arXiv subjects

Javad Salimi Sartakhti

Publications and source records attributed to Javad Salimi Sartakhti.

3 recordsLinked to original sources

arXiv2023

A UML Profile for Bitcoin Blockchain

Blockchain has received attention for its potential use in business. Bitcoin is powered by blockchain, and interest in it has surged in the past few years. It has many uses that need to be modeled. Modeling is used in many walks of life to share ideas, reduce complexity, achieve close alignment of one person viewpoint with another and provide abstractions of a system at some level of precision and detail. Software modeling is used in Model Driven Engineering (MDE), and Domain Specific Languages (DSLs) ease model development and provide intuitive syntax for domain experts. The present study has designed and evaluated a meta-model for the bitcoin application domain to facilitate application development and help in truly understanding bitcoin. The proposed meta-model, including stereotypes, tagged values, enumerations and a set of constraints defined by Object Constraint Language (OCL), was defined as a Unified Modeling Language (UML) profile and was implemented in the Sparx Enterprise Architect (Sparx EA) modeling tool. A case study developed by our meta-model is also presented.

cs.SE↗

arXiv2023

CTRAN: CNN-Transformer-based Network for Natural Language Understanding

Intent-detection and slot-filling are the two main tasks in natural language understanding. In this study, we propose CTRAN, a novel encoder-decoder CNN-Transformer-based architecture for intent-detection and slot-filling. In the encoder, we use BERT, followed by several convolutional layers, and rearrange the output using window feature sequence. We use stacked Transformer encoders after the window feature sequence. For the intent-detection decoder, we utilize self-attention followed by a linear layer. In the slot-filling decoder, we introduce the aligned Transformer decoder, which utilizes a zero diagonal mask, aligning output tags with input tokens. We apply our network on ATIS and SNIPS, and surpass the current state-of-the-art in slot-filling on both datasets. Furthermore, we incorporate the language model as word embeddings, and show that this strategy yields a better result when compared to the language model as an encoder.

cs.CL↗

arXiv2018

Fuzzy Least Squares Twin Support Vector Machines

Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification. It combines the operating principles of Least Squares SVM (LS-SVM) and Twin SVM (T-SVM); it constructs two non-parallel hyperplanes (as in T-SVM) by solving two systems of linear equations (as in LS-SVM). Despite its efficiency, LST-SVM is still unable to cope with two features of real-world problems. First, in many real-world applications, labels of samples are not deterministic; they come naturally with their associated membership degrees. Second, samples in real-world applications may not be equally important and their importance degrees affect the classification. In this paper, we propose Fuzzy LST-SVM (FLST-SVM) to deal with these two characteristics of real-world data. Two models are introduced for FLST-SVM: the first model builds up crisp hyperplanes using training samples and their corresponding membership degrees. The second model, on the other hand, constructs fuzzy hyperplanes using training samples and their membership degrees. Numerical evaluation of the proposed method with synthetic and real datasets demonstrate significant improvement in the classification accuracy of FLST-SVM when compared to well-known existing versions of SVM.

cs.AI↗