Search

The challenges of Reproducibility and Replicability (R & R) in computer science experiments have become a focus of attention in the last decade, as efforts to adhere to good research practices have increased. However, experiments using Deep Learning (DL) remain difficult to reproduce due to the complexity of the techniques used. Challenges such as estimating poverty indicators (e.g., wealth index levels) from remote sensing imagery, requiring the use of huge volumes of data across different geographic locations, would be impossible without the use of DL technology. To test the reproducibility of DL experiments, we report a review of the reproducibility of three DL experiments which analyze visual indicators from satellite and street imagery. For each experiment, we identify the challenges found in the data sets, methods and workflows used. As a result of this assessment we propose a checklist incorporating relevant FAIR principles to screen an experiment for its reproducibility. Based on the lessons learned from this study, we recommend a set of actions aimed to improve the reproducibility of such experiments and reduce the likelihood of wasted effort. We believe that the target audience is broad, from researchers seeking to reproduce an experiment, authors reporting an experiment, or reviewers seeking to assess the work of others. Plain Language Summary: This paper aims to help researchers understand the challenges of reproducing Deep Learning (DL) publications, mitigate reproducibility gaps, and make their own work more reproducible. We build on the work of others and add recommendations organized by (a) the quality of the data set (and associated metadata), (b) the DL methodology, (c) the implementation methodology, and the infrastructure used. To our knowledge, this is the first initiative of its kind to address the problem of reproducibility in remote sensing imagery and DL problems for real‐world tasks. We hope this paper lowers the barrier to entry for the DL community to improve research. Following the lifecycle mantra: reproduce!, then replicate! With the goal of improving reproducibility! Key Points: We discuss the reproducibility challenges faced in research by Deep Learning approaches using Big DataWe provide advice for pre‐screening papers (before experiments) to avoid poorly invested effortWe present a recipe with a set of mitigation strategies to address common errors users (researchers, authors, reviewers) may encounter [ABSTRACT FROM AUTHOR]

Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed‐effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often‐used technique to evaluate high dimensional integrals, to search for D‐efficient designs for estimating parameters in nonlinear mixed‐effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature‐inspired metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

In their paper 'RIS-Assisted Secure UAV Communications with Resource Allocation and Cooperative Jamming', Guo et al. combine an RIS with unmanned aerial vehicles (UAVs) to improve the physical layer security of a wireless network. Current 5G wireless systems depend heavily on the quality of the wireless propagation environment, which had conventionally been modelled as an exogenous entity that can only be adapted and not controlled. This new vision of wireless network design is enabled by an emerging technology of reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs). [Extracted from the article]

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques have made a breakthrough in many different research fields, such as computer vision, natural language processing, network management and mobile communication. One of the primary goals in 5G network pursues high efficiency, higher spectrum and energy efficiency, higher system capacity and data rates, and lower latency with cost reduction. The papers on the topic of "AI-enabled intelligent networks for 5G and beyond" contain novel network architectures and mechanisms by using AI/ML/DL approaches for 5G/B5G networks. [Extracted from the article]

Automation in structural health monitoring (SHM) has greatly benefited from computer science's recent advances. Unlike images, the existing datasets for other types of input, such as vibration‐based damage data, are relatively smaller, less diverse, and highly imbalanced. Therefore, the reliability of data‐driven models developed for safety‐critical assessments can be questionable. This paper proposes a dual Bayesian inference where damage predictions are accompanied by measuring the model's confidence in predictions. First, it is shown how dual classification is integrated with Bayesian inference. Later, we introduce a surrogate deep learning module to transform the raw uncertainty output into an easily interpretable prediction uncertainty index (PUI). The PUI metric can be used to alarm a decision‐maker of the potential mistakes. The proposed dual Bayesian models are investigated on a 2D structure with seven different sensor layouts. Our approach yields increased robustness for different metrics compared with the benchmark. In addition to the performance boost, PUI information paves the way for a risk‐informed implementation of deep learning models in vibration‐based damage diagnosis. [ABSTRACT FROM AUTHOR]

One of the main challenges in computer science and image processing is 2D human pose estimation. Specifically, occlusion and in particular occlusion of human joints caused by camera angle are of paramount importance. In this paper, a new highly accurate network was proposed that can estimate 2D human poses in video images using deep learning. We employ the Single Shot MultiBox Detector network to detect the centre position of each human within a video frame and then use the stacked hourglass network to estimate the 2D human pose. We approximate the human motion as a linear motion between different frames in a certain period; and optimize the human centres based on the local outlier factor and Kalman filters. The same method is applied to optimize the human pose estimations in video, which can address the inaccurate prediction caused by human joints occlusion. The proposed adaptive network is tested using the two well‐known benchmarks for human pose estimation (MPII and Joint‐annotated Human Motion Data Base datasets), and we also generate some 2D human pose estimating qualitative results of single and multiple people in Internet videos. The experimental results show that the proposed network has strong practicability and can achieve high accuracy on adaptive estimating the 2D human pose in video. [ABSTRACT FROM AUTHOR]