In today9;s digital landscape, web applications have become indispensable tools for businesses, facilitating access to vital data and services. However, this ubiquity also exposes them to a myriad of cyber threats,...
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Breakthroughs in natural language processing (NLP) by large-scale language models (LLMs) have led to superior performance in multilingual tasks such as translation, summarization, and Q&A. However, the size and co...
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the proceedings contain 35 papers. the topics discussed include: robust semantic communication driven by knowledge graph;impact of big data analytics and managerial support on CRM: exploring mediating role of marketin...
ISBN:
(纸本)9798350320459
the proceedings contain 35 papers. the topics discussed include: robust semantic communication driven by knowledge graph;impact of big data analytics and managerial support on CRM: exploring mediating role of marketing analytics;an improved and stable routing protocol for cognitive radio based IoT networks;a smart approach using multi–agent system for big data security;integrating trusted computing mechanisms with trust models to achieve zero trust principles;distributed ledger technologies for managing heterogenous computing systems at the edge;secure sparse gradient aggregation in distributed architectures;and internet of things (IoT): a way to expedite production and service performance empirical, evidence from textile industry of United Arab Emirates (UAE).
Electric Vehicles (EVs) uses rechargeable battery packs to store electrical energy, necessitating periodic recharging for continued operation. the efficiency and speed of EV battery charging are crucial factors in the...
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In this paper we apply distributedcomputing to comparative analysis of digitalization efficiency estimation methods tailored to aquaculture. A modified method of digitalization efficiency estimation is presented and ...
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the large scale computer system provides a high performance platform for engineering applications. Mesh generation is the basis for numerical simulation for computing science, which heavily relies on user9; experie...
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Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. this exchange incurs a communication overhead...
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ISBN:
(纸本)9781665460873
Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. this exchange incurs a communication overhead and impacts the performance of FL training. there is limited understanding of how communication protocols specifically contribute to the performance of FL. Such an understanding is essential for selecting the right communication protocol when designing an FL system. this paper presents FedComm, a benchmarking methodology to quantify the impact of optimized application layer protocols, namely Message Queue Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and ZeroMQ Message Transport Protocol (ZMTP), and non-optimized application layer protocols, namely as TCP and UDP, on the performance of FL. FedComm measures the overall performance of FL in terms of communication time and accuracy under varying computational and network stress and packet loss rates. Experiments on a lab-based testbed demonstrate that TCP outperforms UDP as a non-optimized application layer protocol with higher accuracy and shorter communication times for 4G and Wi-Fi networks. Optimized application layer protocols such as AMQP, MQTT, and ZMTP outperformed nonoptimized application layer protocols in most network conditions, resulting in a 2.5x reduction in communication time compared to TCP while maintaining accuracy. the experimental results enable us to highlight a number of open research issues for further investigation. FedComm is available for download from https://***/qub- blesson/FedComm.
Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations9;*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave ...
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Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods withthe use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along withthe actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.
Islanding detection becomes a necessity when the DERs units are required to continue their generation even after the islanding of the μG from the grid. this paper investigates the effectiveness of the active islandin...
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GPU devices are currently seen as one of the trending topics for parallelcomputing. Commonly, GPU applications are developed with programming tools based on compiled languages, like C/C++ and Fortran. this paper pres...
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ISBN:
(纸本)9781665469586
GPU devices are currently seen as one of the trending topics for parallelcomputing. Commonly, GPU applications are developed with programming tools based on compiled languages, like C/C++ and Fortran. this paper presents a performance and programming effort analysis employing the Python high-level language to implement the NAS parallel Benchmark kernels targeting GPUs. We used Numba environment to enable CUDA support in Python, a tool that allows us to implement a GPU application with pure Python code. Our experimental results showed that Python applications reached a performance similar to C++ programs employing CUDA and better than C++ using OpenACC for most NPB kernels. Furthermore, Python codes required less operations related to the GPU framework than CUDA, mainly because Python needs a lower number of statements to manage memory allocations and data transfers. However, our Python versions demanded more operations than OpenACC implementations.
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