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1st International Conference on Logistics, ICL 2024

作者： Mohamed, Marwa F. Elsherif, Samiha M. Eltoukhy, Mohamed Meselhy Suez Canal University Faculty of Computers and Informatics Computer Sciences Department Ismailia Egypt Suez Canal University Faculty of Computers and Informatics Information Systems Department Ismailia Egypt College of Computing and Information Technology at Khulais University of Jeddah Department of Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331518844

The Hajj is an international Islamic event that takes place in Mecca, Saudi Arabia, annually and attracts millions of pilgrims. One of the most important services during the Hajj period is pilgrims' transportation services, they transport pilgrims between the holy sites (e.g., walking facilities, traditional buses, shuttle buses, and the holy sites train). The shuttle bus, among these services, is the busiest for Hajj. It operates during the Hajj and transports most of the pilgrims. For example, during the first Muzdalifah holiday, when on the ninth-day pilgrims move from Arafat to Muzdalifah, the transportation serves more than one million pilgrims. Therefore, there are many transportation companies, and choosing an appropriate company is one of the most important challenges facing Hajj organizers. There are many criteria for selecting transportation companies, such as cost and service quality;therefore, this paper presents a framework that uses the NSGA-III optimization algorithm to choose the best companies based on two objectives (e.g., costs and quality of service). The results of NSGA-III have been compared with heuristic algorithms (e.g., best fit and first fit), and NSGA-III outperformed these algorithms. © 2024 IEEE.

关键词： Bus transportation

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Identifying and Involving the Real End User in software Development: Towards a Pattern Language 27

Identifying and Involving the Real End User in Software Deve...

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Daud Haiderzai, Mohammad Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

software development demand and identification of end users involvement have been increasing rapidly, but identifying the real end user and involving them in software development is challenging for software developers reported in the literature. One of the problems is the difficulty of designing the business domain. It takes longer to find the actual business domain when different and inexpert domain end users are engaged with developers. Another problem is the difficulty of incorporating all types of end users needs in software development. Several expectations and different information make the development process more complex and overload the project scope. software development also takes more time, and the project goal is challenging. The above problems are the leading causes that create a complex process and difficulty in software development. To overcome these problems, we established a pattern language composed of eight organizational patterns observed and documented during in practical work on many projects. These patterns and other known organizational patterns relation are described in a patterns story of a real company. © 2022 ACM.

关键词： software design

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Organizational Patterns for Improving the Understanding between a Customer and the Development Team 27

Organizational Patterns for Improving the Understanding betw...

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Khattab, Mohammad Ismail Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

During a software development process, a customer and the development team need to communicate and understand each other. Poor communication between a customer and the development team is one of the most common challenges in the software development process, which leads to software failures and further increases the budget. In our practical experience and from the literature, we found that lack of customer involvement, no common communication language, complex business processes, end-user resistance toward system development, lack of trust, lack of leadership, and and end-user capacity are the main challenges toward the development team understanding. We bring six newly discovered patterns that address these problems and improve the understanding between a customer and the development team during the software development process. We also bring some of the known organizational patterns that also mitigate these problems into relation to the newly discovered patterns. To create a pattern language from these patterns, we illustrated the sequences of the patterns so that the development team and customer could understand each other. © 2022 ACM.

关键词： software design

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Federated Graph Learning for Cross-Domain Recommendation 38

Federated Graph Learning for Cross-Domain Recommendation

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Yang, Ziqi Peng, Zhaopeng Wang, Zihui Qi, Jianzhong Chen, Chaochao Pan, Weike Wen, Chenglu Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computing and Information Systems The University of Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods. We open source the code at https://***/LafinHana/FedGCDR. © 2024 Neural information processing systems foundation. All rights reserved.

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Temporal Refinement Graph Convolutional Network for Skeleton-Based Action Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1586-1598页

