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12th Nordic Conference on Human-computer Interaction: Participative Computing for Sustainable Futures, NordiCHI 2022

作者： Finne, Rebecca Larsson, Lisa Mylonopoulou, Vasiliki Andreasson, Sebastian Hjelm, Tove Rost, Mattias Weilenmann, Alexandra Torgersson, Olof Chalmers University of Technology Sweden Department of Applied Information Technology University of Gothenburg Sweden Department of Computer Science and Engineering Chalmers University of Technology Sweden University of Gothenburg Sweden

ISBN: (纸本)9781450396998

The personal computer (PC) has been around for more than 35 years by now. Today, we find early adopters of the PC who have been using computers at home for 25 or 30 years and are now themselves in their eighties or nineties. Despite this there is still a lot of research focusing on how to introduce and teach the use of information technology to older people. In this paper we argue that it is time for a shift to designing for digital seniors, i.e., older long-time computer users. Over time this will be the dominating user group and we need to design for continued use of IT rather than guiding older computer novices. The paper also presents the concept Gracefully adaptive user interfaces and provides a case study in the form of a prototype re-design of Facebook aimed at exploring and illustrating how designing for digital seniors can be approached. © 2022 Owner/Author.

关键词： Personal computers

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A Classifier to Detect Stream Applications Based on Network Traffic Statistical Metrics 23

A Classifier to Detect Stream Applications Based on Network ...

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23rd International Arab Conference on Information technology, ACIT 2022

作者： Darwish, Omar Herzallah, Sarah Maabreh, Majdi Al-Eidi, Shouroq Al-Maani, Mahmoud Tashtoush, Yahya Eastern Michigan University Information Security and Applied Computing YpsilantiMI United States Eastern Michigan University Electrical and Computer Engineering YpsilantiMI United States The Hashemite University Department of Information Technology Zarqa Jordan Tafila Technical University Computer Science Department Tafila Jordan Jordan University of Science and Technology Department of Computer Science Irbid Jordan

ISBN: (纸本)9798350320244

One of the most crucial considerations, when considering security vulnerabilities, is network traffic. There is still potential for more research on the inter-arrival time side, even though some studies concentrate on network traffic from the perspective of the packet fields such as packet length and packet number. Inter-arrival timings are crucial to investigate because there are numerous attacks, such as Covert Timing Channels attacks, that heavily rely on them. In this article, we conduct a statistical analysis of the TCP inter-arrival times of two major key streaming programs (Zoom and Skype), which are frequently used, particularly during and following the COVID-19 pandemic. Using two internet-connected devices and the statistical measures of TCP, a dataset of 18,371 instances is created for this use. Five machine learning algorithms are evaluated on balanced and imbalanced forms of the dataset. The results revealed that the traffic of Zoom and Skype calls can be identified by machine learning algorithms with an accuracy of up to 99% by random forest. © 2022 IEEE.

关键词： Transmission control protocol

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Analysis of Viral and Non-viral Categories of Video Through Social Media Using Machine Learning Techniques 15th

Analysis of Viral and Non-viral Categories of Video Through ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Joseph, P. Rose Bindu Amrita Vishwa Vidyapeetham Mysuru Campus Mysuru570026 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9783031889912

People frequently use technological means to collect information from many sources, share it with others on social media, and communicate with friends on these platforms. Social media is therefore a crucial new tool for communication. Collaborative wikis, blogs, and content communities are just a few of the tools and technologies that make up social media. YouTube is the most widely used of these. The influencer’s content gets visible to the users of such platforms. The expansion of social media use has enabled massive and rapid spread ability of content, often called virality. This paper aims to examine the virality of different categories of YouTube videos using machine learning technique, where it plays a major role in analysis of YouTube data such as likes, dislikes, comment count and views. YouTube can serve as a path to virality and fabricate online videos, hence finding the correlation analysis among the videos, which enables us to classify the viral and non-viral video categories, thereby analysing the linear relationship among the features (likes, dislike, views and comment count. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, and Challenges

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IEEE Communications Surveys and Tutorials 2025年

作者： Satpathy, Anurag Sahoo, Manmath Narayan Swain, Chittaranjan Bellavista, Paolo Guizani, Mohsen Muhammad, Khan Bakshi, Sambit Missouri University of Science and Technology Department of Computer Science RollaMO United States National Institute of Technology Visual Surveillance Laboratory Department of Computer Science and Engineering Odisha Rourkela769008 India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India Department of Computer Science and Engineering Italy Machine Learning Department Abu Dhabi United Arab Emirates Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In addition, this survey originally contributes to the literature by proposing a novel taxonomy for existing VNE solutions and providing a thorough comparative study of their strategies. © 1998-2012 IEEE.

