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A Hybrid Approach for Query-Based Data Extraction Using Ensemble BERT Model with Walrus Optimization Algorithm 认领 引用
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作者 Poluru Eswaraiah Uddagiri Sirisha +3 位作者 Shaik Abdul Nabi Revathi Durgam Pallavi Malavath Gilakara Muni Nagamani 《Computers, Materials & Continua》 SCIE EI 2026年第8期1580-1606,共27页
The growing volume of digital text complicates the extraction of relevant information from unstructured data.Transformer models such as BERT,ALBERT,and RoBERTa are powerful,but they may face challenges in hyperparamet... The growing volume of digital text complicates the extraction of relevant information from unstructured data.Transformer models such as BERT,ALBERT,and RoBERTa are powerful,but they may face challenges in hyperparameter optimization and adaptation to new domains.To address this issue,a hybrid ensemble BERT model is suggested,optimized using the Walrus Optimization Algorithm(WaOA).The framework applies PCA to reduce dimensionality,ontology normalization,and K-means clustering to improve semantic comprehension.Experimental results on the SQuAD 2.0 and MS MARCO datasets show that the proposed model outperforms the baseline models.WaOA(Weighted Average of Attention)can improve convergence,reduce training time,and enhance prediction accuracy.The model also improves the semantic relevance of the extracted information.Attention maps visualize the model’s focus on relevant query terms.The method enhances efficiency and cuts redundancy.It also provides a more generalized approach to different query types.The framework promotes consistent and reliable performance across different data conditions,including varying input formats and varying noise levels.It can be generalized to multilingual and domain-specific applications.Overall,the framework provides a scalable and reliable solution to real-world information extraction. 展开更多
关键词 Query-based information extraction ensemble BERT walrus optimization algorithm metaheuristic learning PCA K-means clustering ROUGE t-SNE attention visualization
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A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT 认领 引用
2
作者 Farah Batool Abdul Rehman +3 位作者 Dongsun Kim Assad Abbas Raheel Nawaz Tahir Mustafa Madni 《Computers, Materials & Continua》 SCIE EI 2023年第3期6535-6553,共19页
The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information.Primarily,the proposed approa... The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information.Primarily,the proposed approach minimizes the network size and eliminates undesirable connections.For that,the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer.Therefore,the proposed approach discards the nodes having a rank(α)lesser than 0.5 to reduce the network complexity.αis the variable value represents the rank of each node that varies between 0 to 1.Node with the higher value ofαgets the higher priority and vice versa.The threshold valueα=0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems.Finally,the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information.The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks.Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network.Moreover,the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network.Furthermore,the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops. 展开更多
关键词 Online social network influencer search query-based approach greedy search social internet of things(siot)
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Towards the universal defense for query-based audio adversarial attacks on speech recognition system 认领 引用 被引量：2
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作者 Feng Guo Zheng Sun +1 位作者 Yuxuan Chen Lei Ju 《Cybersecurity》 EI CSCD 2024年第1期53-70,共18页
Recently,studies show that deep learning-based automatic speech recognition(ASR)systems are vulnerable to adversarial examples(AEs),which add a small amount of noise to the original audio examples.These AE attacks pos... Recently,studies show that deep learning-based automatic speech recognition(ASR)systems are vulnerable to adversarial examples(AEs),which add a small amount of noise to the original audio examples.These AE attacks pose new challenges to deep learning security and have raised significant concerns about deploying ASR systems and devices.The existing defense methods are either limited in application or only defend on results,but not on process.In this work,we propose a novel method to infer the adversary intent and discover audio adversarial examples based on the AEs generation process.The insight of this method is based on the observation:many existing audio AE attacks utilize query-based methods,which means the adversary must send continuous and similar queries to target ASR models during the audio AE generation process.Inspired by this observation,We propose a memory mechanism by adopting audio fingerprint technology to analyze the similarity of the current query with a certain length of memory query.Thus,we can identify when a sequence of queries appears to be suspectable to generate audio AEs.Through extensive evaluation on four state-of-the-art audio AE attacks,we demonstrate that on average our defense identify the adversary’s intent with over 90%accuracy.With careful regard for robustness evaluations,we also analyze our proposed defense and its strength to withstand two adaptive attacks.Finally,our scheme is available out-of-the-box and directly compatible with any ensemble of ASR defense models to uncover audio AE attacks effectively without model retraining. 展开更多
关键词 Adversarial attacks Defense Memory mechanism Query-based
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