Knowledge-Based Systems最新文献_第3页

Long-term traffic speed prediction utilizing data augmentation via segmented time frame clustering 通过分段时间框架聚类利用数据增强进行长期交通速度预测

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-22 DOI: 10.1016/j.knosys.2024.112785

Robin Kuok Cheong Chan , Joanne Mun-Yee Lim , Rajendran Parthiban

{"title":"Long-term traffic speed prediction utilizing data augmentation via segmented time frame clustering","authors":"Robin Kuok Cheong Chan , Joanne Mun-Yee Lim , Rajendran Parthiban","doi":"10.1016/j.knosys.2024.112785","DOIUrl":"10.1016/j.knosys.2024.112785","url":null,"abstract":"<div><div>Among many traffic forecasting studies, comparatively fewer studies focus on long-term traffic prediction, such as 24-hour prediction. While traffic data such as traffic speed are easier to obtain, obtaining similarly reliable and accessible feature data with the inclusion of weather or events would be difficult depending on the location or availability of the service providers. Getting these data becomes a more significant issue when considering global coverage. To mitigate the issue of limited feature data, a method to augment already existing data by improving the dataset's quality and ensuring more accurate training via sorting the dataset into appropriate clusters to be used as an additional feature is proposed. This paper proposes a long-term traffic forecasting model that utilizes a novel time-series segmentation method paired with data clustering and classification via Convolutional Neural Network (CNN) to cover the lack of traffic data and features as additional pre-processing before using Long Short-Term Memory (LSTM) for long-term traffic prediction which is not researched as much. This proposed model is called Cluster Augmented LSTM (CAL). The proposed model is compared with existing machine learning models and evaluated using Mean Absolute Percentage Error (MAPE) and Root-Mean-Squared-Error (RMSE) performance metrics. A comparison between LSTM and Gated Recurrent Units (GRU) was conducted, showing that GRU tends to outperform LSTM in most cases. However, the best-performing result for the proposed method still utilizes LSTM. The final results show that the proposed CAL model could achieve better results by 1.42 %-1.76 % and 0.25–0.41 for MAPE and RMSE, respectively.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112785"},"PeriodicalIF":7.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-view attention graph convolutional networks for the host prediction of phages 用于噬菌体宿主预测的多视角注意力图卷积网络

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-22 DOI: 10.1016/j.knosys.2024.112755

Lijia Ma , Peng Gao , Wenxiang Zhou , Qiuzhen Lin , Yuan Bai , Min Fang , Zhihua Du , Jianqiang Li

{"title":"Multi-view attention graph convolutional networks for the host prediction of phages","authors":"Lijia Ma , Peng Gao , Wenxiang Zhou , Qiuzhen Lin , Yuan Bai , Min Fang , Zhihua Du , Jianqiang Li","doi":"10.1016/j.knosys.2024.112755","DOIUrl":"10.1016/j.knosys.2024.112755","url":null,"abstract":"<div><div>Phages play pivotal roles in various biological processes, and the study of host prediction of phages (HPP) has received significant attention in recent years. HPP tries to find the specific bacteria that can be infected by certain phages, which is fundamental for the applications of targeted phage therapies and interventions. However, the existing HPP methods are mainly based on traditional wet-lab experiments which are laborious and time-consuming. Although certain computational methods have emerged to solve those issues, they perform poorly in genomes and contigs of phages as they neglect the similarity between phages in sequences and protein clusters. In this article, we propose a simple but accurate multi-view attention graph convolutional network (called PGCN) for solving the HPP problem. PGCN first constructs two phage similarity networks as a multi-view graph, which captures the similarity between phages in sequences and protein clusters. Then, PGCN uses a graph convolutional network to capture features of phages from the multi-view graph. Finally, PGCN proposes an adaptive attention mechanism to obtain the integrated features of phages from the multi-view features. Experimental results show the superiority of PGCN over the state-of-the-art methods in host prediction. The results also show the excellent performance of PGCN on host prediction in the metagenomes.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112755"},"PeriodicalIF":7.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Lightweight video object segmentation: Integrating online knowledge distillation for fast segmentation 轻量级视频对象分割：整合在线知识提炼，实现快速分割

