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Business Process Remaining Time Prediction Based on Incremental Event Logs

IEEE Transactions on Services computing 2025年第3期18卷 1308-1320页

作者： Guo, Na Liu, Cong Mo, Qi Cao, Jian Ouyang, Chun Lu, Xixi Zeng, Qingtian Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China Nova University of Lisbon NOVA Information Management School Lisbon1070-312 Portugal Shandong University of Technology School of Computer Science and Technology Zibo255000 China Yunnan University School of Software Kunming650091 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200030 China Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Utrecht University Department of Information and Computing Sciences CS Utrecht3584 Netherlands Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China

Predictive Process Monitoring (PPM) aims to predict the future state of running process instances to enable timely interventions to mitigate potential risks. As one of the most fundamental tasks in PPM, process remaining time prediction focuses on preventing timeout occurrences. While various deep learning-based approaches have been developed for this purpose, they often rely on pre-established static prediction models and struggle to maintain accurate predictions when the process undergoes dynamic changes, such as an expanding sales channels. To tackle this challenge, this paper proposes an incremental process remaining time prediction framework by continuously updating the prediction model based on an incremental event log. Specifically, a feature selection strategy is first introduced to extract effective features from event logs. Leveraging effective features can significantly improve the prediction quality by capturing the changes in process information. Then, three incremental log-based updating mechanisms, including period-based updating, quantity-based updating, and concept-drift-based updating, along with a reconstruction strategy, are proposed to dynamically adjust the prediction model in response to business changes. Finally, LSTM, Transformer, and Auto-encoder models are adapted and integrated into the proposed framework. The approach has been implemented and publicly released. Experimental evaluation using nine real-life event logs demonstrate that the proposed framework and its three instantiations (i.e., LSTM-based, Transformer-based, and Auto-encoder-based ones) outperform state-of-the-art techniques in terms of prediction accuracy. © 2008-2012 IEEE.

关键词： Predictive Models Feature Extraction Business Adaptation Models Accuracy Long Short Term Memory Transformers Process Control Computational Modeling Process Monitoring Predictive Process Monitoring Remaining Time Prediction Process Mining Incremental Event Log Business Processes Event Log Prediction Model Prediction Accuracy Dynamic Changes Effects Of Characteristics Long Short Term Memory Static Model Prediction Framework Continuous Updating Incremental Process Reconstruction Scheme Neural Network Training Set Convolutional Neural Network Change Process Hidden Layer Combination Of Features Recurrent Neural Network Flow Control Neural Units Continuous Bag Of Words Mean Absolute Error Values Concept Drift Improve Prediction Accuracy Construction Of Prediction Model Dynamic Nature Of Processes Real Life Scenarios Prediction Task Incremental Learning

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Report on the 11th International Workshop on Quantitative Approaches to software Quality (QuASoQ 2023) 5

Report on the 11th International Workshop on Quantitative Ap...

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Joint of the 5th International Workshop on Experience with SQuaRE Series and its Future Direction and the 11th International Workshop on Quantitative Approaches to software Quality, IWESQ-QuASoQ 2023

作者： Lichter, Horst Sunetnanta, Thanwadee Anwar, Toni Research Group Software Construction RWTH Aachen University Aachen Germany Computer Science Academic Group Faculty of Information And Communication Technology Mahidol University Bangkok Thailand Faculty of Science and Information Technology Computer & Information Systems Universiti Teknologi Petronas Bandar Seri Iskandar Perak Malaysia

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Probabilistic Pca in High Dimensions: Stochastic Dimensionality Reduction on Sparse Multivariate Assets' Bars at High-Risk Regimes

SSRN

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SSRN 2024年

作者： Atwa, Ahmed Nabil Sedky, Ahmed Kholief, Mohamed Department of Information Systems College of Computing & Information Technology Arab Academy for Science Technology and Maritime Transport Abou Kir Alexandria1029 Egypt

We investigate the application of Probabilistic Principal Component Analysis (PPCA) for handling high-dimensional sparse data in the stock market —Equity domain— drawn by Dollar Runs Bars (DRBs). We introduce a novel approach involving the CUMSUM Filter as an event-based sampling technique and the PPCA high-dimensions technique for missing value imputation for transforming sparse data to dense multivariate bars. Our methodology aims to estimate latent returns for assets to gain insights into portfolio behavior during high turbulence/risks in the market microstructure during the imbalance signals. Over 20,320 experiments, we obtained significant statistical results for paired samples, demonstrating average Spearman correlations of 93\% and 94\% for the proposed regimes' test data. The results underscore the model's ability to establish highly correlated relationships among correlated assets at high-risk regimes, suggesting reliable approximations of the reconstructed values derived from the latent variables. © 2024, The Authors. All rights reserved.

