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6th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Huynh, Viet-Tham Nguyen, Tam.V Tran, Minh-Triet Software Engineering Laboratory Faculty of Information Technology University of Science VNU-HCM Viet Nam Vietnam National University Ho Chi Minh City Viet Nam University of Dayton Department of Computer Science United States

ISBN: (纸本)9798350327410

Recognizing hand-drawn sketches is a promising starting point for various applications, such as assisting artists in creating 3D environments for games or virtual environment scenes quickly and efficiently from concept arts. In addition, by understanding drawings, we can generate 3D models that can be used for further design and development. Thus, in this paper, we aim to develop a novel lightweight network that can accurately recognize sketch drawings. We propose a lightweight-yet-efficient neural network based on MobileNetV2 for sketch recognition and employ knowledge distillation to train the proposed model from EfficientNet-B4. To evaluate the accuracy of the proposed method, we collect a dataset of sketches comprising 1800 drawings in 12 categories, ranging from furniture to animals. The experimental results show that our network model achieves an accuracy of 96.7%, with 96.9% precision, 96.7% recall, and 96.7% F1-score. These results demonstrate that the proposed approach has great potential for practical sketch recognition applications, such as interior design or VR scene generation. © 2023 IEEE.

关键词： Distillation

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Building Custom Spreadsheet Functions with Python: End-User software engineering Approach

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Journal of software engineering and Applications 2024年第5期17卷 246-258页

作者： Tamer Bahgat Elserwy Atef Tayh Nour El-Din Raslan Tarek Ali Mervat H. Gheith Department of Software Engineering Faculty of Graduate Studies for Statistical Research (FGSSR) Cairo University Giza Egypt Department of Information Systems Higher Institute of Advanced Studies Giza Egypt

End-user computing empowers non-developers to manage data and applications, enhancing collaboration and efficiency. Spreadsheets, a prime example of end-user programming environments widely used in business for data analysis. However, Excel functionalities have limits compared to dedicated programming languages. This paper addresses this gap by proposing a prototype for integrating Python’s capabilities into Excel through on-premises desktop to build custom spreadsheet functions with Python. This approach overcomes potential latency issues associated with cloud-based solutions. This prototype utilizes Excel-DNA and IronPython. Excel-DNA allows creating custom Python functions that seamlessly integrate with Excel’s calculation engine. IronPython enables the execution of these Python (CSFs) directly within Excel. C# and VSTO add-ins form the core components, facilitating communication between Python and Excel. This approach empowers users with a potentially open-ended set of Python (CSFs) for tasks like mathematical calculations, statistical analysis, and even predictive modeling, all within the familiar Excel interface. This prototype demonstrates smooth integration, allowing users to call Python (CSFs) just like standard Excel functions. This research contributes to enhancing spreadsheet capabilities for end-user programmers by leveraging Python’s power within Excel. Future research could explore expanding data analysis capabilities by expanding the (CSFs) functions for complex calculations, statistical analysis, data manipulation, and even external library integration. The possibility of integrating machine learning models through the (CSFs) functions within the familiar Excel environment.

关键词： End-User software engineering Custom Spreadsheet Functions (CSFs)

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A language for graph database evolution and its implementation in Neo4j 42

A language for graph database evolution and its implementati...

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42nd International Conference on Conceptual Modeling: ER Forum, 7th Symposium on Conceptual Modeling Education, SCME 2023, Project Exhibitions, Posters and Demos, and Doctoral Consortium, ER-Companion 2023

作者： Hausler, Dominique Klettke, Meike Störl, Uta University of Regensburg Data Engineering Group Faculty of Informatics and Data Science Bajuwarenstrasse 4 Regensburg93053 Germany University of Hagen Chair of Databases and Information Systems Faculty of Mathematics and Computer Science Universitätsstrasse 1 Hagen58084 Germany

In this article, we present an evolution language for graph databases and a method to realize evolution operations on graph databases and their schema. Graph database management systems like Neo4j can be used for different scenarios: they can store graphs where no semantic constraints are checked. It is also possible to use them to store graphs where the structure of nodes and relationships is regular, for example, because the nodes and relationships of a graph have been generated by an application. In the latter case, these structures may change, that means they can undergo evolution. Currently, there is no established evolutionary language for graphs. However, evolution operations (such as add, rename, delete, merge, copy, split and move) have already been developed for other database models. In this article, we will not only use the established evolution operations but additionally extend the operations appropriated. After analyzing evolution operators explicitly in the context of graph databases, we also created a new graph-specific operation: transform. Moreover, we suggest an evolution language to graph data and show how it can be executed on the graph database schema as well as on the instances (i.e. the graphs themselves). In doing so, some operations can be implemented native based on Cypher, others non-native using the APOC library for Neo4j. © 2023 CEUR-WS. All rights reserved.

关键词： evolution graph databases evolution graph evolution language graph schema description

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Shedding Light on Glowapp: A Usability Test Evaluation for Enhanced User Experience 9

Shedding Light on Glowapp: A Usability Test Evaluation for E...

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9th International Conference on information Management and Technology, ICIMTech 2024

作者： Sergio Saputro, Immanuela Puspasari Prabowo, Yulius Denny Ticoalu, Voice Esther Mahardini, Karunia Agung BINUS Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Cosmetics is one of the emerging industries in Indonesia. Cosmetology is the practice of using cosmetics and cosmetic products to alter the appearance of the face. When applying makeup, it is essential to tailor it to the occasion and consider the facial structure. Applying appropriate cosmetics may enhance self-assurance. According to the questionnaire findings, women apply makeup more than twice. However, not all of them possess the talent to apply makeup on themselves. They struggle to locate a makeup artist with a style and availability that meets their requirements. Glowapp was developed with booking functionalities. Customers will find it more convenient to schedule an appointment. Blackbox testing was utilized to assess the functionality of the system. The results indicate that the features performed effectively and satisfied user requirements. The usability test is conducted by integrating the eight golden guidelines and ISO-9241-11. The goal is to reduce the time respondents spend filling questionnaires while maintaining usability principles to ensure more accurate answers. Glowapp has a performance score of 93%, with efficiency scoring the highest and effectiveness scoring the lowest. © 2024 IEEE.

