The rapid evolution of software development practices, coupled with the increasing prominence of open-source software (OSS) projects, has positioned versioning systems as invaluable repositories of information for analysis. This paper delves into the factors that shape software development activities by examining documented methodologies and tools used to mine and analyze OSS repositories. Through a detailed review of approaches addressing developer contributions, code modifications, and collaborative workflows, we identify key influences on productivity, code quality, and team dynamics. Our exploration includes an analysis of analytics techniques and tools, such as CVSSearch and CVSAnalY, that facilitate a deeper understanding of developer behaviour and project evolution. Furthermore, we discuss the implications of these insights for enhancing software engineering practices across both OSS and proprietary environments. This paper provides a conceptual framework that connects theoretical insights with practical approaches, offering actionable strategies to improve processes, foster collaboration, and support learning in software development. Ultimately, the findings hold potential value not only for practitioners aiming to optimize workflows but also for educators leveraging OSS environments to teach and evaluate software engineering concepts.

This article provides a research viewpoint on the training procedure of a Bidirectional Encoder Representations from Transformers (BERT) model starting from the beginning, employing the Tensor Processing Unit (TPU), with a particular emphasis on the Spanish language. This article focuses on the techniques and strategies, such as the whole word masking (WWM) technique, that are essential for achieving successful BERT training in multiple languages, including English. The goal is to provide efficient development of natural language processing applications for understanding the Spanish language. The BEThiz model exhibits exceptional proficiency in comprehending contextual cues and producing accurate responses for masked inputs, surpassing other models within the same category and language. This characteristic sets our model apart and solidifies its role as a particularly potent tool in the field of natural language processing.

This paper investigates the signal activity detection problem for spread spectrum signals in low probability of detection communication systems under Gaussian disturbance with unknown covariance matrix. We assume that the receiver is equipped with an adaptive antenna array with general array configuration, comprising of a primary array with high gain beam and a reference array with low gain beam. It has been shown that this reference array structure can benefit interference suppression. Since it is difficult to derive the uniformly most powerful detector for the detection problem, we resort to using the Wald test and generalized likelihood ratio test (GLRT) schemes to design the detectors. Analytical performance of the proposed Wald test and GLRT is derived, indicating that the proposed two detectors bear constant false alarm rate property. Finally, numerical simulations are carried out, which verify the correctness of the theoretical analysis. Besides, it is revealed that the use of reference channel can facilitate to enhance the detection performance, but an increasing in the antenna number does not necessarily lead to an improvement in detection performance.

This study presents a novel partial differential equation (PDE)-based model for neural stem cell (NSC) therapy in spinal cord injury (SCI) treatment. Existing models often fail to accurately represent NSC migration, differentiation, and interaction with biomaterials and immune responses. By integrating scaffoldmediated diffusion and immune response dynamics, the proposed model offers a more realistic framework for regenerative therapy optimization. The model improves upon previous approaches by providing precise control over growth factor distribution, ensuring sustained bioavailability for enhanced NSC survival and functional integration. Applications of this framework extend beyond SCI treatment, with potential implications for neurodegenerative diseases, brain injury repair, and personalized medicine. Future research will focus on experimental validation and machine learning-driven optimization to refine the predictive capabilities of the model.

We study identical semi-passive systems coupled pairwise by heterogeneous, linear or nonlinear passive systems, including systems with memory such as memristive systems. The semi-passive systems are assumed to satisfy a certain input-output symmetry and can for instance be oscillators with an asymptotically stable limit cycle or systems with asymptotically stable equilibrium, but also chaotic systems or neuronal oscillators. We represent the system of coupled semi-passive systems by a graph. The semi-passive systems are associated with the nodes of that graph and the nonlinear passive systems are associated with the graphs edges. To each such system we associate a weight defined as the largest value w.r.t. which the system is input-or output-passive. We derive a sufficient condition for synchronization that is phrased in terms of the algebraic connectivity of the weighted coupling graph, a generalized one-sided Lipschitz-condition associated to the semi-passive system and a constant related to its linear input-output-behavior. We also derive a sufficient condition for output synchronization in a borderline case where our main condition is not applicable.

