Instruction-tuned large language models have demonstrated remarkable capabilities in following human instructions across various domains. However, their proficiency remains notably deficient in many low-resource langu...
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Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and ...
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Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.
Developing control programs for autonomous vehicles is a challenging task, mainly due to factors such as complex and dynamic environments, intricacy of tasks, and uncertain sensor information. To tackle the challenge,...
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Developing control programs for autonomous vehicles is a challenging task, mainly due to factors such as complex and dynamic environments, intricacy of tasks, and uncertain sensor information. To tackle the challenge, this paper harnesses the potential of formal methods and deep reinforcement learning (DRL) for a more comprehensive solution that integrates Generalized Reactivity(1) (GR(1)) synthesis with DRL. The GR(1) synthesis module takes care of high-level task planning, ensuring a vehicle follows a correct-by-construction and verifiable plan for its mission. On the other hand, the DRL model operates as the low-level motion controller, allowing the vehicle to learn from experience and adjust its actions based on real-time sensor feedback. Therefore, the resulting controller for autonomous vehicles is not only guaranteed to finish its designated tasks but also intelligent to handle complex environments. Through comparative experimental studies, we demonstrate that the control program generated by the proposed approach outperforms the ones generated independently utilizing GR(1) reactive synthesis and DRL. IEEE
Training a high-performance deep neural network requires large amounts of data and computational resources. Protecting the intellectual property (IP) and commercial ownership of a deep model is challenging yet increas...
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This study focuses on designing of lead-free double perovskite solar cells (DPSCs). Lead-free organic–inorganic DPSCs have achieved very good efficiency within a short period of active research. Formamidinium based d...
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The pulp and paper industry faces significant en-vironmental challenges, such as air pollution, greenhouse gas emissions, and wastewater discharge, requiring smarter and more sustainable operations. Regulatory bodies ...
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The substantial growth of Internet-of-Things technology and the ubiquity of smartphone devices has increased the public and industry focus on speech emotion recognition (SER) technologies. Yet, conceptual, technical, ...
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Predicting Coronary Artery Disease (CAD) presents a critical and intricate challenge within medical science. Late-stage detection of CAD can gravely affect cardiac and vascular health, often leading to obstructions in...
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This paper presents a novel framework for testing automation applications compliant with the international standard IEC 61499 and including process simulation. The framework enables automation programs to be run in te...
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