Feature trees are the formal basis for algorithms manipulating record like structures in constraint programming, computational linguistics and in concrete applications like software configuration management. Feature t...
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Feature trees are the formal basis for algorithms manipulating record like structures in constraint programming, computational linguistics and in concrete applications like software configuration management. Feature trees model records, and constraints over feature trees yield extensible and modular record descriptions. We introduce the constraint system FT [less-than or equal to] of ordering constraints interpreted over feature trees. Under the view that feature trees represent symbolic information, the relation less than or equal corresponds to the information ordering ("carries less information than"). We present two algorithms in cubic time, one for the satisfiability problem and one for the entailment problem of FT [less-than or equal to] . We show that FT [less-than or equal to] has the independence property. We are thus able to handle negative conjuncts via entailment and obtain a cubic algorithm that decides the satisfiability of conjunctions of positive and negated ordering constraints over feature trees. Furthermore, we reduce the satisfiability problem of Dorre's weak subsumption constraints to the satisfiability problem of FT [less-than or equal to] and improve the complexity bound for solving weak subsumption constraints from O(n5) to O(n3).
Forecasting time series problem occurs in various subject areas. Recently neural network techniques have been used for solving such tasks. However, they have not been sufficiently studied. The article explores the inf...
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Predicting student success is an important task in educational institutions, as it allows for targeted interventions and support systems to enhance educational outcomes. This paper explores the use of SHAP (SHapley Ad...
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A special attention about scalability has to be paid to QoS solutions for Core Internet Networks as they deal with a lot of flows and demand many resources. This paper analyses and proposes integrated solutions from t...
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Software engineering is an important development task in the automation domain due to the increasing number of embedded systems applied for controlling various system functions. In general, those automation systems ar...
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To reduce speech recognition error rate we can use better statistical language models. These models can be improved by grouping words into word equivalence classes. Clustering algorithms can be used to automatically d...
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To reduce speech recognition error rate we can use better statistical language models. These models can be improved by grouping words into word equivalence classes. Clustering algorithms can be used to automatically do this word grouping. We present an incremental clustering algorithm and two iterative clustering algorithms. Also, we compare them with previous algorithms. The experimental results show that the two iterative algorithms perform as well as previous ones. It should be pointed out that one of them, that uses the leaving one out technique, has the ability to automatically determine the optimum number of classes. These iterative algorithms are used by the incremental one. On the other hand, the proposed incremental algorithm achieves the best results of the compared algorithms, its behavior is the most regular with the variation of the number of classes and can automatically determine the optimum number of classes.
The integration and detection of AI (Artificial Intelligence) in a variety of fields, primarily education, are examined in this paper. With an emphasis on virtual assistants and their uses, it explores the potential a...
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作者：
Gancheva, VeskaTechnical University of Sofia
Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria
A growing range of data sets have been created in recent years;these are used by platforms and software applications and kept in remote access repositories. Datasets are therefore more susceptible to harmful attacks. ...
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