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Development of an Integrated AI-WEB System for the Detection and Prevention of Congestion of the Network Infrastructure of a Higher Education Institution 13th

Development of an Integrated AI-WEB System for the Detection...

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13th International Conference on Distributed, Ambient and Pervasive interactions, DAPI 2025, held as part of the 27th HCI International Conference, HCII 2025

作者： Hernandez Collante, Leonel Hernandez Palma, Hugo Wibawa, Aji Prasetya Orozco Bohorquez, Mario Vergara Sotelo, Mario Antonio Faculty of Engineering University Institution of Barranquilla IUB Barranquilla Colombia Department of Industrial Engineering Corporación Universitaria Latinoamericana CUL Barranquilla Colombia Faculty of Engineering Universitas Negeri Malang Malang Indonesia Department of Computer Science and Electronics Universidad de La Costa CUC Barranquilla Colombia Department of Social and Human Sciences Universidad Simón Bolívar Barranquilla Colombia

ISBN: (纸本)9783031929762

This project addresses a critical challenge in university telecommunications: managing and preventing network congestion. With the exponential increase in digital service usage and connectivity demands, the University Institution of Barranquilla (IUB) recognizes the necessity of proactively addressing potential disruptions that could affect the quality and continuity of educational and administrative services. To tackle this issue, we propose the development of an integrated artificial intelligence and web visualization system (AI-WEB) designed to predict and mitigate network congestion. The research follows a mixed-method approach, combining quantitative analysis of network traffic data with qualitative assessments of user experience and system performance. Central to our methodology is implementing a predictive model based on Long Short-Term Memory (LSTM) recurrent neural networks, specifically chosen for their ability to capture and analyze temporal dependencies in sequential data. This model processes historical network traffic patterns to forecast potential congestion scenarios, enabling proactive management strategies. Complementing the predictive model, an adaptive and interactive web-based interface has been developed to provide real-time visualizations of network conditions, predictive congestion alerts, and actionable insights for network administrators. This dual approach—leveraging AI-driven forecasting and an intuitive decision-support system—enhances the efficiency of network resource allocation and improves overall user experience. Future work will focus on refining the LSTM model through continuous training with real-time data, integrating additional machine learning techniques to improve prediction accuracy, and expanding the AI-WEB system's applicability to broader telecommunication environments. By establishing a scalable and replicable framework, this project aims to contribute to developing intelligent network management solutions that foster a

关键词： Resource allocation

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Evoking Stress Reactivity in a Virtual Dance Competition

Evoking Stress Reactivity in a Virtual Dance Competition

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AHFE Virtual Conference on human Factors and Simulation and the International Conference on Digital human Modeling and Applied Optimization, 2020

作者： van Dammen, Lotte Barnett, Neil Conrady, Roselynn Wright, Lucas Thymes, Bradon Shirtcliff, Elizabeth A. Department of Human Development and Family Studies Iowa State University AmesIA United States Virtual Reality Application Center Iowa State University AmesIA United States Department of Mechanical Engineering San Diego State University San DiegoCA United States Hamilton College Physics Department Hamilton ClintonNY United States Computer Science Department Hamilton ClintonNY United States Electrical Engineering and Computer Science Department Howard University WashingtonDC United States

ISBN: (纸本)9783030510633

Stress reactivity involves a physiological response to a stressful task, as a biological mechanism related to mental and physical health outcomes. Traditional stressors evoke social evaluative threat, which is fear of judgment and a threat to social status, typically via public speaking tasks. To be well-suited for virtual reality, however, a novel approach is necessary in the design of new stress tasks. We hypothesized that a virtual reality stress task involving social evaluative threat through a dance competition in front of an audience, elicits a stress response, measured by autonomic nervous system, cortisol, and testosterone reactivity. Participants (n = 18) showed autonomic nervous system reactivity in terms of increased heart rate and decreased respiratory sinus arrhythmia, indicating a stress response. Levels of cortisol increased in response to the dance competition, especially within responders, whereas testosterone levels did not change significantly over time. A virtual reality dance competition involving physical social evaluative threat elicits a stress response. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtual reality

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Player dominance adjustment in games

arXiv

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arXiv 2021年

作者： Xu, Junjie H. Bachelor Program in Information Science and Engineering Department of Human and Computer Intelligence College of Information Science and Engineering Ritsumeikan University

