responded with CRC ideas about the kind of pedagogy they believe would make for a more meaningful computing learning experience: They see computing as a form of power that impacts both good and bad in the world and want computing educators to prepare them to take on these issues of equity, ethics, social responsibility, and underrepresentation in the field. The students' perspectives offer important pedagogical insight into how to support deeper engagement with computing in current CS for All initiatives, while also preparing youth for the rapidly evolving and increasingly complex computing landscape that impacts all of our lives.

关键词： Image edge detection   Self-supervised learning   Complex networks   Deep learning   Matrix decomposition   Computer science   Tensors   Community detection   complex network   contrastive learning   motif  

摘要： Community detection has become a prominent task in complex network analysis. However, most of the existing methods for community detection only focus on the lower order structure at the level of individual nodes and edges and ignore the higher order connectivity patterns that characterize the fundamental building blocks within the network. In recent years, researchers have shown interest in motifs and their role in network analysis. However, most of the existing higher order approaches are based on shallow methods, failing to capture the intricate nonlinear relationships between nodes. In order to better fuse higher order and lower order structural information, a novel deep learning framework called motif-based contrastive learning for community detection (MotifCC) is proposed. First, a higher order network is constructed based on motifs. Subnetworks are then obtained by removing isolated nodes, addressing the fragmentation issue in the higher order network. Next, the concept of contrastive learning is applied to effectively fuse various kinds of information from nodes, edges, and higher order and lower order structures. This aims to maximize the similarity of corresponding node information, while distinguishing different nodes and different communities. Finally, based on the community structure of subnetworks, the community labels of all nodes are obtained by using the idea of label propagation. Extensive experiments on real-world datasets validate the effectiveness of MotifCC.