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22nd IFIP TC 14 international conference on Entertainment Computing, ICEC 2023

22nd IFIP TC 14 International Conference on Entertainment Co...

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22nd IFIP TC 14 international conference on Entertainment Computing, ICEC 2023

ISBN: (纸本)9789819982479

The proceedings contain 48 papers. The special focus in this conference is on Entertainment Computing. The topics include: Seligman’s PERMA Model in Video Games – The Positive Influence of Video Games on Well-Being;the Meaning of Life (in Video Games);Method for Training Player Emotion in a Custom FPS Game Using Heart Rate;split Screen - Split Mind: An Innovative Format for Narrative Reflexive, and Pedagogical Games;accessibility Issues Within Serious Games in engineering Education for Visual Impairment;persuasive System Design for Climate Change Awareness;reasoning of intelligent Virtual Agents with Exceptional Behavior in Rare Scenarios;Game ON! a Gamified Approach to Household Food Waste Reduction;acquiring and Assessing 21st Century Skills Through Game Development: A Case study with an Escape Game Template;semiotic structuring in Movie Narrative Generation;girls vs. Men - The Prevalence of Gender-Biased Language in Popular YouTube Videos;i-Cube, an (In)tangible Interface for Multi-user Generative Art Creation;content Design for learning Support in Museum: Research on Exhibition Support System Using Tactile Senses;Exploring the Influence of Collision Anxiety on Player Experience in XR Games;Mutually Imperceptible Skill Adjustment in VR for Making Hyakunin Isshu Karuta Inclusive;Design of Collaborative learning Experience with AR–AR Collaboration Game for Children at Museum;Unity-VRlines: Towards a Modular eXtended Reality Unity Flight Simulator;augmented Adventures: Using Different Perspectives to Design Novel Tabletop Game Experiences with Augmented Reality;visualising Game Engine Subsystem Coupling Patterns;enhancing stockfish: A Chess Engine Tailored for Training Human Players;a Serious Video Game on Cybersecurity;preface.

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Machine learning based CAD system 21

Machine learning based CAD system

引用

21st IEEE international conference on Sciences and Techniques of Automatic Control and Computer engineering, stA 2022

作者： Neffati, Syrine MacHhout, Mohsen Faculty of Sciences of Monastir Monastir Tunisia

ISBN: (纸本)9781665482615

Classifying brain Magnetic Resonance Images (MRI) as abnormal or healthy can be considered as the key for the preclinical state of a patient. In recent years, various methods were developed in this field. In this paper, a novel MRI classifier based on new Downsized Kernel Principal Component Analysis (DKPCA) and Artificial Neural Network (ANN) is presented. The proposed algorithm, called the DKPCA-ANN classifies brain MRIs as pathological or normal. The proposed study contains three main steps;data acquisition and preprocessing stage, feature extraction and dimensionality reduction stage and finally the classification stage. Initially, to extract the image features, the scheme applied the Discrete Wavelet Transform (DWT). After feature vector normalization the DKPCA is applied to reduce features. The resulted matrix is used by the ANN classifier the predict the results. Seven common brain diseases have been used (Alzheimer's disease, glioma, meningioma, Huntington's disease, Alzheimer's disease plus visual agnosia, sarcoma and Pick's disease) as pathological brains. Brain MRIs were collected from the 'Harvard Medical School'. The findings show that our scheme is robust and effective in comparison with other recent researches. © 2022 IEEE.

关键词： Neural networks

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data-Driven Coordinated Control of AVR and PSS in Power Systems: A Deep Reinforcement learning Method 21

Data-Driven Coordinated Control of AVR and PSS in Power Syst...

引用

21st IEEE international conference on Environment and Electrical engineering / 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC/I and CPS Europe)

作者： Oshnoei, Arman Sadeghian, Omid Mohammadi-Ivatloo, Behnam Blaabjerg, Frede Anvari-Moghaddam, Amjad Aalborg Univ Dept Energy Aalborg Denmark Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

ISBN: (纸本)9781665436137

Y In this paper, a strategy based on deep reinforcement learning (DRL) as an intelligent coordinator for power system stabilizer (PSS) and automatic voltage regulator (AVR) in a two-are power grid is proposed. The proposed coordinator is developed to provide accurate online modification of the gains appearing in the structure of PSS and AVR which avoids unfavorable interactions between PSS and AVR under significant changes in the working point and thereby guaranteeing the stability of the power grid. A Markov decision manner is used to formulate the DRL problem and it is solved through a deep deterministic policy gradient approach with an actor-critic framework. Since the intelligent coordinator relies on the expert's science, some scaling coefficients are added to the coordinator body to achieve optimal performance. To confirm the effectiveness of the presented DRL approach, the design is conducted on Kundur's power grid. Simulations illustrate that the proposed DRL-based control can confirm the stability of the system and attain desired dynamic responses.

