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10th International Conference on Virtual Reality, ICVR 2024

作者： Liang, Hui Xu, Haoming Wang, Yi Pan, Junjun Zhengzhou University of Light Industry Department of Software Engineering Zhengzhou China General Hospital of Pingmei Shenma Group Department of Rehabilitation Pingdingshan China Beihang University Department of Computer Science and Engineering Peking China

ISBN: (纸本)9798350364231

The application of virtual reality (VR) across various sectors is becoming more widespread, playing a pivotal role in the advancement of the metaverse. However, the prevalence of VR sickness undermines user experiences, indicating a pressing need for optimization. Current research primarily focuses on enhancing VR hardware and investigating the relationship between human factors and VR sickness, overlooking the critical aspect of VR content. This article proposes design standards for VR scenarios, grounded in established design psychology theories, and develops six sets of VR scenarios to examine how VR content influences user experiences. We enlisted 150 college students suffering from VR sickness to participate in a study. The findings reveal that incorporating design psychology principles into VR scenarios beneficially affects participants' physiological and emotional responses. Nonetheless, the proposed design standards require further refinement, and future research is needed to validate their effectiveness. © 2024 IEEE.

关键词： Virtual environments

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Automatic identification of secondary narratives in videogames 2

Automatic identification of secondary narratives in videogam...

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2nd Congreso Espanol de Videojuegos, CEV 2023 - 2nd Spanish Video Games Congress

作者： Marín, Alejandro Ruiz, Alejandro León, Carlos Department of Software Engineering and Artificial Intelligence Computer Science Faculty Universidad Complutense de Madrid Madrid28040 Spain

Computational narrative is a complex field. While the computational processing of narratives has been tackled from different perspectives, the literature has focused on the analysis of a main plot, even if it is composed of different narrative sub-plots. However, modern narrative often involves additional threads that complement, justify or enhance the main narrative. This is commonly observed in narrative in video games. In this paper, we present a computational engine that can automatically differentiate between primary and secondary stories. This differentiation is based on the identification of a number of features that are believed to be key factors in the classification. We have analyzed a number of stories by using a simple game prototype with several stories, and a prototype assessment of the effectiveness of the system with human players has been carried out. Results indicate the hypothesized features can certainly be used to identify or produce secondary stories linked to a main plot. © 2023 CEUR-WS. All rights reserved.

关键词： Automation

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Low-confidence Heterogeneous Pixel-Prototype Contrastive Learning for Semi-supervised Medical Image Segmentation

Low-confidence Heterogeneous Pixel-Prototype Contrastive Lea...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, Shihuan Lai, Zhihui Kong, Heng Shenzhen University College of Computer Science and Software Engineering Shenzhen China Baoan Central Hospital of Shenzhen Department of Breast and Thyroid Surgery Shenzhen China

ISBN: (纸本)9798350386226

Medical image segmentation is a challenging task especially when dealing with unlabeled data. It has been proven that the use of complementary information for co-training is effective for medical image segmentation. We find that high-confidence regions are easy to segment, but low-confidence regions are difficult to correctly segment, which results in poor segmentation performance. Therefore, accurate segmentation in low-confidence regions is the effective way to enhance the performance of medical image segmentation. To achieve this goal, we propose a novel co-training strategy named low-confidence heterogeneous pixel-prototype contrastive learning with uncertainty-guide cross supervision for semi-supervised medical image segmentation. Specifically, an uncertainty-guided cross supervision module is firstly designed to estimate the confidence score of predictions from multiple outputs and then perform cross supervision between high-confidence regions. Moreover, we design a low-confidence heterogeneous prototype contrastive learning, which builds the relationship between low-confidence pixel and heterogeneous prototype to mine discriminative information in low-confidence regions so as to improve the segmentation performance. Extensive experiments on medical image datasets demonstrate that our method outperforms state-of-the-art methods in segmentation results. © 2024 IEEE.

关键词： Contrastive Learning

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The Open Source Threat Intelligence Relational Dataset and Its Optimal Implementation

The Open Source Threat Intelligence Relational Dataset and I...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Yang, Yaru Li, Junyu Zhang, Ronghua Sah, Abhishek Pratap Yadav, Amit Khan, Asif Chengdu Neusoft University Department of Computer and Software Chengdu China University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China Charles Darwin University College of Engineering IT and Environment NT Australia Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350361537

Threat intelligence provides a platform for cybersecurity engineers for attack traceability, which provides substantial knowledge database logs to defend against future security threats. Threat intelligence relationship extraction based on deep learning solves the challenge of threat knowledge construction to a certain extent but still faces problems such as lack of open-source datasets and the inability of the model to accurately correlate threat entities with potential relationships. Therefore, for cybersecurity research work, this paper designs a threat ontology, constructs the threat relationship dataset TreatRE by remote supervision, and opens this dataset. The dataset contains 12000 utterances and 12 threat relations from 500 CTIs, and it performs well in multiple relation models trained on deep learning methods. Meanwhile, we propose a multisensory attention-based threat intelligence relationship extraction method MAtt, which combines location perception, self-attention perception, and neuronal memory perception to further improve the threat relationship extraction effect. Experimental results show that the trained model based on TreatRE can more accurately extract the knowledge objects and their relationships described in threat intelligence. An accuracy score of 95.4% can be obtained using the MAtt method, which is 3.48% more than the best baseline compared with the same type of relationship extraction model. © 2024 IEEE.

关键词： Cybersecurity

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410 © 2023 IEEE.

