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7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Ganeshan, M. Rajesh, A. Department of Computer Science and Engineering Jain Deemed to Be University Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Jain University Bangalore India

ISBN: (纸本)9789819907687

Serverless computing has emerged as a promising research subject in computer science as it grows in favor among developers because of its low cost and elasticity. However, one primary concern that has cropped up in recent months with serverless computing is vendor lock-in: It can be hard to port to another vendor's platform without considerable effort and cost. Serverless on AWS is a new way to build cloud-native IoT systems that are highly performant, highly resilient, and low maintenance. We'll implement exciting design concepts for serverless systems on AWS in this paper using the open-source OpenWhisk software. To provide low-cost and maintenance-free IoT services, traditional cloud service providers offer platforms that are progressively migrating toward the serverless approach. OpenWhisk takes care of the infrastructure, servers, and scaling with Docker and containers. Because Apache Open Whisk’s components are built using containers, it provides a wide range of deployment options in cloud environments. Many of today’s prominent Container frameworks such as Kubernetes are available as options. The Kubernetes framework is a well-known open-source container-orchestration system widely used in industrial and academic fields. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Containers

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Multidimensional image morphing-fast image-based rendering of open 3D and VR environments

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 155-172页

作者： Simon SEIBT Bastian KUTH Bartosz von Rymon LIPINSKI Thomas CHANG Marc Erich LATOSCHIK Game Tech Laboratory Faculty of Computer ScienceNuremberg Institute of TechnologyNuremberg 90489Germany Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and ArtsCoburg 96450Germany Human-Computer Interaction Group Institute of Computer ScienceUniversity of WuerzburgWuerzburg 97074Germany

Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and ***,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains *** This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and ***,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image *** this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of *** rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image *** detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in *** addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light *** Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,*** achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering *** Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR ***,the handling of morphing artifacts in the parallax image regions remains a topic for future resea

关键词： computer graphics 3D real-time rendering computer vision Image morphing Virtual reality

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Robustness of generative AI detection: adversarial attacks on black-box neural text detectors

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International Journal of Speech Technology 2024年第4期27卷 861-874页

作者： Fishchuk, Vitalii Braun, Daniel Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of High-tech Business and Entrepreneurship University of Twente Enschede Netherlands

The increased quality and human-likeness of AI generated texts has resulted in a rising demand for neural text detectors, i.e. software that is able to detect whether a text was written by a human or generated by an AI. Such tools are often used in contexts where the use of AI is restricted or completely prohibited, e.g. in educational contexts. It is, therefore, important for the effectiveness of such tools that they are robust towards deliberate attempts to hide the fact that a text was generated by an AI. In this article, we investigate a broad range of adversarial attacks in English texts with six different neural text detectors, including commercial and research tools. While the results show that no detector is completely invulnerable to adversarial attacks, the latest generation of commercial detectors proved to be very robust and not significantly influenced by most of the evaluated attack strategies. © The Author(s) 2024.

关键词： Generative adversarial networks

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Monitoring energy losses of a residential building through thermographic assessments 17

Monitoring energy losses of a residential building through t...

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17th International Conference on Development and Application Systems, DAS 2024

作者： Eugen, Iavorschi Laurențiu, Milici Dan Faculty of Electrical Engineering and Computer Science Department of Electrical Engineering "Stefan cel Mare" University of Suceava Suceava Romania

ISBN: (纸本)9798350349290

One of the significant opportunities for reducing energy waste and carbon emissions is the renovation, modernization, and rehabilitation of existing buildings. Replacing the windows of existing residential buildings with new energy-efficient ones is an easy way to upgrade an existing home into a nearly zero-energy building, NZEB (nearly zero-energy building). There is a wide variety of methods to discover the thermal weak points of a building, and the continuous innovation of construction materials provides solutions to maximize energy savings and minimize heat transfer to the exterior. This document presents a methodology for visualizing and recording the temperature distribution on the investigated surface of the building envelope to identify and locate vulnerable points where heat losses to the exterior are substantial. The objective was to highlight the need for upgrading the insulation system and replacing the existing windows of the analyzed building based on a multi-objective approach to maximize the building's energy performance and increase thermal comfort indoors. Using a Fluke Ti300+ Thermal Camera, energy losses were visualized through thermographic assessments, which could provide an adequate picture for accurately assessing the building's condition before renovation, to evaluate the need for partial renovation. For the case study, a residential building with one floor and mansard roof located in a region with hot summers and cold winters in Romania was chosen. The results showed that there are several options for significantly reducing the building's energy consumption. Thus, multiple energy efficiency solutions were proposed for the building to reduce heat losses. ©2024 IEEE.