Video Games are boring when they are too easy, and frustrating when they are too hard. In terms of providing game experience such as enjoyment to the player by match players with different levels of ability to player ability, We assume that implementing DDA for providing matches between player ability and overall game difficulty to the game, especially the modern game, has limitations in terms of increasing computational cost and complexities in the design of modeling the difficulty in modern games. To overcome limitations underlying the method of providing static difficulty changes to player, and DDA, we proposed a novel idea, "Player Domination adjustment (PDA)." The proposed idea is that to control the AI’s actions based on the player’s inputs so as to adjust the player’s dominant power (e.g. the AI recognizes the player’s attack actions but defends it in a wrong side to let the player incur damage to itself), which was proved as it leads to promotion of game-related self-efficacy in our work. Several pieces of research on were conducted on a social deduction game and a fighting game respectively, show our proposed idea has its potential of promoting User Experience(UX). As in an another study, outperforms DDA in two conducted experiments in terms of health promotion. PDA also suitable in many cases of promoting the health of the player by increasing health metrics of the player such as balancedness (Bal, a term of in use of body parts in body movement). The mechanism is AIs or the whole game environment that selects next actions of AIs/ changes based on player’s health metrics analyzed from his/her input action before such action is executed. With this mechanism we can stealthy encourage players performing the motions considering more healthy. All experimental results demonstrate that PDA is capable of entertaining player, besides, benefits to the design of serious game which uses for health promotion. There is a promising direction for future research in PDA su

关键词： Health

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Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces

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IEEE/CAA Journal of Automatica Sinica 2021年第11期8卷 1817-1826页

作者： Gaofeng Li Dezhen Song Lei Sun Shan Xu Hongpeng Wang Jingtai Liu Nankai University Tianjin 300350China Humanoids and Human Centered Mechatronics Research Line Italian Institute of Technology16163 GenovaItaly Department of Computer Science and Engineering Texas A&M UniversityCollege StationTexas 77843 USA Institute of Robotics and Automatic Information System Nankai UniversityTianjin 300350China Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China

To balance the contradiction between higher flexibility and heavier load bearing capacity,we present a novel deformable manipulator which is composed of active rigid joints and deformable *** deformable link is composed of passive spherical joints with preload forces between socket-ball *** estimate the load bearing capacity of a deformable link,we present a static force-based model of spherical joint with preload force and analyze the static force propagation in the deformable *** yields an important result that the load bearing capacity of a spherical joint only depends on its radius,preload force,and static friction *** further develop a parameter estimation method to estimate the product of preload force and static friction *** experimental results validate our model.80.4%of percentage errors on the maximum payload mass prediction are below 15%.

关键词： Deformable link parameters identification spherical joint with preload force static force-based model

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MLM-Net: Streamlined Multi-Lane Detection Network with Spatio-temporal Memory for Video Instance Lane Detection

MLM-Net: Streamlined Multi-Lane Detection Network with Spati...

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International Conference on Intelligent Transportation

作者： Xiaoqin Wang Yunfei Yin Faliang Huang Xianjian Bao School of Mechanical and Vehicle Engineering Chongqing University Chongqing P. R. China School of Computer Science Chongqing University Chongqing P. R. China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning P. R. China School of Maharishi University of Management Fairfield IOWA USA

Lane detection is a crucial task in autonomous driving as it helps vehicles maintain stable and efficient driving in complex traffic environments. Image-based and video-based lane detection are two common methods, with video-based lane detection being more effective in handling complex scenarios such as lane occlusion and wear. However, most existing video-based lane detection methods suffer from information redundancy, where the same key frames of the same lane are repeatedly calculated during the detection of multiple lanes. Additionally, the spatio-temporal sequence information of video-based lanes is not fully utilized. To address these challenges, MLM-Net is proposed as a streamlined multilane network that facilitates the sharing of spatiotemporal information. The Global Memory Feature (GMF) module is redesigned to mitigate information redundancy across multiple lanes, while the Local Spatio-Temporal Feature (LSTF) module is introduced to enhance the utilization of spatiotemporal consistency information. Additionally, the Enhanced Global Memory Feature (EGMF) module is introduced to extract enhanced features and handle challenging detection scenarios. Experimental results demonstrate that our method achieves state-of-the-art performance for video instance lane detection on the VIL-100 dataset.