作者： Zhuang, Tianming Qin, Zhen Ding, Yi Deng, Fuhu Chen, Leduo Qin, Zhiguang Raymond Choo, Kim-Kwang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu Sichuan610054 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan610054 China School of Advanced Technology Suzhou Jiangsu215123 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human skeleton data, which has served in the aspect of human activity recognition, ought to be the most representative biometric characteristics due to its intuitivity and visuality. The state-of-the-art approaches mainly focus on improving modeling spatial correlations within graph topologies. However, the interframes motional representations are also of vital importance, and we argue that they are worth paying attention to and exploring. Therefore, a temporal refinement module with contrastive learning mechanism is proposed, fuzing as a complementary to the conventional spatial graph convolution layer. In addition, in order to further exploiting the interframe variances and generalizing graph convolutional network (GCN) operation to temporal dimension, a temporal-correlation matrix is introduced to effectively capture dynamic dependencies within frame-pairs, enhancing semantic feature representation. Moreover, since GCN is a typical local operator which lacks of capability to fully model the long-term relations along spatial and temporal variation, to move beyond the limitation, a spatial-temporal cascaded aggregation (STCA) module is designed to enlarge the receptive filter scale. The overall recognition framework consists of three above novelties, which is capable of achieving remarkable performance by evaluating on benchmark datasets (i.e., NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, and Kinetics Skeleton 400). Extensive experiments demonstrate the effectiveness of the proposed framework, e.g., performing recognition accuracy rate of 90.9% and 96.8% on NTU RGB+D 60, 87.9% and 88.9% on NTU RGB+D 120. © 2020 IEEE.

关键词： Convolution

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Efficient 3D Hilbert Curve Encoding and Decoding Algorithms

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Chinese Journal of Electronics 2022年第2期31卷 277-284页

作者： JIA Lianyin LIANG Binbin LI Mengjuan LIU Yong CHEN Yinong DING Jiaman Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Library Yunnan Normal University School of Information Science and Engineering Guangxi University for Nationalities School of Computing Informatics and Decision Systems Arizona State University

Hilbert curve describes a one-to-one mapping between multidimensional space and 1 D *** traditional 3D Hilbert encoding and decoding algorithms work on order-wise manner and are not aware of the difference between different input data and spend equivalent computing costs on them, thus resulting in a low efficiency. To solve this problem, in this paper we design efficient 3D state views for fast encoding and decoding. Based on the state views designed, a new encoding algorithm(JFK-3HE) and a new decoding algorithm(JFK-3HD) are proposed. JFK-3HE and JFK-3HD can avoid executing iteratively encoding or decoding each order by skipping the first 0 s in input data, thus decreasing the complexity and improving the efficiency. Experimental results show that JFK-3HE and JFK-3HD outperform the state-of-the-arts algorithms for both uniform and skew-distributed data.

关键词： order-wise manner multidimensional space encoding state-of-the-arts algorithms encoding algorithm parallel algorithms JFK-3HE statistical distributions decoding solid modelling design efficient 3D JFK-3HD traditional 3D Hilbert encoding equivalent computing costs different input data decoding algorithm Hilbert curve state views iterative methods

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Soft computing Based Metaheuristic Algorithms for Resource Management in Edge computing Environment

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Computers, Materials & Continua 2022年第9期72卷 5233-5250页

作者： Nawaf Alhebaishi Abdulrhman M.Alshareef Tawfiq Hasanin Raed Alsini Gyanendra Prasad Joshi Seongsoo Cho Doo Ill Chul Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Sejong UniversitySeoulKorea School of Software Soongsil UniversitySeoul06978Korea Artificial Intelligence Education Hankuk University of Foreign StudiesDongdaemun-guSeoul02450Korea

In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end *** computing is considered a method of forwarding the processing and communication resources in the cloud towards the *** of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved *** this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing *** SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource *** goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy *** problem is formulated based on the availability of VMs,task characteristics,and queue *** integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data *** experimental validation,a comprehensive set of simulations were performed using the CloudSim *** experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

关键词： Resource scheduling edge computing soft computing fitness function virtual machines

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The Effect of Data Transformation Techniques on Machine Learning Performance: A Case Study on Student Dropout Prediction 5

The Effect of Data Transformation Techniques on Machine Lear...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Liew, Yeong Chuan Lim, Khai Yin Lim, Ting Yee Tan, Choo Jun Chai, Koh Kiong Deng, Xinjie Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Penang Malaysia NEUON AI Sdn Bhd Sarawak Malaysia Institute for Intelligent Systems Research and Innovation Deakin University Geelong Australia

ISBN: (纸本)9798350355925

This study investigates the effects of data transformation techniques on the performance of various machine learning classification models. Specifically, the standard scaler (or Z-score) and min-max scaler techniques coupled with several machine learning and statistical algorithms, including K-Nearest Neighbors, Naive Bayes, Support Vector Machine, and Logistic Regression (LR), are examined systematically. Based on a set of student performance data from an institution, application of the resulting model to predicting student dropout rate is demonstrated. The empirical results indicate that LR with a standard scaler yields the highest AUC (area under the receiver operating characteristic curve) score of 89.95%. © 2024 IEEE.

关键词： Adversarial machine learning

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： Computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

关键词： Computer vision

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