7 research outputs found
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 Autentificaci贸n 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 art铆culo tiene como objetivo principal el desarrollo de un modelo que permita la autentificaci贸n de un usuario para el control de accesos mediante el modelo de Autentificaci贸n de doble factor. Para el desarrollo de dicho modelo presentamos un esquema seguro de autentificaci贸n de dos factores(TFA) basado en la posesi贸n por el usuario de una contrase帽a y un dispositivo con capacidad criptogr谩fica. 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 criptogr谩ficas, el cual es un modelo de doble autentificaci贸n. As铆 mismo se utiliz贸 el lenguaje de programaci贸n cakephp 4.0, adem谩s de utilizar el programa visual studio code para poder realizar los algoritmos requeridos para que funciones el modelo de doble autentificaci贸n
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