We present BALDUR, a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions. To do so, the proposed model combines within a common latent space the different data views to extract the relevant information to solve the classification task and prune out the irrelevant/redundant features/data views. Furthermore, to provide generalizable solutions in small sample size scenarios, BALDUR efficiently integrates dual kernels over the views with a small sample-to-feature ratio. Finally, its linear nature ensures the explainability of the model outcomes, allowing its use for biomarker identification. This model was tested over two different neurodegeneration datasets, outperforming the state-of-the-art models and detecting features aligned with markers already described in the scientific literature.

This article explores hardware backdoors, highlighting examples like Rakshasa and rosenbridge. Discusses prevention and detection methodologies, vulnerability analysis and research tools. It also addresses future research directions and recommendations for industry and academia, emphasizing the importance of a robust approach to hardware security.

Cross-topology generalization is essential for improving the practicality of deep learning models in power system analysis (PSA) tasks. While large pre-trained models hold substantial promise for PSA, existing research lacks insight into which neurons are pivotal for topology generalization, leading to inefficient fine-tuning processes. This letter reveals the adaptation mechanism of deep PSA models to topological changes by introducing a causally-guided framework to analyze functional differences among neurons. A subset of neurons, termed topology-sensitive neurons, is identified as selectively activated by specific topological inputs and shown to causally influence model performance. Using integrated gradient methods, these neurons are localized and targeted with a masked low-rank adaptation (LoRA) strategy, which prioritizes parameter updates based on causal impact rather than indiscriminate fine-tuning. Experimental results demonstrate that focusing on topologysensitive neurons improves fine-tuning efficiency, reducing trainable parameters by 89% while maintaining high accuracy.

With the growing demand for high-definition video streaming services, optimizing network performance is increasingly important. Network Digital Twins (NDTs) offer real-time virtual replicas of physical networks, enabling simulation, prediction, and optimization of network behaviors. This paper proposes a framework that integrates NDTs with buffer control and video caching strategies to enhance video streaming performance. The framework dynamically adjusts buffer sizes and caching policies based on network predictions from the NDT. Simulation results demonstrate that our approach reduces average latency by 25% and buffer underruns by 40% compared to traditional methods. The study highlights the potential of NDTs in improving network management and video streaming services.

In this paper, we propose a novel Privacy-Preserving clearance mechanism for Local Energy Markets (PP-LEM), designed for computational efficiency and social welfare. PP-LEM incorporates a novel competitive game-theoretical clearance mechanism, modelled as a Stackelberg Game. Based on this mechanism, a privacy-preserving market model is developed using a partially homomorphic cryptosystem, allowing buyers’ reaction function calculations to be executed over encrypted data without exposing sensitive information of both buyers and sellers. The comprehensive performance evaluation demonstrates that PP-LEM is highly effective in delivering an incentive clearance mechanism with computational efficiency, enabling it to clear the market for 200 users within the order of seconds while concurrently protecting user privacy. Compared to the state of the art, PP-LEM achieves improved computational efficiency without compromising social welfare while still providing user privacy protection.

Evaluating privacy in data-driven software is hindered by its subjective nature and the multitude of privacyenhancing technologies. To address this, we propose a framework for categorizing data based on its privacy implications. This proposal aims to establish a standardized approach for quantifying privacy across various data sources, aligned with DevPrivOps.

Feature discrimination is a crucial aspect in neural network design, as it directly impacts the network's ability to distinguish between classes and generalize across diverse datasets. The accomplishment of achieving high-quality feature representations ensures high intra-class separability and poses one of the most challenging research directions. While convetional deep neural networks (DNNs) rely on complex transformations and very deep networks to come up with meaningful feature representations, they usually require days of training and consume significant energy amounts. To this end, spiking neural networks (SNNs) offer a promising alternative. SNN's ability to capture temporal and spatial dependencies renders them particularly suitable for complex tasks, where multi-modal data are required. In this paper, we propose a feature discrimination approach for multi-modal learning with SNNs, focusing on audiovisual data. We employ deep spiking residual learning for visual modality processing and a simpler yet efficient spiking network for auditory modality processing. Lastly, we deploy a spiking multilayer perceptron for modality fusion. We present our findings and evaluate our approach against similar works in the field in classification challenges. To the best of our knowledge, this is the first work investigating feature discrimination in SNNs.

