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Cognitive Aspects of Virtual Reality (CVR), IEEE International Conference on

作者： István Károly Boda Erzsébet Tóth László T. Nagy Department of Mathematics and Informatics Debrecen Reformed Theological University Debrecen Hungary Department of Data Science and Visualization University of Debrecen Debrecen Hungary

In our paper we introduce the IT technical language learning material focusing on the specialized use of the English language in the context of information technology (IT) and computing. This material has been developed in the framework of the 3D virtual library project started in 2013 as part of the Cognitive Infocommunications (CogInfoCom) research. The current version of the virtual library has a hypertext or 2D implementation which is freely available in the internet. The advanced 3D presentation of the material uses the 3D features of the MaxWhere Seminar System, arranging and displaying the content of the language learning material in the Fly Podium 3D space. First, we provide a short overview on the virtual library project as an introduction to the IT technical language learning material. In the next section we describe the background and aim of the development of the material, then we present in detail the organization and structure of the 2D and 3D implementation of the learning material. Some inventions and ideas may deserve attention such as the topic map of selected terms, concepts, and texts functioning as a navigational tool, the interactive tests (where the first one is for practice, and the second one is actually a memory game), and the mapping mechanism of the hypertext-based material into the 3D virtual space of the MaxWhere system.

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FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation

arXiv

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arXiv 2023年

作者： Dhar, Mrinal Kanti Zhang, Taiyu Patel, Yash Gopalakrishnan, Sandeep Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53201 United States

This paper presents FUSegNet, a new model for foot ulcer segmentation in diabetes patients, which uses the pretrained EfficientNet-b7 as a backbone to address the issue of limited training samples. A modified spatial and channel squeeze-and-excitation (scSE) module called parallel scSE or P-scSE is proposed that combines additive and max-out scSE. A new arrangement is introduced for the module by fusing it in the middle of each decoder stage. As the top decoder stage carries a limited number of feature maps, max-out scSE is bypassed there to form a shorted P-scSE. A set of augmentations, comprising geometric, morphological, and intensity-based augmentations, is applied before feeding the data into the network. The proposed model is first evaluated on a publicly available chronic wound dataset where it achieves a data-based dice score of 92.70%, which is the highest score among the reported approaches. The model outperforms other scSE-based UNet models in terms of Pratt's figure of merits (PFOM) scores in most categories, which evaluates the accuracy of edge localization. The model is then tested in the MICCAI 2021 FUSeg challenge, where a variation of FUSegNet called x-FUSegNet is submitted. The x-FUSegNet model, which takes the average of outputs obtained by FUSegNet using 5-fold cross-validation, achieves a dice score of 89.23%, placing it at the top of the FUSeg Challenge leaderboard. The source code for the model is available on GitHub. © 2023, CC BY-NC-SA.

关键词： Image segmentation

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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Analyzing the Impact of Security Measures on Wi-Fi Quality of Service

Analyzing the Impact of Security Measures on Wi-Fi Quality o...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Thomas J. Kopcho Mostafa M. Fouda Cameron J. Krome Anna T. Quach Israel G. Olaveson Department of Computer Science Idaho State University Pocatello ID Department of Electrical and Computer Engineering Idaho State University Pocatello ID AI Data & Visualization Idaho National Laboratory Idaho Falls ID

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

This study examines the relationship between Quality of Service (QoS) and security in Wi-Fi networks. Using a detailed dataset consisting of QoS metrics of file transfers under various security configurations, we analyze the impact of these settings on network performance. Our findings provide a quantification to accurately show that different encryption protocols, such as TKIP and CCMP, significantly affect QoS, with disabling security generally improving performance. The study provides a framework for optimizing security settings to enhance both security and QoS in future wireless networks.

关键词： Measurement Protocols Statistical analysis Wireless networks Quality of service Throughput Encryption Security Internet of Things Wireless fidelity

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A Lightweight AI Model for Anomaly Detection in Wireless Networks

A Lightweight AI Model for Anomaly Detection in Wireless Net...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Thomas J. Kopcho Mostafa M. Fouda Cameron J. Krome Department of Computer Science Idaho State University Pocatello ID Department of Electrical and Computer Engineering Idaho State University Pocatello ID AI Data & Visualization Idaho National Laboratory Idaho Falls ID

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Detecting network anomalies is critical for wireless network security and reliability. Traditional AI methods often require substantial computational resources, particularly when deployed on cloud servers, leading to increased network load and latency. In contrast, deploying AI models directly on end devices enables real-time data processing at the source, facilitating faster anomaly detection and mitigation. We propose a lightweight hierarchical AI model where initial inferences are made by a simple model and outputs confidence scores. Using a modified TOPSIS-based method, we determine an optimal confidence score threshold. Inferences below this threshold are forwarded to a more complex model for accurate analysis, reducing overall computational demands while maintaining high detection performance. Our approach is tested on a 5 G network dataset, demonstrating competitive performance compared to other anomaly detection models.

