Journal of Intelligent Information Systems最新文献_第6页

A mutually enhanced multi-scale relation-aware graph convolutional network for argument pair extraction 一种用于参数对提取的相互增强的多尺度关系感知图卷积网络

IF 3.4 3区计算机科学

Journal of Intelligent Information Systems Pub Date : 2023-11-30 DOI: 10.1007/s10844-023-00826-9

Xiaofei Zhu, Yidan Liu, Zhuo Chen, Xu Chen, Jiafeng Guo, Stefan Dietze

{"title":"A mutually enhanced multi-scale relation-aware graph convolutional network for argument pair extraction","authors":"Xiaofei Zhu, Yidan Liu, Zhuo Chen, Xu Chen, Jiafeng Guo, Stefan Dietze","doi":"10.1007/s10844-023-00826-9","DOIUrl":"https://doi.org/10.1007/s10844-023-00826-9","url":null,"abstract":"Argument pair extraction (APE) is a fine-grained task of argument mining which aims to identify arguments offered by different participants in some discourse and detect interaction relationships between arguments from different participants. In recent years, many research efforts have been devoted to dealing with APE in a multi-task learning framework. Although these approaches have achieved encouraging results, they still face several challenging issues. First, different types of sentence relationships as well as different levels of information exchange among sentences are largely ignored. Second, they solely model interactions between argument pairs either in an explicit or implicit strategy, while neglecting the complementary effect of the two strategies. In this paper, we propose a novel Mutually Enhanced Multi-Scale Relation-Aware Graph Convolutional Network (MMR-GCN) for APE. Specifically, we first design a multi-scale relation-aware graph aggregation module to explicitly model the complex relationships between review and rebuttal passage sentences. In addition, we propose a mutually enhancement transformer module to implicitly and interactively enhance representations of review and rebuttal passage sentences. We experimentally validate MMR-GCN by comparing with the state-of-the-art APE methods. Experimental results show that it considerably outperforms all baseline methods, and the relative performance improvement of MMR-GCN over the best performing baseline MRC-APE in terms of F1 score reaches to 3.48% and 4.43% on the two benchmark datasets, respectively.","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"56 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

T-shaped expert mining: a novel approach based on skill translation and focal loss t型专家挖掘:一种基于技能转换和焦点丢失的新方法

IF 3.4 3区计算机科学

Journal of Intelligent Information Systems Pub Date : 2023-11-28 DOI: 10.1007/s10844-023-00831-y

Zohreh Fallahnejad, Mahmood Karimian, Fatemeh Lashkari, Hamid Beigy

{"title":"T-shaped expert mining: a novel approach based on skill translation and focal loss","authors":"Zohreh Fallahnejad, Mahmood Karimian, Fatemeh Lashkari, Hamid Beigy","doi":"10.1007/s10844-023-00831-y","DOIUrl":"https://doi.org/10.1007/s10844-023-00831-y","url":null,"abstract":"Hiring knowledgeable and cost-effective individuals, who use their knowledge and expertise to boost the organization, is extremely important for organizations as they are the most valuable assets. T-shaped experts are the best option based on agile methodology. The T-shaped professional has a deep understanding of one topic and broad knowledge of several others. Compared to other types of professionals, T-shaped professionals are better communicators and cheaper to hire. Finding T-shaped experts in a given skill area requires determining each candidate’s depth of knowledge and shape of expertise. To estimate each candidate’s depth of knowledge in a given skill area, we propose a translation-based method that utilizes two attention-based skill translation models to overcome the vocabulary mismatch between skills and user documents. We also propose two new approaches based on binary cross-entropy and focal loss to determine whether each user is T-shaped. Our experiments on three collections of the StackOverflow dataset demonstrate the efficiency of our proposed method compared to the state-of-the-art approaches.","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"56 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Enhancing anomaly detectors with LatentOut 利用LatentOut增强异常检测器

IF 3.4 3区计算机科学

Journal of Intelligent Information Systems Pub Date : 2023-11-24 DOI: 10.1007/s10844-023-00829-6

