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摘要： As artificial intelligence (AI) grows in popularity and importance—both as a domain within broader computing research and in society at large—increasing focus will need to be paid to the ethical governance of this emerging technology. The attitudes and competencies with respect to AI ethics and policy among post-secondary students studying computer science (CS) are of particular interest, as many of these students will go on to play key roles in the development and deployment of future AI innovations. Despite this population of computer scientists being at the forefront of learning about and using AI tools, their attitudes towards AI remain understudied in the literature. In an effort to begin to close this gap, in fall 2024 we fielded a survey ( $$n=117$$ ) to undergraduate and graduate students enrolled in CS courses at a large public university in the United States to assess their attitudes towards the nascent fields of AI ethics and policy. Additionally, we conducted one-on-one follow-up interviews with 13 students to elicit more in-depth responses on topics such as the use of AI tools in the classroom, ethical impacts of AI, and government regulation of AI. In this paper, we describe the findings of our exploratory study, drawing parallels and contrasts to broader public opinion polling in the United States. We conclude by evaluating the implications of CS student attitudes on the future of AI education and governance.

关键词： Health sciences   Biological sciences   Computer science  

摘要： The detection rate of mitral valve prolapse (MVP) has been increased by using 3D echocardiography. It would be clinically meaningful for accurate assessment of prolapse histology with advanced deep learning algorithms. However, recent studies mainly focus on automatic measurement and reconstruction of cardiac structures using voxel data, while seriously neglecting the automatic diagnosis and visualization using cine data. To address the gap, we propose an artificial intelligence agent to study diagnosis and heatmap for MVP. We include 481 cardiac cycles (8422 frames) from 151 subjects. Our agent achieves the AUC, accuracy and F1 of MVP diagnosis are 99.54%, 95.08%, and 94.12% in patient-levels. The Dice and Iou of heatmap of MVP are 70.35% and 56.39%, which is about 5% higher than that between senior and junior physicians. Our agent would provide standardizing diagnosis and intuitive visualization for the surgical management and treatment plans of MVP.

关键词： Health sciences   Computer-aided diagnosis method   Ultrasound technology   Computer science   Artificial intelligence   Machine learning  

摘要： Ultrasound imaging is critical for clinical diagnostics, providing insights into various diseases and organs. However, artificial intelligence (AI) in this field faces challenges, such as the need for large labeled datasets and limited task-specific model applicability, particularly due to ultrasound's low signal-to-noise ratio (SNR). To overcome these, we introduce the Ultrasound Representation Foundation Model (URFM), designed to learn robust, generalizable representations from unlabeled ultrasound images, enabling label-efficient adaptation to diverse diagnostic tasks. URFM is pre-trained on over 1M images from 15 major anatomical organs using representation-based masked image modeling (MIM), an advanced self-supervised learning. Unlike traditional pixel-based MIM, URFM integrates high-level representations from BiomedCLIP, a specialized medical vision-language model, to address the low SNR issue. Extensive evaluation shows that URFM outperforms state-of-the-art methods, offering enhanced generalization, label efficiency, and training-time efficiency. URFM's scalability and flexibility signal a significant advancement in diagnostic accuracy and clinical workflow optimization in ultrasound imaging.

关键词： Computer science   Machine learning   Quantum information   Quantum physics  

摘要： As a demonstration, artificial-intelligence system helps to make a miniature, high-speed animation of physicists’ favourite feline.

关键词： Physics   Computer science  

摘要： Advancements in classical computing have propelled machine learning applications, yet inherent limitations persist in terms of energy, resource, and speed. Quantum machine learning offers a promising avenue to overcome these limitations but poses its own hurdles. This experimental study investigates the training limits of a real quantum-classical hybrid system on an ion-trap platform using supervised protocols. It addresses the challenges of coupling ion-trap systems with classical processors and highlights the effectiveness of genetic algorithms for NISQ devices and complex binary classification tasks with many local minima. The analysis reveals why gradient-based methods may not be ideal in the NISQ era. Our results provide insights into optimizing hybrid quantum-classical systems, emphasizing the importance of training strategies and hardware design. This work not only enhances understanding of such systems but also illustrates their practical potential in solving real-world problems without relying on classical simulators, bridging the gap between quantum and classical computing paradigms.

关键词： Geography   Computer science   Machine learning  

摘要： The system will save time spent on processing satellite data, researchers say — but they hope the tech firm will share more about how it works.

