Knowledge and Information Systems最新文献_第5页

Minimum spanning tree clustering approach for effective feature partitioning in multi-view ensemble learning 在多视角集合学习中有效划分特征的最小生成树聚类法

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-07-18 DOI: 10.1007/s10115-024-02182-8

Aditya Kumar, Jainath Yadav

引用次数: 0

Robust anomaly detection via adversarial counterfactual generation 通过对抗性反事实生成进行稳健异常检测

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-07-17 DOI: 10.1007/s10115-024-02172-w

Angelica Liguori, Ettore Ritacco, Francesco Sergio Pisani, Giuseppe Manco

{"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":"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 (textrm{ARN}), a semi-supervised anomaly detection and generation method based on adversarial counterfactual reconstruction. (textrm{ARN}) 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.","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}

引用次数: 0

HyperMatch: long-form text matching via hypergraph convolutional networks HyperMatch：通过超图卷积网络进行长文本匹配

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-07-12 DOI: 10.1007/s10115-024-02173-9

Junwen Duan, Mingyi Jia, Jianbo Liao, Jianxin Wang

引用次数: 0

VAE-GNA: a variational autoencoder with Gaussian neurons in the latent space and attention mechanisms VAE-GNA：潜空间高斯神经元的变分自动编码器和注意力机制

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-07-05 DOI: 10.1007/s10115-024-02169-5

Matheus B. Rocha, Renato A. Krohling

{"title":"VAE-GNA: a variational autoencoder with Gaussian neurons in the latent space and attention mechanisms","authors":"Matheus B. Rocha, Renato A. Krohling","doi":"10.1007/s10115-024-02169-5","DOIUrl":"https://doi.org/10.1007/s10115-024-02169-5","url":null,"abstract":"Variational autoencoders (VAEs) are generative models known for learning compact and continuous latent representations of data. While they have proven effective in various applications, using latent representations for classification tasks presents challenges. Typically, a straightforward approach involves concatenating the mean and variance vectors and inputting them into a shallow neural network. In this paper, we introduce a novel approach for variational autoencoders, named VAE-GNA, which integrates Gaussian neurons into the latent space along with attention mechanisms. These neurons directly process mean and variance values through a suitable modified sigmoid function, not only improving classification, but also optimizing the training of the VAE in extracting features, in synergy with the classification network. Additionally, we investigate both additive and multiplicative attention mechanisms to enhance the model’s capabilities. We applied the proposed method to automatic cancer detection using near-infrared (NIR) spectral data, showing that the experimental results of VAE-GNA surpass established baselines for spectral datasets. The results obtained indicate the feasibility and effectiveness of our approach.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"47 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547252","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}

引用次数: 0

Iterative missing value imputation based on feature importance 基于特征重要性的迭代缺失值估算

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-07-05 DOI: 10.1007/s10115-024-02159-7

Cong Guo, Wei Yang, Chun Liu, Zheng Li

引用次数: 0

Deep learning and embeddings-based approaches for keyphrase extraction: a literature review 基于深度学习和嵌入的关键词提取方法：文献综述

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-07-05 DOI: 10.1007/s10115-024-02164-w

Nikolaos Giarelis, Nikos Karacapilidis

{"title":"Deep learning and embeddings-based approaches for keyphrase extraction: a literature review","authors":"Nikolaos Giarelis, Nikos Karacapilidis","doi":"10.1007/s10115-024-02164-w","DOIUrl":"https://doi.org/10.1007/s10115-024-02164-w","url":null,"abstract":"Keyphrase extraction is a subtask of natural language processing referring to the automatic extraction of salient terms that semantically capture the key themes and topics of a document. Earlier literature reviews focus on classical approaches that employ various statistical or graph-based techniques; these approaches miss important keywords/keyphrases, due to their inability to fully utilize context (that is present or not) in a document, thus achieving low F1 scores. Recent advances in deep learning and word/sentence embedding vectors lead to the development of new approaches, which address the lack of context and outperform the majority of classical ones. Taking the above into account, the contribution of this review is fourfold: (i) we analyze the state-of-the-art keyphrase extraction approaches and categorize them upon their employed techniques; (ii) we provide a comparative evaluation of these approaches, using well-known datasets of the literature and popular evaluation metrics, such as the F1 score; (iii) we provide a series of insights on various keyphrase extraction issues, including alternative approaches and future research directions; (iv) we make the datasets and code used in our experiments public, aiming to further increase the reproducibility of this work and facilitate future research in the field.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"57 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547243","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}

