Object detection in low-illumination environments has received limited attention compared to the vast research conducted on object detection under normal illumination. In this paper, we have focused on developing a deep learning-based model capable of detecting objects in low-light environments. The introduction of YOLOv8 in 2023 provided us with a valuable opportunity to evaluate its performance in such conditions. Our experimentation involved the use of the AdamW optimizer to leverage its weight decay capabilities, enabling the model to extract more features from images. We also employed pre-processing techniques to enhance the input images, not visually but to reduce noise and emphasize edges for object detection tasks. Additionally, we explored various training strategies and opted to utilise the ExDARK database, which exclusively contains images captured in low-light conditions. The results were highly encouraging, with our model achieving a mean average precision (mAP) of 71.2% and an average precision (AP) of 77.9%. These results surpassed those of state-of-the-art approaches, validating the effectiveness of our methodology.

In this paper, we propose a novel approach for text detection in Arabic news videos. Firstly, we apply MSER method and morphological operators (open and close) to extract candidate regions of text in image. Then, we use a deep learning method called RatinaNet. It is based in two stages. The first one aims to extract features using residual network (ResNet) and a pyramidal feature network (FPN). In the second step, we use two fully convolutional networks (FCN), one is for the classification task and the other for the bounding box regression task. For training and testing stages, we have used the AcTiVD [18] dataset. Experiments results proves the efficiency and performance of the proposed method.

High-Performance Computing (HPC) platforms are growing in size and complexity. In order to improve the quality of service of such platforms, researchers are devoting a great amount of effort to devise algorithms and techniques to improve different aspects of performance such as energy consumption, total usage of the platform, and fairness between users. In spite of this, system administrators are always reluctant to deploy state of the art scheduling methods and most of them revert to EASY-backfilling, also known as EASY-FCFS (EASY-First-Come-First-Served). Newer methods frequently are complex and obscure and the simplicity and transparency of EASY are too important to sacrifice. In this work, we used execution logs from five HPC platforms to compare four simple scheduling policies: FCFS, Shortest estimated Processing time First (SPF), Smallest Requested Resources First (SQF), and Smallest estimated Area First (SAF). Using simulations, we performed a thorough analysis of the cumulative results for up to 180 weeks and considered three scheduling objectives: waiting time, slowdown and per-processor slowdown. We also evaluated other effects, such as the relationship between job size and slowdown, the distribution of slowdown values, and the number of backfilled jobs, for each HPC platform and scheduling policy. We conclude that one can only gain by replacing EASY-backfilling with SAF with backfilling, as it offers improvements in performance by up to 80% in the slowdown metric while maintaining the simplicity and the transparency of FCFS. Moreover, SAF reduces the number of jobs with large slowdowns and the inclusion of a simple thresholding mechanism guarantees that no starvation occurs. Finally, we propose SAF as a new benchmark for future scheduling studies.

Despite the impressive growth and size of super-computers, the computational power they provide still cannot match the demand. Efficient and fair resource allocation is a critical task. Super-computers use Resource and Job Management Systems to schedule applications, which is generally done by relying on generic index policies such as First Come First Served and Shortest Processing time First in combination with Backfilling strategies. Unfortunately, such generic policies often fail to exploit specific characteristics of real workloads. In this work, we focus on improving the performance of online schedulers. We study mixed policies, which are created by combining multiple job characteristics in a weighted linear expression, as opposed to classical pure policies which use only a single characteristic. This larger class of scheduling policies aims at providing more flexibility and adaptability. We use space coverage and black-box optimization techniques to explore this new space of mixed policies and we study how can they adapt to the changes in the workload. We perform an extensive experimental campaign through which we show that (1) even the best pure policy is far from optimal and that (2) using a carefully tuned mixed policy would allow to significantly improve the performance of the system. (3) We also provide empirical evidence that there is no one size fits all policy, by showing that the rapid workload evolution seems to prevent classical online learning algorithms from being effective.