Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Quer...
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In this study, we use the CIMulator platform to evaluate the performance of neuromorphic accelerators with novel Hf0.5Zr0.5O2 (HZO) ferroelectric fin field-effect transistor (FefinFET) as synaptic device. The MNIST ha...
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One of the most prevalent findings in the autonomous transportation literature is the strong connection between human trust and intentions to use autonomous vehicles (AVs). Indeed, trust is widely regarded as an essen...
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One of the most prevalent findings in the autonomous transportation literature is the strong connection between human trust and intentions to use autonomous vehicles (AVs). Indeed, trust is widely regarded as an essential antecedent for the adoption of AVs, a finding based primarily on survey-based methodologies. However, psychological theory has long suggested that self-reported measures of intention are fraught with contradiction – people often say one thing and do another. We examine this potential dichotomy here by experimentally characterizing the relationship between self-reported trust and behavioral decisions to use AVs. An initial survey of 444 participants assessed trust in AVs, identifying three trust categories: high, moderate, and low. Results demonstrated that people trust human rideshare drivers more than AVs. A subsequent in-lab behavioral study with 72 of these participants involved choosing a ride in an AV or a human-driven vehicle. Contrary to prevailing assumptions, our results reveal a deep chasm between intention and behavior: 97 % of participants, regardless of trust rating, chose to ride in the AV. This finding indicates that situational context, curiosity, and immediate circumstances heavily influence decision-making, mediating (and even overshadowing) self-reported trust levels. Employing Cognitive Dissonance Theory, we offer potential explanations for why participants reconciled their initial distrust with their subsequent actions. Our findings challenge the narrative that self-reported trust determines AV adoption and highlight the importance of situational factors in shaping user behavior. Using these results, we offer new insights and guidance for deploying AVs, suggesting that controlled, low-risk environments could facilitate broader acceptance of this emerging technology, providing a practical solution to the AV trust problem.
Speech content is closely related to the stability of speaker embeddings in speaker verification tasks. In this paper, we propose a novel architecture based on self-constraint learning (SCL) and reconstruction task (R...
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Manila Bay is a significant contributor to the Philippines' fish production, but its resources have been depleted due to overfishing, pollution, and damage, leading to a decline in fish catch and a shift towards l...
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The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of information and Communication Technologies (ICTs). Within the immersive SPG e...
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A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,w...
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A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.
In a music scenario, both auditory and visual elements are essential to achieve an outstanding performance. Recent research has focused on the generation of body movements or fingering from audio in music performance....
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Defect detection is a critical component in ensuring the highest quality of printed circuit board manufacture. The work investigates the application of transfer learning to identify YOLOv8 design flaws. The YOLOv8 mod...
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PCB defect detection and classification is vital in electronics manufacturing to ensure high-quality and safe products. Manual inspection is time-consuming, costly, and prone to errors. To address this, automated meth...
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