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Business Process Remaining Time Prediction Based on Incremental Event Logs

IEEE Transactions on Services Computing 2025年第03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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Extracurricular Environment for Electronics Packaging Knowledge Integration 46

Extracurricular Environment for Electronics Packaging Knowle...

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46th International Spring Seminar on Electronics technology, ISSE 2023

作者： Moisa, Cosmin Codreanu, Norocel Svasta, Paul Negrea, Cǎtǎlin Cenuşǎ, Mihai Ionescu, Ciprian Advanced Projects Software and Central Technologies SC Continental Automotive Romania SRL Timisoara Romania University Politehnica of Bucharest Faculty of Electronics Telecommunications and Information Technology Bucharest Romania Research and Development DarkNote Engineering Timisoara Romania SC Continental Automotive Romania SRL Iasi Romania

ISBN: (纸本)9798350334845

This paper describes a series of electronics packaging scale-up events proved to be prolific and full of maturity. The organizing structure and the topics are consistent, with pragmatic implications and transparency in the proposed solving with industrial conciseness and level of detail required. Given the high focus on functional and constructive details of the subjects revealed to the contestants (who are bachelor and master students), which are similar to industry requirements, it is worthy to understand, based on this paper, the basics of the environment, established three more than decades ago. The students who have participated proved their competitive potential and there is a high return on investment (ROI) in specialists and experts for the IEEE EPS community. By our active involvement, we hope to give to the next generations of hardware engineers the chance to apply their electronics knowledge in the vast field of the electronics industry. © 2023 IEEE.

关键词： E-learning

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AI-Powered Conceptual Model for Scrum Framework

AI-Powered Conceptual Model for Scrum Framework

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Computing and Information technology (ICCIT-1441), 2020 International Conference on

作者： Rana Mdallal Dania Alsawalmeh Dania Jarad Ammar Odeh Department of Software Engineering/Enterprise Systems Engineering (ESE) Princess Sumaya University for Technology (PSUT) Department of Computer Science/Cybersecurity Princess Sumaya University for Technology (PSUT)

Scrum is an incremental, interactive agile framework widely used to develop practical problems; the Scrum team focuses on achieving a common goal each cycle, "Sprint Goal" instead of the traditional sequential technique for delivering products. Although many organizations are adopting Scrum, it is still following the traditional manual approaches. Even though many tools are available in the market to facilitate project management and Scrum master tasks, the support is still limited and lacks the utilization of Artificial Intelligence science. This study focuses on integrating the Scrum framework with Artificial Intelligence to build a predicting model that can assist project management "in particular the Scrum master" to predict the achievement of the Scrum Team per Sprint using Story Points; this prediction is built based on several factors that can affect the burndown charts per Sprint.

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Capability-based IoT access control using blockchain

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Digital Communications and Networks 2021年第4期7卷 463-469页

作者： Yue Liu Qinghua Lu Shiping Chen Qiang Qu Hugo’Connor Kim-Kwang Raymond Choo He Zhang School of Computer Science and Engineering University of New South WalesAustralia Data61 CSIROAustralia Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesChina Department of Information Systems and Cyber Security University of Texas at San AntonioUSA Software Institute Nanjing UniversityChina

Internet of Things(IoT)devices facilitate intelligent service delivery in a broad range of settings,such as smart offices,homes and ***,the existing IoT access control solutions are mainly based on conventional identity management schemes and use centralized *** are knowm security and privacy limitations with such schemes and architectures,such as the single-point failure or surveillance(e.g.,device tracking).Hence,in this paper,we present an architecture for capability-based IoT access control utilizing the blockchain and decentralized identifiers to manage the identity and access control for IoT ***,we propose a protocol to provide a systematic view of system interactions,to improve *** also implement a proof-of-concept prototype of the proposed approach and evaluate the prototype using a real-world use *** evaluation results show that the proposed solution is feasible,secure,and scalable.

关键词： Blockchain Internet of things Capability-based access control Identity management Architecture design

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Intrusion Detection Mechanism Using Deep Learning

Intrusion Detection Mechanism Using Deep Learning

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IoT Based Control Networks and Intelligent systems (ICICNIS), International Conference on

作者： P. Ananthi K. Nirmaladevi Naveen Kumar S Department of Computer Technology-PG Kongu Engineering College Erode Tamilnadu Department of Computer Applications Madanapalle Institute of Technology & Science M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

In today's connected and data-driven world, networks and digital systems need to be protected from malicious attacks. The effectiveness of conventional Intrusion Detection systems (IDS) in recognizing and impeding novel threats and intricate methodologies has its limits. This study introduces a new method for improving network security by using deep learning techniques in the design and development of an intrusion detection system (IDS). The suggested Intrusion Detection System uses deep learning techniques, specifically Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to identify and react to known and new security threats. Data can be taught to be encoded into a lower-dimensional representation and then decoded back into its original form using autoencoders (AEs). Autoencoders are a cutting-edge intrusion detection system that can lower false positives and raise overall detection accuracy because of their capacity to automatically learn from and adjust to the ever-changing threat landscape. They enhance computer networks capacity to spot irregularities and possible security threats. With the deep learning model obtaining a high accuracy of 99% in real-world threat detection, the results show that deep learning is effective in developing intrusion detection systems.

