--Graph Neural Networks (GNNs) have achieved significant success in machine learning, with wide applications in social networks, bioinformatics, knowledge graphs, and other fields. Most research assumes ideal closed-set environments. However, in real-world open-set environments, graph learning models face challenges in robustness and reliability due to unseen classes. This highlights the need for Graph Open-Set Recognition (GOSR) methods to address these issues and ensure effective GNN application in practical scenarios. Research in GOSR is in its early stages, with a lack of a comprehensive benchmark spanning diverse tasks and datasets to evaluate methods. Moreover, traditional methods, Graph Out-of-Distribution Detection (GOODD), GOSR, and Graph Anomaly Detection (GAD) have mostly evolved in isolation, with little exploration of their interconnections or potential applications to GOSR. T o fill these gaps, we introduce G-OSR, a comprehensive benchmark for evaluating GOSR methods at both the node and graph levels, using datasets from multiple domains to ensure fair and standardized comparisons of effectiveness and efficiency across traditional, GOODD, GOSR, and GAD methods. The results offer critical insights into the generalizability and limitations of current GOSR methods and provide valuable resources for advancing research in this field through systematic analysis of diverse approaches. RAPH learning, as a significant research direction in machine learning, has been widely applied in social network analysis, recommendation systems, bioinformatics, knowledge graphs, traffic planning, and the fields of chemistry and materials science [1]. Graph Neural Networks (GNNs) have demonstrated superior performance in various node classification and graph classification tasks [2]. These methods typically follow a closed-set setting, which assumes that all test classes are among the seen classes accessible during training [3]. However, in real-world scenarios, due to undersampling, out-of-distribution, or anomalous samples, it is highly likely to encounter samples belonging to novel unseen classes, which can significantly impact the safety and robustness of models [4], as illustrated in Figure 1. Guangyao Chen is with Cornell University, Ithaca, NY, USA. Wentao Zhang is with Peking University, Beijing, China. Zhongyi Han is with King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. Rundong He and Yilong Yin are the corresponding authors. Closed-set classification cannot identify unseen classes, while open-set recognition can identify unseen classes and classify nodes belonging to seen classes.

Mosques are worship places of Allah and must be preserved clean, immaculate, provide all the comforts of the worshippers in them. The prophet's mosque in Medina/ Saudi Arabia is one of the most important mosques for Muslims. It occupies second place after the sacred mosque in Mecca/ Saudi Arabia, which is in constant overcrowding by all Muslims to visit the prophet Mohammad's tomb. This paper aims to propose a smart dome model to preserve the fresh air and allow the sunlight to enter the mosque using artificial intelligence techniques. The proposed model controls domes movements based on the weather conditions and the overcrowding rates in the mosque. The data have been collected from two different resources, the first one from the database of Saudi Arabia weather's history, and the other from Shanghai Technology Database. Congested Scene Recognition Network (CSRNet) and Fuzzy techniques have applied using Python programming language to control the domes to be opened and closed for a specific time to renew the air inside the mosque. Also, this model consists of several parts that are connected for controlling the mechanism of opening/closing domes according to weather data and the situation of crowding in the mosque. Finally, the main goal of this paper has been achieved, and the proposed model has worked efficiently and specifies the exact duration time to keep the domes open automatically for a few minutes for each hour head.

Yemen's Houthi fighters said they fired 14 drones at several Saudi cities on Saturday, including at Saudi Aramco facilities in Jeddah, with the Saudi state news agency reporting that the Saudi-led coalition attacked 13 targets in Yemen during a military operation against the group. Yahya Saree, the Houthi military spokesman, said in a televised press conference on Saturday that the group had attacked Aramco's refineries in Jeddah as well as military targets in Riyadh, Jeddah, Abha, Jizan and Najran. Saree said the attacks were in response to the escalation of "aggression" by the Saudi-led Arab coalition "and the continuation of its crimes and siege" of Yemen. However, inaccuracies were reported in Saree's statement – it mentioned the wrong name for the international airport in Jeddah and the wrong location for King Khalid base, saying it was in Riyadh when it is actually in the south of the kingdom. While there has been no comment from the Saudi-led coalition on the drone-attack claims, the Saudi Press Agency (SPA) said the coalition's operation in Yemen on Saturday hit weapons depots, air defence systems and drones' communication systems in the capital Sanaa as well as Saada and Marib provinces.

