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Found 13 papers, 2 papers with code
Choosing the Right Path for AI Integration in Engineering Companies: A Strategic Guide
This paper presents a framework for EPC companies in their transformation towards AI.
EdgeFL: A Lightweight Decentralized Federated Learning Framework
By leveraging EdgeFL, software engineers can harness the benefits of federated learning while overcoming the challenges associated with existing FL platforms/frameworks.
5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul
Fast and reliable wireless communication has become a critical demand in human life.
Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning
A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection.
On the Assessment of Benchmark Suites for Algorithm Comparison
In this paper, we propose the use of an item response theory (IRT) model, the Bayesian two-parameter logistic model for multiple attempts, to statistically evaluate these aspects with respect to the empirical success rate of algorithms.
Real-time End-to-End Federated Learning: An Automotive Case Study
With the development and the increasing interests in ML/DL fields, companies are eager to apply Machine Learning/Deep Learning approaches to increase service quality and customer experience.
The use of incentives to promote Technical Debt management
When developing software, it is vitally important to keep the level of technical debt down since it is well established from several studies that technical debt can, e. g., lower the development productivity, decrease the developers' morale, and compromise the overall quality of the software.
Software Engineering
End-to-End on-device Federated Learning: A case study
Because of its characteristics such as model-only exchange and parallel training, the technique can not only preserve user data privacy but also accelerate model training speed.
Machine Learning Algorithms for Data Labeling: An Empirical Evaluation
While many of these algorithms are available in open-source packages, there is no research that investigates how these algorithms compare to each other in different types of datasets and with different percentages of available labels.
HPM-Frame: A Decision Framework for Executing Software on Heterogeneous Platforms
Heterogeneous computing is one of the most important computational solutions to meet rapidly increasing demands on system performance.
Software Engineering
Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions
The online appendix provides a step-by-step guide on how to perform the analysis of the models discussed in this paper, including the code for the statistical models, the data transformations and the discussed tables and figures.
Methodology
Engineering AI Systems: A Research Agenda
In this paper, we provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that we have studied.
Software Engineering Challenges of Deep Learning
A set of seven projects have been selected to describe the potential with this new technology and to identify associated main challenges.
