Source Code Availability Research Articles

Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools

The majority of Ethical Hacking (EH) tools utilised in penetration testing are developed by practitioners within the industry or underground communities. Similarly, academic researchers have also contributed to developing security tools. However, there appears to be limited awareness among practitioners of academic contributions in this domain, creating a significant gap between industry and academia’s contributions to EH tools. This research paper aims to survey the current state of EH academic research, primarily focusing on research-informed security tools. We categorise these tools into process-based frameworks (such as PTES and Mitre ATT&CK) and knowledge-based frameworks (such as CyBOK and ACM CCS). This classification provides a comprehensive overview of novel, research-informed tools, considering their functionality and application areas. The analysis covers licensing, release dates, source code availability, development activity, and peer review status, providing valuable insights into the current state of research in this field.

Seq_B_LSTM_CNN_HPO: Rare Mendelian Diseases to Genotypes Associations from Multiple Data Sources

Motivation: Genotype-Phenotype annotations have become a crucial tool for studying the abnormalities in phenotype diseases. These abnormalities and relations can help to understand more the complex, and hidden information. This information clearly describes the genetic mutations causes in the organisms such as human. Several systems and algorithms have been proposed and implemented to solve this issue, since the digital information is provided for free online from different resources that describe the human mutations and the different variations in genes. Machine learning, especially deep artificial neural network, has proven its ability to overcome the limitations of these traditional algorithms and remarkably performing at extraordinary accuracies compared to conventional methods such as statistical techniques and others. Results: In this paper, a multilabel hyper-artificial neural networks model classifier is proposed and implemented for predicting rare mendelian diseases. It is called Seq_B_LSTM_CNN_HPO. The proposed system trained on more than 50 features obtained from four data sources, Gene Ontology (GO), Human Phenotype Ontology (HPO), UniProtKB, and Gene Expressions to learn complex features and relations. The proposed system was tested on UniProtKB dataset and compared with different proposed systems in the fields. The experiment was performed on human organism for variety of analytical study in order to find new relations between phenotype diseases. The tabulated results are evaluated using six different unique evaluation metrics with outstanding results scores of Fmax, Precision, Recall, AUPR, AUROC, Smin with scores of 0.894, 0.902, 0.886, 0.711, 0.631, 0.384 which outperformed several proposed systems in the literature. Data and Source Code Availability: The source code is provided at GitHub repository and the dataset is uploaded at Google_Drive

A Collection of 30 Multidimensional Functions for Global Optimization Benchmarking

A collection of thirty mathematical functions that can be used for optimization purposes is presented and investigated in detail. The functions are defined in multiple dimensions, for any number of dimensions, and can be used as benchmark functions for unconstrained multidimensional single-objective optimization problems. The functions feature a wide variability in terms of complexity. We investigate the performance of three optimization algorithms on the functions: two metaheuristic algorithms, namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and one mathematical algorithm, Sequential Quadratic Programming (SQP). All implementations are done in MATLAB, with full source code availability. The focus of the study is both on the objective functions, the optimization algorithms used, and their suitability for solving each problem. We use the three optimization methods to investigate the difficulty and complexity of each problem and to determine whether the problem is better suited for a metaheuristic approach or for a mathematical method, which is based on gradients. We also investigate how increasing the dimensionality affects the difficulty of each problem and the performance of the optimizers. There are functions that are extremely difficult to optimize efficiently, especially for higher dimensions. Such examples are the last two new objective functions, F29 and F30, which are very hard to optimize, although the optimum point is clearly visible, at least in the two-dimensional case.

Deep reinforcement learning in computer vision: a comprehensive survey

Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision. In this work, we provide a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision. We start with comprehending the theories of deep learning, reinforcement learning, and deep reinforcement learning. We then propose a categorization of deep reinforcement learning methodologies and discuss their advantages and limitations. In particular, we divide deep reinforcement learning into seven main categories according to their applications in computer vision, i.e. (i) landmark localization (ii) object detection; (iii) object tracking; (iv) registration on both 2D image and 3D image volumetric data (v) image segmentation; (vi) videos analysis; and (vii) other applications. Each of these categories is further analyzed with reinforcement learning techniques, network design, and performance. Moreover, we provide a comprehensive analysis of the existing publicly available datasets and examine source code availability. Finally, we present some open issues and discuss future research directions on deep reinforcement learning in computer vision.