关键词： Computational modeling Wireless networks Real-time systems Servers Security Reliability Artificial intelligence Anomaly detection Load modeling Testing

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Enhancing medical vision-language contrastive learning via inter-matching relation modelling

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IEEE Transactions on Medical Imaging 2025年第6期PP卷 PP页

作者： Li, Mingjian Meng, Mingyuan Fulham, Michael Feng, David Dagan Bi, Lei Kim, Jinman University of Sydney Biomedical data analysis and visualization lab School of Computer Science NSW Australia Royal Prince Alfred Hospital Department of Molecular Imaging NSW Australia Shanghai Jiao Tong University Institute of Translational Medicine Shanghai China

Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using six public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, linear classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities. © 2025 IEEE.

关键词： Contrastive Learning

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Audio spectrogram analysis in IoT paradigm for the classification of psychological-emotional characteristics

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International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Kumar, Ankit Singh, Sushil Kumar Bhardwaj, Indu Singh, Prakash Kumar Khanna, Ashish Brahma, Biswajit Dr. APJ Abdul Kalam Technical University Lucknow India Department of Computer Engineering Marwadi University Rajkot India Department of Electronics and Communication Engineering Galgotias University U.P. Greater Noida India Rajkiya Engineering College U.P Mainpuri India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology (GGSIPU) New Delhi India Senior Data and Visualization Engineer McKesson Corporation USA 32559 Lake Bridgeport St Fremont CA United States

Psychological activities have various dimensions in which they correlate with their respective behavior generated by the human body. Understanding the relationship of psychological events from external action units is one of the research subjects to explore various human behavior and their dependencies. The study of psychological analysis in the medical field is very time-consuming and costly. It requires constant monitoring of the patient for some time and various interrogation sessions to finalize the emotional severity of an individual. The challenges in exploring human emotions propel the requirement of computer vision techniques in this field. The proposed study explicitly evaluates the recognition of psychological-emotional activities with the help of an audio spectrogram of people fetched through an IoT (Internet of Things) device comprising a microphone to investigate its correlation with psychological events. The audio samples are collected in an asymmetric environment where the chances of the noise are random. Noise cancellation, low power consumption, and sensitivity controls are some of the prominent features of the microphone IoT that have been used to extract raw audio samples. The proposed system follows the extraction of features such as mel-frequency cepstral coefficients (MFCC), harmonic to noise rate (HNR), zero crossing rate (ZCR), and Generative Adversarial Networks (GAN) from the audio spectrogram. The study uses a deep learning-based model containing a convolutional neural network model to recognize and classify different psychological-emotional stages including happiness, anger, disgust, surprise, fear, and sadness from audio spectrogram features. The average accuracy of the classification model for the recognition of all emotions is found to be 99.42% in a maximum of 312 iterations. The model is found to be robust for various applications such as preventing suicidal cases, improving decision-making in the diagnosis of depression patients, im

关键词： Audio spectrogram Convolutional neural network (CNN) Emotion recognition Generative adversarial networks (GAN) Harmonic to noise rate (HNR) Mel-frequency cepstral coefficient (MFCC) Zero crossing rate (ZCR)

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Blockchain and PUF-based secure key establishment protocol for cross-domain digital twins in industrial Internet of Things architecture

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Journal of Advanced Research 2024年 62卷 155-163页

作者： Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Kharel, Rupak Das, Ashok Kumar Shetty, Sachin Rodrigues, Joel J.P.C. School of Psychology and Computer Science University of Central Lancashire Preston United Kingdom Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Software Engineering The University of Lahore Lahore 54590 Pakistan Department of Computer Science University of Sahiwal Punjab Sahiwal 57000 Pakistan Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Department of Modeling Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center and Center for Cybersecurity Education and Research Old Dominion University Suffolk 23435 VA United States COPELABS Lusófona University Campo Grande 376 Lisbon 1749-024 Portugal

