In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approa...
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In integrated sensing and communication (ISAC) systems, communication signals can easily be wiretapped by targets (potential eavesdroppers) or detected by wardens. Different from previous works that address a single s...
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When flying a satellite, various operations on-board the satellite require determining quite a precise orientation to properly function, such as Earth or Sun observation, downlink communication and similar. This resul...
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Ternary Content Addressable Memory (TCAM) is widely used in development areas such as network routers and machine learning, which are receiving significant attention. Ferroelectric field-effect transistors (FeFETs) ar...
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This paper presents a novel method for optimizing meteorological sensor placement over large geographic areas, leveraging genetic algorithm (GA) and classified Light Detection And Ranging (LiDAR) data. The airborne Li...
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single-inductor multiple-output (SIMO) boost converter faces an issuce of mutual interference and cross-regulation among output voltages. This paper proposes a ripple-based non-cross-regulation controller suitable for...
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The SM4 algorithm is now widely used to ensure the security of data transmission, but its physical implementation is still vulnerable to side channel attack. By studying the structure of SM4 algorithm, this paper prop...
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Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most ...
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This paper investigates the active reconfigurable intelligent surfaces (RIS)-assisted integrated sensing and communication (ISAC) system, in which a dual-functional base station (BS) simultaneously transmits communica...
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In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the ***,it uses co-occurrence techniques and tries...
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In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the ***,it uses co-occurrence techniques and tries to combine nodes’textual content for *** still do not,however,directly simulate many interactions in network *** order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling ***,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation ***,the Commuting Matrix for massive node pair paths is used to improve computational ***,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson *** addition,we also consider solving the model’s parameters by applying variational *** results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational *** on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.
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