scholarly journals An analysis between exact and approximate algorithms for the k-center problem in graphs

2022 ◽  
Vol 12 (2) ◽  
pp. 2058
Author(s):  
Velin Kralev ◽  
Radoslava Kraleva ◽  
Viktor Ankov ◽  
Dimitar Chakalov
Keyword(s):  
Operating System ◽  
Execution Time ◽  
Source Code ◽  
Exact Algorithm ◽  
Approximate Solutions ◽  
Approximate Algorithm ◽  
Approximate Algorithms ◽  
Optimal Solutions ◽  
Center Problem ◽  
Computation Complexity

<span lang="EN-US">This research focuses on the k-center problem and its applications. Different methods for solving this problem are analyzed. The implementations of an exact algorithm and of an approximate algorithm are presented. The source code and the computation complexity of these algorithms are presented and analyzed. The multitasking mode of the operating system is taken into account considering the execution time of the algorithms. The results show that the approximate algorithm finds solutions that are not worse than two times optimal. In some case these solutions are very close to the optimal solutions, but this is true only for graphs with a smaller number of nodes. As the number of nodes in the graph increases (respectively the number of edges increases), the approximate solutions deviate from the optimal ones, but remain acceptable. These results give reason to conclude that for graphs with a small number of nodes the approximate algorithm finds comparable solutions with those founds by the exact algorithm.</span>

scholarly journals Information processing for Similar Source code using LSH Algorithm

2021 ◽  
Vol 23 (07) ◽  
pp. 23-34
Author(s):  
Mrs. Vani Dave ◽  
◽  
Mr Sanjeev Kumar shukla ◽  
Keyword(s):  
Information Processing ◽  
Execution Time ◽  
Source Code ◽  
Locality Sensitive Hashing ◽  
Source File ◽  
C Language ◽  
Fast Search

In this study, we propose a method to quickly search for similar source files for a given source file as a method to examine the origin of reused code. By outputting not only the same contents but also similar contents, it corresponds to the source file that has been changed during reuse. In addition, locality-sensitive hashing is used to search from a large number of source files, enabling fast search. By this method, it is possible to know the origin of the reused code. A case study was conducted on a library that is being reused written in C language. Some of the changes were unique to the project, and some were no longer consistent with the source files. As a result, it was possible to detect the source files that were reused from among the 200 projects with 92% accuracy. In addition, when we measured the execution time of the search using 4 files, the search was completed within 1 second for each file.

scholarly journals Computing Simplicial Depth by Using Importance Sampling Algorithm and Its Application

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fanyu Meng ◽  
Wei Shao ◽  
Yuxia Su
Keyword(s):  
Importance Sampling ◽  
Testing Machine ◽  
Exact Algorithm ◽  
Real Data ◽  
Exact Algorithms ◽  
Approximate Algorithm ◽  
Sampling Algorithm ◽  
Simplicial Depth ◽  
Low Efficiency ◽  
Np Problem

Simplicial depth (SD) plays an important role in discriminant analysis, hypothesis testing, machine learning, and engineering computations. However, the computation of simplicial depth is hugely challenging because the exact algorithm is an NP problem with dimension d and sample size n as input arguments. The approximate algorithm for simplicial depth computation has extremely low efficiency, especially in high-dimensional cases. In this study, we design an importance sampling algorithm for the computation of simplicial depth. As an advanced Monte Carlo method, the proposed algorithm outperforms other approximate and exact algorithms in accuracy and efficiency, as shown by simulated and real data experiments. Furthermore, we illustrate the robustness of simplicial depth in regression analysis through a concrete physical data experiment.

Proposal of Iterative Genetic Algorithm for Test Suite Generation

2021 ◽  
Vol 12 (1) ◽  
pp. 111-130
Author(s):  
Ankita Bansal ◽  
Abha Jain ◽  
Abhijeet Anand ◽  
Swatantra Annk
Keyword(s):  
Genetic Algorithm ◽  
Execution Time ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Computation Complexity ◽  
Test Suite Generation ◽  
Adaptive Nature ◽  
Efficiency And Reliability

Huge and reputed software industries are expected to deliver quality products. However, industry suffers from a loss of approximately $500 billion due to shoddy software quality. The quality of the product in terms of its accuracy, efficiency, and reliability can be revamped through testing by focusing attention on testing the product through effective test case generation and prioritization. The authors have proposed a test-case generation technique based on iterative listener genetic algorithm that generates test cases automatically. The proposed technique uses its adaptive nature and solves the issues like redundant test cases, inefficient test coverage percentage, high execution time, and increased computation complexity by maintaining the diversity of the population which will decrease the redundancy in test cases. The performance of the technique is compared with four existing test-case generation algorithms in terms of computational complexity, execution time, coverage, and it is observed that the proposed technique outperformed.

Appreciative Inquiry: A Life-Giving Personal Operating System

2021 ◽  
Vol 23 (1) ◽  
pp. 61-79
Author(s):  
Lindsey N. Godwin ◽  
Jackie Stavros
Keyword(s):  
Operating System ◽  
Appreciative Inquiry ◽  
Source Code ◽  
The World

Similar to how the technologies we use have an operating system (OS) to ensure information and work flows smoothly, Godwin and Stavros suggest that each of us has a personal OS that impacts the information we process from the world around us and our subsequent actions. Using Appreciative Inquiry principles as the source code for an AI-based personal operating system (the aiOS) and the AI 5D cycle as one of the most popular applications of the aiOS, they conclude with an invitation to the global AI community to submit their favorite aiOS applications for future featuring on the AI Commons.