关键词： Heat transfer

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Social and Non-social Reward Learning Contexts for Detection of Major Depressive Disorder Using EEG: A Machine Learning Approach 16th

Social and Non-social Reward Learning Contexts for Detecti...

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16th International Conference on Brain Informatics, BI 2023

作者： Ghattas, Philopateer Gamal, Mai Eldawlatly, Seif Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt

ISBN: (纸本)9783031430749

Major Depressive Disorder (MDD) is a leading cause of disability globally and a major cause of suicide deaths. Improving our understanding of MDD is expected to inspire better objective diagnostic and treatment tools which may decrease the burden of disease worldwide. MDD is associated with social impairments and reward processing aberrations. However, the interaction between social cognition and reward learning in MDD is not well understood. In this work, we aim to study the effect of integrating social information with reward learning in MDD using an EEG-based machine learning approach. We recorded EEG data from subjects during their participation in a reward learning experiment in social and non-social contexts. We then extracted linear and nonlinear features from the EEG data to detect MDD using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers. Our results show that the data collected during social contexts achieved broadly higher classification accuracies compared to non-social contexts reaching 80% using multi-channels. Moreover, single-channel data achieved comparable and even better accuracies than multi-channels with also superior performance in social contexts reaching 85.7%. In the sub-band classification analysis, beta and alpha bands were considerably better than theta and delta bands. Surprisingly, the non-social context had the highest accuracy in the beta band while the social context had the highest accuracy in the alpha band. The results were consistent when using single features compared to combining the features. These findings show the notable role of social information with reward processing in advancing our understanding of MDD and its subtypes which can guide the improvement of diagnostic and personalized treatment tools. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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Advanced dairy cow monitoring: enhanced detection with precision 3D tracking

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Multimedia Tools and Applications 2025年第18期84卷 19117-19146页

作者： Wang, Ranran Li, Yingxiu Yue, Peng Yuan, Chunhong Tian, Fuyang Lu, Xin College of Mechanical and Electronic Engineering Shandong Agricultural University Shandong Tai’an271018 China Department of Computer Science Faculty of Engineering University of Sheffield SheffieldS1 4DP United Kingdom Faculty of Agriculture Iwate University Iwate Morioka020-8550 Japan Faculty of Science and Engineering Iwate University Iwate Morioka020-8550 Japan

Ensuring the welfare of dairy cows requires precise monitoring of their daily exercise to evaluate their physical health. This necessitates innovative methods beyond traditional motion sensors. We present a novel method that integrates an enhanced YOLOv5s object detection model with the DeepSORT multi-object tracking algorithm to meticulously track dairy cow movements, providing holistic information about their health. Our research started with the establishment of a dedicated dataset tailored for cow detection. We then segmented the detection scope to focus on specific regions of interest. Within the modified YOLOv5s model, we replaced the standard CSPDarknet53 backbone with DenseNet to achieve deep separable convolution and feature reorganization modules, leading to reduced parameters, augmented feature expression, and better information flow. In particular, the SPD-Conv module was incorporated to retain intricate details, essential for detecting smaller and low-resolution targets. The transition from Generalized Intersection over Union (GIoU) Loss to Complete Intersection over Union (CIoU) loss improved detection accuracy and sped up model convergence. Our clustering approach, based on the elbow rule, optimized K-means clustering, enhancing speed and precision. For multi-object tracking, the DeepSORT model was tailored to cater to varying cow sizes, and we chose an algorithm to associate appearance information. We converted pixel data into real-world distances, providing exact 3D cow movement metrics. Experimental validation confirmed the efficacy of our approach. Our enhanced model surpassed the original YOLOv5s in performance by 11.1% for accuracy (97.4%), 9.6% for recall (97.8%), and 11.0% for average accuracy (98.2%). The comprehensive accuracy stood at 92.1% for our model. In conclusion, our innovative methodology offers a non-invasive means to monitor dairy cow exercise, paving the way for advanced health assessment techniques in the dairy sector. © The Aut

关键词： K-means clustering

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A Study of Deep Learning Techniques for Predicting Breast Cancer Types

A Study of Deep Learning Techniques for Predicting Breast Ca...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Ashwini, P. Suguna, N. Vadivelan, N. Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Associate Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Professor Department of Computer Science and Engineering Teegala Krishna Reddy Engineering College Hyderabad India

