Today, we witness an unprecedented surge of data traffic due to the explosive growth of smart devices and intelligent applications. Hence, supporting the Data Center Networks (DCNs) infrastructure by accommodating myriads of servers becomes natural and inevitable. This vision can be realized by providing an interconnecting architecture with higher scalability and remarkable capacity while maintaining fault tolerance, minimum latency, and cost and power efficiency. Motivated by this trend, this paper proposes a new network based on dual-centric dubbed RibsNet, which is a symmetric network composed of two layers of switches and dual-port servers. The proposed network enables the servers to establish all kinds of connections (server-switch and server-server), thus enhancing network fault tolerance with high bisection width and maximizing its performance and reliability. RibsNet allows the gradual addition of servers while preserving all its topological properties; it has a stable and low diameter, which does not exceed six regardless of the DCN’s size. Furthermore, an efficient and fault tolerant routing scheme, exclusively designed for this network, is proposed to enhance the routing efficiency and address different types of failures. Theoretical analysis proves that RibsNet strikes a good balance among several connections within the network and brings outstanding gains in terms of incremental scalability, bisection width, and cost and power savings against various cutting-edge DCNs architectures. Simulation results demonstrate RibsNet as a promising structure that outperforms BCube, DCell, HSDC, and FiConn in terms of latency and throughput by approximately 8%, 6%, 7%, and 26%, respectively.

The escalating count of online services and substantial influx of data triggers the rapid expansion of data center networks (DCNs). DCN should ensure high scalability, low latency, and cost-efficiency, allowing it to adapt swiftly to the dynamic demands of evolving services. The task of constructing a DCN that marries flexibility, cost-effectiveness, low latency, and robust scalability has emerged as a formidable challenge. Driven by this prevailing tendency, this paper proposes a novel server-centric network termed $AlveoliNet$ . $AlveoliNet$ is a symmetric network encompassing several clusters formed of two switch layers and two server types: Symmetric and Binary servers. The Symmetric servers have two ports, implying lower cable complexity and construction cost. To perform remarkably with respect to fault tolerance, incremental scalability, and wide bisection width, the Binary servers are endowed with multi-port in accordance with the network level. $AlveoliNet$ supports incremental incorporation of servers while maintaining its fundamental topological characteristics; its diameter cannot be beyond five hops, irrespective of the DCN size. Additionally, two routing algorithms are designed exclusively for this architecture. The performance evaluations and empirical findings illustrate that $AlveoliNet$ achieves a harmonious equilibrium between flexibility, remarkable fault-tolerance, incremental scalability, cost-efficiency, and power savings compared to cutting-edge DCN structures. Specifically, the evaluation results demonstrate that for 103,680 nodes operating at 10 GB/s, the construction cost of $AlveoliNet$ is approximately 28.8%, 75.7%, 74.6%, 43.2%, and 60.5% of the cost of $Fat$ - $Tree$ , $Totoro$ , $DCell$ , $BCube$ , and $LaScaDa$ , respectively. Furthermore, the simulation outcomes reveal $AlveoliNet$ as a highly promising architecture, surpassing $Totoro$ , $DCell$ , $BCube$ , $LaScaDa$ , and $FiConn$ regarding latency by approximately 12%, 14%, 18%, 4%, and 31%, respectively.

Arabic encompasses numerous dialects, including the Yemeni dialect, which is notably underrepresented in speech emotion recognition (SER) research. To support deep learning-based emotion recognition in Yemeni speech, there is a critical need for high-quality audio datasets that capture the emotional nuances specific to Yemeni Arabic. The limited availability of such resources has hindered research progress in this domain, particularly for Arabic and its dialects. In this paper, we introduce the Yemeni Speech Emotion Dataset (YSED), the first of its kind for Yemeni Arabic. YSED comprises audio recordings from 71 Yemeni volunteers (37 males and 34 females) aged between 15 and 45 years, expressing five emotions: anger, sadness, happiness, fear, and neutrality. Each participant recorded five sentences per emotion, with four repetitions for emotional states, and one for neutral, resulting in an initial 6035 sentence recordings. Recording duration ranged from 2 to 7 s using the simulated and induced approach. After evaluation by six judges, a total of 1432 recordings were validated, with an overall duration of 59 min and 10 s. The analysis achieved a Fleiss' Kappa coefficient of 0.9, indicating a high level of inter-annotator agreement. The YSED dataset is expected to be a valuable resource for researchers in emotion analysis and application development, enhancing the processing of Yemeni speech at an emotional level and informing services and social interactions. This work represents a significant step toward enriching data sources for understanding emotions in Yemeni Arabic and other languages and cultures. The dataset will be accessible at the following link: https://zenodo.org/records/15227219.

Network interconnection critically impacts the performance of data centers (DCs) and high-performance computing (HPC) systems, with scalability becoming vital as computing demands grow. This necessitates interconnection architectures that meet stringent latency, bandwidth, cost, and power consumption requirements. Optical interconnections provide cost-efficiency, reduced power consumption, and scalability to fulfill bandwidth needs. However, optical switches lack optical buffers, complicating the operation of all optical networks. To this end, we propose HiveNet , a novel hybrid interconnect architecture based on dual-port nodes and arrayed waveguide grating routers (AWGRs). HiveNet integrates low-radix electrical switches at lower layers to reduce cable complexity and construction costs, while AWGR-based optical connections at upper layers ensure fast switching and high bandwidth. The dual-port capability enables robust fault tolerance and supports all communication types (node-switch and node-node). Furthermore, a customized routing algorithm significantly enhances performance. Simulations conducted under various traffic patterns demonstrate that HiveNet achieves controlled delay and superior aggregate throughput. For large-scale networks (with 104,976 nodes at 10 Gb/s), HiveNet reduces construction costs by 49.3%, 26.4%, 32.7%, 54.1%, and 59.3% compared to Fat - Tree , H - LION , Leaf - Spine , BCube , and Lotus , respectively. Additionally, HiveNet decreases power consumption by 34.8%, 48.2%, 29.8%, and 23.1% compared to Fat - Tree , BCube , Leaf - Spine , and Lotus , respectively.

The Yemeni Music Genres Dataset (YMGD) is the first curated benchmark for Arabic Yemeni musical traditions, comprising a balanced collection of five genres, Sana'ani, Hadhrami, Tihami, Lahji, and Adeni, with 230 audio recordings per class, each standardized to 30 s. In addition to the audio content, the dataset includes comprehensive metadata for each track, including artist name, genre label, and title, enabling structured analysis and reproducibility. The dataset was manually collected from publicly available sources, primarily YouTube, and subsequently annotated by domain experts in Yemeni music to ensure high labeling fidelity. Inter-annotator agreement was quantified using Fleiss’ Kappa, yielding a score of 0.85, indicating strong consistency and reliability in the annotation process. This dataset provides a robust foundation for a wide range of research applications, including music information retrieval, the development and evaluation of machine learning models for genre classification, recommendation systems, and computational cultural analysis. By combining expert validation with balanced representation and standardized preprocessing, it establishes a high-quality benchmark resource for both research and educational use in low-resource musical domains. The dataset will be accessible at the following link: https://doi.org/10.5281/zenodo.19543208.