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International Conference on Informatics and Computing (ICIC)

作者： Falentino Sembiring Rieska Rahayu Ayuningsih Muhamad Muslih Muhammad Andy Maulana Gina Wahdayani Futri Mohd Zainuri Saringat Rachma Siva Ainunnisa Anggun Fergina Information Systems Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This research focuses on image classification, grouping images based on shared characteristics. The study aims to identify the highest-potential category within various fields, from high to low potential. By understanding existing potential, researchers can inform targeted strategies for future development. The methodology combines Logistic Regression in Orange Data Mining to enhance classification accuracy. Hierarchical Clustering is used to analyze 7,777 image results. Findings reveal that Tourism (C3) has the most potential, followed by Culinary (C2) and Culture (C4), while Handcrafts (C1) exhibit the least potential. In conclusion, logistic regression successfully identifies influential aspects of Sukabumi through Instagram images, aiding potential development

关键词： Deep learning Logistic regression Accuracy Web sites Multimedia communication Data mining Cultural differences Informatics Image classification

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Visualizing Student Attention in Smart Classrooms: A Preliminary Study 15

Visualizing Student Attention in Smart Classrooms: A Prelimi...

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15th International Conference on Innovations in information Technology, IIT 2023

作者： Parambil, Medha Mohan Ambali Alhammadi, Amna Mohammed Abdulla Alnajjar, Fady Trabelsi, Zouheir Ali, Luqman Swavaf, Muhammed United Arab Emirate University Department of Information Systems and Security College of IT Al Ain United Arab Emirates United Arab Emirate University Department of Computer Science and Software Engineering College of IT Al Ain United Arab Emirates

ISBN: (纸本)9798350382396

In recent years, there has been a swift progression in employing novel methods in classrooms to enhance students' academic achievements, especially in line with the growing digitization of education. Such methods often encompass systems like facial recognition to monitor various aspects of students, including attendance, emotional states, and attention. These tools are capable of evaluating students' presence and engagement in class, offering quantifiable metrics regarding their concentration and emotions. However, a prominent challenge has been the translation of this data into an accessible form that enables educators to assess and enhance their teaching techniques swiftly. Our suggested solution tackles this issue by offering a real-Time visual depiction of students' classroom status through different visualization techniques. These visual aids allow teachers to promptly recognize trends in student focus, thus aiding in the strategic alteration of teaching styles. Furthermore, these visual representations can be tailored to display various metrics and applied to tasks beyond monitoring attention, like overseeing attendance or assessing student progress. By integrating these advanced visualizations into the educational process, both teaching efficacy and the learning experience for students and teachers alike can be substantially elevated. © 2023 IEEE.

关键词： Students

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Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 127-143页

作者： Abdullah A.Asiri Ahmad Shaf Tariq Ali Muhammad Aamir Ali Usman Muhammad Irfan Hassan A.Alshamrani Khlood M.Mehdar Osama M.Alshehri Samar M.Alqhtani Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients *** in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original *** artiﬁcially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented *** over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artiﬁcially realistic data for MRI *** GAN network contains mainly two parts known as generator and ***,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural *** this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been *** proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

关键词： GAN network CE-MRI images convolutional neural network brain tumor classiﬁcation

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Simulated annealing for resource and power allocation in 5G and B5G D2D communications 6

Simulated annealing for resource and power allocation in 5G ...

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6th International Conference on Pattern Analysis and Intelligent systems, PAIS 2024

作者： Benbraika, Mohamed Kamel Bourekkache, Samir Kraa, Okba Himeur, Yassine Telli, Khaled Ouamane, Abdelmalik University of El-Oued Computer Science Department El-Oued Algeria Mohamed Khider University Smart Computer Science Laboratory Biskra Algeria Mohamed Khider University Energy Systems Modeling Laboratory Biskra Algeria University of Dubai College of Engineering And Information Technology United Arab Emirates Mohamed Khider University Laboratory of LI3C Biskra Algeria

ISBN: (纸本)9798350350265

Device-to-device connection is a captivating new approach that holds great potential for the next generation of mobile networks, among others 5G and beyond. One of the reasons for this is that it has the capacity to boost system throughput, reduce the burden on the network core, and enhance spectral efficiency. Therefore, in order to make the most of these advantages, it is of the utmost importance to enhance the allocation of resources and power in order to reduce the amount of interference caused by co-channels. This paper proposes utilizing a based simulated annealing (SAA) technique to enhance combined radio spectrum and energy allocation. The SAA is made up of a phase that involves heating, which is then followed by slow and steady cooling. This is a method that is very often used in the field of metallurgy. Doing so results in an ordered and therefore stronger metal with more flexibility and less energy. The simulation outcomes show that the proposed approach surpasses both the random solution and Genetic approach (GA) in terms of time to stabilize to an optimal solution, throughput, and obtaining a feasible solution. © 2024 IEEE.

关键词： Genetic algorithms

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Towards Combating Frequency Simplicity-biased Learning for Domain Generalization 38

Towards Combating Frequency Simplicity-biased Learning for D...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： He, Xilin Hu, Jingyu Lin, Qinliang Luo, Cheng Xie, Weicheng Song, Siyang Khan, Muhammad Haris Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at AdvFrequency. © 2024 Neural information processing systems foundation. All rights reserved.

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Intelligent Daycare: Enhancing Child Safety with IoT and Machine Learning Innovations

Intelligent Daycare: Enhancing Child Safety with IoT and Mac...

