Abstract Siamese Neural Networks (SNNs) have shown promise in addressing a variety of tasks, even with limited data availability. However, their adoption is hindered by the lack of transparency in their decision-making processes. A key challenge in explaining SNNs lies in the absence of an inverse mapping between high-dimensional input feature vectors and the low-dimensional embedding space. Therefore, computing direct distances between input features becomes meaningless. Existing autoencoder-based explanation methods face several limitations. These include poor image reconstruction quality due to insufficient data and the omission of final distance layer of the SNN during the explanation process. While the Siamese Network Explainer (SINEX) can explain audio and grayscale images, it does not support RGB images. To overcome these challenges, we propose a method called Features Distance-based eXplanation (FDbX). This approach identifies salient features using ridge regression, trained on perturbed SLIC-segmented images. To enhance the selection of important features, we incorporate Bayesian analysis, which assigns importance scores to features. To provide a comprehensive explanation of the decision route, we construct a mathematical model that represents important features and their Hamming distances as a bipartite graph. In this graph, nodes represent features and edges denote distances between feature pairs. The resulting explanation heatmaps highlight critical image segments, offering more intuitive and visually informative explanations than existing methods. We evaluate stability and faithfulness of our method using stability indices such as $$R^2$$ and mean squared error. To the best of our knowledge, this is the first work to introduce Variable and Coefficient Stability Indices for image datasets.

Building upon pre-trained ViT models, many advanced methods have achieved significant success in COVID-19 classification. Many scholars pursue better performance by increasing model complexity and parameters. While these methods can enhance performance, they also require extensive computational resources and extended training times. Additionally, the persistent challenge of overfitting, due to limited COVID-19 dataset sizes, remains a hurdle. To address these challenges, we proposed a novel method to optimize pre-trained transformer models for efficient COVID-19 classification with stochastic configuration networks (SCNs), referred to as OPT-CO. We proposed two optimization methods: sequential optimization (SeOp) and parallel optimization (PaOp), by incorporating optimizers in a sequential and parallel manner, respectively. Our method can enhance model performance without necessitating a significant parameter expansion. Additionally, we introduced OPT-CO-SCN to avoid overfitting problems through the adoption of random projection for head augmentation. The experiments were carried out to evaluate the performance of our proposed model based on two publicly available datasets. Based on the evaluation results, our method achieved superior, performance surpassing other state-of-the-art methods.

The interlinked processing units in modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoC) architecture is becoming a de facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS; therefore, Voltage Frequency Island (VFI) has been recently adopted for effective energy management mechanism in the large-scale multiprocessor chip designs. In this article, we investigated energy-efficient and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI-based NoC-MPSoCs (VFI-NoC-HMPSoC) with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we proposed a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI-based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent--inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We introduced a notion of Energy Gradient (EG) that guides EGD in its search for island voltage settings and minimize the total energy consumption. We conducted the experiments on eight real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ∼24% and ∼30% over CA-TMES-Search and CA-TMES-Quick, respectively.

Online social networks (OSNs) have attracted millions of users worldwide over the last decade. In response to a series of urgent issues faced by existing OSNs, such as information overload, single-point failure, and the privacy issue, this paper introduces a self-organized decentralized OSN (SDOSN) over a social overlay resembling real-life social graph. The social overlay considers social relationship and semantic content of users and focuses on the key OSNs functionality of efficient information dissemination and service discovery. Then a swarm intelligence search method is proposed to facilitate adaptive learning and effective service discovery in decentralized environments. Our evaluation, performed in simulation over a real-world dataset, shows that the proposed approach achieves better performance comparing with the state-of-the-art methods on different network structures.

Similarly to social networks where people are connected by their social relationships, two autonomous peer nodes can be connected in unstructured peer-to-peer (P2P) networks if users in those nodes are interested in each other's data. The similarity between P2P networks and social networks, where peer nodes are people and connections are relationships, leads us to believe that human strategies in social networks are useful for improving the performance of resource discovery by self-organising autonomous peers on unstructured P2P networks. In this paper, we present an efficient social-like peer-to-peer (ESLP) model for resource discovery by mimicking different human behaviours in social networks.