引用次数: 0

Taxonomy of deep learning-based intrusion detection system approaches in fog computing: a systematic review 基于深度学习的雾计算入侵检测系统方法分类学：系统综述

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-07-05 DOI: 10.1007/s10115-024-02162-y

Sepide Najafli, Abolrazl Toroghi Haghighat, Babak Karasfi

{"title":"Taxonomy of deep learning-based intrusion detection system approaches in fog computing: a systematic review","authors":"Sepide Najafli, Abolrazl Toroghi Haghighat, Babak Karasfi","doi":"10.1007/s10115-024-02162-y","DOIUrl":"https://doi.org/10.1007/s10115-024-02162-y","url":null,"abstract":"The Internet of Things (IoT) has been used in various aspects. Fundamental security issues must be addressed to accelerate and develop the Internet of Things. An intrusion detection system (IDS) is an essential element in network security designed to detect and determine the type of attacks. The use of deep learning (DL) shows promising results in the design of IDS based on IoT. DL facilitates analytics and learning in the dynamic IoT domain. Some deep learning-based IDS in IOT sensors cannot be executed, because of resource restrictions. Although cloud computing could overcome limitations, the distance between the cloud and the end IoT sensors causes high communication costs, security problems and delays. Fog computing has been presented to handle these issues and can bring resources to the edge of the network. Many studies have been conducted to investigate IDS based on IoT. Our goal is to investigate and classify deep learning-based IDS on fog processing. In this paper, researchers can access comprehensive resources in this field. Therefore, first, we provide a complete classification of IDS in IoT. Then practical and important proposed IDSs in the fog environment are discussed in three groups (binary, multi-class, and hybrid), and are examined the advantages and disadvantages of each approach. The results show that most of the studied methods consider hybrid strategies (binary and multi-class). In addition, in the reviewed papers the average Accuracy obtained in the binary method is better than the multi-class. Finally, we highlight some challenges and future directions for the next research in IDS techniques.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"14 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141577121","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}

引用次数: 0

LightCapsGNN: light capsule graph neural network for graph classification LightCapsGNN：用于图分类的光胶囊图神经网络

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-07-04 DOI: 10.1007/s10115-024-02170-y

Yucheng Yan, Jin Li, Shuling Xu, Xinlong Chen, Genggeng Liu, Yang-Geng Fu

{"title":"LightCapsGNN: light capsule graph neural network for graph classification","authors":"Yucheng Yan, Jin Li, Shuling Xu, Xinlong Chen, Genggeng Liu, Yang-Geng Fu","doi":"10.1007/s10115-024-02170-y","DOIUrl":"https://doi.org/10.1007/s10115-024-02170-y","url":null,"abstract":"Graph neural networks (GNNs) have achieved excellent performances in many graph-related tasks. However, they need appropriate pooling operations to deal with the graph classification tasks, and thus, they may suffer from some limitations such as information loss and ignorance of the part-whole relationships. CapsGNN is proposed to solve the above-mentioned issues, but suffers from high time and space complexities leading to its poor scalability. In this paper, we propose a novel, effective and efficient graph capsule network called LightCapsGNN. First, we devise a fast voting mechanism (called LightVoting) implemented via linear combinations of K shared transformation matrices to reduce the number of trainable parameters in the voting procedure. Second, an improved reconstruction layer is proposed to encourage our model to capture more informative and essential knowledge of the input graph. Third, other improvements are combined to further accelerate our model, e.g., matrix capsules and a trainable routing mechanism. Finally, extensive experiments are conducted on the popular real-world graph benchmarks in the graph classification tasks and the proposed model can achieve competitive or even better performance compared to ten baselines or state-of-the-art models. Furthermore, compared to other CapsGNNs, the proposed model reduce almost (99%) learnable parameters and (31.1%) running time.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"37 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547249","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}

