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SSRN 2024年

作者： Zhang, Shasha Zhou, Jie Ma, Yanfang Pan, Haiyu Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Mathematics and Science College Shanghai Normal University Shanghai200234 China Computer Science and Information Engineering College Changzhou Institute of Technology Jiangsu Changzhou213032 China

Fuzzy concurrent game structures (FCGSs), an extension of concurrent game structures in the fuzzy setting, combine both fuzzy and nondeterministic choices. They are therefore a powerful semantic foundation for modeling fuzzy systems that interact with their environments. In recent years, refinement relations from the field of formal verification, including trace containment and simulation, have been successfully introduced to special classes or related ones of FCGSs, such as fuzzy transition systems and fuzzy automata, and have been developed quickly. However, these refinement relations are not directly applicable to FCGSs as they constrain the possible behaviors of the environment. To further satisfy the need for analysis and verification of fuzzy systems, this paper introduces alternating refinement relations for FCGSs. First, we present two general notions of alternating trace containment for FCGSs. Although these notions coincide with the corresponding classical ones, we show that they do not coincide within FCGSs. Then, we provide algorithms to solve the problems of deciding two types of alternating trace containments in finite FCGSs under memoryless strategies. On the other hand, we propose two types of alternating simulations for FCGSs via weight functions and closed sets, and discover a novel result that they have different discriminatory abilities, with one alternating simulation implying the other. Finally, we investigate the relationships between two types of alternating trace containments and two types of alternating simulations. © 2024, The Authors. All rights reserved.

关键词： Game theory

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability 2

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katru, Chandrasekhar Rao Srinivasan, Sathish Kumar, Mohan Yadwad, Sunita Satish, G. Maranan, Ramya Lead Software Engineer Indian LandSC29707 United States Machine Learning & AI Development Oracle Cloud Infrastructure Division San FranciscoCA United States Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Parul institute of Engineering and Technology Parul University Computer science and Engineering Faculty of Engineering and Technology Gujarat India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Telangana Sheriguda India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance. © 2025 IEEE.

关键词： Infrastructure as a service (IaaS)

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A Robust Methodology for Fruit Quality Prediction and Estimation Using Intelligent Learning Based Image Processing Logic

A Robust Methodology for Fruit Quality Prediction and Estima...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Malathi K Vunnava Dinesh Babu M. Ramkumar M. Ayyadurai Vivek K Hamzeh J Aljawawdeh Department of CSE Saveetha School of Engineering Saveetha Institute of Science and Technology (SIMATS) Saveetha University Chennai Department of CSE RV Institute of Technology Guntur Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Fruit quality prediction plays a vital role in agricultural and food industries, ensuring that only high-quality produce reaches the consumer. Traditional methods of assessing fruit quality rely on manual inspection, which is both time-consuming and prone to human error. This paper presents a robust methodology for fruit quality prediction and estimation, utilizing a hybrid deep learning model that integrates DenseN et for feature extraction with LightGBM for efficient classification. The proposed model is designed to automate the process of fruit quality assessment by analyzing key attributes such as color, texture, and shape from high-resolution fruit images. The Fruit360 dataset was used for training and testing, with images classified into categories such as fresh, ripe, overripe, and damaged. A comprehensive comparison with nine existing models, including SVM, Random Forest, and XGBoost, was conducted using performance metrics such as accuracy, precision, recall, and Fl-score. The proposed model achieved an accuracy of 96.78%, significantly outperforming all other models. Furthermore, the model's precision and recall reached 95.33% and 95.67%, respectively, demonstrating its reliability in distinguishing high-quality fruits from low-quality ones.

关键词： Training Support vector machines Accuracy Shape Computational modeling Estimation Predictive models Feature extraction Reliability Testing

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

arXiv

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arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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Optimizing Heart Failure Predictive Accuracy: An Effective Approach Using SMOTE Techniques

