作者：
Trufas, DafinaLOS
Faculty of Mathematics and Computer Science University of Bucharest Institute for Logic and Data Science Bucharest Romania
In this paper we present a formalization of Intuitionistic Propositional Logic in the Lean proof assistant. Our approach focuses on verifying two completeness proofs for the studied logical system, as well as explorin...
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作者：
Puri, ChetanReddy, K.T.V.
Department of Computer Science and Design Wardha India
Department of Artificial Intelligence and Data Science Wardha India
Fetal growth restriction and preterm delivery proceed to be major around the world wellbeing concerns, with serious consequences for the wellbeing of moms and babies. Provoke and exact estimating of these issues is ba...
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Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static user-item interactions, LSTM mod...
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Recent advances in text-to-speech, particularly those based on Graph Neural Networks (GNNs), have significantly improved the expressiveness of short-form synthetic speech. However, generating human-parity long-form sp...
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Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artifici...
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Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.
Color pencil drawing is well-loved due to its rich *** paper proposes an approach for generating feature-preserving color pencil drawings from *** mimic the tonal style of color pencil drawings,which are much lighter ...
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Color pencil drawing is well-loved due to its rich *** paper proposes an approach for generating feature-preserving color pencil drawings from *** mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction *** lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph *** saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious ***,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are *** experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.
This study examines the adherence of ChatGPT (GPT-3.S and GPT-4) to fairness principles, specifically proportionality and equality, in negotiation scenarios. Three distinct negotiation contexts were explored: work-stu...
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Demands on the performance of database systems continue to increase. In state-of-the-art database systems, the storage engine is a major source of performance bottlenecks, and it is important to harness parallelism by...
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With the rapid advancement of the Internet of Things (IoT), its applications are becoming increasingly essential in actual application. Specifically, the recent surge in electric vehicles has spurred significant advan...
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Dear editor,Masking is generally utilized to construct the first-order protection for cryptographic algorithms,but such protected designs are still susceptible to higher-order power analysis ***-order differential pow...
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Dear editor,Masking is generally utilized to construct the first-order protection for cryptographic algorithms,but such protected designs are still susceptible to higher-order power analysis ***-order differential power analysis (DPA)[1–4]can break first-order masking countermeasures by combining the leakages of the two secret shares into a signal that is correlated with the target intermediate variable.
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