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ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph
Published 2024-06-01“…Robo or unsolicited calls have become a persistent issue in telecommunication networks, posing significant challenges to individuals, businesses, and regulatory authorities. …”
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62
VO2 based polarization-independent dual-wavelength plasmonic switches using U and C shaped nanostructures
Published 2025-02-01“…These switches can be employed in telecommunication networks, optical communications, and integrated photonic circuits.…”
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63
Internet das coisas (IoT) e novas dinâmicas da produção agrícola no campo brasileiro
Published 2022-09-01“…Since the advent of the Internet of Things, the growth rate of data production and its transmission speed have increased exponentially and demand the connection of rural establishments and production stages to telecommunications networks. The analysis of the agents involved in the development, diffusion and connection of these systems and the assessment of their impacts on agricultural production allow us to visualize two complementary and contradictory processes in the Brazilian countryside: 1) the deepening of inequalities, arising from the socially and spatially selective diffusion of digital technologies; 2) the potential mitigation of the deleterious effects of the first trend, that is, of the disparities between agents of the upper and lower circuit of agrarian economies, through the diffusion and trivialization of the use of information technologies and internet infrastructures in the context of family farming, valuing the diverse knowledge of small and medium producers and cooperatives.…”
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64
Evaluation and optimization of carbon emission for federal edge intelligence network
Published 2024-03-01“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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Current Aspects of Legal Regulation for Monitoring Electronic Communications and Removal of Information from Electronic Communication Networks in Ukraine
Published 2021-09-01“…The legislator made an attempt to legally regulate the implementation of operative and technical measures in electronic communication networks in the Criminal Procedural Code of Ukraine dated from April 13, 2012, which provided the removal of information from transport telecommunication networks. Relevant norms were also provided in the Law of Ukraine “On Operative and Search Activities”. …”
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66
A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System
Published 2024-01-01“…Overall, the proposed CNN-LSTM PCM can effectively compensate for residual phase fluctuations generated by the optical path asymmetry, providing a potential option for achieving stable RF transmission in telecommunication networks.…”
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Abnormal link detection algorithm based on semi-local structure
Published 2022-02-01“…With the research in network science, real networks involved are becoming more and more extensive.Redundant error relationships in complex systems, or behaviors that occur deliberately for unusual purposes, such as wrong clicks on webpages, telecommunication network spying calls, have a significant impact on the analysis work based on network structure.As an important branch of graph anomaly detection, anomalous edge recognition in complex networks aims to identify abnormal edges in network structures caused by human fabrication or data collection errors.Existing methods mainly start from the perspective of structural similarity, and use the connected structure between nodes to evaluate the abnormal degree of edge connection, which easily leads to the decomposition of the network structure, and the detection accuracy is greatly affected by the network type.In response to this problem, a CNSCL algorithm was proposed, which calculated the node importance at the semi-local structure scale, analyzed different types of local structures, and quantified the contribution of edges to the overall network connectivity according to the semi-local centrality in different structures, and quantified the reliability of the edge connection by combining with the difference of node structure similarity.Since the connected edges need to be removed in the calculation process to measure the impact on the overall connectivity of the network, there was a problem that the importance of nodes needed to be repeatedly calculated.Therefore, in the calculation process, the proposed algorithm also designs a dynamic update method to reduce the computational complexity of the algorithm, so that it could be applied to large-scale networks.Compared with the existing methods on 7 real networks with different structural tightness, the experimental results show that the method has higher detection accuracy than the benchmark method under the AUC measure, and under the condition of network sparse or missing, It can still maintain a relatively stable recognition accuracy.…”
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