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-22 DOI: 10.1016/j.knosys.2024.112759

Zhiqiang Hou , Chenxu Wang , Sugang Ma , Jiale Dong , Yunchen Wang , Wangsheng Yu , Xiaobao Yang

{"title":"Lightweight video object segmentation: Integrating online knowledge distillation for fast segmentation","authors":"Zhiqiang Hou , Chenxu Wang , Sugang Ma , Jiale Dong , Yunchen Wang , Wangsheng Yu , Xiaobao Yang","doi":"10.1016/j.knosys.2024.112759","DOIUrl":"10.1016/j.knosys.2024.112759","url":null,"abstract":"<div><div>The typical shortcoming of STM (Space-Time Memory Network) mode video object segmentation algorithms is their high segmentation performance coupled with slow processing speeds, which poses challenges in meeting real-world application demands. In this work, we propose using an online knowledge distillation method to develop a lightweight video segmentation algorithm based on the STM mode, achieving fast segmentation while maintaining performance. Specifically, we utilize a novel adaptive learning rate to tackle the issue of inverse learning during distillation. Subsequently, we introduce a Smooth Block mechanism to reduce the impact of structural disparities between the teacher and student models on distillation outcomes. Moreover, to reduce the fitting difficulty of the student model on single-frame features, we design the Space-Time Feature Fusion (STFF) module to provide appearance and position priors for the feature fitting process of each frame. Finally, we employ a simple Discriminator module for adversarial training with the student model, to encourage the student model to learn the feature distribution of the teacher model. Extensive experiments show that our algorithm attains performance comparable to the current state-of-the-art on both DAVIS and YouTube datasets, despite running up to <span><math><mo>×</mo></math></span>4 faster, with <span><math><mo>×</mo></math></span>20 fewer parameters and <span><math><mo>×</mo></math></span>30 fewer GFLOPS.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112759"},"PeriodicalIF":7.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

UrduHope: Analysis of hope and hopelessness in Urdu texts 乌尔都语希望：分析乌尔都语文本中的希望与绝望

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-22 DOI: 10.1016/j.knosys.2024.112746

Fazlourrahman Balouchzahi , Sabur Butt , Maaz Amjad , Grigori Sidorov , Alexander Gelbukh

{"title":"UrduHope: Analysis of hope and hopelessness in Urdu texts","authors":"Fazlourrahman Balouchzahi , Sabur Butt , Maaz Amjad , Grigori Sidorov , Alexander Gelbukh","doi":"10.1016/j.knosys.2024.112746","DOIUrl":"10.1016/j.knosys.2024.112746","url":null,"abstract":"<div><div>Hope is a crucial aspect of human psychology that has received considerable attention due to its role in facing challenges in human life. However, current research predominantly focuses on hope as positive anticipation, overlooking its counterpart, hopelessness. This paper addresses this gap by presenting an expanded framework for analyzing hope speech in social media, incorporating hope and hopelessness. Drawing on insights from psychology and Natural Language Processing (NLP), we argue that a comprehensive understanding of human emotions necessitates considering both constructs. We introduce the concept of hopelessness as a distinct category in hope speech analysis and develop a novel dataset for Urdu, an underrepresented language in NLP research. We proposed a semi-supervised annotation procedure by utilizing Large Language Models (LLMs) along with human annotators to annotate the dataset and explored various learning approaches for hope speech detection, including traditional machine learning models, neural networks, and state-of-the-art transformers. The findings demonstrate the effectiveness of different learning approaches in capturing the nuances of hope speech in Urdu social media discourse. The hope speech detection task was modeled in two subtasks: a binary classification of Urdu tweets to Hope and Not Hope classes and then a multiclass classification of Urdu tweets into Generalized, Realistic, and Unrealistic Hopes, along with Hopelessness, and Not Hope (Neutral) categories. The best results for binary classification were obtained with Logistic Regression (LR) with an averaged macro F1 score of 0.7593, and for the multiclass classification experiments, transformers outperformed other experiments with an averaged macro F1 score of 0.4801.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112746"},"PeriodicalIF":7.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Multi-domain dialogue state tracking via dual dynamic graph with hierarchical slot selector 通过带分层插槽选择器的双动态图进行多域对话状态跟踪

