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11th International Conference on information and Education technology, ICIET 2023

作者： Rodriguez, Ramon Abisado, Mideth Montefalcon, Myron Darrel Rhald Padilla, Jay Serrano, Elcid Nu-Dasmarinas National University College of Computing and Information Technologies Philippines Mapua University School of Information Technology Philippines

ISBN: (纸本)9781665465489

In today's age, the use and access to technology have become essential aspects of every individual. However, digital immigrants, individuals raised before the digital age, need help to cope with this advancement. In this study, the Education technology Office conducted an onsite learning intervention to teach the digital immigrant faculty members about using Microsoft Applications such as Office365, Microsoft Teams, and Microsoft Forms. The faculty members' facial expressions during the intervention were recorded using a web camera. Five consented videos of 1 hour were obtained in the data collection process. The video dataset was processed using the OpenFace toolkit to detect the facial features per frame. Afterward, feature selection and extraction of Action units were applied before fitting to the LSTM model. The LSTM model yielded a recognition accuracy of 97.3% on the validation set. In the analysis of facial expressions based on time, it was observed that as time progressed during the learning intervention, there were not many changes in the emotion of the participants. Also, the most frequent emotion recognized in different timestamps (initial, middle, final) part was 'happy,' which may indicate that the participant has enjoyed the intervention and may suggest that it is effective. While other emotions were also evidently recognized, such as 'sad' and 'surprised', which might indicate the difficulty or challenges encountered by the digital immigrants during the learning intervention. © 2023 IEEE.

关键词： Emotion Recognition

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Water and Energy Management in the Research and Recreation Area of Telkom University through Energy Use Intensity (EUI) 7

Water and Energy Management in the Research and Recreation A...

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7th International Conference on Data Science and Its Applications, ICoDSA 2024

作者： Abdillah, Muhamad Rizqi Yasirandi, Rahmat Nuha, Hilal Hudan School of Computing Telkom University Bandung Indonesia King Mongkut's Institute of Technology Faculty of Information Technology Bangkok Thailand

ISBN: (纸本)9798350365351

Water and energy are important aspects for plants and animals. Agritech can help solve the problem of water and energy use in plants. By using sensors and automatic control technology. Agritech can help to significantly reduce water and energy use and increase the efficiency of water and energy use in plants. Research and Recreation Area (RRA) is one of the priority areas at Telkom University because RRA is part of a green campus that prioritizes efforts to make the campus an environmentally friendly and sustainable environment. RRA already has its own solar panels, fishponds, hydroponics, and greenhouse but still not integrated with each other. The purpose of this research is to implement water and energy demand management in the ecosystem at RRA and conduct an efficiency analysis on the implementation of water and energy demand management in the ecosystem at RRA. The method used in this research is Energy Use Intensity (EUI). EUI is used to analyse energy use based on the area of a building. © 2024 IEEE.

关键词： Energy utilization

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SketchBoard: Sketch-Guided Storyboard Generation for Game Characters in the Game Industry 22

SketchBoard: Sketch-Guided Storyboard Generation for Game Ch...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Chan, Elicia Min Yi Seow, Chee Kiat Tan, Esther Su Wee Wang, Min Yau, Peter Chunyu Cao, Qi School of Computing Science University of Glasgow Singapore School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9798331527471

Research outcomes have been reported to various industrial applications of Artificial Intelligence generated content (AIGC). This paper explores AI assisted creative production of storyboard and game characters in the game industry, by leveraging Stable Diffusion 1.5 coupled with Control Nets (SD-1.5+CN) for image generations and Generative Pretrained Transformer (GPT) of OpenAI for narrative generations. Experiment evaluations are conducted for visual appeal and narrative flows of the generated contents using SketchBoard. The results illustrate SketchBoard with weighted prompts achieving a higher Contrastive Language-Image Pre-Training (CLIP) score than the baseline models. In addition to reducing the time and effort required for manual creation, it obtains strong alignments between the generated images and provided text prompts. User study is conducted where the user feedback indicates a high level of satisfaction, with 92% of participants expressing enjoyment and contentment with SketchBoard. Its ability to generate visually appealing storyboard frames with coherent narratives offers opportunities for efficient storyboard creation, paving the way for future advancements in automated storyboard generation. © 2024 IEEE.

关键词： Game design

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Ozone Concentration Forecasting: Assessing the Efficacy of MLP, DNN, and XGBoost in Environmental Bench-AQ Dataset

Ozone Concentration Forecasting: Assessing the Efficacy of M...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Mohamed Abdul Kader Jailani, N. Mara, Geeta C. Reva University School of Computer Science & Applications Bangalore India Reva University School of Computing & Information Technology Bangalore India

ISBN: (纸本)9798350359688

This study investigates the prediction of tropospheric ozone concentration, a critical aspect of air quality assessment. We utilized machine learning techniques, including Multilayer Perceptron (MLP), Deep Neural Network (DNN), and XGBoost, and analyzed extensive air quality datasets that incorporated ozone-specific metrics and environmental variables. The findings revealed that XGBoost notably excelled over MLP and DNN in predicting average ozone concentrations and Aot40 metrics, as evidenced by its lower Mean Absolute Error (MAE) values. This research highlights the importance of precise feature selection in machine learning and demonstrates the potential of advanced ensemble methods like XGBoost in environmental modeling. It suggests the need for future exploration in integrating a broader range of environmental variables and applying these findings in real-world scenarios, especially in urban planning and public health. © 2024 IEEE.