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Online Deep Squat Evaluation: Leveraging Subject-Specific Adaptation and Information Retention

Online Deep Squat Evaluation: Leveraging Subject-Specific Ad...

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Consumer Life Tech (ICLT), IEEE

作者： Sara Sardari Bahareh Nakisa Sara Sharifzadeh Alireza Daneshkhah Seng W. Loke Michael J. Duncan Matteo Crotti Vasile Palade Research Centre for Computational Science and Mathematical Modelling Coventry University Coventry UK School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Geelong Vic Australia Department of Computer Science Swansea University Swansea UK School of Mathematics and Data Science Emirates Aviation University Dubai UAE Centre for Sport Exercise and Life Sciences Coventry University Coventry UK Department of Human and Social Sciences University of Bergamo Bergamo Italy

ISBN: (数字)9798331519339

ISBN: (纸本)9798331519346

Evaluating deep squats accurately during automatic physical rehabilitation monitoring across different subjects remains challenging due to inter-subject variability and limited labelled data. The challenges include: 1) conventional methods presuppose that a "one-model-fits-all" approach works for activity evaluation, ignoring that subject-specific differences can lead to suboptimal results if these differences are not considered. 2) Previous studies focus on offline learning, where models are trained on the entire dataset, which can be updated later through retraining. This approach neglects the need for continual learning, where models adapt sequentially to new subjects while retaining past knowledge to prevent catastrophic forgetting. This study addresses these challenges by proposing a novel continual meta-learning approach and a memory buffer to provide personalized deep squat evaluations. Using Azure Kinect sensors, we collected RGB-D videos and 3D skeletal data from 33 participants performing deep squats, annotated with Functional Movement Screen (FMS) scores. Our model dynamically adapts to new participants while retaining knowledge from previous ones, preventing performance degradation over time. Experimental results demonstrate that our approach outperforms a model without a buffer memory technique by retaining learned knowledge across participants and adapting to new individuals with minimal data.

关键词： Metalearning Continuing education Adaptation models Three-dimensional displays Frequency modulation Skeleton Sensors Quality assessment Monitoring Videos

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An Adaptive Bionic Sensor: Enhancing Ankle Joint Tracking with High Sensitivity and Superior Cushioning Performance

SSRN

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SSRN 2024年

作者： Jin, Jianqiao Zhang, Chen Zhao, Jianyuan Yu, Minghan Lei, Ming Jin, Chun Yin, Rui Zhao, Weiwei Sauvage Laboratory for Smart Materials The School of Integrated Circuit Harbin Institute of Technology Shenzhen518055 China Shenzhen Key Laboratory of Flexible Printed Electronics Technology Harbin Institute of Technology Shenzhen518055 China State Key Laboratory of Advanced Welding & Joining Harbin Institute of Technology Harbin150001 China Human-Computer Interaction Design Lab School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

Flexible sensors are renowned for their rapid responsiveness, high flexibility, and outstanding mechanical properties, making them ideal for applications in wearable devices, sports and health monitoring, and human-machine interfaces. To enhance the sensing performance of these flexible sensors, researchers have drawn inspiration from nature, creating various bionic microstructures. However, these structures often manifest in the macroscopic form of thin films, which do little to improve impact resistance and joint protection. In response, this work achieves a highly sensitive and impact-resistant pressure sensor by integrating bionic principles at both micro and macro levels. Specifically, at the micro level, a muscle-like hydrogel system consisting of MXene nanosheets incorporated into the double network of polyvinyl alcohol and sodium alginate matrix is selected. At the macro level, the bionic prototypes of mimosa leaves and durian spikes with good energy absorption structure, together with octopus’ sucker with good adsorption capacity are selected, and the synergistic construction of the three structures is realized through 3D printing methods. The obtained sensor has an appropriate response range, sensitivity and stability. More importantly, the displacement deformation of the hydrogel can be reduced by 91.22%, while its adhesion strength can be increased by 57.5% compared to the unstructured hydrogel. As a proof-of-concept, the proposed bionic flexible sensor can be used to monitor the motion status of the human ankle joint in real-time, thereby assisting users in making real-time judgments on gait health. © 2024, The Authors. All rights reserved.

