Knowledge and Information Systems最新文献

{"title":"Complementary incomplete weighted concept factorization methods for multi-view clustering","authors":"Ghufran Ahmad Khan, Jalaluddin Khan, Taushif Anwar, Zaid Al-Huda, Bassoma Diallo, Naved Ahmad","doi":"10.1007/s10115-024-02197-1","DOIUrl":"https://doi.org/10.1007/s10115-024-02197-1","url":null,"abstract":"<p>The main aim of traditional multi-view clustering is to categorize data into separate clusters under the assumption that all views are fully available. However, practical scenarios often arise where not all aspects of the data are accessible, which hampers the efficacy of conventional multi-view clustering techniques. Recent advancements have made significant progress in addressing the incompleteness in multi-view data clustering. Still, current incomplete multi-view clustering methods overlooked a number of important factors, such as providing a consensus representation across the kernel space, dealing with over-fitting issue from different views, and looking at how these multiple views relate to each other at the same time. To deal these challenges, we introduced an innovative multi-view clustering algorithm to manage incomplete data from multiple perspectives. Additionally, we have introduced a novel objective function incorporating a weighted concept factorization technique to tackle the absence of data instances within each incomplete viewpoint. We used a co-regularization constraint to learn a common shared structure from different points of view and a smooth regularization term to prevent view over-fitting. It is noteworthy that the proposed objective function is inherently non-convex, presenting optimization challenges. To obtain the optimal solution, we have implemented an iterative optimization approach to converge the local minima for our method. To underscore the effectiveness and validation of our approach, we conducted experiments using real-world datasets against state-of-the-art methods for comparative evaluation.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"57 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperparameter elegance: fine-tuning text analysis with enhanced genetic algorithm hyperparameter landscape","authors":"Gyananjaya Tripathy, Aakanksha Sharaff","doi":"10.1007/s10115-024-02202-7","DOIUrl":"https://doi.org/10.1007/s10115-024-02202-7","url":null,"abstract":"<p>Due to the significant participation of the users, it is highly challenging to handle enormous datasets using machine learning algorithms. Deep learning methods are therefore designed with efficient hyperparameter sets to enhance the processing of the vast corpus. Different hyperparameter tuning models have been used previously in various studies. Still, tuning the deep learning models with the greatest possible number of hyperparameters has not yet been possible. This study developed a modified optimization methodology for effective hyperparameter identification, addressing the shortcomings of the previous studies. To get the optimum outcome, an enhanced genetic algorithm is used with modified crossover and mutation. The method has the ability to tune several hyperparameters simultaneously. The benchmark datasets for online reviews show outstanding results from the proposed methodology. The outcome demonstrates that the presented enhanced genetic algorithm-based hyperparameter tuning model performs better than other standard approaches with 88.73% classification accuracy, 87.31% sensitivity, 90.15% specificity, and 88.58% F-score value for the IMDB dataset and 92.17% classification accuracy, 91.89% sensitivity, 92.47% specificity, and 92.50% F-score value for the Yelp dataset while requiring less processing effort. To further enhance the performance, attention mechanism is applied to the designed model, achieving 89.62% accuracy, 88.59% sensitivity, 91.89% specificity, and 89.35% F-score with the IMDB dataset and 93.29% accuracy, 92.04% sensitivity, 93.22% specificity, and 92.98% F-score with the Yelp dataset.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"18 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive moving average Q-learning","authors":"Tao Tan, Hong Xie, Yunni Xia, Xiaoyu Shi, Mingsheng Shang","doi":"10.1007/s10115-024-02190-8","DOIUrl":"https://doi.org/10.1007/s10115-024-02190-8","url":null,"abstract":"<p>A variety of algorithms have been proposed to address the long-standing overestimation bias problem of Q-learning. Reducing this overestimation bias may lead to an underestimation bias, such as double Q-learning. However, it is still unclear how to make a good balance between overestimation and underestimation. We present a simple yet effective algorithm to fill in this gap and call Moving Average Q-learning. Specifically, we maintain two dependent Q-estimators. The first one is used to estimate the maximum expected Q-value. The second one is used to select the optimal action. In particular, the second estimator is the moving average of historical Q-values generated by the first estimator. The second estimator has only one hyperparameter, namely