Emerging technologies evolve dynamically in response to societal needs and global disruptions, reflecting the interplay between technological advancement and public perception. This study demonstrates how the identification and prioritization of emerging technologies are influenced by shifts in public attention as captured through Wikipedia activity during the COVID-19 pandemic. By combining Wikipedia statistics and social network analysis, the proposed hybrid framework enables the detection and evaluation of technologies from both social and technical perspectives. The empirical analysis of point-of-care testing and e-healthcare reveals contrasting driving factors-market-oriented vs. technology-oriented dynamics-illustrating how emerging technologies develop under a major global event. These findings provide evidence that open, data-driven approaches can support governments and affiliated organizations in identifying emerging technologies in a timely manner and managing them. Furthermore, the study makes methodological contributions to data-driven decision making, suggesting that future research should incorporate heterogeneous data sources from diverse domains to strengthen evidence-based predictive foresight.
Blockchain networks (BCNs) deployed in a cloud infrastructure support secure service transaction but may be compromised due to the rapid advancement of quantum computing. The proposed approach presents a zero-trust enabled, post-quantum blockchain framework to enhance security and trust in federated multi-domain cloud network services. A zero-trust approach performs continuous authentication of services and ensures the integrity of operational metrics. It leverages security by integrating a lattice-based post-quantum cryptography (PQC) algorithm that uses the N-th degree truncated polynomial ring method optimized with the Toom-Cook approach, highlighting security against quantum threats with computational efficiency. This study incorporated a PQC-verified inter-ledger smart contract to enable federated trust in a multi-ledger design, which ensures secure cross-ledger interoperability. The experimentation showed reduced average time-to-write of 16.4 seconds by Quorum with 821 polynomial degree and average instantiation time of 4.39 seconds for log retrieval in the network. The validation time recorded by the proposed framework is 57.89% less than that of the classical blockchain at high transaction loads. Quorum achieved the lowest per-call overhead with latency of 38 milliseconds and throughput of 787 transactions per second (TPS), outperforming Hyperledger Fabric and Ethereum. The proposed framework garnered an average empirical federated trust score of 91.3%.
This study explores the challenge of aspect-based sentiment analysis (ABSA), aiming to enhance the identification and interpretation of fine-grained sentiment information in textual data. ABSA holds practical value in e-commerce, social media, and market analysis. In this paper, we propose a composite deep learning model (CDLM) that integrates bidirectional long short-term memory networks (BiLSTM) and deep pyramid convolutional neural networks (DPCNN) to capture contextual relationships among words, improving feature representation and classification efficiency. To improve information retrieval precision, we introduce an attention mask matrix that emphasizes salient terms while suppressing irrelevant content. A self-attention mechanism is employed to capture global dependencies between different parts of the sentence, enhancing the model's ability to interpret complex syntactic structures. Finally, we incorporate a graph attention network with a dynamically updated adjacency matrix to adaptively refine the graph structure, thereby enhancing the extraction of semantic information from sentences and improving sentiment classification performance, particularly in cases involving multiple sentiment elements. Experiments demonstrate that our model achieves competitive results across three benchmark datasets. By integrating several advanced deep learning techniques, this study demonstrates potential for practical engineering applications, such as user sentiment feedback analysis. The source code is publicly available on GitHub (https://github.com/fortytwo1111/cdlm).
The expansion of the attack surface due to the proliferation of cloud environments has driven a rapid increase in advanced cyber threats. Recently, visualization-based deep learning malware detection systems that convert input into grayscale images have been researched to counter these threats. However, deep learning models are vulnerable to adversarial attacks that mislead malware classifiers into classifying the attacks as benign due to their sensitivity to minor perturbations. Such vulnerability poses a critical security threat in cloud environments where accurate malware detection is vital for protecting sensitive data. Existing purification methods suffer from over-purification, which damages input data features, causing information loss. To address this problem, this study proposes a generative adversarial network-based multi-teacher distilled purification (GAN-MDPuri) scheme that distills knowledge from teacher models. The GAN-MDPuri scheme employs the competitive learning structure of GANs to achieve balanced knowledge distillation from a purification teacher network that removes perturbations and a reconstruction teacher network that restores normal sample input. Unlike existing knowledge distillation approaches that require complex weight adjustment mechanisms, the proposed scheme dynamically balances distinct knowledge during GAN training. The student model trained using this approach removes perturbations while preserving critical input features. The validation of the GAN-MDPuri scheme demonstrates that it achieved a high average adversarial malware detection accuracy of 0.9588 on large-scale datasets. Furthermore, the proposed model detected adversarial malware, reducing the adversarial attack success rate by an average of 0.3297 compared with existing purification schemes.
Lip reading, or visual speech recognition, interprets spoken words by analyzing lip movements and facial cues. While visual-only and audio-only systems show promise, their performance degrades significantly in noisy or unconstrained real-world environments. This study proposes an enhanced multimodal lip reading framework combining visual and audio information to improve recognition accuracy and robustness. The visual stream employs a custom convolutional neural network (CNN) for spatial feature extraction from mouth regions, with the audio stream using Mel-frequency cepstral coefficients modeled with stacked long short-term memory (LSTM) layers. The extracted features are fused and processed through bidirectional LSTM layers to capture temporal dependencies. An evaluation on the lip reading in the wild (LRW) dataset demonstrates that our proposed audio-visual fusion model substantially outperforms unimodal baselines, achieving 87.3% accuracy with notable robustness under noisy conditions. Five-fold cross-validation confirms model reliability and generalization capability, with ablation studies validating the contribution of each component. Our proposed CNN-BiLSTM framework demonstrates the effectiveness of multimodal learning for enhanced lip reading performance, providing a computationally efficient solution that is practically deployable in assistive technologies and challenging acoustic environments on resource-constrained devices. While the current implementation focuses on isolated word recognition, this work lays the foundation for future continuous lip reading systems.
