H.3.8. Natural Language Processing
Mohammad Hadi Goldani; Saeedeh Momtazi; Reza Safabakhsh
Abstract
The widespread use of web-based forums and social media has led to an increase in news consumption. To mitigate the impact of misinformation on users' health-related decisions, it is crucial to develop machine learning models that can automatically detect and combat fake news. In this paper, we propose ...
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The widespread use of web-based forums and social media has led to an increase in news consumption. To mitigate the impact of misinformation on users' health-related decisions, it is crucial to develop machine learning models that can automatically detect and combat fake news. In this paper, we propose a novel multilingual model with dynamic transformer model called Hybrid CapsNet for Covid-19 fake news detection in English and Persian languages. Our model incorporates two dynamic pre-trained representation models that incrementally uptrain and update the word embeddings in the training phase., dynamic RoBERTa for English and dynamic ParsBERT for Persian, and two parallel classifiers with new loss function namely margin loss. By utilizing dynamic transformer and both Deep Convolutional Neural Networks (DCNN) and Capsule Neural Networks (CapsNet), we achieve better performance than state-of-the-art baselines. To evaluate the proposed model, we use two recent Covid-19 datasets in English and Persian. Our results, in terms of F1-score, demonstrate the effectiveness of the Hybrid CapsNet model. Our model outperforms existing baselines, suggesting that it can be an effective tool for detecting and combating fake news related to Covid-19 in multiple languages. Overall, our study highlights the importance of developing effective machine learning models for combating misinformation during critical events such as the Covid-19 pandemic. The proposed model has the potential to be applied to other languages and domains and can be a valuable tool for protecting public health and safety.
H.3. Artificial Intelligence
M. Taghian; A. Asadi; R. Safabakhsh
Abstract
The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved ...
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The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning. In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with deep reinforcement learning is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. In addition, the effects of different structures for the encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.
H.3.2.6. Games and infotainment
A. Torkaman; R. Safabakhsh
Abstract
Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent ...
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Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope with the uncertainty existing in these games, we design a Bayesian network whose parameters are learned from an unlabeled game-logs dataset; so it does not require a human expert’s knowledge. We evaluate our model on StarCraft which is considered as a unified test-bed in this domain. The model is compared with that proposed by Synnaeve and Bessiere. Experimental results on recorded games of human players show that the proposed model can predict the opponent’s future decisions more effectively. Using this model, it is possible to create an adaptive game intelligence algorithm applicable to RTS games, where the concept of build order (the order of building construction) exists.
