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Titlebook: Making the Tunisian Resurgence; Mahmoud Sami Nabi Book 2019 The Editor(s) (if applicable) and The Author(s), under exclusive license to Sp

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ntelligent education system services. However, most existing KT models oversimplify the modeling of the knowledge state, ignoring two practical issues in the learning process: (1) the mutual influence of learning ability and knowledge mastery and (2) the process of knowledge internalization. This pa
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Mahmoud Sami Nabiellent results, especially graph attention network models (GATs). Existing GATs ignore the factual correlation between different relations in the same pair of entities based on the global graph structure. To solve this problem, we propose a model RISDF based on the graph attention network with relat
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Mahmoud Sami Nabirvised learning model do not provide an option to detect such instances, so they miss-classify such instances with 100% probability. Open Set Recognition (OSR) and Non-Exhaustive Learning (NEL) are potential solutions to overcome this problem. Most existing methods of OSR first classify members of e
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Mahmoud Sami Nabi model updates while safeguarding the confidentiality of individual updates. Despite widespread claims regarding SecAgg’s privacy-preserving capabilities, a formal analysis of its privacy is lacking, making such presumptions unjustified. In this paper, we delve into the privacy implications of SecAg
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Mahmoud Sami Nabi model updates while safeguarding the confidentiality of individual updates. Despite widespread claims regarding SecAgg’s privacy-preserving capabilities, a formal analysis of its privacy is lacking, making such presumptions unjustified. In this paper, we delve into the privacy implications of SecAg
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Mahmoud Sami Nabi is how to decipher the user intent with limited interactions. Recent progress regards the combination of consecutive items in the session as intent. However, these methods, which merely depend on the session, ignore the fact that such limited interaction within the session may not entirely express
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