教学课程资源库 更多>>

关键词： Artificial intelligence   Ethics   Psychology   Computer science  

摘要： This article argues that existing approaches to programming ethical AI fail to resolve a serious moral-semantic trilemma, generating interpretations of ethical requirements that are either too semantically strict, too semantically flexible, or overly unpredictable. This paper then illustrates the trilemma utilizing a recently proposed 'general ethical dilemma analyzer,' GenEth. Finally, it uses empirical evidence to argue that human beings resolve the semantic trilemma using general cognitive and motivational processes involving 'mental time-travel,' whereby we simulate different possible pasts and futures. I demonstrate how mental time-travel psychology leads us to resolve the semantic trilemma through a six-step process of interpersonal negotiation and renegotiation, and then conclude by showing how comparative advantages in processing power would plausibly cause AI to use similar processes to solve the semantic trilemma more reliably than we do, leading AI to make better moral-semantic choices than humans do by our very own lights.

关键词： case study   computer science   coding   cross-curricular   middle-school   assessment   animated narrative  

摘要： Coding is increasingly popular in schools around the world and is often taught by non-specialist teachers as an integrated task with other subject areas. In this article, we explore the relationship between computer science (CS) concepts and students' multimodal expression in a coding animated narrative (CAN) task in the context of an integrated English-Technology unit of learning. Through this collective case study, we explore how CS concepts underpin semiotic elements of an animated narrative, analyse the factors that influence the extent to which students exercise those concepts, and reveal the tensions and opportunities that a CAN task may present for learning computer science concepts in regular, non-specialist, cross-curricular classrooms. The findings suggest that CAN tasks are unique in presenting opportunities for students to learn challenging CS concepts such as synchronisation and parallelism. At the same time, CAN tasks present tensions for teaching CS concepts in non-specialist classrooms, where student projects are often judged on their visual qualities. In such settings, procedural, rather than conceptual knowledge, may be a more efficient route to creative outcomes. It also means that drawing skills need to be prioritised. Role specialisation often led to better quality projects but at the expense of individual students' conceptual development in computer science.

关键词： Quaternions   Mathematical models   Adaptation models   Convergence   Sun   Learning systems   Computer science   Complex representation   dynamic Sylvester quaternion matrix equation (DSQME)   finite-time convergence   zeroing neural network (ZNN)  

摘要： This article aims to studying how to solve dynamic Sylvester quaternion matrix equation (DSQME) using the neural dynamic method. In order to solve the DSQME, the complex representation method is first adopted to derive the equivalent dynamic Sylvester complex matrix equation (DSCME) from the DSQME. It is proven that the solution to the DSCME is the same as that of the DSQME in essence. Then, a state-of-the-art neural dynamic method is presented to generate a general dynamic-varying parameter zeroing neural network (DVPZNN) model with its global stability being guaranteed by the Lyapunov theory. Specifically, when the linear activation function is utilized in the DVPZNN model, the corresponding model [termed linear DVPZNN (LDVPZNN)] achieves finite-time convergence, and a time range is theoretically calculated. When the nonlinear power-sigmoid activation function is utilized in the DVPZNN model, the corresponding model [termed power-sigmoid DVPZNN (PSDVPZNN)] achieves the better convergence compared with the LDVPZNN model, which is proven in detail. Finally, three examples are presented to compare the solution performance of different neural models for the DSQME and the equivalent DSCME, and the results verify the correctness of the theories and the superiority of the proposed two DVPZNN models.

关键词： computer science   algorithm   string transformation   information entropy   Hilbert space filling curve  

摘要： This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows-Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied.