the moving average parameter. This parameter controls the dependence between the second estimator and the first estimator, ranging from independent to identical. Based on Moving Average Q-learning, we design an adaptive strategy to select the moving average parameter, resulting in AdaMA (<u>Ada</u>ptive <u>M</u>oving <u>A</u>verage) Q-learning. This adaptive strategy is a simple function, where the moving average parameter increases monotonically with the number of state–action pairs visited. Moreover, we extend AdaMA Q-learning to AdaMA DQN in high-dimensional environments. Extensive experiment results reveal why Moving Average Q-learning and AdaMA Q-learning can mitigate the overestimation bias, and also show that AdaMA Q-learning and AdaMA DQN outperform SOTA baselines drastically. In particular, when compared with the overestimated value of 1.66 in Q-learning, AdaMA Q-learning underestimates by 0.196, resulting in an improvement of 88.19%.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"372 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disease outbreak prediction using natural language processing: a review","authors":"Avneet Singh Gautam, Zahid Raza","doi":"10.1007/s10115-024-02192-6","DOIUrl":"https://doi.org/10.1007/s10115-024-02192-6","url":null,"abstract":"<p>Research on disease outbreak prediction has suddenly received an enormous interest owing to the COVID-19 pandemic. Natural language processing using user-generated text data has proven to be quite effective for the same. Disease outbreaks that occur frequently can be easily predicted, but novel disease outbreaks are difficult to predict. This review work attempts to summarize the research concerning disease outbreaks and the use of datasets such as news headlines, tweets, and search engine queries using natural language processing techniques. Existing state-of-the-art systems have been analytically discussed with their contributions and limitations. This work is an insight into the existing research in the domain of disease outbreak prediction. A total of 146 articles were reviewed in this study, and results show that news and Twitter datasets are being used most to predict disease outbreaks. This research underlines the fact that numerous works are available in the literature based on specific outbreak-related Internet-sourced text data, viz. news, tweets, and search engine queries. However, this becomes a limitation for any disease outbreak prediction system as it can predict only specific disease outbreaks and motivates the development of systems capable of disease outbreak prediction without any bias.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"43 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An empirical study of a novel multimodal dataset for low-resource machine translation","authors":"Loitongbam Sanayai Meetei, Thoudam Doren Singh, Sivaji Bandyopadhyay","doi":"10.1007/s10115-024-02087-6","DOIUrl":"https://doi.org/10.1007/s10115-024-02087-6","url":null,"abstract":"<p>Cues from multiple modalities have been successfully applied in several fields of natural language processing including machine translation (MT). However, the application of multimodal cues in low-resource MT (LRMT) is still an open research problem. The main challenge of LRMT is the lack of abundant parallel data which makes it difficult to build MT systems for a reasonable output. Using multimodal cues can provide additional context and information that can help to mitigate this challenge. To address this challenge, we present a multimodal machine translation (MMT) dataset of low-resource languages. The dataset consists of images, audio and corresponding parallel text for a low-resource language pair that is Manipuri–English. The text dataset is collected from the news articles of local daily newspapers and subsequently translated into the target language by translators of the native speakers. The audio version by native speakers for the Manipuri text is recorded for the experiments. The study also investigates whether the correlated audio-visual cues enhance the performance of the machine translation system. Several experiments are conducted for a systematic evaluation of the effectiveness utilizing multiple modalities. With the help of automatic metrics and human evaluation, a detailed analysis of the MT systems trained with text-only and multimodal inputs is carried out. Experimental results attest that the MT systems in low-resource settings could be significantly improved up to +2.7 BLEU score by incorporating correlated modalities. The human evaluation reveals that the type of correlated auxiliary modality affects the adequacy and fluency performance in the MMT systems. Our results emphasize the potential of using cues from auxiliary modalities to enhance machine translation systems, particularly in situations with limited resources.