Predicting of Open Source Software Component Reusability Level Using Object-Oriented Metrics by Taguchi Approach

Component-based software development (CBSD) is an efficient approach used by software developers to develop new software. The commercial off the shelf (COTS) and open-source software (OSS) are two styles to implement CBSD. The COTS provides the interface and depicts the black-box behavior, but does not support several software quality characteristics. On the other hard, OSS is a more efficient approach compared to COTS due to its source code availability. This research aims to identify the reusability level of OSS components from an online repository of OSS. The OSS components are classified based on Chidamber and Kemerer reusability metrics (CK-metrics). This paper proposed a mathematical model to establish the relationship between the reusability of CK-metrics. Reusability level of OSS component has been measured and most effective CK-metrics obtained by applying the Taguchi design and analysis of variance (ANOVA). The input parameters for the experimental design are evaluated based on the OSS repository. Performance analysis has been carried out based upon the interaction effect between the reusability of CK-metrics. Main effect plots are created to identify the most reusable component of the OSS. The genetic algorithm (GA) is used to predict the optimized value of the different control parameters. The results indicate that the OSS component reusability level is 0.698194. The reusability of software has a significant effect on the quality of software. The quality of software can be improved by increasing the reusability of software components.

Integrating GLEAMS sedimentation into RZWQM for pesticide sorbed sediment runoff modeling

Sediment transport from agricultural fields to native waterways is a significant pollution vector, not just for the bulk sediment, but also for additional mass of pesticides traveling offsite that are sorbed to soil particles. Existing field scale models that track plant growth as well as the fate and transport of applied pesticides lack an integrated sediment transport component. This study sought to address this lack of available modeling tools for researchers and regulators by integrating the sediment and surface flow components of Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) model into the mature Root Zone Water Quality Model (RZWQM) to create a derivative model named RZWQM-Sed. Previous research identified RZWQM as a quick running, agricultural field scale model that accurately estimated offsite transport of solutes. Unlike other well performing field scale agricultural models, the full source code of RZWQM was available for modification and extension. However, RZWQM lacked a sediment component and thus could not measure all pollutants moving offsite. GLEAMS sedimentation was chosen for integration due to its well documented history, compatibility with the RZWQM codebase, and source code availability. Sensitivity analysis of the RZWQM runoff variables showed that the residual water content, saturated water content, and bubbling pressure from the Brooks-Corey equation had the highest influence for RZWQM followed by the saturated hydraulic conductivity and the non-Brooks-Corey bubbling pressure. Analysis of GLEAMS variables showed the most significant variables are the USLE parameters (cfact, pfact, ksoil) and Manning's N. The latter variable only showed sensitivity at very high surface roughness while the USLE parameters had a linear relationship over the entire domain. The integrated model was calibrated and validated using multiple real-world datasets spanning ranges of space and time. The final model performed well in the primary task of predicting the mass of sorbed chemicals in the tailwater (Nash-Sutcliffe coefficient > 0.3).

Schroedinger’s Code: A Preliminary Study on Research Source Code Availability and Link Persistence in Astrophysics

Abstract We examined software usage in a sample set of astrophysics research articles published in 2015 and searched for the source codes for the software mentioned in these research papers. We categorized the software to indicate whether the source code is available for download and whether there are restrictions to accessing it, and if the source code is not available, whether some other form of the software, such as a binary, is. We also extracted hyperlinks from one journal’s 2015 research articles, as links in articles can serve as an acknowledgment of software use and lead to the data used in the research, and tested them to determine which of these URLs are still accessible. For our sample of 715 software instances in the 166 articles we examined, we were able to categorize 418 records as according to whether source code was available and found that 285 unique codes were used, 58% of which offered the source code for download. Of the 2558 hyperlinks extracted from 1669 research articles, at best, 90% of them were available over our testing period.