关键词： Virtual machine

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Effective Prediction of Energy Consumption in Automated Guided Vehicles with Recurrent and Convolutional Neural Networks

Effective Prediction of Energy Consumption in Automated Guid...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Benecki, Pawel Kostrzewa, Daniel Grzesik, Piotr Shubyn, Bohdan Syu, Jia-Hao Lin, Jerry Chun-Wei Sunderam, Vaidy Mrozek, Dariusz Silesian University of Technology Department of Applied Informatics Gliwice Poland Lviv Polytechnic National University Department of Telecommunications Lviv Ukraine National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Emory University Department of Computer Science AtlantaGA30322 United States

ISBN: (纸本)9798350324457

Detection and prediction of failures in Automated Guided Vehicles (AGV) are essential for the uninterrupted operation of production plants. Anomaly detection is usually achieved by comparing expected measurement values with actual observations. Thus, it is crucial to predict telemetry signals properly. In this paper, we research the prediction of energy consumption using state-of-the-art Artificial Neural Networks architectures (SCINet) compared with other Recurrent Neural Network (RNN) approaches on the data streams acquired from CoBotAGV. We especially focus on the possibility of applying feature weighting. We show that it can improve prediction capabilities. We also investigate resource utilization in terms of time to fit the embedded AGV environment. © 2023 IEEE.

关键词： anomaly detection automated guided vehicles feature weighting Industry 4.0 time series prediction

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Exploration of Ripe Raw and Defective Tomatoes with Deep Learning Techniques 2

Exploration of Ripe Raw and Defective Tomatoes with Deep Lea...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Vanitha, C.N. Sathyamoorthy, Malathy Dhanaraj, Rajesh Kumar Rai, Priti Geetha, V. Manikantan, M. Kongu Engineering College Science and Engineering Department of Computer Erode India Kpr Institute of Engineering and Technology Department of Information Technology Coimbatore India Pune India Gl Bajaj Institute of Management Greater Noida India Reva University School of Applied Sciences Department of Computer Science Bangalore India Kongu Engineering College Dept. of CT-UG Perundurai India

ISBN: (纸本)9798350325799

Due to great need of agricultural operations, there was a shortage of workers during the harvest period. This results in a bottleneck in the operations of agricultural organizations. One of the most common problems in these sectors is the hazardous practice of farming. This type of farming operation makes vegetables very costly and unattractive. The reduction of costs is controlled by the need for labour. This issue is solved by adapting to new technology and compensating the labour deficiency. The algorithms that are processed in this method are Convolutional Neural Network (CNN), Object Detection and Generative Adversarial Network (GAN). These algorithms will enable the model to recognise faulty tomatoes. This model accurately identifies raw, ripe and defective tomatoes using deep learning methods and it is highly helpful for the betterment of agriculture. © 2023 IEEE.

关键词： Object detection

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Evaluating the Performance of Subjective Weighting Methods for Multi-Criteria Decision-Making using a novel Weights Similarity Coefficient 27

Evaluating the Performance of Subjective Weighting Methods f...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Shekhovtsov, Andrii Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

In every decision-making problem which involves two or more criteria, there is to identify the relative importance of those criteria in order to make a proper decision. Very often, a decision-makers employee, for this purpose, subjective weighting methods, such as Analytic Hierarchy Process (AHP) or RANking COMparison (RANCOM). However, there is no simple way to compare the quality of the weights identification using different methods. To address this issue, this paper proposes a simple but efficient Weights Similarity Coefficient, which allows us to evaluate the relative performance of different subjective weighting methods. Furthermore, we propose a framework that utilizes the proposed coefficient in order to provide more complete comparison results. To demonstrate the applicability and effectiveness of the proposed framework, a case study is performed to compare two popular weighting methods: AHP and RANCOM. The results of the comparison prove that proposed coefficient with the combination of the proposed framework is an efficient instrument for weighing methods comparison. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： AHP Criteria weights Manhattan distance MCDA RANCOM SPOTIS

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Lightweight human pose estimation based on high-resolution network 3

Lightweight human pose estimation based on high-resolution n...

