Network Science and Graph Analytics

A novel framework for evaluating developers’ code comprehension proficiency through technical and non-technical skills Context: Code comprehension is an essential software maintenance skill, where technical skills are often considered the primary benchmark for evaluating developers’ proficiency, overlooking the significant role of non-technical skills. Objective: Our work aims to propose a generalized framework for measuring developers’ code comprehension proficiency by integrating technical and non-technical skills, inspired by cognitive attraction networks, and conducting an empirical study to evaluate code comprehension proficiency based on selective skills. Methods: The generalized framework evaluates developers’ technical and non-technical skills separately using collected data and computes their respective indices to derive an overall measure of code comprehension ability, represented as the comprehension measure index (CMI). Additionally, an empirical study with 158 participant...

Exploring interconnections among atoms, brain, society, and cosmos with network science and explainable machine learning This paper presents a methodology combining Network Science (NS) and Explainable Machine Learning (XML) that could hypothetically uncover shared principles across seemingly disparate scientific domains. As an example, it presents how the approach could be applied to four fields: materials science, neuroscience, social science, and cosmology. The study focuses on criticality, a phenomenon associated with the transition of complex systems between states, characterized by sudden and significant behavioral shifts. By proposing a five-step methodology-ranging from relational data collection to cross-domain analysis with XML-the paper offers a hypothetical framework for potentially identifying criticality-related features in these fields and transferring insights across disciplines. The results of domains cross-fertilization could support practical applications, such as im...

Three-branch neural network for No-Reference Quality assessment of Pan-Sharpened Images Pan-Sharpening (PS) techniques aim to enhance the spatial resolution of low-resolution multispectral images by leveraging data from high-resolution panchromatic images. Their comparison typically relies on the quality assessment of the resulting Full-Resolution (FS) pan-sharpened images. However, in the absence of a reference image, a dedicated No-Reference (NR) method must be employed. Therefore, this paper introduces a novel approach called the Three-Branch Neural Network for No-Reference Quality Assessment of Pan-Sharpened Images (TBN-PSI). The network consists of three subnetworks designed for perceptual processing of image channels, featuring shared extraction of low-level features and high-level semantics. Extensive experimental evaluation demonstrates the superiority of the approach over the state-of-the-art NR PS image quality assessment methods, using six datasets containing diverse satelli...

Relay Selection for Covert Communication with an Active Warden We consider covert communication with multiple relays and an active warden who not only sends jamming signals but also aims to detect the covert transmission. In the relay system with the active warden, the most critical factor is the channel between the relay and the warden, as the warden leverages this channel to transmit jamming signals while trying to detect the presence of covert communication. To mitigate the impact of the active warden, we propose a relay selection scheme that selects the relay with the minimum channel gain to the warden.  We analyze the performance of the proposed scheme and demonstrate how increasing the number of relays leads to performance improvements based on analytical results. Numerical results show that the analytical predictions closely match the simulations, and our proposed scheme effectively increases the covert rate while minimizing the threat posed by the active warden. In two-pha...

Relay Selection for Covert Communication with an Active Warden We consider covert communication with multiple relays and an active warden who not only sends jamming signals but also aims to detect the covert transmission. In the relay system with the active warden, the most critical factor is the channel between the relay and the warden, as the warden leverages this channel to transmit jamming signals while trying to detect the presence of covert communication. To mitigate the impact of the active warden, we propose a relay selection scheme that selects the relay with the minimum channel gain to the warden. We analyze the performance of the proposed scheme and demonstrate how increasing the number of relays leads to performance improvements based on analytical results. Numerical results show that the analytical predictions closely match the simulations, and our proposed scheme effectively increases the covert rate while minimizing the threat posed by the active warden. In two-phase DF ...

Adaptive Autoencoder-Based Intrusion Detection System with Single Threshold for CAN Networks The controller area network (CAN) protocol, widely used for in-vehicle communication, lacks built-in security features and is inherently vulnerable to various attacks. Numerous attack techniques against CAN have been reported, leading to intrusion detection systems (IDSs) tailored for in-vehicle networks. In this study, we propose a novel lightweight unsupervised IDS for CAN networks, designed for real-time, on-device implementation. The proposed autoencoder model was trained exclusively on normal data. A portion of the attack data was utilized to determine the optimal detection threshold using a Gaussian kernel density estimation function, while the frame count was selected based on error rate analysis. Subsequently, the model was evaluated using four types of attack data that were not seen during training. Notably, the model employs a single threshold across all attack types, enabling detect...