Fabrizio Angiulli, Fabio Fassetti, Luca Ferragina

{"title":"Enhancing anomaly detectors with LatentOut","authors":"Fabrizio Angiulli, Fabio Fassetti, Luca Ferragina","doi":"10.1007/s10844-023-00829-6","DOIUrl":"https://doi.org/10.1007/s10844-023-00829-6","url":null,"abstract":"({{textbf{Latent}}varvec{Out}}) is a recently introduced algorithm for unsupervised anomaly detection which enhances latent space-based neural methods, namely (Variational) Autoencoders, GANomaly and ANOGan architectures. The main idea behind it is to exploit both the latent space and the baseline score of these architectures in order to provide a refined anomaly score performing density estimation in the augmented latent-space/baseline-score feature space. In this paper we investigate the performance of ({{textbf{Latent}}varvec{Out}}) acting as a one-class classifier and we experiment the combination of ({{textbf{Latent}}varvec{Out}}) with GAAL architectures, a novel type of Generative Adversarial Networks for unsupervised anomaly detection. Moreover, we show that the feature space induced by ({{textbf{Latent}}varvec{Out}}) has the characteristic to enhance the separation between normal and anomalous data. Indeed, we prove that standard data mining outlier detection methods perform better when applied on this novel augmented latent space rather than on the original data space.","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"77 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A transformer-based framework for predicting geomagnetic indices with uncertainty quantification 基于变压器的不确定量化地磁指数预测框架

IF 3.4 3区计算机科学

Journal of Intelligent Information Systems Pub Date : 2023-11-18 DOI: 10.1007/s10844-023-00828-7

Yasser Abduallah, Jason T. L. Wang, Haimin Wang, Ju Jing

{"title":"A transformer-based framework for predicting geomagnetic indices with uncertainty quantification","authors":"Yasser Abduallah, Jason T. L. Wang, Haimin Wang, Ju Jing","doi":"10.1007/s10844-023-00828-7","DOIUrl":"https://doi.org/10.1007/s10844-023-00828-7","url":null,"abstract":"Geomagnetic activities have a crucial impact on Earth, which can affect spacecraft and electrical power grids. Geospace scientists use a geomagnetic index, called the Kp index, to describe the overall level of geomagnetic activity. This index is an important indicator of disturbances in the Earth’s magnetic field and is used by the U.S. Space Weather Prediction Center as an alert and warning service for users who may be affected by the disturbances. Another commonly used index, called the ap index, is converted from the Kp index. Early and accurate prediction of the Kp and ap indices is essential for preparedness and disaster risk management. In this paper, we present a deep learning framework, named GNet, to perform short-term forecasting of the Kp and ap indices. Specifically, GNet takes as input time series of solar wind parameters’ values, provided by NASA’s Space Science Data Coordinated Archive, and predicts as output the Kp and ap indices respectively at time point (varvec{t + w}) hours for a given time point (varvec{t}) where (varvec{w}) ranges from 1 to 9. GNet combines transformer encoder blocks with Bayesian inference, which is capable of quantifying both aleatoric uncertainty (data uncertainty) and epistemic uncertainty (model uncertainty) in making predictions. Experimental results show that GNet outperforms closely related machine learning methods in terms of the root mean square error and R-squared score. Furthermore, GNet can provide both data and model uncertainty quantification results, which the existing methods cannot offer. To our knowledge, this is the first time that Bayesian transformers have been used for geomagnetic activity prediction.","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"2 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

EqBal-RS: Mitigating popularity bias in recommender systems EqBal-RS:减轻推荐系统中的流行偏见

3区计算机科学

Journal of Intelligent Information Systems Pub Date : 2023-11-07 DOI: 10.1007/s10844-023-00817-w

Shivam Gupta, Kirandeep Kaur, Shweta Jain

引用次数: 0

BMDF-SR: bidirectional multi-sequence decoupling fusion method for sequential recommendation 序列推荐的双向多序列解耦融合方法

