scholarly journals JavaSMT 3: Interacting with SMT Solvers in Java

2021 ◽  
pp. 195-208
Author(s):  
Daniel Baier ◽  
Dirk Beyer ◽  
Karlheinz Friedberger
Keyword(s):  
Programming Languages ◽  
Satisfiability Modulo Theories ◽  
Performance Characteristics ◽  
Enabling Technology ◽  
Smt Solvers ◽  
Computer Aided ◽  
And Performance ◽  
Lock In Effect ◽  
Lock In ◽  
Update Process

AbstractSatisfiability Modulo Theories (SMT) is an enabling technology with many applications, especially in computer-aided verification. Due to advances in research and strong demand for solvers, there are many SMT solvers available. Since different implementations have different strengths, it is often desirable to be able to substitute one solver by another. Unfortunately, the solvers have vastly different APIs and it is not easy to switch to a different solver (lock-in effect). To tackle this problem, we developed JavaSMT, which is a solver-independent framework that unifies the API for using a set of SMT solvers. This paper describes version 3 of JavaSMT, which now supports eight SMT solvers and offers a simpler build and update process. Our feature comparisons and experiments show that different SMT solvers significantly differ in terms of feature support and performance characteristics. A unifying Java API for SMT solvers is important to make the SMT technology accessible for software developers. Similar APIs exist for other programming languages.

Acoustic and Performance Characteristics of an Ideally Twisted Rotor in Hover

2021 ◽  
Author(s):  
Nicole A. Pettingill ◽  
Nikolas S. Zawodny ◽  
Christopher Thurman ◽  
Leonard V. Lopes
Keyword(s):  
Performance Characteristics ◽  
And Performance

The Capital Gains Lock-In Effect and Earnings Quality

2019 ◽  
Author(s):  
Stephen G. Dimmock ◽  
Fan Feng ◽  
Huai Zhang
Keyword(s):  
Earnings Quality ◽  
Capital Gains ◽  
Lock In Effect ◽  
Lock In

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

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