Existing learning models partition the generated representations using hyperplanes which form well defined groups of similar embeddings that is uniquely mapped to a particular class. However, in practical applications...
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Background: In the wake of escalating cyber threats and the indispensability of ro-bust network security mechanisms, it becomes crucial to understand the evolving landscape of cryptographic research. Recognizing the s...
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The advances from the last few decades in the fields of ML (Machine Learning), DL (Deep Learning), and semantic computing are now changing the shape of the healthcare system. But, unlike physical health problems, diag...
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Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can l...
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Sequence-to-sequence models are fundamental building blocks for generating abstractive text summaries, which can produce precise and coherent summaries. Recently proposed, different text summarization models aimed to ...
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Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running gra...
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Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.
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%.
Traffic rerouting is a technique used to optimize traffic flow and reduce congestion by redirecting vehicles to alternate routes. The work done in this research focuses on a specific case scenario covering a 25 k...
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Omnidirectional images provide an immersive viewing experience in a Virtual Reality (VR) environment, surpassing the limitations of traditional 2D media beyond the conventional screen. This VR technology allows users ...
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Omnidirectional images provide an immersive viewing experience in a Virtual Reality (VR) environment, surpassing the limitations of traditional 2D media beyond the conventional screen. This VR technology allows users to interact with visual information in an exciting and engaging manner. However, the storage and transmission requirements for 360-degree panoramic images are substantial, leading to the establishment of compression frameworks. Unfortunately, these frameworks introduce projection distortion and compression artifacts. With the rapid growth of VR applications, it becomes crucial to investigate the quality of the perceptible omnidirectional experience and evaluate the extent of visual degradation caused by compression. In this regard, viewport plays a significant role in omnidirectional image quality assessment (OIQA), as it directly affects the user’s perceived quality and overall viewing experience. Extracting viewports compatible with users viewing behavior plays a crucial role in OIQA. Different users may focus on different regions, and the model’s performance may be sensitive to the chosen viewport extraction strategy. Improper selection of viewports could lead to biased quality predictions. Instead of assessing the entire image, attention can be directed to areas that are more importance to the overall quality. Feature extraction is vital in OIQA as it plays a significant role in representing image content that aligns with human perception. Taking this into consideration, the proposed ATtention enabled VIewport Selection (ATVIS-OIQA) employs attention based view port selection with Vision Transformers(ViT) for feature extraction. Furthermore, the spatial relationship between the viewports is established using graph convolution, enabling intuitive prediction of the objective visual quality of omnidirectional images. The effectiveness of the proposed model is demonstrated by achieving state-of-the-art results on publicly available benchmark datasets, n
Wireless networks such as MANETs present unique challenges due to their dynamic and decentralized nature. Efficient routing protocols are essential for achieving reliable and robust communication in such networks. In ...
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