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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Amer Abu-Jassar Mohammad Hamdan Kawther Hamash Sardor Boboyorov Vyacheslav Lyashenko Department of Computer Science College of Information Technology Amman Arab University Amman Jordan College of software engineering Amman Arab University Amman Jordan WellStar school of nursing Kennesaw state university Georgia USA Tashkent Medical Academy Termiz branch Termiz Uzbekistan Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper addresses the challenge of enhancing the quality of medical data interpretation, specifically using color microscopic medical images for effective visualization and further analysis. The method employed involves increasing contrast by adjusting the brightness intensity values within the Lab color model, which also considers the reference white point for horizontal light. To determine the parameters for the contrasting procedure, two strategies for selecting gamma correction of the input image are explored, based on the dynamics of the resulting image quality for various no-reference quality metrics. By analyzing these dynamics, the choice of correction conditions for the contrasting procedure can be justified, rather than relying on static metrics. The paper investigates the application of these proposed strategies on different types of images, presents a formal generalization of the strategies for selecting correction parameters, and offers practical recommendations for their use.

关键词： Measurement Image quality Visualization Image color analysis Microscopy Computational modeling Data visualization Quality assessment Computational intelligence Biomedical imaging

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GIS-based cost distribution analysis of new consumer connections to an urban power grid

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Geo-Spatial information Science 2015年第4期18卷 183-192页

作者： Sergey V.KOSYAKOV Artur M.SADYKOV Department of Software Engineering Ivanovo State Power Engineering UniversityIvanovoRussia Department of Geographical Information Systems Ivanovo State Power Engineering UniversityIvanovoRussia

In this paper,we propose a method of cost distribution analysis of new consumer connections to a city power grid by accounting for spatial restrictions and characteristics of existing *** practice,the calculation of connection costs for each new consumer includes the network design and financial *** suggest that connection costs should be calculated for the whole city based on the normative parameters at the stage when the object location is selected by investors and when power grid development is planned by power *** proposed method enables the modeling of new power line connection routes from every parcel of city land to possible points of connection to the operating networks based on the raster design of the *** optimal path is chosen by one criterion consisting of two components:the costs of both laying new power lines and providing sufficient power reserve in the chosen network connection *** as a computer program,the method has been used to calculate the costs of connections to low-voltage power lines.

关键词： GIS least-cost path analysis grid-connections urban power grid

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SFedXL: Semi-Synchronous Federated Learning With Cross-Sharpness and Layer-Freezing

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IEEE Internet of Things Journal 2025年

作者： Zhao, Mingxiong Zhao, Shihao Feng, Chenyuan Yang, Howard H. Niyato, Dusit Quek, Tony Q. S. Yunnan University National Pilot School of Software Kunming650500 China Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China EURECOM Sophia Antipolis06410 France Zhejiang University University of Illinois Urbana-Champaign Institute Zhejiang University Haining314400 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an Unmanned Aerial Vehicle (UAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a UAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by UAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different UAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed Semi-synchronous FL with Cross-Sharpness and Layer-Freezing (SFedXL), tailored for a UAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a trade-off of increased local computation latency. © 2014 IEEE.

关键词： Federated learning

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Echo State Network With Probabilistic Regularization for Time Series Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1743-1753页

作者： Xiufang Chen Mei Liu Shuai Li the Engineering Research Center of Open Source Software and Real-Time Systems Ministry of EducationSchool of Information Science and EngineeringLanzhou UniversityLanzhou 730000China IEEE

Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational ***,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world *** work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness ***,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the ***,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical ***,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed *** source code is publicly available at https://***/LongJinlab/probabilistic-regularized-echo-state-network.

关键词： Echo state network(ESN) noise probabilistic regularization robustness

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IoT device identification based on network communication analysis using deep learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9113-9129页

作者： Kotak, Jaidip Elovici, Yuval Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

Attack vectors for adversaries have increased in organizations because of the growing use of less secure IoT devices. The risk of attacks on an organization’s network has also increased due to the bring your own device (BYOD) policy which permits employees to bring IoT devices onto the premises and attach them to the organization’s network. To tackle this threat and protect their networks, organizations generally implement security policies in which only white-listed IoT devices are allowed on the organization’s network. To monitor compliance with such policies, it has become essential to distinguish IoT devices permitted within an organization’s network from non-white-listed (unknown) IoT devices. In this research, deep learning is applied to network communication for the automated identification of IoT devices permitted on the network. In contrast to existing methods, the proposed approach does not require complex feature engineering of the network communication, because the ’communication behavior’ of IoT devices is represented as small images which are generated from the device’s network communication payload. The proposed approach is applicable for any IoT device, regardless of the protocol used for communication. As our approach relies on the network communication payload, it is also applicable for the IoT devices behind a network address translation (NAT) enabled router. In this study, we trained various classifiers on a publicly accessible dataset to identify IoT devices in different scenarios, including the identification of known and unknown IoT devices, achieving over 99% overall average detection accuracy. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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Personalization of learning activities within a virtual environment for training based on fuzzy logic theory

Personalization of learning activities within a virtual envi...

