This paper presents a pilot middleware system, AirSync, that is designed to enable cloud integration for remote monitoring and prediction of air quality data generated by the air quality sensors using indoor LoRa comm...
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An Internet of Mobile Things (IoMT) refers to an internetworked group of pervasive devices that coordinate their motion and task execution through frequent status and data exchange. An IoMT could be serving critical a...
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The rapid identification and diagnosis of viruses through saliva are highly desirable for molecular diagnostics but face significant challenges due to the low concentration of viruses and interference from other biomo...
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This paper proposes a methodology for contradiction detection in cross lingual texts about the Nakba. We outline a pipeline that includes text translation using Google’s Gemini for context-aware translations, followe...
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In recent years, artificial intelligence has entered the public eye across various fields, enhancing productivity and increasing social welfare. However, it has also raised significant security and privacy concerns du...
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Completing standardization of the VVC in July 2020, the Joint Video Experts Team of ISO/IEC SC29/WG05 launched the "Beyond VVC" exploration by developing the Enhanced Compression Model (ECM) which keeps inte...
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Loop-free routing approaches based on distance information combine a Local Ordering Condition (LOC) with a distributed reordering computation (DRC). The LOC allows routers to determine whether they can independently c...
To bolster Sybil resistance and establish persistent identities in new permissionless blockchain networks, we introduce delay towers, leveraging Verifiable Delay Functions (VDFs) to implement a proof of elapsed time (...
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The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Rec...
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The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Recognition(HAR)***,the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained *** paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain,which reduces the model’s depth and accelerates activity *** traditional pruning methods that focus on the spatial domain and the importance of filters,this method converts sensor data,such as HAR data,to the frequency domain for *** emphasizes the low-frequency components by calculating their energy spectral density ***,filters that meet the predefined thresholds are retained,and redundant filters are removed,leading to a significant reduction in model size without compromising performance or incurring additional computational ***,the proposed algorithm’s effectiveness is empirically validated on a standard five-layer CNNs backbone *** computational feasibility and data sensitivity of the proposed scheme are thoroughly ***,the classification accuracy on three benchmark HAR datasets UCI-HAR,WISDM,and PAMAP2 reaches 96.20%,98.40%,and 92.38%,***,our strategy achieves a reduction in Floating Point Operations(FLOPs)by 90.73%,93.70%,and 90.74%,respectively,along with a corresponding decrease in memory consumption by 90.53%,93.43%,and 90.05%.
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