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"3 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141867047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Caption matters: a new perspective for knowledge-based visual question answering","authors":"Bin Feng, Shulan Ruan, Likang Wu, Huijie Liu, Kai Zhang, Kun Zhang, Qi Liu, Enhong Chen","doi":"10.1007/s10115-024-02166-8","DOIUrl":"https://doi.org/10.1007/s10115-024-02166-8","url":null,"abstract":"<p>Knowledge-based visual question answering (KB-VQA) requires to answer questions according to the given image with the assistance of external knowledge. Recently, researchers generally tend to design different multimodal networks to extract visual and text semantic features for KB-VQA. Despite the significant progress, ‘caption’ information, a textual form of image semantics, which can also provide visually non-obvious cues for the reasoning process, is often ignored. In this paper, we introduce a novel framework, the Knowledge Based Caption Enhanced Net (KBCEN), designed to integrate caption information into the KB-VQA process. Specifically, for better knowledge reasoning, we make utilization of caption information comprehensively from both explicit and implicit perspectives. For the former, we explicitly link caption entities to knowledge graph together with object tags and question entities. While for the latter, a pre-trained multimodal BERT with natural implicit knowledge is leveraged to co-represent caption tokens, object regions as well as question tokens. Moreover, we develop a mutual correlation module to discern intricate correlations between explicit and implicit representations, thereby facilitating knowledge integration and final prediction. We conduct extensive experiments on three publicly available datasets (i.e., OK-VQA v1.0, OK-VQA v1.1 and A-OKVQA). Both quantitative and qualitative results demonstrate the superiority and rationality of our proposed KBCEN.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"11 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An adaptive and late multifusion framework in contextual representation based on evidential deep learning and Dempster–Shafer theory","authors":"Doaa Mohey El-Din, Aboul Ella Hassanein, Ehab E. Hassanien","doi":"10.1007/s10115-024-02150-2","DOIUrl":"https://doi.org/10.1007/s10115-024-02150-2","url":null,"abstract":"<p>There is a growing interest in multidisciplinary research in multimodal synthesis technology to stimulate diversity of modal interpretation in different application contexts. The real requirement for modality diversity across multiple contextual representation fields is due to the conflicting nature of data in multitarget sensors, which introduces other obstacles including ambiguity, uncertainty, imbalance, and redundancy in multiobject classification. This paper proposes a new adaptive and late multimodal fusion framework using evidence-enhanced deep learning guided by Dempster–Shafer theory and concatenation strategy to interpret multiple modalities and contextual representations that achieves a bigger number of features for interpreting unstructured multimodality types based on late fusion. Furthermore, it is designed based on a multifusion learning solution to solve the modality and context-based fusion that leads to improving decisions. It creates a fully automated selective deep neural network and constructs an adaptive fusion model for all modalities based on the input type. The proposed framework is implemented based on five layers which are a software-defined fusion layer, a preprocessing layer, a dynamic classification layer, an adaptive fusion layer, and an evaluation layer. The framework is formalizing the modality/context-based problem into an adaptive multifusion framework based on a late fusion level. The particle swarm optimization was used in multiple smart context systems to improve the final classification layer with the best optimal parameters that tracing 30 changes in hyperparameters of deep learning training models. This paper applies multiple experimental with multimodalities inputs in multicontext to show the behaviors the proposed multifusion framework. Experimental results on four challenging datasets including military, agricultural, COIVD-19, and food health data provide impressive results compared to other state-of-the-art multiple fusion models. The main strengths of proposed adaptive fusion framework can classify multiobjects with reduced features automatically and solves the fused data ambiguity and inconsistent data. In addition, it can increase the certainty and reduce the redundancy data with improving the unbalancing data. The experimental results of multimodalities experiment in multicontext using the proposed multimodal fusion framework achieve 98.45% of accuracy.