关键词： Sodium alginate

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Gesture interaction in virtual reality

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Virtual Reality & Intelligent Hardware 2019年第1期1卷 84-112页

作者： Yang LI Jin HUANG Feng TIAN Hong-An WANG Guo-Zhong DAI Beijing Key Laboratory of Human-Computer Interaction Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Chinese Academy of SciencesBeijing 100190China

With the development of virtual reality(VR)and human-computer interaction technology,how to use natural and efficient interaction methods in the virtual environment has become a hot topic of *** is one of the most important communication methods of human beings,which can effectively express users'*** the past few decades,gesture-based interaction has made significant *** article focuses on the gesture interaction technology and discusses the definition and classification of gestures,input devices for gesture interaction,and gesture interaction recognition *** application of gesture interaction technology in virtual reality is studied,the existing problems in the current gesture interaction are summarized,and the future development is prospected.

关键词： Virtual reality Gesture interaction Gesture recognition

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Future intelligent autonomous robots, ethical by design. Learning from autonomous cars ethics

arXiv

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arXiv 2021年

作者： Dodig-Crnknovic, Gordana Holstein, Tobias Pelliccione, Patrizio Interaction Design Unit Department of Computer Science and Engineering Chalmers | University of Technology Gothenburg Sweden Division of Computer Science and Software Engineering School of Innovation Design and Engineering Mälardalen University Västerås Sweden Software Engineering Division Department of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden Department of Information Engineering Computer Science and Mathematics University of L'Aquila L'Aquila Italy

Development of the intelligent autonomous robot technology presupposes its anticipated beneficial effect on the individuals and societies. In the case of such disruptive emergent technology, not only questions of how to build, but also why to build and with what consequences are important. Thus, questions of values, good and bad, right and wrong, must be addressed in the context of the development, implementation, testing, use and disposal of technology. The field of ethics of intelligent autonomous robotic cars is a good example of research with actionable practical value, where a variety of stakeholders, including the legal system and other societal and governmental actors, as well as companies and businesses, collaborate bringing about shared view of ethics and societal aspects of technology. It could be used as a starting platform for the approaches to intelligent autonomous robot development considering human-machine interfaces in different phases of the life cycle of technology. Drawing from our work on ethics of autonomous intelligent robocars, and the existing literature on ethics of robotics, our contribution consists of a set of values and ethical principles with identified challenges and proposed approaches for meeting them. This may help stakeholders in the field of intelligent autonomous robotics to connect ethical principles with their applications. Our recommendations of ethical requirements for autonomous cars can be used for other types of intelligent autonomous robots, with the caveat for social robots that require more research regarding interactions with the users. We emphasize that existing ethical frameworks need to be applied in a context-sensitive way, by assessments in interdisciplinary, multi-competent teams through multi-criteria analysis. Furthermore, we argue for the need of a continuous development of ethical principles, guidelines, and regulations, informed by the progress of technologies and involving relevant stakeholders. Copyright

关键词： Intelligent robots

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A Step-by-Step Training Method for Multi Generator Gans with Application to Anomaly Detection and Cybersecurity

SSRN

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SSRN 2022年

作者： Adiban, Mohammad Siniscalchi, Sabato Marco Salvi, Giampiero Department of Electronic Systems NTNU Trondheim Norway Department of Human Centred Computing Monash University Melbourne Australia KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden

Anomaly and cyberattack detection are problems where the data is often highly unbalanced towards normal observations. Furthermore, the anomalies observed in real applications may be significantly different from the ones contained in the training data. It is, therefore, desirable to study methods that are able to detect anomalies only based on the distribution of the normal data. To address this problem, we propose a novel objective function for generative adversarial networks (GANs), referred to as STEP-GAN. STEP-GAN simulates the distribution of possible anomalies by learning a modified version of the distribution of the task-specific normal data. It leverages multiple generators in a step-by-step interaction with a discriminator in order to capture different modes in the data distribution. The discriminator is optimized to distinguish not only between normal data and anomalies but also between the different generators, thus encouraging each generator to model a different mode in the distribution. This reduces the well-known mode collapse problem in GAN models considerably. We tested our method in the areas of power systems and network traffic control systems (NTCSs) using two publicly available highly imbalanced datasets, ICS (Industrial Control System) security dataset and UNSW-NB15, respectively. In both application domains, STEP-GAN outperforms the state-of-the-art systems as well as the two baseline systems we implemented as a comparison. © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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