A Survey of Performance Analysis Tools for OpenMP and MPI

Objective: Due to the recent development of multiple parallel programming tools with varying features, it is difficult to choose the best tool according to the needs of the user. Methods: This problem is addressed by making a comparative analysis study of different features like license type, source code availability, targeted platforms and languages supportedby these diverse tools. There are different parallel programming languages that support the present multi-core architecture like Message Passing Interface (MPI) and Open Multi-Processing (OpenMP). These are widely used to provide different performance characteristics of parallelism in different test cases. The new architecture and the complexity strengthens the need to monitor and analyze the performance of the various OpenMP kernels and constructs on multi-core processors. Findings: There are many papers that have been published in the past but non of them focuses on a comparative study among the performance analysis tools (PATs) that we mostly opt for. This paper intends to analyze the parallel computing ability of OpenMP and MPI, besides helping the user to understand which tool suites his task the best. Improvement: This study shows that MPI offers the best performance characteristics in the field of shared memory programming whereas OpenMP is a better choice because of the global style of the resulting program. It also provides a roadmap to select the best tool when designing a parallel programming system.

안드로이드 기반 자연과학 교육용 디지털 앱북 개발을 위한 사용자 상호작용 라이브러리

The digital app-book is an advanced form of the electronic book (e-book), which attracts a lot of interests by the help of video, sound, sensors and a variety of interactions. As mobile devices have evolved, the demand of digital app-books is also rising substantially. However, the distribution of digital app-book contents is hard to meet the demand because the digital app-book requires a lot of programming cost for the interaction. To resolve this problem, Was verified and implementation as a library function of the interaction between device and user. The proposed library consists of three parts (user action recognition, device action, and content action) and provides various user-device interaction functions by combining methods of each part, which can support source code reusability, easy understanding and availability, and wide expandibility. The library was used in the development of natural science education app-book contents. As a result, it could reduce a lot of code lines and facilitate more rapid app-book development. Keyword : E-Book, Digital App-Book, Mobile Application 정규논문 (Regular Paper) 방송공학회논문지 제20권 제1호, 2015년 1월 (JBE Vol. 20, No. 1, January 2015) http://dx.doi.org/10.5909/JBE.2015.20.1.110 ISSN 2287-9137 (Online) ISSN 1226-7953 (Print) 이강운 외 2인 : 안드로이드 기반 자연과학 교육용 디지털 앱북 개발을 위한 사용자 상호작용 라이브러리 111 (Kang-Woon Lee et al. : User Interaction Library for Natural Science Education Digital App-Book on Android Platform)

Cloud4Psi: cloud computing for 3D protein structure similarity searching.

Summary: Popular methods for 3D protein structure similarity searching, especially those that generate high-quality alignments such as Combinatorial Extension (CE) and Flexible structure Alignment by Chaining Aligned fragment pairs allowing Twists (FATCAT) are still time consuming. As a consequence, performing similarity searching against large repositories of structural data requires increased computational resources that are not always available. Cloud computing provides huge amounts of computational power that can be provisioned on a pay-as-you-go basis. We have developed the cloud-based system that allows scaling of the similarity searching process vertically and horizontally. Cloud4Psi (Cloud for Protein Similarity) was tested in the Microsoft Azure cloud environment and provided good, almost linearly proportional acceleration when scaled out onto many computational units.Availability and implementation: Cloud4Psi is available as Software as a Service for testing purposes at: http://cloud4psi.cloudapp.net/. For source code and software availability, please visit the Cloud4Psi project home page at http://zti.polsl.pl/dmrozek/science/cloud4psi.htm.Contact: dariusz.mrozek@polsl.pl

Simulation of the on Demand Software Model, Including Vendors Offering Source Code Availability

In recent years, the power balance between software firms and enterprise customers has been heavily changing. The contractual power of customers is increasing and the firms must take this fact into account. Stated the common need to save money, the opportunities to adopt new software solutions with tight budgets, or with substantial savings, are limited. The better way to obtain operational savings seems to be shifting to the use of open source software, obtaining in this way all the benefits of this software typology: lower costs, greater adherence to open standards, a broader vendor choice and service suppliers, no vendor lock-in and flexible, incremental architectures. In this work, a business model is proposed to analyse the competition among on-demand customer relationship management (CRM) firms, in particular among firms offering CRM products with or without source code availability. The model can also be customized to study other kinds of markets, and other assumptions can be made for studying their implications to the software business.