关键词： Linear systems

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Author profiling from Romanized Urdu text using transfer learning models

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Neural computing and Applications 2025年第6期37卷 4455-4470页

作者： Ali, Abid khan, Muhammad Sohail Khan, Muhammad Amin Khan, Sajid Ullah Khan, Faheem Department of Computer Software Engineering University of Engineering & Technology Mardan KPK Mardan23200 Pakistan Islamabad H-11/4 Islamabad44000 Pakistan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Engineering Gachon University Seongnam-Si Korea Republic of

This research concentrates on author profiling using transfer learning models for classifying age and gender. The investigation encompassed a diverse set of transfer learning techniques, including Roberta, BERT, ALBERT, Distil BERT, Distil Roberta, ELECTRA, and XLNet. Through meticulous evaluation using metrics such as the Matthews Correlation Coefficient, Accuracy, Precision, Recall, and F1 Score, the study examined the efficacy of these models. The curated dataset was divided for gender and age tasks, resulting in robust gender prediction with the XLNet model and age prediction with the BERT model. Notably, the XLNet model achieved the highest MCC (0.7946), Accuracy (0.8957), Precision (0.8992), Recall (0.8957), and F1 Score (0.8958) values in gender classification, while the BERT model excelled in age prediction with an MCC of (0.7338), Accuracy of (0.8220), Precision of (0.8324), Recall of (0.8220), and F1 Score of (0.8243). Visualized outcomes provide valuable insights into the model’s performance nuances, paving the way for their practical implementation. This research offers novel contributions to author profiling tasks, bridging the gap between theory and real-world applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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INVESTIGATION OF HOW SOCIAL MEDIA INFLUENCED THE ENDSARS PROTEST IN LAGOS, NIGERIA

arXiv

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arXiv 2023年

作者： Augustine, Christopher American University of Nigeria Department of Information Systems School of Information Technology & Computing Yola Nigeria

Social media are tools for digital communication and information exchange. Social media have been used to promote awareness of various socio-political, economic, and environmental issues efficiently and effectively. Nigerians use social media platforms, including Facebook, Twitter, WhatsApp, Instagram, etc., to interact with one another and shape public discourse and policies. Hence, social media has become means for communication and raising awareness among citizens and government about public policies. A good example is a petition on social media on disbandment (End) of the Special Anti-Robbery Squad (SARS) of the Nigerian Police Force. Consequently, the research investigates how social media impacted the EndSARS protests in Lagos, Nigeria. The study was necessary because most of the reflections available in the literature on social media-driven protests are based mainly on dualist assumptions. This is to say that scholars present technology (social media), everyday life experiences that lead to protests, and the societies (contexts) where the protest occur as different entities. This is not in line with insights in the socio-materiality theory that is driven by notions in the interpretivism philosophy. Consequently, this study aims to carry out a survey that will show how social media, everyday life experiences, and contexts where protests were held are interwoven. The aim will enable the presentation of a more appropriate explanation of phenomena surrounding the impact of social media on protests. The study is an inductive qualitative study and adopted the interpretive case study. The research context was selected based on the convenient sampling technique, while the study participants were selected based on the snowball sampling technique. Data were collected from study participating using the unstructured in-depth interview technique. Data collected in the study were analyzed using the thematic data analysis technique. Study findings reveal how the Nigerian so

关键词： Social networking (online)

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Mining Top-k Frequent Patterns over Streaming Graphs 28th

Mining Top-k Frequent Patterns over Streaming Graphs

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Wang, Xi Zhang, Qianzhen Guo, Deke Zhao, Xiang Institute for Quantum Information and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9783031306747

Mining top-k frequent patterns is an important operation on graphs, which is defined as finding k interesting subgraphs with the highest frequency. Most existing work assumes a static graph. However, graphs are dynamic in nature, which is described as streaming graphs. Mining top-k frequent patterns in streaming graphs is challenging due to the streaming nature of the input and the exponential time complexity of the problem. A naive solution is to calculate approximations of the frequent patterns in the streaming graph and then find the top-k answers, which is a memory- and time-consuming method. In this paper, we design a novel auxiliary data structure, FPC, to detect valid subgraph patterns and their frequency in real-time. We first convert each newly produced subgraph into a sequence and then map it into corresponding tracks in FPC based on hash functions. We theoretically prove that FPC can provide unbiased estimation and then give an error bound of our algorithm. In addition, we propose a vertical hashing and candidate buckets sampling technique to further improve FPC with higher space utilization and higher accuracy. Extensive experiments confirm that our approach generates high-quality results compared to the baseline method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hash functions

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Pretrained Model with CNN for Lung Cancer Identification and Classification 7

Pretrained Model with CNN for Lung Cancer Identification and...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Kanwal, Shaista Ali, Sajid Alahmadi, Tahani Jaser Assam, Muhammad Ansari, Ghulam Jillani Bhatti, Uzair Aslam University of Education Department of Information Sciences Lahore Pakistan College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Department of Information Systems P.O. Box 84428 Riyadh Saudi Arabia University of Science and Technology Department of Software Engineering KPK Bannu Pakistan School of Information and Communication Engineering Hainan University China