ISBN: (纸本)9789819947164

Breast cancer is today’s deadly health issue which causes high mortality in woman worldwide. The preliminary detection and classification may help for proper treatment of the same. Understanding the causes of this cancer using traditional machine learning techniques misread feature extraction are leading to a complication. Traditional machine learning models are best suited for feature-based raw data. The novel deep learning techniques have been introduced for diagnosis of breast abnormalities using various imaging modalities like mammogram, magnetic resonance imaging (MRI) and ultrasound with high accuracy. This survey focuses on challenges of classical machine learning models and supervene efficient prediction models using novel deep learning techniques. This review presents comparison analysis of traditional machine learning and deep learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Dynamic L-Budget Clustering of Curves 19

Dynamic L-Budget Clustering of Curves

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19th Scandinavian Symposium on Algorithm Theory, SWAT 2024

作者： Buchin, Kevin Buchin, Maike Gudmundsson, Joachim Plätz, Lukas Thiel, Lea Wong, Sampson Faculty of Computer Science TU Dortmund University Germany Faculty of Computer Science Ruhr University Bochum Germany Faculty of Engineering The University of Sydney Australia Department of Computer Science University of Copenhagen Denmark

ISBN: (纸本)9783959773188

A key goal of clustering is data reduction. In center-based clustering of complex objects therefore not only the number of clusters but also the complexity of the centers plays a crucial role. We propose LBudget Clustering as unifying perspective on this task, optimizing the clustering under the constraint that the summed complexity of all centers is at most L. We present algorithms for clustering planar curves under the Fréchet distance, but note that our algorithms more generally apply to objects in metric spaces if a notion of simplification of objects is applicable. A scenario in which data reduction is of particular importance is when the space is limited. Our main result is an efficient (8 + Ε)-approximation algorithm with a (1 + Ε)-resource augmentation that maintains an L-budget clustering under insertion of curves using only O(LΕ−1) space and O∗(L3 log L + L2 log(r∗/r0)) time where O∗ hides factors of Ε−1 © Kevin Buchin, Maike Buchin, Joachim Gudmundsson, Lukas Plätz, Lea Thiel, and Sampson Wong;licensed under Creative Commons License CC-BY 4.0.

关键词： Approximation algorithms

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A Nobel Approach to Identify the Rainfall Prediction Using Deep Convolutional Neural Networks Algorithm 6th

A Nobel Approach to Identify the Rainfall Prediction Using D...

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6th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Desai, Prachi Gandhi, Ankita Acharya, Mitali Department of Computer Science Engineering Faculty of Engineering and Technology Parul University Vadodara India

ISBN: (纸本)9789811935749

It is unlikely to have a big impact on a country's economic *** also helps to reduce the loss of life and property caused by natural *** study of rainfall prediction using machine learning techniques, with a special emphasis on *** India, about 70% of the population is dependent on agriculture and related *** forecasting has been an issue of major technical and economic importance in the agricultural *** rain prediction model is still mostly based on artificial neural networks, and it has only been used in India so *** the present study, a comparative analysis of the two rainfall forecasting techniques, and is more *** today’s technology, the ability to predict the rainfall is not very well-informed about the complexity of the *** methods used are the methods of statistical and mathematical methods that don’t work if there is a situation of a nonlinear *** existing installation may fail when the level of complexity of the information contained in it, increase it in the ***, this is the best way to get to the waiting;in the rain, it is a study of deep learning and neural networks, and genetic algorithms, is that it gives you more precise, it is used to predict the *** order to come up with a good rain is a joint, an appraisal is not *** simplest and easiest approach to get on a larger scale is to use weather *** the present study, it can be used in all types of weather stations, and the forecast will give access to some parts of the country.A technique for predicting month-to-month rain for a specific area in India using a deep CNN learning method as a replacement fortuning *** CNN was compared against an ANN. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rain

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Graphene oxide-coated microbottle resonators for relative humidity sensing

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Optoelectronics Letters 2024年第7期20卷 400-405页

作者： Huda Adnan Zain Malathy Batumalay Md Ashadi Md Johari Hazli Rafis Abdul Rahim Sulaiman Wadi Harun Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Data Science and Information Technology INTI International UniversityNilai 71800Malaysia1 Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia

Graphene oxide(GO)is a 2D coating material used to improve fiber optics sensors’response to relative *** resonators(MBRs)have garnered more attention as sensing media *** MBR with a 190μm diameter was coated with ***,tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25℃.The MBR showed a higher Q factor before and after GO *** sensitivity of 0.115 dB/%RH was recorded with the 190μm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR *** results show that the MBR can be used in fiber optic sensing applications for environmental sensing.

关键词： humidity oxide bottle

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