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3rd International Conference on Machine Learning and computer Application, ICMLCA 2022

作者： Yang, Zhiwen Yang, Ruan Yang, Yunong College of Computer and Information Science Chongqing Normal University Chongqing China College of Education Science Chongqing Normal University Chongqing China Digital Education Engineering Center for Technology and Applied Research Chongqing Normal University Chongqing China

ISBN: (纸本)9781510664814

Most of the existing methods of human pose estimation methods focus on improving the accuracy of prediction results, but due to network parameters and high computational complexity, a great deal of computing resources are needed. In this paper, a lightweight human pose estimation method based on high-resolution network is proposed. The bottleneck module and the basic module in the high-resolution network are redesigned by using the depth separable convolution instead of the ordinary convolution and integrating attention mechanism, which ensures the accuracy of the network and greatly reduces the number of parameters and computational complexity of the model. Experimental results on the COCO VAL2017 dataset show an 84.5% reduction in the number of model parameters, a 73.9%% reduction in computational complexity, and 0.2% increase in the accuracy of human keypoint detection compared to the high-resolution network. © 2023 SPIE.

关键词： Convolution

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Ensemble of Fully Convolutional Neural Networks with End-to-End Learning for Small Object Semantic Segmentation 1

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10th International Conference on Robot Intelligence technology and Applications, RiTA 2022

作者： Lam, Ken Lun Abdullah, Azizi Albashish, Dheeb Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bandar Baru Bangi43600 Malaysia Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

ISBN: (数字)9783031268892

ISBN: (纸本)9783031268885

Small object segmentation is important due to its vast application potential for biomedical and surveillance. This paper describes several ensemble methods that combine different fully convolutional neural networks (FCNNs) for accurate semantic segmentation of aerial images. One problem with a single model for accurate small object segmentation is that the small object classes are suppressed by large object classes, which can increase overfitting problems and hinder generalization performance. Furthermore, using the single model for segmentation has particular strengths and weaknesses, and the segmentation accuracy changes with the different features used for training. Thus, this paper describes several ensemble methods that combine multiple fully convolutional networks for accurate semantic segmentation of unmanned aerial vehicles (UAVs) images. The proposed method starts with performing object localization for each single FCNN model. After that, each model’s channels of feature map outputs are combined to guide the training of the ensemble segmentation network. We have performed experiments on UAVid benchmark dataset. The results show that the proposed ensemble approach outperforms the single model that directly uses the channels of the single model’s feature map outputs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Comparison of multi-criteria decision methods for customer-centered decision making: A practical study case 27

Comparison of multi-criteria decision methods for customer-c...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Shekhovtsov, Andrii Dobryakova, Larisa Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

In this paper, we show the practical application of two recently proposed Multi-Criteria Decision Analysis (MCDA) methods, namely the Stable Preference Ordering Towards the Ideal Solution (SPOTIS) and the Reference Ideal Method (RIM) methods. Both of these methods can utilize the Expected Solution Point (ESP) concept, which allows it to easily reflect the decision-maker's or the customer's preferences and expectations. We show the comparison of those methods in the practical study case of laptop choice for the customer with specific needs and expectations for the hardware expressed using ESP. The comparison also includes rankings built toward an optimal solution to underline the necessity of such an approach as the Expected Solution Point. We also use the recently developed Ranking Comparison (RANCOM) method to identify criteria weights to include customer preferences in the weights. The paper also contains a broad discussion of the obtained results and limitations of the presented methods. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： ESP MCDA RANCOM RIM SPOTIS Target-based decision-making

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