ISBN: (纸本)9798350350890

Lung cancer impacts both genders, and its early detection is key to lowering death rates. Current deep learning techniques for automated identification and classification of lung carcinoma face issues with interpretability, data variability handling, and broader applicability. In our research, we propose a novel method that merges a convolutional neural network (CNN) with a streamlined RESNET18 framework. This approach is designed for lung cancer detection and categorization from CT scans, involving image preprocessing like resizing and rescaling. It effectively differentiates between healthy and cancerous tissues, further classifying them as either Normal, Benign, or Malignant, including variants such as adenocarcinoma and squamous cell carcinoma. Our CNN+RESNET18 model boasts an impressive 99% accuracy, supporting radiologists in the intricate task of image analysis, reducing risks of incorrect or delayed diagnosis, and fostering prompt treatment for improved patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Enhancing handwritten text feature extraction through key point detection and graph representation

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Soft computing 2024年第21期28卷 12977-12990页

作者： Rahman, Atta Ur Alahmadi, Tahani Jaser Alsenani, Yousef S. Ali, Sania Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technologies P.O. Box 23460 Topi Pakistan P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science University of Science and Technology Bannu P.O. Box 28100 Khyber Pakhtunkhwa Bannu Pakistan

Handwriting typically consists of a wide range of writing forms with substantial differences in the placements and size of those writing shapes. The arrangement, organization, and spatial association of individual letters, words, and phrases on a written page are all examples of handwritten text structure. This study proposed a novel approach in which a handwritten text structure is primarily transformed into a Cartesian XY-coordinate system while maintaining its shape characteristics. The points on the contour of handwritten text with local maximum bending values are considered hypothetical breakdown points called Key Points (KPs). From the skeletal representation of handwriting, this approach first identifies KPs and their coordinate positions. After that, KPs are transformed into a graph-based representation with vertices and edges. With the use of geometric graphs, this representation attempts to capture the spatial and temporal organization of handwriting. Previous techniques focused on statistical methods, which offer fixed-size descriptions;however, graph-based representations are flexible in size and reveal the relationship between text structure. Graph representations enable the incorporation of contextual information by integrating extra features which improve text accuracy and understanding. The proposed approach is tested for writer identification and verification tasks on four benchmark datasets (CERUG-EN, CVL, Firemaker, and IAM) and one custom-built dataset. The findings demonstrated that the proposed approach obtained state-of-the-art accuracies across all the mentioned datasets. Particularly, it achieved the highest accuracy of 99.75% for identification and 99.81% for verification on the Firemaker dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Feature extraction

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Amplifying the Voice of Blacks in computing

Amplifying the Voice of Blacks in Computing

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Black Issues in computing Education (BICE)

作者： Cheryl Swanier Cheryl Seals Dale-Marie Wilson Manning College of Information and Computing Sciences University of Massachusetts USA Computer Science and Software Engineering Auburn University USA Department of Software and Information Systems University of North Carolina at Charlotte USA

ISBN: (数字)9798350372915

ISBN: (纸本)9798350372922

This workshop is designed to intentionally share experiences and connect participants to their greatest asset, their voice. There is power in your voice. Engaging interaction, thought-provoking storytelling, and authentic truth telling will open the way for understanding the value of harnessing voice power when navigating professionally and personally. Despite diversity, equity, and inclusion efforts, stated commitments to diversity, the demographic characteristics of the professoriate and industry look remarkably like the faculty and professionals of 50 years ago for Blacks in computing. Blacks in computing constantly face discrimination in the form of microaggressions and white fragility. This discrimination has not translated to greater Black faculty and professional representation in their career advancement. Instead, the health of both faculty and professionals are being adversely impacted, as they are experiencing insomnia, anxiety, depression, emotional distress, and physical exhaustion. This disruption of norms has a negatively impacted the productivity of both Black men and women at work and can lead to their demotion and dismissal. Many times, these struggles are not shared, and their voices are silenced. This workshop primarily addresses how Blacks in computing can use their voice to advance their careers and utilize proven voice positioning strategies to harness their voice power and to maximize professional and personal wellbeing. These voice strategies will facilitate both Black men and women in their career advancement, skill development, and preparation for entry into their next career level. A failure to address these challenges has negative implications for both academia and industry

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MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts

arXiv

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arXiv 2024年

作者： Zhou, Jianan Cao, Zhiguang Wu, Yaoxin Song, Wen Ma, Yining Zhang, Jie Xu, Chi College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology Netherlands Institute of Marine Science and Technology Shandong University China Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark instances. We further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution data. The source code is available at: https://***/RoyalSkye/Routing-MVMoE. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicle routing

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