关键词： PLq-ROFS   Heronian mean operators   Maximizing deviation methodology   MOORA methodology   CS applications in an educational sector  

摘要： Computer science (CS) has changed educational processes in a wide range of academic fields and is now considered to be an essential part of modern education. This manuscript presents a novel methodology in which CS can be used to solve judgment concerns in education. The conventional education system frequently finds it difficult to deal with the ambiguities and linguistic expressions that are an integral component of human judgment and choice-making processes. By combining probabilistic uncertainty with linguistic concepts, a probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) presents an efficient mathematical methodology for describing inaccurate and unclear facts to handle these concerns. For aggregating the assessment data, we first propose the PLq-ROF Heronian mean operators. The maximizing deviation methodology is a helpful tool when dealing with situations where the knowledge about the attribute weights is either partially or fully unknown. To find attribute weights, this study presents the PLq-ROF-maximizing deviation methodology. We also propose the multi-objective optimization by ratio analysis (MOORA) methodology with PLq-ROFNs to evaluate the options and identify an optimal choice. Furthermore, a case study related to the judgment process on CS applications in education is provided from the PLq-ROFS perspective to illustrate the effectiveness and use of the developed technique. The numerical results show that A4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbb {A}}_{4}$$\end{document} (namely: skill development) is the best CS application in education.

关键词： Biological system modeling   Predictive models   Information diffusion   Computational modeling   Topology   Vectors   Context modeling   Device-to-device communication   Computer science   Accuracy   Deep diffuse model   group relationship   influence factor   information diffusion   intelligence of social things (IoST)   user relationships  

摘要： In the intelligence of social things (IoST) paradigm, where interconnected devices and social networks create a dynamic ecosystem, understanding information diffusion is essential. IoST integrates user interactions, device behaviors, and contextual factors, adding complexity to information networks and necessitating accurate prediction models. This work analyses user behavior in terms of both group and individual relationships and presents an information propagation prediction model that combines information propagation topology features with user relationship representations. Information diffusion prediction analyzes patterns of spread in networks to understand and forecast propagation processes. Existing studies emphasize social and dynamic influence relationships within user groups but often neglect user similarity in group relations and intrinsic factors affecting individual sharing decisions. To address these gaps, a novel model is proposed, combining user relationship representations and diffusion topological features. At the group level, a user cooccurrence graph captures similarity relationship, integrating these with diffusion topology to analyze group interactions. At the individual level, user-specific feature representations and influence factor vectors address intrinsic motivations for sharing. Experimental results validate the model's efficacy, achieving performance improvements on public datasets. On the Memetracker dataset, the model increased MAP@k by 6.54% and hits@k by 2.75%, demonstrating its ability to capture both group and individual dynamics for enhanced diffusion prediction.

关键词： robotization   technological change   worker-level data   tasks  

摘要： Technological change has led to a decline in the share of routine and physical jobs, and a rise in the share of abstract and social ones at the economy level. However, much less is known about how these trends unfold at the individual level. Do workers' tasks become more or less routine and physical? Do workers shift towards more social and abstract activities? This paper is the first to explore these questions in the context of robotization. We use survey data from 20 European countries to develop worker-level indices of physical, routine, abstract and social tasks, which we link to industry-level robotization exposure. Using instrumental variable techniques, we find that robotization reduces physically demanding tasks but increases routine tasks, while also limiting opportunities for cognitively challenging work and human interaction. This study provides a worker-centric perspective on the relationship between technology and task composition, revealing insights that aggregate analyses miss.

关键词： Social networking (online)   Behavioral sciences   Minimization   Infectious diseases   Adaptation models   Spirals   Predictive models   Linear programming   Computer science   User experience   Human social behavior   influence minimization   Markov chain   rumor suppression   social networks  

摘要： With the continuous development of Internet technology, social networks have provided convenient conditions for information dissemination. The rapid dissemination of information has provided us with great convenience, but some criminals extensively spread rumors based on such convenience, adversely affecting social stability. In this context, two key challenges arise: the survivability of rumors, which refers to their persistence and long-term impact, and the dynamic viewpoint changes of individuals, which influence how rumors spread and diminish over time. This article proposes a dynamic-susceptible-exposed-infected-recovered (DSEIR) rumor propagation model based on human social behavior to solve the spread of rumors problem. This model considers the characteristics of rumors spread in social networks with the Markov chain and makes the simulation more authentic. To suppress rumor propagation, we introduce the concept of rumor survivability and propose a dynamic truth movement blocking strategy, which adapts to people's evolving viewpoints to curb the influence of rumors effectively. Finally, we analyze the proposed model and blocking strategy in four real networks. The experimental results show that the proposed propagation model can authentically simulate the propagation of rumors in social networks and the proposed blocking strategy efficiently suppresses the rumor propagation. We have released our code here: https://***/gf9264/DSEIR.