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-22 DOI: 10.1016/j.knosys.2024.112754

Yeseul Gong, Heeseon Kim, Seokju Hwang, Donghyun Kim, Kyong-Ho Lee

{"title":"Multi-domain dialogue state tracking via dual dynamic graph with hierarchical slot selector","authors":"Yeseul Gong, Heeseon Kim, Seokju Hwang, Donghyun Kim, Kyong-Ho Lee","doi":"10.1016/j.knosys.2024.112754","DOIUrl":"10.1016/j.knosys.2024.112754","url":null,"abstract":"<div><div>Dialogue state tracking aims to maintain user intent as a consistent state across multi-domains to accomplish natural dialogue systems. However, previous researches often fall short in capturing the difference of multiple slot types and fail to adequately consider the selection of discerning information. The increase in unnecessary information correlates with a decrease in predictive performance. Therefore, the careful selection of high-quality information is imperative. Moreover, considering that the types of essential and available information vary for each slot, the process of selecting appropriate information may also differ. To address these issues, we propose HS2DG-DST, a Hierarchical Slot Selector and Dual Dynamic Graph-based DST. Our model is designed to provide maximum information for optimal value prediction by clearly exploiting the need for differentiated information for each slot. First, we hierarchically classify slot types based on the multiple properties. Then, two dynamic graphs provide highly relevant information to each slot. Experimental results on MultiWOZ datasets demonstrate that our model outperforms state-of-the-art models.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112754"},"PeriodicalIF":7.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

CaMo: Capturing the modularity by end-to-end models for Symbolic Regression

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-22 DOI: 10.1016/j.knosys.2024.112747

Jingyi Liu , Min Wu , Lina Yu , Weijun Li , Wenqiang Li , Yanjie Li , Meilan Hao , Yusong Deng , Shu Wei

{"title":"CaMo: Capturing the modularity by end-to-end models for Symbolic Regression","authors":"Jingyi Liu , Min Wu , Lina Yu , Weijun Li , Wenqiang Li , Yanjie Li , Meilan Hao , Yusong Deng , Shu Wei","doi":"10.1016/j.knosys.2024.112747","DOIUrl":"10.1016/j.knosys.2024.112747","url":null,"abstract":"<div><div>Modularity is a ubiquitous principle that permeates various aspects of nature, society, and human endeavors, from biological systems to organizational structures and beyond. In the context of Symbolic Regression, which aims to find the explicit expressions from observed data, modularity could be viewed as a type of knowledge to capture the salient substructure to achieve higher fitting results. Symbolic Regression is essentially a composition optimization problem thus remaining valuable sub-structures can provide efficiency to the subsequent search. In this paper, we propose to acquire modularity in a search process and use the term <em>module</em> indicating the useful sub-structure. Specifically, the end-to-end model is chosen to incorporate the module into the search procedure for its scalability and generalization ability. Modules are considered high-order knowledge and act as fundamental operators, expanding the search library of Symbolic Regression. The proposed algorithm enables self-learning or self-evolution of modules as part of the learning component. Additionally, a module extraction strategy generates modules hierarchically from the expression tree, along with a module update mechanism designed to eliminate unnecessary modules while incorporating new useful ones effectively. Experiments were conducted to evaluate the effectiveness of each component.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"309 ","pages":"Article 112747"},"PeriodicalIF":7.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 Google Trend enhanced deep learning model for the prediction of renewable energy asset price 用于预测可再生能源资产价格的谷歌趋势增强型深度学习模型