关键词： Deep learning Accuracy Autoencoders Intrusion detection Network security Threat assessment Real-time systems Computer security Optimization Long short term memory

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Emergency Vehicle Navigation in Connected Autonomous systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年第12期12卷 20670-20677页

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Girivel, Niveditha Subramanian Sundaram, Varsha Mani Shanmuga Khowaja, Sunder Ali Dev, Kapal Anna University NGNLab Department of Computer Technology MIT Campus Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Citicorp Services Private Ltd Software Development Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Glasnevin Campus DublinD09 V209 Ireland Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Johannesburg2006 South Africa Chitkara University Institute of Engineering and Technology Centre for Research Impact Outcome Chitkara University Punjab Rajpura140401 India

Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite Emergency Vehicle (EV) travel in the context of Connected AVs (CAVs) and ensured secured communication employing Public Key Infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV's signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and Road Side Units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV's lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an Adaptive Smart Leader Selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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RDG-TE: Link reliability-aware DRL-GNN-based traffic engineering in SDN

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Expert systems with Applications 2025年 265卷

作者： Farhan, Muhammad Shah, Nadir Wang, Lei Muntean, Gabriel-Miro Song, Houbing Herbert School of Software Dalian University of Technology Dalian China Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Key Lab of Ubiquitous Network and Service Software of Liaoning Province China School of Electronic Engineering Dublin City University Dublin Ireland Department of Information Systems University of Maryland Baltimore United States

The existing advanced machine learning approaches based on Graph Neural Networks (GNN) for efficient traffic engineering (TE) in software Defined Networking (SDN) overlook consideration of link reliability values. Link reliability is a very important parameter, directly linked to end-to-end delay for data packet transmission, and can be used to improve the delivery performance. This research article proposes two versions of RDG-TE, a novel model that integrates Deep Reinforcement Learning (DRL) and GNN in order to solve efficiently network TE problems by considering link reliability in both model training and reward computation. The proposed model enables improved performance by more accurately predicting the SDN network behavior in case of link failure. Testing results show that, in comparison to the closest state-of-art approach, our proposed approach reduces the number of disturbed flows by 24.19% and the hop count by 35.38% while also increases the reliable route prediction accuracy by 40.17%. © 2024 Elsevier Ltd

关键词： Deep reinforcement learning

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Temporal Inconsistency-Based Active Learning

Temporal Inconsistency-Based Active Learning

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tianjiao Wan Yutao Dou Kele Xu Zijian Gao Bo Ding Dawei Feng Huaimin Wang National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

Deep supervised learning has demonstrated strong capabilities; however, such progress relies on massive and expensive data annotation. Active Learning (AL) has been introduced to selectively annotate samples, thus reducing the human labeling effort. Previous AL research has focused on employing recently trained models to design sampling strategies, based on uncertainty or representativeness. Drawing inspiration from the issue of model forgetting, we propose a novel AL framework called Temporal Inconsistency-Based Active Learning (TIR-AL). In this framework, multiple snapshots of the models across consecutive cycles are jointly utilized to select samples with higher temporal inconsistency, by computing the proposed self-weighted nuclear norm metric. Furthermore, we introduce a consistency regularization term to mitigate the issue of forgetting. Together, these components make full use of the potential of data and facilitate effective interaction within the AL loop. To demonstrate the efficacy of TIR-AL, we conducted a set of experiments illustrating how our approach outperforms state-of-the-art methods without incurring any additional training costs.

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Rsta: A Reversible Signal Transmission Architecture for Breaking the Limitation of Unidirectional Transmission

SSRN

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SSRN 2025年

作者： Liu, Xin Cui, Shuang Zhang, Xun Zhou, Fuxing Sheng, Kaikai Wang, Bin Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian116622 China

Molecular circuits are essential tools for biosensing and molecular information processing. However, the long-distance and reversible transmission of orthogonal signals remains a significant challenge in molecular circuits, preventing molecular signals from achieving reversible transmission similar to that of electrical signals in wires. To overcome the limitations of unidirectional signal transmission and facilitate the integration of antagonistic circuits. We propose a reversible signal transmission architecture (RSTA) with wire-like characteristics to achieve reversible signal transmission based on fundamental molecular devices known as reversible signal fan-out devices (RSFDs). The polarity of the input signal dictates the transmission direction, establishing the initial condition for the output signals. Cascaded RSFDs within RSTA offer two key advantages. First, the temporal response to signals. Second, the reversible transformation of opposite circuit types without structural modification. We experimentally validated the principles and functionality of RSTA using temporal logic device and integrated molecular circuits. This work advances the field of reversible molecular signal transmission, enabling the rational design of complex molecular circuits and molecular information processing systems. We envision applications of this technology in nanomachines, molecular computing, and biosensing. © 2025, The Authors. All rights reserved.

关键词： Temporal logic

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A New RISC-V CPU for Safety-Critical systems

A New RISC-V CPU for Safety-Critical Systems

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Mediterranean Conference on Embedded Computing (MECO)

作者： Ján Mach Lukáš Kohútka Pavel Čičák Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Bratislava Slovakia

In this paper, we propose a novel hardening technique against Single Event Effects, which enables high-frequency operation and does not cause large power consumption and area overhead. The protection is based on redundancy, separation of the pipeline into two main sections, and is fully applicable at Register Transfer Level. It provides fast detection of faults, simple recovery by a flush of the pipeline, and allows a large prediction unit to be unprotected. We present the protection scheme at the core with the RISC-V instruction set. Simulations confirm that the protection can handle the injected faults. Synthesis shows that protection lowers the maximum frequency by only 3.9%. The area increase is only 108% and can be even lower. Total power consumption increased by 119%.

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