9,210 research outputs found

    The role of digital communication in developing administrative work in higher education institutions

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    In higher education institutions, effective digital communication is crucial for achieving administrative goals, such as improving student services, managing resources, and facilitating collaboration among staff members. By exploring the impact of copresence factors on digital communication effectiveness, higher education institutions can gain a deeper understanding of the factors that influence their digital communication and develop strategies that optimize its efficiency. The study applied a quantitative research approach through a questionnaire survey to collect required responses from employees who are working in the higher education institutions of Jordan with a total of 304 participants. The findings of this study indicate that copresence factors play a significant role in the effectiveness of digital communication within higher education institutions in Jordan. The results support the framework developed by others and suggest that self-copresence and partner-copresence have a positive impact on the efficiency of communication. This highlights the importance of considering the presence of individuals during digital communication and the impact it can have on the quality of the exchange. In conclusion, the study sheds light on the importance of correspondence in digital communication and its impact on the efficiency of communication within higher education institutions. The findings can help in the development of strategies and practices for enhancing the effectiveness of digital communication and improving administrative work in higher education institutions in Jordan

    Augmented Reality in Learning Settings: A Systematic Analysis of its Benefits and Avenues for Future Studies

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    Despite its increasing use in various settings, Augmented Reality (AR) technology is still often considered experimental, partly due to a lack of clear understanding of the benefits of using AR. This study systematically reviews research on the use of AR in learning settings. Our analysis of 93 relevant articles offers 21 benefits related to the learning gains and outcomes of using AR. Our study shows that the positive effects of using AR on learners’ motivation and joy have been well-studied, whereas the effects on independent learning, concentration, spontaneous learning, critical thinking, and practical skills have not yet been examined in detail. Beyond classifying and discussing the benefits of using AR in learning settings, we elaborate avenues for future studies. We specifically point to the importance of conducting long-term studies to determine the value of using AR in learning beyond the initial novelty and exploring the integration of AR with other technologies

    Unleashing the power of artificial intelligence for climate action in industrial markets

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    Artificial Intelligence (AI) is a game-changing capability in industrial markets that can accelerate humanity's race against climate change. Positioned in a resource-hungry and pollution-intensive industry, this study explores AI-powered climate service innovation capabilities and their overall effects. The study develops and validates an AI model, identifying three primary dimensions and nine subdimensions. Based on a dataset in the fast fashion industry, the findings show that the AI-powered climate service innovation capabilities significantly influence both environmental and market performance, in which environmental performance acts as a partial mediator. Specifically, the results identify the key elements of an AI-informed framework for climate action and show how this can be used to develop a range of mitigation, adaptation and resilience initiatives in response to climate change

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Deep Learning Techniques for Electroencephalography Analysis

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    In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective

    Orientation-Aware 3D SLAM in Alternating Magnetic Field from Powerlines

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    Identifying new sensing modalities for indoor localization is an interest of research. This paper studies powerline-induced alternating magnetic field (AMF) that fills the indoor space for the orientation-aware three-dimensional (3D) simultaneous localization and mapping (SLAM). While an existing study has adopted a uniaxial AMF sensor for SLAM in a plane surface, the design falls short of addressing the vector field nature of AMF and is therefore susceptible to sensor orientation variations. Moreover, although the higher spatial variability of AMF in comparison with indoor geomagnetism promotes location sensing resolution, extra SLAM algorithm designs are needed to achieve robustness to trajectory deviations from the constructed map. To address the above issues, we design a new triaxial AMF sensor and a new SLAM algorithm that constructs a 3D AMF intensity map regularized and augmented by a Gaussian process. The triaxial sensor’s orientation estimation is free of the error accumulation problem faced by inertial sensing. From extensive evaluation in eight indoor environments, our AMF-based 3D SLAM achieves sub-1m to 3m median localization errors in spaces of up to 500 m2 , sub-2° mean error in orientation sensing, and outperforms the SLAM systems based on Wi-Fi, geomagnetism, and uniaxial AMF by more than 30%

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Breaking Virtual Barriers : Investigating Virtual Reality for Enhanced Educational Engagement

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    Virtual reality (VR) is an innovative technology that has regained popularity in recent years. In the field of education, VR has been introduced as a tool to enhance learning experiences. This thesis presents an exploration of how VR is used from the context of educators and learners. The research employed a mixed-methods approach, including surveying and interviewing educators, and conducting empirical studies to examine engagement, usability, and user behaviour within VR. The results revealed educators are interested in using VR for a wide range of scenarios, including thought exercises, virtual field trips, and simulations. However, they face several barriers to incorporating VR into their practice, such as cost, lack of training, and technical challenges. A subsequent study found that virtual reality can no longer be assumed to be more engaging than desktop equivalents. This empirical study showed that engagement levels were similar in both VR and non-VR environments, suggesting that the novelty effect of VR may be less pronounced than previously assumed. A study against a VR mind mapping artifact, VERITAS, demonstrated that complex interactions are possible on low-cost VR devices, making VR accessible to educators and students. The analysis of user behaviour within this VR artifact showed that quantifiable strategies emerge, contributing to the understanding of how to design for collaborative VR experiences. This thesis provides insights into how the end-users in the education space perceive and use VR. The findings suggest that while educators are interested in using VR, they face barriers to adoption. The research highlights the need to design VR experiences, with understanding of existing pedagogy, that are engaging with careful thought applied to complex interactions, particularly for collaborative experiences. This research contributes to the understanding of the potential of VR in education and provides recommendations for educators and designers to enhance learning experiences using VR

    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
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