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-21 DOI: 10.1016/j.knosys.2024.112733

Lalatendu Mishra , Balaji Dinesh , P.M. Kavyassree , Nachiketa Mishra

{"title":"A Google Trend enhanced deep learning model for the prediction of renewable energy asset price","authors":"Lalatendu Mishra , Balaji Dinesh , P.M. Kavyassree , Nachiketa Mishra","doi":"10.1016/j.knosys.2024.112733","DOIUrl":"10.1016/j.knosys.2024.112733","url":null,"abstract":"<div><div>This study investigates the predictive efficiency of various forecasting models for renewable energy asset prices, using oil price and investor sentiment. For renewable energy assets, renewable energy exchange-traded funds (ETFs) are considered in this study. We construct two sentiment indices using the first principal component: a fund-level investor sentiment index based on traditional indices (the Relative Strength Index and the Psychological Line Index) and the Google Trend Index derived from search trend data with keywords related to respective renewable energy ETFs. In this study, we propose a prediction model along with a deep learning framework, integrating both sentiment indices. We predict ETF log returns and conditional volatility using machine learning and deep learning models. To enhance predictive accuracy, we modify both the traditional sentiment and Google Trend indices. The results assert that models incorporating both the modified fund-level investor sentiment and Google Trends indices outperform unmodified indices. This study underscores the effectiveness of integrating multi-source sentiment for improved predictive performance, with a significant contribution by the Google Trend Index. Our model, particularly the CNN-LSTM, outperforms the CNN and BiLSTM models, as validated through Modified Diebold-Mariano tests. In addition to this benchmark, we perform additional benchmarking with forecasting techniques used in the latest ETF study and verify the robustness of our model. The findings of this study will be useful for different stakeholders of the renewable energy sector.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112733"},"PeriodicalIF":7.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

CATrack: Condition-aware multi-object tracking with temporally enhanced appearance features CATrack：利用时间增强型外观特征进行条件感知多目标跟踪

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-21 DOI: 10.1016/j.knosys.2024.112760

Yanchao Wang , Run Li , Dawei Zhang , Minglu Li , Jinli Cao , Zhonglong Zheng

{"title":"CATrack: Condition-aware multi-object tracking with temporally enhanced appearance features","authors":"Yanchao Wang , Run Li , Dawei Zhang , Minglu Li , Jinli Cao , Zhonglong Zheng","doi":"10.1016/j.knosys.2024.112760","DOIUrl":"10.1016/j.knosys.2024.112760","url":null,"abstract":"<div><div>Multiple Object Tracking (MOT) is a critical task in computer vision with a wide range of practical applications. However, current methods often use a uniform approach for associating all targets, overlooking the varying conditions of each target. This can lead to performance degradation, especially in crowded scenes with dense targets. To address this issue, we propose a novel Condition-Aware Tracking method (CATrack) to differentiate the appearance feature flow for targets under different conditions. Specifically, we propose three designs for data association and feature update. First, we develop an Adaptive Appearance Association Module (AAAM) that selects suitable track templates based on detection conditions, reducing association errors in long-tail cases like occlusions or motion blur. Second, we design an ambiguous track filtering Selective Update strategy (SU) that filters out potential low-quality embeddings. Thus, the noise accumulation in the maintained track feature will also be reduced. Meanwhile, we propose a confidence-based Adaptive Exponential Moving Average (AEMA) method for the feature state transition. By adaptively adjusting the weights of track and detection embeddings, our AEMA better preserves high-quality target features. By integrating the above modules, CATrack enhances the discriminative capability of appearance features and improves the robustness of appearance-based associations. Extensive experiments on the MOT17 and MOT20 benchmarks validate the effectiveness of the proposed CATrack. Notably, the state-of-the-art results on MOT20 demonstrate the superiority of our method in highly crowded scenarios.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112760"},"PeriodicalIF":7.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online learning discriminative sparse convolution networks for robust UAV object tracking 在线学习判别稀疏卷积网络，实现稳健的无人飞行器目标跟踪