Muneer Lyati is an engineer and mechanic from Saudi Arabia. Muneer Lyati was born in Mecca, Saudi Arabia, on November 16, 1982. He received a bachelor's degree in engines and vehicles from Jeddah College of Technology. Muneer Lyati strives to be a trustworthy engineer who delivers professional results to all of his customers. He became one of Saudi Arabia's most sought-after engine and vehicle specialists thanks to his extensive mechanical engineering background and strong management and communication skills.

Unlike its intercept, a linear classifier's weight vector cannot be tuned by a simple grid search. Hence, this paper proposes weight vector tuning of a generic binary linear classifier through the parameterization of a decomposition of the discriminant by a scalar which controls the trade-off between conflicting informative and noisy terms. By varying this parameter, the original weight vector is modified in a meaningful way. Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning. This yields computational savings as the proposed tuning method reduces to tuning a scalar compared to tuning the native hyperparameter, which may involve repeated weight vector generation along with its burden of optimization, dimensionality reduction, etc., depending on the classifier. It is also found that weight vector tuning significantly improves the performance of Linear Discriminant Analysis (LDA) under high estimation noise. Proceeding from this second finding, an asymptotic study of the misclassification probability of the parameterized LDA classifier in the growth regime where the data dimensionality and sample size are comparable is conducted. Using random matrix theory, the misclassification probability is shown to converge to a quantity that is a function of the true statistics of the data. Additionally, an estimator of the misclassification probability is derived. Finally, computationally efficient tuning of the parameter using this estimator is demonstrated on real data. Alouni, and T. Y. Al-Naffouri are with the Electrical and Computer Engineering Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; emails: {lama.niyazi,

Following the recent progress in Terahertz (THz) signal generation and radiation methods, joint THz communications and sensing applications are shaping the future of wireless systems. Towards this end, THz spectroscopy is expected to be carried over user equipment devices to identify material and gaseous components of interest. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. In this paper, we present an overview of these techniques, with an emphasis on signal pre-processing (standard normal variate normalization, min-max normalization, and Savitzky-Golay filtering), feature extraction (principal component analysis, partial least squares, t-distributed stochastic neighbor embedding, and nonnegative matrix factorization), and classification techniques (support vector machines, k-nearest neighbor, discriminant analysis, and naive Bayes). We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band. Lastly, we investigate the performance and complexity trade-offs of the studied methods in the context of joint communications and sensing; we motivate the corresponding use-cases, and we present few future research directions in the field.

JEDDAH: Saudi Arabia will be a global artificial intelligence (AI) leader by 2030, a prominent Saudi expert has said. Dr. Abdullah bin Sharaf Al-Ghamdi, president of the Saudi Data and Artificial Intelligence Authority (SDAIA), made the comments during a media briefing on shaping new frontiers at the International Media Center in Riyadh ahead of the G20 Leaders' Summit. Last year, the authority developed the Estishraf Platform, an AI-based platform that utilizes data to create diversified insights and respond to the top priorities of decision-makers in Saudi Arabia. "Through this platform we were able to earn revenues amounting to SR43 billion ($11.5 billion), only in 2019," said Al-Ghamdi. "Undoubtedly, this is an excellent indicator for the great opportunities the national economy is waiting for after AI has become a knowledge-based economy."

I used to see the young people in the city mostly spending their time at the eateries, playing games online, driving out to the desert. Coffee shops were just a place to drink coffee or spend some time chilling out. That scenario is slowly changing. A new group of youngsters are emerging in Jeddah who love music, art, fashion as well as creating their own startups. Many of these youngsters are women.

A fashion show in Jeddah, Saudi Arabia, that used drones to walk clothes down a runway has been ripped apart by Arab fashion elites and critics who compared the dresses to ghosts and dementors. Ali Nabil Akbar tells BBC Arabic he thought showing the dresses via drone during the Saturday show at Hilton Hotel was "suitable for Ramadan." "The idea is that we want to add things that are simple yet beautiful," Akbar tells the BBC. "Even the décor and set-up of the hall was organized beautifully, everything involved innovation." I'm dying at this fashion show in Saudi they weren't allowed female models pic.twitter.com/5xxpMBk4Nr

Mads Mikkelsen recently talked about the time "Death Stranding" creator Hideo Kojima explained to him the plot of the upcoming open world action video game. The "Hannibal" actor also reacted to the rumor that Oscar-winning actress Emma Stone could be part of the Kojima Productions' project. According to Metal Gear Informer, a video showing Mikkelsen's attendance at the Saudi Comic Con in Jeddah in mid-February recently surfaced online. In the video, the Danish actor apparently talks about his involvement in the production of the video game and other stuff related to it. At one point in the video, Mikkelsen tells the audience about the time Kojima explained to him what "Death Stranding" is really about.