关键词： power system stability deep reinforcement learning coordinated control interconnected power system

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learning Advisor Networks for Noisy Image Classification 21st

Learning Advisor Networks for Noisy Image Classification

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Ricci, Simone Uricchio, Tiberio Del Bimbo, Alberto Univ Florence Florence Italy

ISBN: (纸本)9783031064302;9783031064296

In this paper, we introduced the novel concept of advisor network to address the problem of noisy labels in image classification. Deep neural networks (DNN) are prone to performance reduction and overfitting problems on training data with noisy annotations. Weighting loss methods aim to mitigate the influence of noisy labels during the training, completely removing their contribution. This discarding process prevents DNNs from learning wrong associations between images and their correct labels but reduces the amount of data used, especially when most of the samples have noisy labels. Differently, our method weighs the feature extracted directly from the classifier without altering the loss value of each data. The advisor helps to focus only on some part of the information present in mislabeled examples, allowing the classifier to leverage that data as well. We trained it with a meta-learning strategy so that it can adapt throughout the training of the main model. We tested our method on CIFAR10 and CIFAR100 with synthetic noise, and on Clothing1M that contains real-world noise, reporting state-of-the-art results.

关键词： Meta learning Advisor network Noisy labels

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Software and Hardware Processing Method for Blockchain Light Node SPV Verification 21

Software and Hardware Processing Method for Blockchain Light...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Ma, Zhan'Gang Yan, Jiandong Zou, Feng Cao, Xixin Peking University School of Software and Microelectronics Dept. Integrated Circuits and Intelligent Systems Beijing China Ruiyuan Venture Capital Co. Ltd. Changzhou Zhonglou Jinlong Holding Group Dept. Development Planning and Strategic Inverstment Changzhou China

ISBN: (纸本)9798350329223

With the application of blockchain light nodes in embedded devices, how to alleviate computing pressure brought by complex operations such as transaction's SPV Verification for CPU of embedded devices and improve the performance of devices in these aspects has gradually become a research topic in industry and academia. This paper proposes a series of methods to improve the performance of blockchain SPV Verification from the perspectives of system architecture and hash computing unit: (1) According to the computational characteristics of SPV Verification, this paper customizes macro instructions and microinstructions for the coprocessor to meet the requirements of flexibility;The built-in dedicated cache holds transaction data fetched from external memory and intermediate data generated by internal Hash Computing Unit, which not only prepares transaction data for hash computation, but also avoids frequent access to the bus and external memory. (2) Techniques like two-round unfolded computing, timing-balanced pipeline architecture and optimized adders are adopted to improve the performance of SHA256 computation. (3) When double hash computing is required for transactions, Hash Computing Unit can directly perform the second hash computation based on the first hash computation, reducing the frequency of accessing to external memory, thereby improving the performance to a certain extent. Through these methods, the performance of the hardware coprocessor for SVP verification of transactions is more than double that of traditional solutions. © 2023 IEEE.

关键词： Blockchain

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Multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel dataset and Deep-learning Experiments

Multimodality for Diagnosis of Asian Choroidal Vasculopath...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Cho, Daehyun Kim, Young Ho Ahn, Somin Oh, Jaeryung Wallraven, Christian Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Ophthalmology Korea University College of Medicine Seoul Korea Republic of Department of Ophthalmology Dongguk University Ilsan Hospital Goyang Korea Republic of Department of Artificial Intelligence and Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9783031845246

Deep learning algorithms show tremendous potential for clinical decision-making - for example, in providing automated diagnoses of imaging data. However, typical clinical datasets often are limited in size, modalities, and contain heterogeneous, incomplete data, which presents challenges for deep learning frameworks that necessitate larger, uniform datasets, complicating their deployment especially with new types of disease models. In this work, we present a case study for deep learning in such a challenging setting in the context of diagnosing Asian choroidal Vasculopathy (ACV), which is a retinopathy profile currently under discussion in ophthalmology to be differentiated from age-related macular degeneration (AMD). We first introduce a novel, human-annotated multimodal dataset for ACV versus AMD diagnosis incorporating four different imaging modalities. We next explore the usefulness of "foundation models" for this data, compared to traditional dataset-specific training. Most importantly, we investigate which of the four modalities is most discriminative and whether bi-modal classification is able to enhance performance across multiple fusion approaches. We also discuss first results of salient features using explainability techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Modernizing Educational Programs by Integrating AI and Advanced Technologies Topics to Enhance Competitiveness

Modernizing Educational Programs by Integrating AI and Advan...

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Technology Enhanced learning in Higher Education (TELE), international conference on

作者： Saidakhror Saidakhmedovich Gulyamov Odilbek Khazratkulov Sardor Bazarov Institute of Advanced Training and Statistical Research Tashkent Uzbekistan Tashkent State University of Law Tashkent Uzbekistan

ISBN: (数字)9798350353532

ISBN: (纸本)9798350353549

This paper argues for the imperative of modernizing educational programs by integrating dedicated modules on artificial intelligence, machine learning, intelligent systems, extended reality, data science and other advanced technology topics. This can address the skills gap faced by graduates and enhance the competitiveness, relevance and future-readiness of higher education. Recommendations are provided for updating standards, faculty development, industry linkages and accreditation frameworks to enable nation-wide adoption. Curriculum modernization can activate lifelong learning mindsets vital for the 21st century.