An Exact Algorithm for Large-Scale Continuous Nonlinear Resource Allocation Problems with Minimax Regret Objectives

2021 ◽  
Author(s):  
Jungho Park ◽  
Hadi El-Amine ◽  
Nevin Mutlu
Keyword(s):  
Resource Allocation ◽  
Objective Function ◽  
Large Scale ◽  
Computational Study ◽  
Optimal Solution ◽  
Exact Algorithm ◽  
Minimax Regret ◽  
Optimal Solutions ◽  
Heuristic Approaches ◽  
Exact Approach

We study a large-scale resource allocation problem with a convex, separable, not necessarily differentiable objective function that includes uncertain parameters falling under an interval uncertainty set, considering a set of deterministic constraints. We devise an exact algorithm to solve the minimax regret formulation of this problem, which is NP-hard, and we show that the proposed Benders-type decomposition algorithm converges to an [Formula: see text]-optimal solution in finite time. We evaluate the performance of the proposed algorithm via an extensive computational study, and our results show that the proposed algorithm provides efficient solutions to large-scale problems, especially when the objective function is differentiable. Although the computation time takes longer for problems with nondifferentiable objective functions as expected, we show that good quality, near-optimal solutions can be achieved in shorter runtimes by using our exact approach. We also develop two heuristic approaches, which are partially based on our exact algorithm, and show that the merit of the proposed exact approach lies in both providing an [Formula: see text]-optimal solution and providing good quality near-optimal solutions by laying the foundation for efficient heuristic approaches.

Structural Coverage Analysis Methods

2019 ◽  
pp. 36-63
Author(s):  
Parnasi Retasbhai Patel ◽  
Chintan M. Bhatt
Keyword(s):  
Execution Time ◽  
Source Code ◽  
Second Phase ◽  
Coverage Criteria ◽  
Analysis Methods ◽  
Coverage Analysis ◽  
Structural Coverage ◽  
Test Suit

Structural coverage analysis for any code is a very common approach to measure the quality of any test suit. Structural coverage determines which structure of the software or which portion is not exercised. This chapter describes two different phases to achieve structural coverage analysis using DO-178B/C standards. Statement coverage is the very basic coverage criteria which involves execution of all the executable statements in the source code at least once. Analysis of structural coverage can be done by capturing the amount of code that is covered by the airborne software. The first phase contains the instrumentation procedure which instruments the source code at execution time, and the second phase is generating a report that specifies which portion of source code is executed and which one is not in the form of a percentage.

scholarly journals Capybara: equivalence ClAss enumeration of coPhylogenY event-BAsed ReconciliAtions

2020 ◽  
Vol 36 (14) ◽  
pp. 4197-4199
Author(s):  
Yishu Wang ◽  
Arnaud Mary ◽  
Marie-France Sagot ◽  
Blerina Sinaimeri
Keyword(s):  
Source Code ◽  
Supplementary Information ◽  
Optimal Solutions ◽  
Supplementary Data ◽  
Huge Number ◽  
Tree Reconciliation ◽  
Class Enumeration ◽  
Suboptimal Solutions ◽  
The Many ◽  
Event Based

Abstract Motivation Phylogenetic tree reconciliation is the method of choice in analyzing host-symbiont systems. Despite the many reconciliation tools that have been proposed in the literature, two main issues remain unresolved: (i) listing suboptimal solutions (i.e. whose score is ‘close’ to the optimal ones) and (ii) listing only solutions that are biologically different ‘enough’. The first issue arises because the optimal solutions are not always the ones biologically most significant; providing many suboptimal solutions as alternatives for the optimal ones is thus very useful. The second one is related to the difficulty to analyze an often huge number of optimal solutions. In this article, we propose Capybara that addresses both of these problems in an efficient way. Furthermore, it includes a tool for visualizing the solutions that significantly helps the user in the process of analyzing the results. Availability and implementation The source code, documentation and binaries for all platforms are freely available at https://capybara-doc.readthedocs.io/. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

A Reusable Device Driver Framework for the Sensor Nodes in Internet of Things

2012 ◽  
Vol 468-471 ◽  
pp. 60-63
Author(s):  
Xiao Fan Wu ◽  
Jia Jun Bu ◽  
Chun Chen
Keyword(s):  
Operating System ◽  
Internet Of Things ◽  
Embedded System ◽  
Sensor Node ◽  
Rapid Development ◽  
Source Code ◽  
Device Driver ◽  
Sensor Nodes ◽  
C Programming Language ◽  
C Programming

Due to the rapid development of Internet of Things (IoT), kinds of sensor nodes have been introduced to the different applications. Because of the variety of MCUs, sensors and radio modules, it’s challenging to reuse the device drivers between different sensor node platforms. To address this issue, a reusable device driver framework is proposed in this paper. Comparing with existed work, our framework is flexible, efficient, and easy to learn. The flexibility is achieved by layered encapsulation, which decouples the device driver with the sensor node operating system kernel. Our framework gives the reusability at the source code level, so it’s efficient. At the end, our framework is implemented in C programming language, which is the most common tool adopted by embedded system developing. This framework has applied to SenSpire OS, a micro-kernel real-time operating system for IoT sensor nodes.

Sign in / Sign up

Export Citation Format

Share Document