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Proposing a more efficient Fermat factorization technique to enhance large composite integer factorization algorithms, particularly for RSA cryptography. The method decreases processing time and addresses constant time complexity, showcasing its potential for factorizing modulus N. We analyze the Fermat factorization theorem, establish its factorization bound, and connect it with integer sequences. The study raises questions about generalizing the technique for factoring N, independent of the difference between prime numbers p and q. Overall, identifying formulas for integer sequences is emphasized as a potential key to achieving linear time factorization for any composite integer.

In this article, we propose to build ML models to recover links between pull-requests and issues using two Machine Learning algorithms (KMeans and BIRCH) based on lexical and semantic weighting measurements. These models are evaluated using PI-Link ground-truth dataset. The obtained results show that pull-request and issue links can be recovered with an accuracy of 91.5% using BIRCH clustering algorithm.

In this paper, we propose, PI-Link, a new significant and reliable ground-truth dataset composed of 50369 links that explicitly connect 34732 Issues with 50369 Pull-Requests. These links are automatically extracted from all (907,139) Android projects in GitHub created between January 1, 2011 and January 1, 2021. To better organize and store the collected data, we propose a metamodel based on the concepts of Issues and Pull Requests. Moreover, we analyze the relationships between Issues and their linked Pull Requests based on four features related to their titles, bodies, labels and comments.

Numerous applications that rely on assymmetric cryptography use the RSA algorithm. It can be applied to digital signatures and the encryption of sensitive data. The secure storage of the private key is essential for the algorithm’s strength. Finding ways to use factorization or other heuristics to determine the value of the private key was the goal of several academic efforts. The Euler totient or the Carmichael functions are both used in this study to analyze the private key properties and demonstrate the existence of many private keys for the same public key. We further demonstrate that a universal key that complies with the FIPS standard exists. Moreover, by taking advantage of a condition imposed by FIPS recommendations, we present a new method for attacking the RSA modulus (N). The attack is based on the value of the private key being greater than 2^(n/2) with n representing the modulus size.

The CoMe4ACloud approach proposes a generic model-based architecture for autonomic management of Cloud systems. We derive a generic unique Autonomic Manager (AM) capable of managing any Cloud service, regardless of the layer. This AM is based on a constraint solver which aims at finding the optimal configuration for the modeled XaaS, i.e. the best balance between costs and revenues while meeting the constraints established by the SLA.

Object-oriented Application Programing Interfaces (APIs) support software reuse by providing pre-implemented functionalities. Due to the huge number of included classes, reusing and understanding large APIs is a complex task. Otherwise, software components are accepted to be more reusable and understandable entities than object-oriented ones. Thus, in this paper, we propose an approach for reengineering object-oriented APIs into component-based ones. We mine components as a group of classes based on the frequency they are used together and their ability to form a quality-centric component. To validate our approach, we experimented on 100 Java applications that used four APIs.

Cloud computing is an emerging and fast-growing computing paradigm that has gained great interest from both industry and academia. Consequently, many researchers are actively involved in cloud computing research projects. One major challenge facing cloud computing researchers is the lack of a comprehensive cloud computing experimental tool to use in their studies. This paper introduces CloudExp, a modeling and simulation environment for cloud computing. CloudExp can be used to evaluate a wide spectrum of cloud components such as processing elements, data centers, storage, networking, Service Level Agreement (SLA) constraints, web-based applications, Service Oriented Architecture (SOA), virtualization, management and automation, and Business Process Management (BPM). Moreover, CloudExp introduces the Rain workload generator which emulates real workloads in cloud environments. Also, MapReduce processing model is integrated in CloudExp in order to handle the processing of big data problems.