The identification of real and fake news has become difficult task in the recent years due to more similar feature between them. As the issues of fake news are leading the miscommunication to the reads, various automa...
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Traditional healthcare focused on hospitals and clinics proved out to be inadequate, especially during the COVID-19 pandemic and various emergency crisis, Care Compass, is an innovative and affordable smart wearable d...
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Federated learning (FL) has emerged as an efficient technique to train machine learning (ML) models across decentralized devices without sharing any raw data for preserving privacy. However, it faces challenges in com...
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ISBN:
(纸本)9783031814037;9783031814044
Federated learning (FL) has emerged as an efficient technique to train machine learning (ML) models across decentralized devices without sharing any raw data for preserving privacy. However, it faces challenges in communication overhead and resource constraints, particularly in real-time applications of Internet of Things (IoT). To address these challenges, we propose a novel method called Hashcash and Jaya-based communication efficient Federated learning (HJCFL). It consists of three main phases: local aggregator selection, clustering of IoT devices and communication efficient FL process. It uses Hashcash for strategically selecting local aggregators and Jaya based algorithm for clustering IoT devices. The usage of Hashcash function has an added advantage over existing aggregator selection techniques because of it's simplicity, proof-of-work and adjustable difficulty level. To the best of our knowledge, none of the existing techniques uses Hashcash for the same. Moreover, unlike most of the existing schemes, the proposed method utilizes all the available devices and therefore it converges faster with significantly less number of global rounds than others. Through extensive simulations, we demonstrate the effectiveness of the proposed HJCFL as compared to two baseline techniques. The result's validity is further confirmed using a widely recognized statistical technique, i.e., analysis of variance (ANOVA), followed by a least significant difference (LSD) post-hoc analysis.
In generative zero-shot learning, the visual distribution mismatch between synthetic and real samples is a challenge due to the lack of effective constraints for unseen class visual features. To address this, we propo...
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Nowadays, modern industries and smart technologies are focused on the application of intelligent technology. However, most employees still lack high ability in embedded systems. To solve this problem, the engineering ...
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This paper presents PathFinder, an intelligent career development system that leverages large language models (LLMs) to bridge the gap between individuals' current skills and their career aspirations. The system c...
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In the current era where data and information is the main fuel for business, education, banking, marketing and digital applications used by people. These applications consist of personal and sensitive data. These digi...
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Digital technology is becoming an important traction to lead the transformation of education, and secondary education as a cradle of scientific and technological talents, and further promote the development of digital...
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Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretr...
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Knowledge tracing (KT) is a pivotal component of online education systems, aiming to assess and predict students' knowledge states based on their learning history. Existing knowledge tracing models have achieved c...
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ISBN:
(纸本)9789819601158;9789819601165
Knowledge tracing (KT) is a pivotal component of online education systems, aiming to assess and predict students' knowledge states based on their learning history. Existing knowledge tracing models have achieved considerable success, yet they have neglected the frequency of interactions between students and knowledge points. Frequency information can aid in more stable modeling of students' knowledge states. In this study, we incorporate frequency data into our model's question embeddings, enabling it to consider the frequency of student-knowledge concept interactions. Moreover, the order within students' learning sequences is crucial, but knowledge tracing models based on the transformer architecture, with their attention mechanisms, are insensitive to the sequence order. We propose the N-transformer structure that combines transformer with order-sensitive RNNs, effectively enhancing the model's sensitivity to the order in students' learning sequences. Subsequently, we employ a simpler and more effective method for prior knowledge modeling, directly extracting prior knowledge related to the current exercise from the model's predicted knowledge state at the previous time step. Finally, we have designed a decay function for the attention mechanism based on the Ebbinghaus forgetting curve to simulate the students' forgetting behavior. Ultimately, we conducted experiments on four datasets, Our model has achieved up to a 3% increase in AUC compared to the baseline on some datasets.
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