Introduction: The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical and digital domains is crucial for unlocking the full potential of the IIoT, and digital twins can facilitate this integration by providing a virtual representation of real-world entities. Objectives: By combining digital twins with the IIoT, industries can simulate, predict, and control physical behaviors, enabling them to achieve broader value and support industry 4.0 and 5.0. Constituents of cooperative IIoT domains tend to interact and collaborate during their complicated operations. Methods: To secure such interaction and collaborations, we introduce a blockchain-based cross-domain authentication protocol for IIoT. The blockchain maintains only each domain's dynamic accumulator, which accumulates crucial materials derived from devices, decreasing the overhead. In addition, we use the on-chain accumulator to effectively validate the unlinkable identities of cross-domain IIoT devices. Results: The implementation of the concept reveals the fact that our protocol is efficient and reliable. This efficiency and reliability of our protocol is also substantiated through comparison with state-of-the-art literature. In contrast to related protocols, our protocol exhibits a minimum 22.67% increase in computation cost efficiency and a 16.35% rise in communication cost efficiency. Conclusion: The developed protocol guarantees data transfer security across the domain and thwarts IoT devices from potential physical attacks. Additionally, in order to protect privacy, anonymity and unlinkability are also guaranteed. © 2024

关键词： Digital twins Industrial Internet of Things (IIoT) Key agreement Mutual authentication Physically Unclonable Functions (PUFs)

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Investigating the Influence of Hyperparameters on the Optimal Time-Series Prediction Ability of Generative Large Language Models

Investigating the Influence of Hyperparameters on the Optima...

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Information Technology and data science (CITDS), IEEE Conference on

作者： Arpad Pandy Róbert Lakatos Péter Pollner Eszter Csernai András Hajdu Department of Data Science and Visualization Faculty of Informatics University of Debrecen Debrecen Hungary Health Services Management Training Centre Semmelweis University Budapest Hungary GE HealthCare Chicago Illinois United States

ISBN: (数字)9798350387889

ISBN: (纸本)9798350387896

This study investigates the optimal hyperparameter settings for Large Language Models (LLMs) in predicting time-series data. We conducted extensive experiments using both synthetic datasets and real agricultural data to evaluate the performance of several LLMs. Our findings reveal that the predictive accuracy of these models is highly dependent on the configuration of the temperature and Top- p hyperparameters. Specifically, models demonstrated optimal performance when the ratio between these two parameters was maintained below a threshold, with the most effective ratios being under 2.0 for Mistral-small and around 4.0 for Gemma-7B. Furthermore, Mistral-small showed lower variability in performance across different settings, indicating higher determinism. By aggregating data from multiple experimental conditions, we identified that maintaining a temperature to Top-p ratio below 1.36 covers 50% of top-performing pairs for Mistral-small and below 2.75 for Gemma-7B. If we consider only hyperparameters, that has such ratios, we can decrease the MSE value by 32% on average. These insights provide a foundation for enhancing the accuracy of LLMs in time-series forecasting, offering practical guidelines for hyperparameter optimization. Our results underscore the potential of LLMs to outperform traditional methods, particularly in datasets with periodic patterns, thereby contributing to the growing literature on the application of generative models in time-series analysis.

关键词： Temperature distribution Temperature dependence Accuracy Large language models Time series analysis Predictive models data models Forecasting Tuning Synthetic data

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A Survey of Research on Few-Shot Relation Classification

SSRN

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SSRN 2024年

作者： Zhang, YaChuan Guo, Yi Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Engineering Research Center of Big Data Internet Audience Shanghai China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China

Relation classification is a crucial sub-task of information extraction, playing a significant role in extracting structured information from vast texts and constructing knowledge graphs. However, in practical scenarios, the availability of labeled data is often limited, and some datasets exhibit a long-tail distribution. To address these challenges, few-shot learning relation classification (FSRC) has emerged as a promising approach. The purpose of FSRC is to learn high-quality features with insufficient data for distinguishing the relation between given entities in a sentence. This task presents greater challenges compared to traditional relation classification tasks. Recently, FSRC has gained a lot of attention, prompting numerous researchers to provide innovative methods, datasets, and ideas to tackle this challenge. This paper focuses on relation classification based on few-shot learning and provides a comprehensive review of state-of-the-art techniques. Based on distinct research characteristics, we classified FSRC methods into five categories: metric-based method, optimization-based method, pre-trained and LLMs based method, prompt-based method, and lower source situation. We analyze the advantages and disadvantages of each category, provide a summary of the datasets, metrics, and results obtained by representative models. Finally, the paper concludes the current limitations of the few-shot relation classification and proposes future research directions. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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