关键词： Industries Leadership Ethics Extended reality Machine learning Dynamic scheduling Accreditation

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Small-Scale dataset for Machine learning Based Network Intrusion Detection 21

Small-Scale Dataset for Machine Learning Based Network Intru...

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21st IEEE international Colloquium on Signal Processing and Its Applications, CSPA 2025

作者： Ridwan, Mohammad Azmi Radzi, Nurul Asyikin Mohamed Samidi, Faris Syahmi Sulaiman, Suhaila Azmi, Kaiyisah Hanis Mohd Ahmad, Wan Siti Halimatul Munirah Wan Institute of Power Engineering Universiti Tenaga Nasional Selangor Kajang 43000 Malaysia MJIIT-UTM Department of Electronic Systems Engineering Kuala Lumpur 54100 Malaysia Institute for Research Development & Innovation Imu Kuala Lumpur 57000 Malaysia

ISBN: (纸本)9798331522193

An intrusion detection system (IDS) was introduced to detect various intrusive attempts on the network. Several machine learning (ML) techniques have been incorporated to build robust and intelligent IDS. Researchers are maximizing public intrusion datasets, such as the KDD CUP 99, NSL-KDD and the CICIDS-2018 datasets, to train the ML algorithms. However, public intrusion datasets have large data sizes, which cause a high training time for the ML algorithms. Moreover, public intrusion datasets have high-dimensional input features, thus requiring tedious feature extraction methods. Furthermore, these datasets have an imbalance ratio between normal and attack traffic, which leads to less efficient training supplied to the ML algorithms, resulting in low accuracy. An intrusion dataset that can reduce the training and processing time whilst having a balanced dataset between normal and attack traffic must be proposed to overcome these issues. Hence, a small-scale and robust intrusion dataset is developed in this paper to train the ML classifier. Simulation results show that the proposed dataset improves the accuracy of the ML classifier by 19.15%, 99.26% and 40.48% considering accuracy, recall and F-measure, respectively, compared with the CICIDS-2018 public dataset. © 2025 IEEE.

关键词： dataset Denial-of-Service Attack Intrusion Detection Machine learning

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PhyFlow: Physics-Guided Deep learning for Generating Interpretable 3D Flow Fields 21

PhyFlow: Physics-Guided Deep Learning for Generating Interpr...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Muralidhar, Nikhil Bu, Jie Cao, Ze Raj, Neil Ramakrishnan, Naren Tafti, Danesh Karpatne, Anuj Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Virginia Tech Dept Mech Engn Blacksburg VA USA

ISBN: (纸本)9781665423984

Generating flow fields (such as pressure and velocity fields) in 3D space is a fundamental task in computational fluid dynamics (CFD), with applications across a vast spectrum of science and engineering problems. An important class of fluid flow problems in CFD is multi-phase flow, where dispersed solid particles are present in the fluid flow. Despite recent developments in deep learning (DL) for CFD applications, current state-of-the-art is still unable to model 3D flow fields, especially in multi-phase flow settings. It is with this goal that we introduce PhyFlow , a novel physics-guided deep learning architecture for modeling 3D multi-phase fluid flows, designed to mimic the popular projection method for solving fluid flows in CFD simulations. We demonstrate that PhyFlow generates high quality flow fields and yields a 49.61% improvement over other state-of-the-art baselines. We also test the quality of PhyFlow based fields by employing them in downstream tasks like particle drag force prediction and demonstrate state-of-the-art results, improving upon the previous best models by 9.89%. Finally, we demonstrate the consistency of PhyFlow predictions with known underlying physics governing equations. Our source code and data are available online*.

关键词： Physics-guided ML Deep learning CFD

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Detecting Fake News in MANET Messaging Using an Ensemble Based Computational Social System 21st

Detecting Fake News in MANET Messaging Using an Ensemble Bas...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Ramkissoon, Amit Neil Goodridge, Wayne Univ West Indies Dept Comp & Informat Technol St Augustine Trinidad Tobago

ISBN: (纸本)9783031133244;9783031133237

Mobile Adhoc Networks (MANETs) are utilised in a variety of mission-critical situations and as such, it is important to detect any fake news that exists in such networks. This research proposes an Ensemble Based Computational Social System for fake news detection in MANET messaging. As such this research combines the power of Veracity, a unique, computational social system with that of Legitimacy, a dedicated ensemble learning technique, to detect fake news in MANET messaging. Veracity uses five algorithms namely, VerifyNews, CompareText, PredictCred, CredScore and EyeTruth for the capture, computation and analysis of the credibility and content data features using computational social intelligence. To validate Veracity, a dataset of publisher credibility-based and message content-based features is generated to predict fake news. To analyse the data features, Legitimacy, a unique ensemble learning prediction model is used. Four analytical methodologies are used to analyse these experimental results. The analysis of the results reports a satisfactory performance of the Veracity architecture combined with the Legitimacy model for the task of fake news detection in MANET messaging.

关键词： Computational social system Content Credibility Ensemble learning Fake news detection MANET

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