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-21 DOI: 10.1016/j.knosys.2024.112742

Qi Xu , Zhuoming Xu , Huabin Wang , Yun Chen , Liang Tao

{"title":"Online learning discriminative sparse convolution networks for robust UAV object tracking","authors":"Qi Xu , Zhuoming Xu , Huabin Wang , Yun Chen , Liang Tao","doi":"10.1016/j.knosys.2024.112742","DOIUrl":"10.1016/j.knosys.2024.112742","url":null,"abstract":"<div><div>Despite the remarkable empirical success for UAV object tracking, current convolutional networks usually have three unavoidable limitations: (1) The feature maps produced by convolutional layers are difficult to interpret. (2) The network needs to be trained offline on a large-scale auxiliary training set, resulting in the feature extraction ability of the trained network depending on the categories of the training set. (3) The performance of networks suffers from sensitivity to hyper-parameters (such as learning rate and weight decay) when the network needs online fine-tuning. To overcome the three limitations, this paper proposes a Discriminative Sparse Convolutional Network (DSCN) that exhibits good layer-wise interpretability and can be trained online without requiring any auxiliary training data. By imposing sparsity constraints on the convolutional kernels, DSCN furnishes the convolution layer with an explicit data meaning, thus enhancing the interpretability of the feature maps. These convolutional kernels are directly learned online from image blocks, which eliminates the offline training process on auxiliary data sets. Moreover, a simple yet effective online tuning method with few hyper-parameters is proposed to fine-tune fully connected layers online. We have successfully applied DSCN to UAV object tracking and conducted extensive experiments on six mainstream UAV datasets. The experimental results demonstrate that our method performs favorably against several state-of-the-art tracking algorithms in terms of tracking accuracy and robustness.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112742"},"PeriodicalIF":7.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 learning process modeling for session-aware knowledge tracing 加强学习过程建模，实现会话感知知识追踪

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-21 DOI: 10.1016/j.knosys.2024.112740

Chunli Huang , Wenjun Jiang , Kenli Li , Jie Wu , Ji Zhang

{"title":"Enhancing learning process modeling for session-aware knowledge tracing","authors":"Chunli Huang , Wenjun Jiang , Kenli Li , Jie Wu , Ji Zhang","doi":"10.1016/j.knosys.2024.112740","DOIUrl":"10.1016/j.knosys.2024.112740","url":null,"abstract":"<div><div>Session-aware knowledge tracing tries to predict learners’ performance, by splitting learners’ sequences into sessions and modeling their learning within and between sessions. However, there still is a lack of comprehensive understanding of the learning processes and session-form learning patterns. Moreover, the knowledge state shifts between sessions at the knowledge concept level remain unexplored. To this end, we conduct in-depth data analysis to understand learners’ learning processes and session-form learning patterns. Then, we perform an empirical study validating knowledge state shifts at the knowledge concept level in real-world educational datasets. Subsequently, a method of Enhancing Learning Process Modeling for Session-aware Knowledge Tracing, ELPKT, is proposed to capture the knowledge state shifts at the knowledge concept level and track knowledge state across sessions. Specifically, the ELPKT models learners’ learning process as intra-sessions and inter-sessions from the knowledge concept level. In intra-sessions, fine-grained behaviors are used to capture learners’ short-term knowledge states accurately. In inter-sessions, learners’ knowledge retentions and decays are modeled to capture the knowledge state shift between sessions. Extensive experiments on four real-world datasets demonstrate that ELPKT outperforms the existing methods in learners’ performance prediction. Additionally, ELPKT shows its ability to capture the knowledge state shifts between sessions and provide interpretability for the predicted results.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"309 ","pages":"Article 112740"},"PeriodicalIF":7.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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