Optimizing Heart Failure Predictive Accuracy: An Effective A...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Ahmed Baber Faizan Ahmed Attique Ur Rehman Sabeen Javaid Menwa Alshammeri Azka Mir Deepak Kumar Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science College of Computer and Information Sciences Al Jouf University Saudia Arabia Division of Research and Development Lovely Professional University Phagwara KKR and KSR institute of technology and sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Heart failure occurs when the heart's lower cham-bers (ventricles) weaken and can't pump blood effectively. The heart contains 2 ventricles that are left and right. When left ventricular failure occurs, it causes shortness of breath and fatigue, and when right ventricular failure occurs, it causes peripheral and abdominal fluid accumulation. Heart failure is a disorder in breathing and oxygen supply to the whole body. As we know, Heart is responsible for circulating blood all over the body parts. When it stops pumping, blood can no longer circulate effectively causing fluid to build up in the lungs causing difficulty in breathing and in the legs, causing swelling. The fluid buildup can cause shortness of breath and swelling of the legs and feet. As a result, fluctuation in breathing takes place and, in the end, the patient dies. This study investigates the use of machine learning tools, like k-Nearest Neighbors, Decision Trees, Naïve Bayes, and Random Forests, to identify heart failure. We are proposing a model for Heart Failure Prediction using a machine learning algorithm and a dataset to determine the performance in terms of accuracy. As a result, the Random Forest classifier along with the SMOTE operator provides the highest accuracy of 91.12%, precision of 87.88%, recall of 95.39%, and F1 measure of 91.48% among the models considered. This research is a very important contribution to healthcare for effective predictions of this disease, as the number of patients is increasing day by day. The results hold significance for future modifications too in medical diagnostics and helping material for more accurate detection methodologies.

关键词： Heart Legged locomotion Accuracy Fluids Machine learning algorithms Pumps Nearest neighbor methods Cardiovascular diseases Random forests Blood

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TalkingGaussian: Structure-Persistent 3D Talking Head Synthesis via Gaussian Splatting

arXiv

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arXiv 2024年

作者： Li, Jiahe Zhang, Jiawei Bai, Xiao Zheng, Jin Ning, Xin Zhou, Jun Gu, Lin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University China Institute of Semiconductors Chinese Academy of Sciences China School of Information and Communication Technology Griffith University Australia RIKEN AIP Japan The University of Tokyo Japan

Radiance fields have demonstrated impressive performance in synthesizing lifelike 3D talking heads. However, due to the difficulty in fitting steep appearance changes, the prevailing paradigm that presents facial motions by directly modifying point appearance may lead to distortions in dynamic regions. To tackle this challenge, we introduce TalkingGaussian, a deformation-based radiance fields framework for high-fidelity talking head synthesis. Leveraging the point-based Gaussian Splatting, facial motions can be represented in our method by applying smooth and continuous deformations to persistent Gaussian primitives, without requiring to learn the difficult appearance change like previous methods. Due to this simplification, precise facial motions can be synthesized while keeping a highly intact facial feature. Under such a deformation paradigm, we further identify a face-mouth motion inconsistency that would affect the learning of detailed speaking motions. To address this conflict, we decompose the model into two branches separately for the face and inside mouth areas, therefore simplifying the learning tasks to help reconstruct more accurate motion and structure of the mouth region. Extensive experiments demonstrate that our method renders high-quality lip-synchronized talking head videos, with better facial fidelity and higher efficiency compared with previous methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Exploring Sensory Conflict Effect Due to Upright Redirection While Using VR in Reclining & Lying Positions 22

Exploring Sensory Conflict Effect Due to Upright Redirection...

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35th Annual ACM Symposium on User Interface software and Technology, UIST 2022

作者： Luo, Tianren He, Zhenxuan Cai, Chenyang Han, Teng Pan, Zhigeng Tian, Feng Institute of Software Chinese Academy of Sciences Beijing China College of Information Engineering Capital Normal University Beijing China College of Information Technology Beijing University of Technology Beijing China College of Artifcial Intelligence Nanjing University of Information Science and Technology Jiangsu Nanjing China College of Computer Science and Technology University of Chinese Academy of Sciences China College of Artifcial Intelligence University of Chinese Academy of Sciences China

ISBN: (纸本)9781450393201

When users use Virtual Reality (VR) in nontraditional postures, such as while reclining or lying in relaxed positions, their views lean upwards and need to be corrected, to make sure they see upright contents and perceive the interactions as if they were standing. Such upright redirection is excepted to cause visual-vestibular-proprioceptive conflict, affecting users' internal perceptions (e.g., body ownership, presence, simulator sickness) and external perceptions (e.g., egocentric space perception) in VR. Different body reclining angles may affect vestibular sensitivity and lead to the dynamic weighting of multi-sensory signals in the sensory integration. In the paper, we investigated the impact of upright redirection on users' perceptions, with users' physical bodies tilted at various angles backward and views upright redirected accordingly. The results showed that upright redirection led to simulator sickness, confused self-awareness, weak upright illusion, and increased space perception deviations to various extents when users are at different reclining positions, and the situations were the worst at the 45° conditions. Based on these results, we designed some illusion-based and sensory-based methods, that were shown effective in reducing the impact of sensory conflict through preliminary evaluations. © 2022 ACM.