关键词： Multilayer neural networks

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RoboMP2: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models 41

RoboMP2: A Robotic Multimodal Perception-Planning Framework ...

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41st International Conference on Machine Learning, ICML 2024

作者： Lv, Qi Li, Hao Deng, Xiang Shao, Rui Wang, Michael Yu Nie, Liqiang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Engineering Great Bay University China School of Computing and Information Technology Great Bay University China

Multimodal Large Language Models (MLLMs) have shown impressive reasoning abilities and general intelligence in various domains. It inspires researchers to train end-to-end MLLMs or utilize large models to generate policies with human-selected prompts for embodied agents. However, these methods exhibit limited generalization capabilities on unseen tasks or scenarios, and overlook the multimodal environment information which is critical for robots to make decisions. In this paper, we introduce a novel Robotic Multimodal Perception-Planning (RoboMP2) framework for robotic manipulation which consists of a Goal-Conditioned Multimodal Preceptor (GCMP) and a Retrieval-Augmented Multimodal Planner (RAMP). Specially, GCMP captures environment states by employing a tailored MLLMs for embodied agents with the abilities of semantic reasoning and localization. RAMP utilizes coarse-to-fine retrieval method to find the k most-relevant policies as in-context demonstrations to enhance the planner. Extensive experiments demonstrate the superiority of RoboMP2 on both VIMA benchmark and real-world tasks, with around 10% improvement over the baselines. Copyright 2024 by the author(s)

关键词： Semantics

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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Velocity control of a Stephenson III six-bar linkage-based gait rehabilitation robot using deep reinforcement learning

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Neural computing and Applications 2025年第7期37卷 5671-5682页

作者： Kapsalyamov, Akim Brown, Nicholas A. T. Goecke, Roland Jamwal, Prashant K. Hussain, Shahid School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Clinical Sciences Queensland University of Technology BrisbaneQLD Australia School of Systems and Computing UNSW CanberraACT Australia Department of Electrical and Computer Engineering Nazarbayev University Nur-Sultan City Kazakhstan

Lower limb rehabilitation robots can help to improve the locomotor capabilities of patients experiencing gait impairments and help medical workers by reducing strain on them. However, since commercially available exoskeletons are expensive and there is a lack of number of physiotherapists many patients are still not able to get proper rehabilitation training. The closed-loop linkage mechanisms have recently drawn much attention in the realization of gait rehabilitation robots. Such mechanisms are affordable and capable of providing suitable trajectories for gait training therapy. In this work, we have proposed a fully operational one degree-of-freedom mechanism which can generate complex naturalistic lower limb trajectories. Although in theory, it is assumed that the constant speed applied at the input crank is sufficient to control the system, in reality, the external forces exerted by human legs and the inertia of the links can greatly alter the rotational velocity at the crank, which may negatively affect the training process. Therefore, we have explored the performance of a deep reinforcement learning-based control algorithm designed to regulate the speed of the input crank to reach satisfactory performance needed for gait rehabilitation training. Experimental evaluations with healthy human subjects were conducted to demonstrate that the mechanism is capable of directing lower limbs on naturalistic gait trajectories with a required walking speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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Computer Systems Science & Engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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A comprehensive Study on Security Threats in Autonomous Vehicles: Safeguarding the Future 1

A comprehensive Study on Security Threats in Autonomous Vehi...

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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Poddar, Srisha Deepak Murali, M. Raj Asekaran, P. Prabakaran, N. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Sona College of Technology Department of Information Technology Salem India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chengalpattu603203 India

ISBN: (纸本)9798350361780

Autonomous vehicles, commonly referred to as self- driving vehicles, have the capability to operate and perform necessary functions without the need for human intervention. This is made possible with advanced technologies such as cameras, ultrasonic and infrared sensors, INS, GPS, DSRC, RADAR, LiDAR, and onboard computers, which work together to map the vehicle's position and surroundings. However, because these technologies are easily susceptible to manipulation, autonomous vehicles are at risk of cyber-attacks if hackers succeed to find vulnerabilities in the vehicle's system or in the electronic system of the company producing the vehicle. This study aims to investigate how machine learning and deep learning methods can be utilized to improve the defence against cyber threats aimed at autonomous vehicles by analysing several computational algorithms. © 2024 IEEE.

关键词： Autonomous vehicles

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