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2017 International Conference on E-Learning, EL 2017

作者： Mohamed, Fahim Abdeslam, Jakimi Lahcen, El Bermi Software Engineering and Information Systems Engineering Team Computer Sciences Department Moulay Ismaïl University FST Errachidia Morocco

ISBN: (纸本)9789898533630

Virtual Environments for Training (VET) are useful tools for visualization, discovery as well as for training. VETs are based on virtual reality technique to put learners in training situations that emulate genuine situations. VETs have proven to be advantageous in putting learners into varied training situations to acquire knowledge and competencies, especially when these situations are taking place in uncontrolled circumstances, or when they are dangerous, unrealizable, or expensive to establish in reality. However individual learners find it difficult to select suitable activities for their particular situations because there is no personalized service to respond to the user needs. The solution is to generate learning activities based on learner's profile. Yet, a learner's profile may contain uncertain data (such as the desired level of difficulty, etc). This paper presents an attempt to introduce the concepts of fuzzy set theory the design of an online educational module. Such a module can deal with uncertainties in the knowledge acquisition, representation and decision making. The fuzzy logic principles are used to create the learner profile and to provide the appropriate learning activities to each learner according to his/her profile.

关键词： Fuzzy logic

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Business Process Remaining Time Prediction Based on Incremental Event Logs

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IEEE Transactions on Services Computing 2025年

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： incremental event log Predictive process monitoring process mining remaining time prediction

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K-eNSC: K-estimation for Normalized Spectral Clustering

K-eNSC: K-estimation for Normalized Spectral Clustering

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2020 International Conference on Intelligent systems and Computer Vision (ISCV)

作者： Mouden, Zakariyaa Ait El Jakimi, Abdeslam Moulay Ismail University Software Engineering Information Systems Engineering Research Team Faculty of Sciences and Techniques Errachidia Meknes Morocco

ISBN: (数字)9781728180410

ISBN: (纸本)9781728180410

In machine learning, the power of an approach is measured by its capability to be adapted for different applications and using different formats of data. Spectral Clustering is an unsupervised method that can be adopted for many research fields in and beyond computer science. In this paper, we present and analyze the existing algorithms of spectral clustering, and based on their limits we propose our modified version to deal with the most common challenge in this context which is the dynamic estimation of the output number of clusters. © 2020 IEEE.

关键词： Machine learning

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Accuracy reliability evaluation of trusted CNC machine tool based on belief reliability

Accuracy reliability evaluation of trusted CNC machine tool ...

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2024 International Conference on Computer Application and information Security, ICCAIS 2024

作者： Hu, Xuan Du, Jun Li, Xuetong Ye, Changnan Li, Haifeng CEPREI Laboratory Guangdong Guangzhou 510610 China The Ministry of Industry and Information Technology Key Laboratory of Performance and Reliability Testing and Evaluation for Basic Software and Hardware Guangdong Guangzhou 510610 China HuaTech Technology Co. Ltd. Beijing 100195 China Inner Mongolia First Machinery Group Co. Ltd. Inner Mongolia Baotou 014030 China China Space Sanjiang Group Co. Ltd. Hubei Xiaogan 432100 China School of Reliability and Systems Engineering Beihang University Beijing 100191 China

ISBN: (纸本)9781510689312

At present, few studies in the existing literature analyze the evaluation methods for the accuracy reliability of trusted computer numerical control (CNC) machine tool. This study aims to create a systematic method to solve the problem of how to conduct a comprehensive and scientific evaluation of trusted CNC machine tool based on belief reliability theory. Firstly, a definition of accuracy reliability is presented and the factors that affect the accuracy reliability of trusted CNC machine tool are analyzed. Then, a full failure mode method is used to perform failure mode and effects analysis (FMEA) on “mechanical failures”. And a traditional failure chain method is used to perform the FMEA on the other failures, with a focus on “security protection failures”. Moreover, a method for determining the corresponding epistemic uncertainty factor based on the application effectiveness evaluation of security risk factor for trusted CNC machine tool accuracy reliability is presented. Similarly, a method for determining the corresponding epistemic uncertainty factor based on the application effectiveness evaluation of FMEA for trusted CNC machine tool accuracy reliability is presented. Finally, the accuracy of spindle and feeding shaft of trusted CNC machine tool is selected as the performance indicator to measure the overall accuracy of machine tool, and the trusted CNC machine tool accuracy reliability is evaluated based on the belief reliability theory. The results of case studies reveal that the existence of epistemic uncertainty can lower people’s expectations of product reliability. In addition, product reliability can be improved by continuously reducing epistemic uncertainty. © The Authors.

关键词： accuracy belief reliability Computer numerical control machine tool epistemic uncertainty factor trusted CNC system

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Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and Technology in Computer Science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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