关键词： Image classification

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A Survey on the Emerging Trend of Human Microchip Implants And Their Potential

A Survey on the Emerging Trend of Human Microchip Implants A...

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2023 Advances in science and engineering Technology International Conferences, ASET 2023

作者： Al Mansouri, Ltifa Mohammed Khalifa Alnajjar, Fady College of It United Arab Emirates University Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781665454742

Human microchip implants are small devices that are implanted into the body for various purposes including storing and transmitting information, monitoring health or location, and providing access to facilities or systems. Although human microchip implants offer numerous advantages, their adoption remains low in the public sector. This article presents a comprehensive survey on the emerging trend of human microchip implants and their potential uses in various fields. In addition to introducing the general concept of microchip implants, the work discusses their current and potential future applications. To better understand the direction of this trend, recent case studies of existing applications of human microchip implants are analyzed and presented. The article also explores the barriers to the adoption of human microchip implants and discusses their potential impact on humans, whether positive or negative. © 2023 IEEE.

关键词： Near field communication

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computer-Aided Potato Disease Detection by Using Deep Learning Techniques

Computer-Aided Potato Disease Detection by Using Deep Learni...

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International Conference on Green Energy, Computing, and Intelligent Technology, GEn-CITy 2023

作者： Razaq, Fareeha Bilal, Muhammad Ramzan, Muhammad Naveed, Muhammad Razzaq, Samreen Department of Computer Science University of Sargodha Sargodha Pakistan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (纸本)9789819998326

Potato is the most widely grown and consumed food throughout the world. There are a number of potato crop diseases that affect production, and these diseases differ in symptoms, circumstances, and controls. Early detection and recognition of disease information can aid in disease prevention and production. This paper presents deep learning models using proposed CNN and pre-trained models for potato disease detection and classification. The proposed model is more efficient and accurate at detection and classification. To perform classification, we used two datasets: PLD and PlantVillage. The Xception CNN model serves as the foundation for our proposed model. It achieved 1.00 accuracy, 0.99 precision, 1.00 recall, and 0.99 F1-score on the PLD dataset. On the PlantVillage dataset, it achieved 1.00 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score. We also compared the results of Inception-ResNet-V2, MobileNet-V2, VGG-19, Inception-V3, and Xception models with the performance of our proposed model. For three classes of potato leaves, the proposed CNN model produced more accurate results than other pre-trained models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

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computers, Materials & Continua 2022年第12期73卷 4695-4711页

作者： Mohammed Altaf Ahmed Sara A Althubiti Dronamraju Nageswara Rao ELaxmi Lydia Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science and Engineering Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Cyberattacks are developing gradually sophisticated,requiring effective intrusion detection systems(IDSs)for monitoring computer resources and creating reports on anomalous or suspicious *** the popularity of Internet of Things(IoT)technology,the security of IoT networks is developing a vital *** of the huge number and varied kinds of IoT devices,it can be challenging task for protecting the IoT framework utilizing a typical *** typical IDSs have their restrictions once executed to IoT networks because of resource constraints and ***,this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning(BAIDS-DFPDL)model in IoT *** presented BAIDS-DFPDLmodelmainly focuses on the identification and classification of intrusions in the IoT *** accomplish this,the presented BAIDS-DFPDL model follows blockchain(BC)technology for effective and secure data transmission among the ***,the presented BAIDSDFPDLmodel designs Differential Flower Pollination based feature selection(DFPFS)technique to elect ***,sailfish optimization(SFO)with Restricted Boltzmann Machine(RBM)model is applied for effectual recognition of *** simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions.

关键词： Internet of things feature selection intrusion detection blockchain security deep learning

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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computer Vision and Control Algorithm for Sustainable and Precision Agriculture

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SN computer science 2025年第4期6卷 1-13页

作者： Adebayo, Segun Aworinde, Halleluyah O. Ojerinde, Stephen A. Oyedoja, Kayode O. Adeniyi, Abidemi Emmanuel Olanloye, Odunayo Dauda Osueke, Christian O. Ogundokun, Roseline Oluwaseun Das, Abhishek Mechatronics Engineering Bowen University Iwo Nigeria Computer Science Bowen University Iwo Nigeria Computer Science Western Illinois University IL United States Technology Education Emmanuel Alayande University of Education Oyo Nigeria Department of Software Engineering Kaunas University of Technology Kaunas Lithuania Department of Computer Science Landmark University Omu-Aran Kwara State Nigeria Department of Computer Science and Engineering Aliah University Kolkata India Tshwane University of Technology Pretoria South Africa Durban University of Technology Durban South Africa Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The world population continues to increase with a large proportion of this increase happening in Africa just as predicated by Food and Agriculture Organization of the United Nation. To meet the demand of feeding this increasing population, innovative farming practices are being developed to increase the yield of the major stable food supply. This research developed a computer vision and control algorithm on a quadcopter for farm surveillance, crop protection from the invasion of pest, early disease detection as well as crop yield estimation. A process of developing a model capable of detecting and classifying these pests in the environment was presented in this study. The process involves selecting, preprocess and transforming the sensed data from a vision sensor to train classification algorithm and to detect and track object of interest in the environment. This study addresses a notable research gap in computer vision, control systems, big data analytics, and robotics, specifically within the context of smart farming. It primarily utilizes conventional learning algorithms, including Artificial Neural Networks and Support Vector Machines, typically found in widely-used commercial software. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Algorithm computer vision Machine learning Quadcopter UAV

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