关键词： Attitudes   computer science   country   gender  

摘要： The current study compared American, Korean, and Indonesian middle and high school students' CS attitudes. Concurrently, this study also examined whether the items in the CS attitudes scale exhibit country and gender measurement biases. We gathered data on CS attitudes from middle and high school students in the US, Korea, and Indonesia. The participating students took the same (translated) previously validated CS attitudes scale. We ran a unidimensional IRT, differential item functioning (DIF), a two-way ANOVA, and the Kruskal-Wallis H test. Despite the valid instrument, we found it inappropriate as is for international comparison studies because students from different countries interpreted some items differently. We then compared gender-based differences in CS attitudes across countries. The results revealed no significant differences between males and females in the Indonesian middle school data, whereas male students had significantly higher CS attitudes than female students in both American and Korean student data. Furthermore, we found the same pattern in gender differences in Korean and Indonesian high school students' CS attitudes scores as in the middle school study. These findings underscore the importance of a country's sociocultural context in influencing gap and diversity in secondary school students' CS attitudes.

关键词： family   science   knowledge   and technology   sex and gender  

摘要： Although women's representation in science, technology, engineering, and mathematics (STEM) employment has increased significantly over the past few decades, their presence remains low in fields like computer science. Using the National Science Foundation's Scientists and Engineers Statistical Data System (SESTAT), this paper assesses the factors shaping employment in STEM and non-STEM occupations among men and women with bachelor's degrees in computer science. Our results reveal that women with degrees in computer science are far less likely than their male counterparts to be employed in STEM occupations, particularly in computer science jobs. But family factors do not have the expected association with employment in computer science jobs. Men who are parents and childless women are more likely to work in non-STEM jobs versus computer science jobs, relative to childless men. Furthermore, the gender gap in employment in computer science jobs is larger among those graduating in the new millennium, suggesting that other factors are at play.

关键词： Learning systems   Distance learning   Computer aided instruction   Task analysis   Computational modeling   Computer science   Philosophical considerations   Democratized learning   distributed artificial intelligences (AIs)   hierarchical learning   self-organization  

摘要： Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems toward large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to study a generalization in distributed learning systems that go beyond existing mechanisms such as federated learning (FL). Moreover, such learning systems rely on hierarchical self-organization of well-connected distributed learning agents who have limited and highly personalized data and can evolve and regulate themselves based on the underlying duality of specialized and generalized processes. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this article. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, hierarchical generalized learning problems in recursive forms are formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical update mechanisms. To that end, a distributed learning algorithm, namely DemLearn, is proposed. Extensive experiments on benchmark MNIST, Fashion-MNIST, FE-MNIST, and CIFAR-10 datasets show that the proposed algorithm demonstrates better results in the generalization performance of learning models in agents compared to the conventional FL algorithms. The detailed analysis provides useful observations to further handle both the generalization and specialization performance of the learning models in Dem-AI systems.

关键词： Delays   Servers   Routing   Redundancy   Liquid crystal displays   Intelligent transportation systems   Computer science   In-network caching   energy efficiency   information centric networking   caching advantage   content placement  

摘要： The Information Centric Networking (ICN) is a future internet architecture to support efficient content distribution in a vehicular environment. In-network caching in ICN provides a realistic solution for vehicular communication due to storage of content replicas inside network vehicles. However, the challenge still exists while caching content replicas in resource constraint vehicles ( such as limited power and cache capacity) to minimize the communication latency. To address the above mentioned challenge, this paper proposes EPC - an ICN based Energy efficient Placement of Content chunk that fits well in a vehicular environment. The proposed resource management strategy mainly aims to reduce the content fetching delay by caching content replicas towards the network edge router. The EPC strategy decides on placement of content chunks on each vehicle by jointly considering residual power of current vehicle, local popularity of content, and caching gain. The EPC supports efficient utilization of network available resources by allowing only vehicles with their residual power greater than threshold to perform chunk caching and hence, further offers reduced content duplication in the whole network. The effectiveness of the proposed scheme is evaluated in Icarus- an ICN simulator for analyzing the performance of ICN caching and routing strategies. The EPC outperforms various state of the art caching strategies approximately by 30% when gets evaluated in terms of offered cache hit ratio, content retrieval delay, and the average number of hops utilized for fetching the requested content.

ISBN: (纸本)9783959773041