84 research outputs found
CAUSES AND WAYS OF OCCURRENCE OF OCCASIONAL WORDS IN THE LANGUAGE OF THE PRESS
No full textThis article contains an analytical overview of opinions of modern linguists on the causes of neologisms appearance and the principles of their differentiation. The author identifies the criteria for attributing the word to occasional words by comparing them with neologisms. Following other researchers, the author believes that the distinctive feature of occasional words and neologisms is that they belong to speech and language, respectively. The distinctive features of occasional words according to M.V. Radchenko are novelty, one-time use, and the fact that their appearance is usually dictated by the creative challenges faced by the author. However, these do not allow reckoning the occasional words among speech. The author is convinced that the occasional use, first of all, is in violation of grammar, word forming, lexical and semantic rules of the language. This is a motivated irregularity that is rationally organized. The author describes the causes of neologisms appearance in the language of the media. The article details the derivation aspect of occasional words. On the examples of journalistic texts, M.V. Radchenko describes the regulatory and non-normative methods of occasional words forming. Finally, she concludes that for the occasional word forming the regulatory models already existing in the language are often used
Bi-objective Heterogeneous Consolidation in Cloud Computing
No full textIn this paper, we address the problem of power-aware Virtual Machines (VMs) consolidation considering resource contention. Deployment of VMs can greatly influence host performance, especially, if they compete for resources on insufficient hardware. Performance can be drastically reduced and energy consumption increased. We focus on a bi-objective experimental evaluation of scheduling strategies for CPU and memory intensive jobs regarding the quality of service (QoS) and energy consumption objectives. We analyze energy consumption of the IBM System x3650 M4 server, with optimized performance for business-critical applications and cloud deployments built on IBM X-Architecture. We create power profiles for different types of applications and their combinations using SysBench benchmark. We evaluate algorithms with workload traces from Parallel Workloads and Grid Workload Archives and compare their non-dominated Pareto optimal solutions using set coverage and hyper volume metrics. Based on the presented case study, we show that our algorithms can provide the best energy and QoS trade-offs
InnoCSE 2017 : Innovative Approaches in Computer Science within Higher Education : Proceedings of the 1st International Workshop on Innovative Approaches in Computer Science within Higher Education, Chelyabinsk, Russia, May 26th, 2017
No full textInnoCSE 2017 : Innovative Approaches in Computer Science within Higher Education : Proceedings of the 1st International Workshop on Innovative Approaches in Computer Science within Higher Education, Chelyabinsk, Russia, May 26th, 2017
No full textDistributed virtual test benches: usage of computer aided engineering systems in distributed computing environment
No full textГлеб Игоревич Радченко, кандидат физико-математических наук, кафедра системного программирования, Южно-Уральский государственный университет (Челябинск, Россия), [email protected].
Gleb Radchenko, Candidate of Physico-mathematical Sciences, Department of Systems
Programming, South Ural State University ( Chelyabinsk, Russia), [email protected].Рациональной альтернативой созданию собственного суперкомпьютерного
центра для решения сложных задач инженерного моделирования является аренда вычислительных и программных ресурсов в режиме удаленного доступа у центров коллективного пользования. Однако при этом возникает целый комплекс проблем, связанных с организацией прозрачного и безопасного доступа к таким ресурсам. В статье предложено описание технологии CAEBeans, обеспечивающей автоматизированную генерацию проблемно-ориентированных грид-сервисов, позволяющих использовать программные системы для инженерного проектирования и анализа в распределенных вычислительных средах.
Renting of hardware and software resources by means of remote access is an efficient alternative to creating a own supercomputer center to meet the challenges of engineering modeling. However, there is a range of issues, associated with organization of a transparent and secure access to remote resources. A description of CAEBeans technology is proposed in this article. This technology porvide automated generation of problem-oriented grid services, enable the use of CAE software systems in distributed computing
environments
Uncertainty Estimation in Multi-Agent Distributed Learning
No full textTraditionally, IoT edge devices have been perceived primarily as low-power
components with limited capabilities for autonomous operations. Yet, with
emerging advancements in embedded AI hardware design, a foundational shift
paves the way for future possibilities. Thus, the aim of the KDT NEUROKIT2E
project is to establish a new open-source framework to further facilitate AI
applications on edge devices by developing new methods in quantization,
pruning-aware training, and sparsification. These innovations hold the
potential to expand the functional range of such devices considerably, enabling
them to manage complex Machine Learning (ML) tasks utilizing local resources
and laying the groundwork for innovative learning approaches.
In the context of 6G's transformative potential, distributed learning among
independent agents emerges as a pivotal application, attributed to 6G networks'
support for ultra-reliable low-latency communication, enhanced data rates, and
advanced edge computing capabilities.
Our research focuses on the mechanisms and methodologies that allow edge
network-enabled agents to engage in collaborative learning in distributed
environments. Particularly, one of the key issues within distributed
collaborative learning is determining the degree of confidence in the learning
results, considering the spatio-temporal locality of data sets perceived by
independent agents.Comment: Poster for SAL Symposium on 6G. 22 November 2023 - 23 November 2023
Linz, Austri
Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices
No full textInitially considered as low-power units with limited autonomous processing, Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This advancement has vastly amplified their computational capabilities, emphasizing the practicality of edge AI. Such progress introduces new challenges of optimizing AI tasks for the limitations of energy and network resources typical in Edge computing environments. Our study explores methods that enable distributed data processing through AI-enabled edge devices, enhancing collaborative learning capabilities. A key focus of our research is the challenge of determining confidence levels in learning outcomes, considering the spatial and temporal variability of data sets encountered by independent agents. To address this issue, we investigate the application of Bayesian neural networks, proposing a novel approach to manage uncertainty in distributed learning environments
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