3区计算机科学

Journal of Intelligent Information Systems Pub Date : 2023-11-06 DOI: 10.1007/s10844-023-00825-w

Aohua Gao, Jiwei Qin, Chao Ma, Tao Wang

引用次数: 0

Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems 学习自动编码器集成，用于检测物联网生态系统中的恶意软件隐藏通信

3区计算机科学

Journal of Intelligent Information Systems Pub Date : 2023-11-03 DOI: 10.1007/s10844-023-00819-8

Nunziato Cassavia, Luca Caviglione, Massimo Guarascio, Angelica Liguori, Marco Zuppelli

{"title":"Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems","authors":"Nunziato Cassavia, Luca Caviglione, Massimo Guarascio, Angelica Liguori, Marco Zuppelli","doi":"10.1007/s10844-023-00819-8","DOIUrl":"https://doi.org/10.1007/s10844-023-00819-8","url":null,"abstract":"Abstract Modern IoT ecosystems are the preferred target of threat actors wanting to incorporate resource-constrained devices within a botnet or leak sensitive information. A major research effort is then devoted to create countermeasures for mitigating attacks, for instance, hardware-level verification mechanisms or effective network intrusion detection frameworks. Unfortunately, advanced malware is often endowed with the ability of cloaking communications within network traffic, e.g., to orchestrate compromised IoT nodes or exfiltrate data without being noticed. Therefore, this paper showcases how different autoencoder-based architectures can spot the presence of malicious communications hidden in conversations, especially in the TTL of IPv4 traffic. To conduct tests, this work considers IoT traffic traces gathered in a real setting and the presence of an attacker deploying two hiding schemes (i.e., naive and “elusive” approaches). Collected results showcase the effectiveness of our method as well as the feasibility of deploying autoencoders in production-quality IoT settings.","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"7 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135820617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Finding a needle in a haystack: insights on feature selection for classification tasks 大海捞针:对分类任务特征选择的见解

3区计算机科学

Journal of Intelligent Information Systems Pub Date : 2023-11-03 DOI: 10.1007/s10844-023-00823-y

Laura Morán-Fernández, Verónica Bolón-Canedo

{"title":"Finding a needle in a haystack: insights on feature selection for classification tasks","authors":"Laura Morán-Fernández, Verónica Bolón-Canedo","doi":"10.1007/s10844-023-00823-y","DOIUrl":"https://doi.org/10.1007/s10844-023-00823-y","url":null,"abstract":"Abstract The growth of Big Data has resulted in an overwhelming increase in the volume of data available, including the number of features. Feature selection, the process of selecting relevant features and discarding irrelevant ones, has been successfully used to reduce the dimensionality of datasets. However, with numerous feature selection approaches in the literature, determining the best strategy for a specific problem is not straightforward. In this study, we compare the performance of various feature selection approaches to a random selection to identify the most effective strategy for a given type of problem. We use a large number of datasets to cover a broad range of real-world challenges. We evaluate the performance of seven popular feature selection approaches and five classifiers. Our findings show that feature selection is a valuable tool in machine learning and that correlation-based feature selection is the most effective strategy regardless of the scenario. Additionally, we found that using improper thresholds with ranker approaches produces results as poor as randomly selecting a subset of features.","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"29 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Leveraging neighborhood and path information for influential spreaders recognition in complex networks 利用邻域和路径信息识别复杂网络中有影响力的传播者

3区计算机科学

Journal of Intelligent Information Systems Pub Date : 2023-11-01 DOI: 10.1007/s10844-023-00822-z

Aman Ullah, JinFang Sheng, Bin Wang, Salah Ud Din, Nasrullah Khan

引用次数: 0

Session-aware recommender system using double deep reinforcement learning 会话感知推荐系统使用双重深度强化学习

3区计算机科学

Journal of Intelligent Information Systems Pub Date : 2023-11-01 DOI: 10.1007/s10844-023-00824-x

Purnima Khurana, Bhavna Gupta, Ravish Sharma, Punam Bedi

引用次数: 0