In our work, we demonstrate a novel Neural Network (NN) based methodology for generating S-Parameters based on using the meshed and discretized geometry as input to the NN. Our approach would enable a designer to rapidly optimize the performance of vias across an entire substrate. This performance is possible while also achieving a computational time improvement of approximately double that in other NN-based works.

This paper proposes a control system to guide drones in delivering packages autonomously. The objective is to guide the quadrotor to take off from a ground vehicle, go to the delivery point, return to the ground vehicle, and land on it. The drone should disregard the ground vehicle as its leader when flying toward the delivery point and resume its leadership after delivering the parcel. To ensure this, the leader-follower formation control paradigm is adopted, allowing controlling the follower to track the leader's movement as a reference trajectory. To fully accomplish the delivery task, the delivery drone will follow two leaders at two different moments: the delivery point in the delivery step and the ground vehicle when returning to it. The delivery drone should also avoid obstacles, such as buildings and trees, to which the well-known potential field theory is adopted, thus completing the controller design. Results of an experiment on a lab scale are presented and discussed, which validates the proposed control system. Consequently, the correctness of the underlying hypothesis that the leader-follower control paradigm is a feasible solution to control a delivery drone in accomplishing package delivery is confirmed. This is the main contribution of the study. Moreover, a possible generalization of the proposed control system for a set of delivery drones is outlined.

Interface defect response time is a key parameter in MOS devices interface defect density measurements such as the hi-lo CV measurement as well as the conductance measurement. It is widely accepted that interface defects at energies close to the band edge have too short a response time to be measured properly by these techniques, unless very high measurement frequencies are employed, which is challenging experimentally. Consequently, near band edge interface defects are difficult to measure. Here we show that the time constant of band edge defects are not as short as commonly believed. Specifically, we show that as the capacitor is biased into accumulation to measure defect levels near the band edge, the defect response time levels off instead of continue to decrease. As a result, the commonly available 1MHz measurement frequency is more than adequate even for defects near the band edge.

This paper presents the design, fabrication, and application of a strain gauge system for measuring mechanical strain in various materials. Utilizing constantan wire and polyimide film as a substrate, we demonstrate the process of creating a functional strain gauge and its calibration. Experimental results reveal a linear relationship between applied strain and electrical resistance change, confirming the gauge’s effectiveness for real-world applications.

The next generation of Wi-Fi is meant to achieve ultra-high reliability for wireless communication. Several approaches are available to this extent, some of which are being considered for inclusion in standards specifications, including coordination of access points to reduce interference. In this paper, we propose a centralized architecture based on digital twins, called WiTwin, with the aim of supporting wireless stations in selecting the optimal association according to a set of parameters. Unlike prior works, we assume that Wi-Fi 7 features like multi-link operation (MLO) are available. Moreover, one of the main goals of this architecture is to preserve communication quality in the presence of mobility, by helping stations to perform reassociation at the right time and in the best way.

Epilepsy is a common neurological disorder, and electroencephalogram (EEG) is widely used for its detection and analysis. EEG-based functional brain networks play a key role in revealing epileptic seizure patterns. Multilayer networks, which can capture multiple types of interactions, have shown great potential in this domain. However, previous analysis frameworks often simplified multilayer networks into single-layer subnetworks, ignoring the multilayer structure, or adopted a multiplex analysis framework, ignoring the importance of inter-layer connections. In this study, we proposed a multi-level analysis framework for frequency-based multilayer networks, ranging from supra-adjacency matrix, subnetwork, node, layer, and global levels. We applied this framework to the TUH EEG Seizure Corpus (TUSZ) to investigate the phase synchronization changes between the interictal (between seizures) and ictal (during seizures) phases of epileptic seizures. Compared to the interictal phase, the ictal phase showed increased local connectivity, decreased overall network connectivity, and a shift from random to regular network organization. Meanwhile, cross-frequency coupling (CFC) analysis suggested that δ-γ (frontal and parietal-occipital regions) and θ-α (frontal and right frontal-temporal regions) bands are potentially associated with seizure propagation or termination. These findings provide insights for future analysis of CFC in seizures and intervention strategies by interpreting seizure propagation networks. In conclusion, our framework enables efficient multilayer network analysis in epilepsy, facilitating the identification of key frequency bands and brain regions in CFC analysis.