Computational reflection and its application to platform verification

The complexity of modern hardware design has created the need for higher levels of abstraction, where system modeling is used to integrate modules into complex System-on-Chip (SoCs) platforms. SystemC, and its TLM (Transaction Level Modeling) extensions, have been used for this purpose mainly because of their fast prototyping and simulation features, which allow for early design space exploration. This paper proposes an approach to explore and interact with SystemC models by means of an introspection technique known as Computational Reflection. We use reflection to implement a white-box introspection mechanism called ReflexBox. We show that ReflexBox is a fast, non-intrusive technique that can be used to dynamically gather and inject stimuli into any SystemC module, without the need to use a proprietary SystemC implementation, change the SystemC library, instrument or even inspect the module source code. Our approach can be used to support many different verification tasks like platform debugging, performance evaluation and communication analysis. To show ReflexBox effectiveness we used it in three platforms case studies to address tasks like register inspection, performance analysis and signal replaying for testbench reuse. In all cases we assumed no source code availability and measured the impact on the overall platform performance.

MarkerSet: a marker selection tool based on markers location and informativity in experimental designs

BackgroundThe recent sequencing of full genomes has led to the availability of many SNP markers which are very useful for the mapping of complex traits. In livestock production, there are still no commercial arrays and many studies use home-made sets of SNPs. Thus, the current methodologies for SNP genotyping are still expensive and it is a crucial step to select the SNPs to use. Indeed, the main factors affecting the power of the linkage analyses are the density of the genetic map and the heterozygosity of markers in tested animal parents.FindingsThis is why we have developed a PERL program selecting a defined number of markers based on their locations on the genome and their informativity in specific experimental designs. As an option, different experimental designs can be combined in order to select the best possible common marker set. The program has been tested using different conditions of marker informativity and density with both real and simulated datasets. The results show the efficiency of our program to select the most informative markers even if there is a wide range of informativity for whole genome scan mapping analyses. In case of combination of different experimental crosses, the multidesign mode can optimize the SNP markers selection.ConclusionWritten in PERL, it assures a maximum portability to other operating systems (OS) and the source code availability for user modifications. Except for the simulation mode which could be time consuming, MarkerSet can compute results in a very short time.

Source Code Implications for Malcode

The advent of source code availability within the malicious code world has serious implications. The nature and volume of attacks have been changed forever as a result. This article reviews a brief history of source code implications and identifies current trends and implications of source code availability to malicious actors.

University methodology for internetworking principles and design projects

An undergraduate engineering internetworking learning environment that presents both internetworking principles and laboratory experimentation is described. The learning environment uses the source code availability of the Linux operating system as a case study of the implementation issues and ramifications of internet networking infrastructures. Laboratory use of experimentation with internetworking equipment and software allows interaction with internetworking principles and fundamentals as well as implementation and performance issues. The objectives of this environment include providing a comprehensive mechanism whereby students are exposed to fundamentals and principles that may readily be applied to experimental-based internetwork research and internetwork product development. A follow-on capstone design environment is also briefly described.

Criteria for comparing the adaptability of forest growth models

We develop criteria that assess the adaptability of forest growth simulation models for extension into new populations and applications. The most important criteria summarize the infrastructure of the model: portability, extendibility, source code availability, and adequate documentation. We apply these criteria to a suite of stand growth models for simulation of a wide range of management alternatives in the Pacific Northwest. None of the candidate models is fully adaptable, but the Forest Vegetation Simulator (FVS) came closest.