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"13 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal intelligent information retrieval and reliable storage scheme for cloud environment and E-learning big data analytics","authors":"Chandrasekar Venkatachalam, Shanmugavalli Venkatachalam","doi":"10.1007/s10115-024-02152-0","DOIUrl":"https://doi.org/10.1007/s10115-024-02152-0","url":null,"abstract":"<p>Currently, online learning systems in the education sector are widely used and have become a new trend, generating large amounts of educational data based on students’ activities. In order to improve online learning experiences, sophisticated data analysis techniques are required. Adding value to E-learning platforms through the efficient processing of big learning data is possible with Big Data. With time, the E-learning management system’s repository expands and becomes a rich source of learning materials. Subject matter experts may benefit from using E-learning resources to reuse previously created content when creating online content. In addition, it might be beneficial to the students by giving them access to the pertinent documents for achieving their learning objectives effectively. An improved intelligent information retrieval and reliable storage (OIIRS) scheme is proposed for E-learning using hybrid deep learning techniques. Assume that relevant E-learning documents are stored in cloud and dynamically updated according to users’ status. First, we present a highly robust and lightweight crypto, i.e., optimized CLEFIA, for securely storing data in local repositories that improve the reliability of data loading. We develop an improved butterfly optimization algorithm to provide an optimal solution for CLEFIA that selects private keys. In addition, a hybrid deep learning method, i.e., backward diagonal search-based deep recurrent neural network (BD-DRNN) is introduced for optimal intelligent information retrieval based on keywords rather than semantics. Here, feature extraction and key feature matching are performed by the modified Hungarian optimization (MHO) algorithm that improves searching accuracy. Finally, we test our proposed OIIRS scheme with different benchmark datasets and use simulation results to test the performance.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"25 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust anomaly detection via adversarial counterfactual generation","authors":"Angelica Liguori, Ettore Ritacco, Francesco Sergio Pisani, Giuseppe Manco","doi":"10.1007/s10115-024-02172-w","DOIUrl":"https://doi.org/10.1007/s10115-024-02172-w","url":null,"abstract":"<p>The capability to devise robust outlier and anomaly detection tools is an important research topic in machine learning and data mining. Recent techniques have been focusing on reinforcing detection with sophisticated data generation tools that successfully refine the learning process by generating variants of the data that expand the recognition capabilities of the outlier detector. In this paper, we propose <span>(textrm{ARN})</span>, a semi-supervised anomaly detection and generation method based on adversarial counterfactual reconstruction. <span>(textrm{ARN})</span> exploits a regularized autoencoder to optimize the reconstruction of variants of normal examples with minimal differences that are recognized as outliers. The combination of regularization and counterfactual reconstruction helps to stabilize the learning process, which results in both realistic outlier generation and substantially extended detection capability. In fact, the counterfactual generation enables a smart exploration of the search space by successfully relating small changes in all the actual samples from the true distribution to high anomaly scores. Experiments on several benchmark datasets show that our model improves the current state of the art by valuable margins because of its ability to model the true boundaries of the data manifold.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"10 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141717792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HyperMatch: long-form text matching via hypergraph convolutional networks","authors":"Junwen Duan, Mingyi Jia, Jianbo Liao, Jianxin Wang","doi":"10.1007/s10115-024-02173-9","DOIUrl":"https://doi.org/10.1007/s10115-024-02173-9","url":null,"abstract":"<p>Semantic text matching plays a vital role in diverse domains, such as information retrieval, question answering, and recommendation. However, longer texts present challenges, including noise, long-range dependency, and cross-sentence inference. Graph-based approaches have shown effectiveness in addressing these challenges, but traditional graph structures struggle to model complex higher-order relationships in long-form texts. To overcome this limitation, we propose <b>HyperMatch</b>, a hypergraph-based method for long-form text matching. HyperMatch leverages hypergraph modeling to capture high-order relationships and enhance matching performance. Our approach involves constructing a keyword graph using document keywords as nodes, connecting sentences to nodes based on inclusion relationships, creating a hypergraph based on sentence similarity across nodes, and utilizing hypergraph convolutional networks to aggregate matching signals. Extensive experiments on benchmark datasets demonstrate the superiority of our model over state-of-the-art long-form text matching approaches.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"41 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}