Understanding issues related to personal data and data protection in open source projects on GitHub

Context: Data protection regulations such as the GDPR and the CCPA affect how software may handle the personal data of its users and how consent for handling of such data may be given. Prior literature focused on how this works in operation, but lacks a perspective of the impact on the software development process. Objective: Within our work, we will address this gap and explore how software development itself is impacted. We want to understand which data protection-related issues are reported, who reports them, and how developers react to such issues. Method: We will conduct an exploratory study based on issues that are reported with respect to data protection in open source software on GitHub. We will determine the roles of the actors involved, the status of such issues, and we use inductive coding to understand the data protection issues. We qualitatively analyze the issues as part of the inductive coding and further explore the reasoning for resolutions. We quantitatively analyze the relation between the roles, resolutions, and data protection issues to understand correlations.Comment: Registered Report with Continuity Acceptance (CA) for submission to Empirical Software Engineering granted by RR-Committee of the MSR'2

Modelo de Autentificación de Doble Factor

The main objective of this paper is the development of a model that allows the authentication of a user for access control using the Two-Factor Authentication model. For the development of such a model we present a secure two-factor authentication (TFA) scheme based on the user's possession of a password and a cryptographically capable device. The security of this model is end-to-end in the sense that whoever wants to access in a fraudulent way is going to find it difficult and thus guarantee the security of the user of the system, the algorithm used was Cryptographic Networks, which is a double authentication model. Also the programming language cakephp 4.0 was used, in addition to using the visual studio code program to perform the algorithms required for the double authentication model to work.El presente artículo tiene como objetivo principal el desarrollo de un modelo que permita la autentificación de un usuario para el control de accesos mediante el modelo de Autentificación de doble factor. Para el desarrollo de dicho modelo presentamos un esquema seguro de autentificación de dos factores(TFA) basado en la posesión por el usuario de una contraseña y un dispositivo con capacidad criptográfica. La seguridad de este modelo es de extremo a extremo en el sentido de que el que quiera acceder de una manera fraudulenta se le va a complicar y asi garantizar la seguridad del usuario de dicho sistema, se tuvo como algoritmo Redes criptográficas, el cual es un modelo de doble autentificación. Así mismo se utilizó el lenguaje de programación cakephp 4.0, además de utilizar el programa visual studio code para poder realizar los algoritmos requeridos para que funciones el modelo de doble autentificación

Demystifying Dependency Bugs in Deep Learning Stack

Deep learning (DL) applications, built upon a heterogeneous and complex DL stack (e.g., Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software and hardware dependencies across the DL stack. One challenge in dependency management across the entire engineering lifecycle is posed by the asynchronous and radical evolution and the complex version constraints among dependencies. Developers may introduce dependency bugs (DBs) in selecting, using and maintaining dependencies. However, the characteristics of DBs in DL stack is still under-investigated, hindering practical solutions to dependency management in DL stack. To bridge this gap, this paper presents the first comprehensive study to characterize symptoms, root causes and fix patterns of DBs across the whole DL stack with 446 DBs collected from StackOverflow posts and GitHub issues. For each DB, we first investigate the symptom as well as the lifecycle stage and dependency where the symptom is exposed. Then, we analyze the root cause as well as the lifecycle stage and dependency where the root cause is introduced. Finally, we explore the fix pattern and the knowledge sources that are used to fix it. Our findings from this study shed light on practical implications on dependency management

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table