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International Conference on Communication and Electronics systems (ICCES)

作者： Amisha Prathyanga Pasindu Shyaminda Pasan Chamikara Sachin Lakshan Samantha Thelijjagoda Dharshana Kasthurirathna Department of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Child safety and well-being in daycare settings are important, thus it is vital that the persons in charge be attentive to them. This research tries to solve the problem by introducing a smart and detailed monitoring system that utilizes new technology to enhance safety and support caretakers. The system has various innovative features, such as child missing detection, behavior and facial expression analysis, cry analysis and categorization, and fall detection. Using high-resolution cameras and on-device Edge AI capabilities, it ensures real-time surveillance of children's activities while protecting privacy. The approach integrates behavior and facial expression analysis to assess emotional states, cry recognition to discover childcare needs, as well as a useful fall detection system that can tell the difference between falls and other movements. All these parts synergize to hand over the necessary information to the caregivers on time and in a way that they can act upon. The quantitative experiments in real-time childcare centers show high accuracy through all the components, attesting to the system's reliability and durability. This research has important significance in improving the safety and the frequency response in daycare centers because it gives caregivers automated tools to quickly address the needs of children. While promising, the system has drawbacks, such as potential biases in data models and a reliance on ideal environmental circumstances. Future work intends to improve flexibility across varied daycare situations, improve accuracy through larger datasets, and investigate ethical frameworks for wider application.

关键词： Privacy Ethics Accuracy Surveillance Machine learning Edge AI Real-time systems Safety Time factors Fall detection

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Towards robust neural networks via a global and monotonically decreasing robustness training strategy

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Frontiers of information Technology & Electronic engineering 2023年第10期24卷 1375-1389页

作者： Zhen LIANG Taoran WU Wanwei LIU Bai XUE Wenjing YANG Ji WANG Zhengbin PANG Institute for Quantum Information&State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China State Key Laboratory of Computer Science Institute of Software Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100190China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China Key Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China

Robustness of deep neural networks(DNNs)has caused great concerns in the academic and industrial communities,especially in safety-critical *** of verifying whether the robustness property holds or not in certain neural networks,this paper focuses on training robust neural networks with respect to given ***-of-the-art training methods,interval bound propagation(IBP)and CROWN-IBP,perform well with respect to small perturbations,but their performance declines significantly in large perturbation cases,which is termed“drawdown risk”in this ***,drawdown risk refers to the phenomenon that IBPfamily training methods cannot provide expected robust neural networks in larger perturbation cases,as in smaller perturbation *** alleviate the unexpected drawdown risk,we propose a global and monotonically decreasing robustness training strategy that takes multiple perturbations into account during each training epoch(global robustness training),and the corresponding robustness losses are combined with monotonically decreasing weights(monotonically decreasing robustness training).With experimental demonstrations,our presented strategy maintains performance on small perturbations and the drawdown risk on large perturbations is alleviated to a great *** is also noteworthy that our training method achieves higher model accuracy than the original training methods,which means that our presented training strategy gives more balanced consideration to robustness and accuracy.

关键词： Robust neural networks Training method Drawdown risk Global robustness training Monotonically decreasing robustness

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LUMOS-DM: Landscape-Based Multimodal Scene Retrieval Enhanced by Diffusion Model 30th

LUMOS-DM: Landscape-Based Multimodal Scene Retrieval Enhance...

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30th International Conference on MultiMedia Modeling, MMM 2024

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

ISBN: (纸本)9783031533013

information retrieval is vital in our daily lives, with applications ranging from job searches to academic research. In today’s data-driven world, efficient and accurate retrieval systems are crucial. Our research focuses on video data, using a system called LUMOS-DM: Landscape-based Multimodal Scene Retrieval Enhanced by Diffusion Model. This system leverages Vision Transformer and Diffusion Models, taking user-generated sketch images and text queries as input to generate images for video retrieval. Initial testing on a dataset of 100 h of global landscape videos achieved an 18.78% at Top-20 accuracy rate and 36.45% at Top-100 accuracy rate. Additionally, video retrieval has various applications, including generating data for advertising and marketing. We use a multi-modal approach, combining sketch and text descriptions to enhance video content retrieval, catering to a wide range of user needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diffusion

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A Privacy Preserving Method for IoT Forensics

A Privacy Preserving Method for IoT Forensics

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Wenzheng Chen, Boxi Fu, Xiao Gu, Qing Shi, Jin Du, Xiaojiang Zhou, Xiaoyang Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University School of Information Management Nanjing China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Innovation Research Institute Nanjing China

ISBN: (纸本)9798350351255

The diversity of the Internet of Things (IoT) poses challenges to privacy protection, especially in the field of digital forensics. How to ensure that only the private information of the suspect is provided, and not the irrelevant information of other users is disclosed is crucial, especially when obtaining evidence in the complex IoT environment. To the best of our knowledge, there are few studies on protecting the privacy of irrelevant users in the IoT forensics. However, it is very important to ensure that the evidence does not violate the privacy of other users when collecting evidence, because it directly determines whether the evidence is legal and whether it can be admissible in court. In this paper, a new method based on data provenance graph is designed to solve the privacy protection problem of IoT forensics. The key idea of this method is to protect privacy by dividing multi-user information and protecting it from an encryption perspective. The method consists of three main phrases: distinguishing different users' data provenance graphs using traversal search, node abstraction, and hiding techniques, utilizing pseudo-random dual-key negotiation methods tailored for the scenario to enhance privacy protection for unrelated users, and employing identity authentication technology to facilitate better investigation and extraction of data provenance graph information of criminal accomplices in specific scenarios. Example proves that this method has practical significance and promising application prospects in protecting the privacy of unrelated users in IoT forensics while ensuring evidence accessibility in special criminal scenarios. © 2024 IEEE.

关键词： Differential privacy

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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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