关键词： Virtual reality

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SCAF-DG: A Multi-site Medical Image Denoising With A Domain-Generalized Spatial-Channel Attention Fusion

SCAF-DG: A Multi-site Medical Image Denoising With A Domain-...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Linda Delali Fiasam Yunbo Rao Collins Sey Benjamin Klugah-Brown Obed Nartey Tettey Esther Stacy E.B. Aggrey Isaac Osei Agyemang Isaac Adjei-Mensah Yang Yang School of Information and Software Engineering University of Electronic Science Technology of China Chengdu China School of Life Science and Technology University of Electronic Science Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science Technology of China Chengdu China Sichuan Institute of Computer Sciences Chengdu China

Images from multiple medical sites usually contain varying noise levels that can affect the generalization performance of the denoising models. Domain generalization (DG), which seeks to learn a model that can generalize to an unseen test domain, has not been extensively explored in medical image denoising for multi-site images. We present an approach for multi-site medical image denoising, which addresses the issue of varying noise levels across different imaging sites. Our proposed method, called SCAF-DG (Spatial-Channel Attention Fusion for Domain Generalization), employs two attention units to extract both domain-specific channel features and mutually-invariant spatial features from the noisy images. These features are then combined to form a rich feature space. Additionally, we utilized skip connections to preserve structural information during the denoising process. We evaluate SCAF-DG on publicly available brain magnetic resonance image (MRI) datasets from three different imaging sites, and demonstrate an average increase of 1.01 in peak signal-to-noise ratio (PSNR) and 0.016 in structural similarity index measure (SSIM) for denoising images. Our results demonstrate that SCAF-DG has the potential to generalize well to new, unseen test domains, and outperforms existing state-of-the-art denoising models on multi-site medical images.

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Hybrid of Ant Colony and Artificial Bee Colony Optimization for Resource Allocation in Cognitive Radio Networks

Hybrid of Ant Colony and Artificial Bee Colony Optimization ...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： P Lakshmiramana Mohammed Al-Farouni P Senthil Shashidhara K S Pramod Mutalik Computer Science and Technology Madanapalle Institute of Technology and Science Angallu Madanapalle India The Islamic university Najaf Iraq Department of Information Technology S.A. Engineering College Chennai India Dept. of ECE Nitte Meenakshi Institute of Technology Bengaluru India Software Engineer Visvesvaraya Technological University Raichur Karnataka India

The resource allocation in heterogeneous cognitive radio networks (CRN) system has been considered a key technology in wireless networks. However, the main challenge of the CRN system is resource allocation with minimized and maximum transmission in the spectrum band, enhancing optimum throughput increase, load balance and global optimization. The proposed method is hybrid optimization, comprising Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC), allocating resources while considering power, throughput and equal to distributed the allocation. The load balancing technique should ensure virtual independent task scheduling approaches of the hybrid optimization and optimize scheduling and resource utilization to maximize throughput and create load balancing. Performance analysis of the proposed method was compared using resource allocation with existing load balancing and scheduling algorithms. Although the proposed ACO-ABC method exhibits high performance compared to existing methods such as Dynamic Fuzzy-based Primary User Aware Clustering (DFPC), non-orthogonal multiple access (NOMA) and Hybrid Tabu Search-based Stimulated Algorithm (HTSA), experimental results demonstrate high performance achieving a Packet Delivery Ratio (PDR) of 97%, Delay of 0.009s, Energy consumed of 0.01 (nJ), and Throughput of 2.0 (Mpbs).

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DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization

arXiv

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arXiv 2024年

Radiance fields have demonstrated impressive performance in synthesizing novel views from sparse input views, yet prevailing methods suffer from high training costs and slow inference speed. This paper introduces DNGaussian, a depth-regularized framework based on 3D Gaussian radiance fields, offering real-time and high-quality few-shot novel view synthesis at low costs. Our motivation stems from the highly efficient representation and surprising quality of the recent 3D Gaussian Splatting, despite it will encounter a geometry degradation when input views decrease. In the Gaussian radiance fields, we find this degradation in scene geometry primarily lined to the positioning of Gaussian primitives and can be mitigated by depth constraint. Consequently, we propose a Hard and Soft Depth Regularization to restore accurate scene geometry under coarse monocular depth supervision while maintaining a fine-grained color appearance. To further refine detailed geometry reshaping, we introduce Global-Local Depth Normalization, enhancing the focus on small local depth changes. Extensive experiments on LLFF, DTU, and Blender datasets demonstrate that DNGaussian outperforms state-of-the-art methods, achieving comparable or better results with significantly reduced memory cost, a 25× reduction in training time, and over 3000× faster rendering speed. Code is available at: https://***/Fictionarry/DNGaussian. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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