关键词： Black-box testing

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Interference-Limits Aware Power and Receiver Optimization for 5G NR-U and Wi-Fi in the 6 GHz Shared-Spectrum

Interference-Limits Aware Power and Receiver Optimization fo...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Rasika Nilaweera Kalahe-Wattege Fernando Beltran Jairo Gutierrez Information Systems The University of Auckland Auckland New Zealand Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Adjacent Channel Interference (ACI) presents a significant concern for densely deployed Wi-Fi networks in the $\mathbf{6 G H z}$ spectrum. Coexisting Wi-Fi and 5G may be exposed to ACI due to the limitations in the devices’ receiver filtering capabilities or nearby user devices’ high transmit power. Since users of 5G and Wi-Fi networks operate in close proximity in frequency, space, and time, harmful ACI between 5 G and $\mathrm{Wi}-\mathrm{Fi}$ is unavoidable. This study investigates interference-limits aware approaches for enhancing throughput in distributed 5G NR-U-enabled femtocells and interference mitigation in Wi-Fi6E/7 network. Users act rationally and non-cooperatively to optimize their own utility. We have developed a non-cooperative Stackelberg game model to maximize throughput for 5G and mitigate ACI in $\mathrm{Wi-Fi}$ user devices. Our analysis explores the conditions necessary for the existence of a Stackelberg equilibrium within this context. The effectiveness of the proposed model is further demonstrated through simulation results.

关键词： Performance evaluation 5G mobile communication Radio transmitters Receivers Games Throughput Resource management Femtocells Wireless fidelity Optimization

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User Acceptance of New HCI Technologies 6

User Acceptance of New HCI Technologies

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Katona, Jozsef Kovari, Attila Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Engineering University of Dunaujvaros Department of Natural Sciences Dunaujvaros Budapest Hungary

ISBN: (纸本)9798350328752

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs. © 2023 IEEE.

关键词： Human computer interaction

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Integration of Smart Traffic Fog Nodes and Homes through a Model Driven Approach 2

Integration of Smart Traffic Fog Nodes and Homes through a M...

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2nd International Conference on Computing and information Technology, ICCIT 2022

作者： Alotaibi, Saud S. Rashid, Muhammad Azam, Farooque Abbas, Waseem Rasheed, Yawar Sinky, Mohammed H Anwar, Muhammad Waseem Umm Al-Qura University Department of Information Systems Makkah Saudi Arabia Umm Al-Qura University Computer Engineering Department Makkah Saudi Arabia H-12 Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665436052

Evolving concepts of smart cities include smart traffic lights and smart homes. These devices keep on observing the activities of users and share it with the cloud for further processing and decisions accordingly. This makes these systems complex ones. Therefore, a model-driven framework is needed which can integrate smart traffic lights with smart homes for the minimization of inherent development complexity. This article proposes a model driven framework, which integrates the smart traffic Fog nodes and smart homes. The basic objective is to share the mutual data related to traffic using cloud computing technology. To achieve this, a meta model is proposed. Subsequently, a Sirius based graphical modeling tool is developed which may be used to model the complex scenarios of smart lights and smart homes. A model to text transformation code is also written to automatically generate the target java code from the developed model. The validity of the proposed model has been demonstrated through a real world case study. In future, the proposed framework can be extended by incorporating the concepts of security and integrity of data. © 2022 IEEE.

关键词： Fog

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Assessing Quality of Service Performance in SDN Controllers: POX vs. OpenDaylight

Assessing Quality of Service Performance in SDN Controllers:...

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Advancements in Computing (ICAC), International Conference on

作者： Shashika Lokuliyana Pipuni Wijesiri Anuradha Jayakody Pradeep K.W. Abeygunawardhana Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This research offers a comparative analysis of Qual-ity of Service (QoS) within software Defined Networks (SDN) by assessing the POX and OpenDaylight controllers across various network topologies. The results show that OpenDaylight has a 32.59% higher average throughput than POX, demonstrating its superior data handling capabilities. Conversely, POX exhibits a 54.53% lower average delay and an 85.05% higher average jitter compared to OpenDaylight, highlighting its effectiveness in maintaining lower latency and variability in packet transmission times. These contrasting performances provide essential insights for network engineers in selecting the optimal SDN controller based on specific network demands, significantly influencing the overall network efficiency and stability.

关键词： Systematics Network topology Quality of service Jitter Throughput Real-time systems Stability analysis Delays software defined networking Optimization

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Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

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computers, Materials & Continua 2022年第6期71卷 5441-5457页

作者： Mahmoud Ragab Ehab Bahauden Ashary Maha Farouk S.Sabir Adel A.Bahaddad Romany F.Mansour Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)*** has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of ***,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data *** this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical *** output of optimization is either 0 or 1 i.e.,odd or even in *** this output value,the data is identified according to the ***,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter *** the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by *** motivation of the minimal objective is to remain ***,some computations can be performed more efficiently by the proposed *** testing,the test data was evaluated by minimal loss *** outcomes were assessed in terms of accuracy,computational time,and so *** this,databases like Lymphography,Dermatology,and Arrhythmia were used.

关键词： Medical data classification feature selection qkm classifier ltsa optimization

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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