引用次数: 0

Latent side-information dynamic augmentation for incremental recommendation 用于增量推荐的潜在侧信息动态增强技术

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-06-26 DOI: 10.1007/s10115-024-02165-9

Jing Zhang, Jin Shi, Jingsheng Duan, Yonggong Ren

{"title":"Latent side-information dynamic augmentation for incremental recommendation","authors":"Jing Zhang, Jin Shi, Jingsheng Duan, Yonggong Ren","doi":"10.1007/s10115-024-02165-9","DOIUrl":"https://doi.org/10.1007/s10115-024-02165-9","url":null,"abstract":"The incremental recommendation involves updating existing models by extracting information from interaction data at current time-step, with the aim of maintaining model accuracy while addressing limitations including parameter dependencies and inefficient training. However, real-time user interaction data is often afflicted by substantial noise and invalid samples, presenting the following key challenges for incremental model updating: (1) how to effectively extract valuable new knowledge from interaction data at the current time-step to ensure model accuracy and timeliness, and (2) how to safeguard against the catastrophic forgetting of long-term stable preference information, thus preserving the model’s sensitivity during cold-starts. In response to these challenges, we propose the Incremental Recommendation with Stable Latent Side-information Updating (SIIFR). This model employs a side-information augmenter to extract valuable latent side-information from user interaction behavior at time-step T, thereby sidestepping the interference caused by noisy interaction data and acquiring stable user preference. Moreover, the model utilizes rough interaction data at time-step (T+1), in conjunction with existing side-information enhancements to achieve incremental updates of latent preferences, thereby ensuring the model’s efficacy during cold-start. Furthermore, SIIFR leverages the change rate in user latent side-information to mitigate catastrophic forgetting that results in the loss of long-term stable preference information. The effectiveness of the proposed model is validated and compared against existing models using four popular incremental datasets. The model code can be achieved at: https://github.com/LNNU-computer-research-526/FR-sii.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"245 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510288","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}

引用次数: 0

An overview of semantic-based process mining techniques: trends and future directions 基于语义的流程挖掘技术概览：趋势和未来方向

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-06-26 DOI: 10.1007/s10115-024-02147-x

Fadilul-lah Yassaanah Issahaku, Ke Lu, Fang Xianwen, Sumaiya Bashiru Danwana, Husein Mohammed Bandago

{"title":"An overview of semantic-based process mining techniques: trends and future directions","authors":"Fadilul-lah Yassaanah Issahaku, Ke Lu, Fang Xianwen, Sumaiya Bashiru Danwana, Husein Mohammed Bandago","doi":"10.1007/s10115-024-02147-x","DOIUrl":"https://doi.org/10.1007/s10115-024-02147-x","url":null,"abstract":"Process mining algorithms essentially reflect the execution behavior of events in an event log for conformance checking, model discovery, or enhancement. Domain experts have developed several process mining algorithms based on theoretical frameworks such as linear integer programming, heuristics, and genetic algorithms, region-based and semantic-based approaches. The idea is to generate insightful representations of these processes of information systems to enable process mining practitioners to gain insight into their systems. Recently, there has been a shift toward semantic-based approaches for process mining since they not only discover enhanced models but also emphasize context. To this effect, this paper conducts a comprehensive review of 30 articles on semantic process mining techniques. It was found that 44.7% of all works used semantics for process discovery, 23.7% for model enhancement, and conformance checking was the least with 10.5%. We further indicate the benefits and contributions of these methods to process mining. Challenges, opportunities, and prospective future research areas are also discussed.\u0000","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"19 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510287","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}

引用次数: 0