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arXiv 2024年

作者： Zhang, Wenxiao Kong, Xiangrui Braunl, Thomas Hong, Jin B. Dept. of Computer Science and Software Engineering The University of Western Australia Perth Australia Dept. of Electrical Electronic and Computer Engineering The University of Western Australia Perth Australia

Embodied AI systems, including AI-powered robots that autonomously interact with the physical world, stand to be significantly advanced by Large Language Models (LLMs), which enable robots to better understand complex language commands and perform advanced tasks with enhanced comprehension and adaptability, highlighting their potential to improve embodied AI capabilities. However, this advancement also introduces safety challenges, particularly in robotic navigation tasks. Improper safety management can lead to failures in complex environments and make the system vulnerable to malicious command injections, resulting in unsafe behaviours such as detours or collisions. To address these issues, we propose SafeEmbodAI, a safety framework for integrating mobile robots into embodied AI systems. SafeEmbodAI incorporates secure prompting, state management, and safety validation mechanisms to secure and assist LLMs in reasoning through multi-modal data and validating responses. We designed a metric to evaluate mission-oriented exploration, and evaluations in simulated environments demonstrate that our framework effectively mitigates threats from malicious commands and improves performance in various environment settings, ensuring the safety of embodied AI systems. Notably, In complex environments with mixed obstacles, our method demonstrates a significant performance increase of 267% compared to the baseline in attack scenarios, highlighting its robustness in challenging conditions. © 2024, CC BY.

关键词： Mobile robots

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Enhancing Women's Safety Through Customized Smartphone Sensor Data: A Human Activity Recognition System 9

Enhancing Women's Safety Through Customized Smartphone Senso...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Islam, Yamina Nowrin, Sanjida Faqih, Majed Ibrahim Sultana, Fozia Bahadur, Erfanul Hoque Masum, Abdul Kadar International Islamic University Chittagong Dept. of Computer Science and Engineering Chattogram Bangladesh Signal Corps Institute Director of Research and Development Saudi Arabia Hiperdyne Artificial Intelligence Engineer Tokyo Japan Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350319651

Human activity recognition (HAR) research using smartphone sensor technology has changed people's daily lives and opened the door for numerous exciting applications due to its extensive applications in addressing real-world humancentric issues. These studies, however, mostly concentrated on a woman's daily activities and behaviours when she is attacked, such as running, walking, sleeping, eating, drinking, lying, straight punching, front kicking, and so forth. The article offers a method to foresee human behaviour using a deep learning model and evaluates the efficiency of the approach using real-world data in response to this. We offer an LSTM structure for detecting the intensity of HAR in order to track activity patterns and build a neural network with binary classification skills to evaluate the situation of women. The proposed model uses the dataset to identify human activity, which records 30 volunteers' body movements while they engage in 18 physical activities. Feature engineering is used to extract additional variables from the data in addition to that. Our proposed model achieves a 90.89% accuracy. © 2023 IEEE.

关键词： Smartphones

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Comparing Emotion Detection Methods in Online Classrooms: YOLO Models, Multimodal LLM, and Human Baseline

Comparing Emotion Detection Methods in Online Classrooms: YO...

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IEEE Education engineering (EDUCON)

作者： Medha Mohan Ambali Parambil Salah Bouktif Munkhjargal Gochoo Fady Alnajjar Dept. Computer Science and Software Engineering United Arab Emirates University Al Ain UAE

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

The COVID-19 pandemic has transformed learning environments, challenging educators to understand students' behaviors during the online mode of learning, in particular emotions associated with students' attention during virtual classrooms. As learning transitions between physical and virtual spaces, the ability to interpret student attention and engagement has become complex. In response to this challenge, our research investigates the use of GPT-4o, a multimodal large language model, for identifying student emotions by analyzing images in diverse learning settings. The study involved analyzing online classroom images featuring 149 faces, utilizing three distinct approaches: a computer vision model (YOLO), the multimodal LLM (GPT-4o), and a human-annotated baseline. The analysis systematically categorized facial expressions into eight emotional categories: Happy, Sad, Angry, Neutral, Contempt, Disgust, Fear, and Surprise. The findings indicate that multimodal LLMs can effectively detect student emotions, achieving an average accuracy of 93.8%, which aligns with the human baseline accuracy of 97.0%. In contrast, YOLO models maintained an average accuracy of 81.9%, performing well for basic emotions but struggling with subtle expressions. This research contributes to enhancing educational practices by providing valuable insights regarding the application of multimodal LLMs to assist educators in comprehending student emotions within both physical and digital classroom settings.

关键词： YOLO Emotion recognition Privacy Accuracy Annotations Pandemics Scalability Large language models Real-time systems Faces

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Optimized Crop Detection Using IoT and Deep Neural Networks 5

Optimized Crop Detection Using IoT and Deep Neural Networks

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Choudhury, Tanupriya Rohini, A. Mahdi, Hussain Falih Uttarakhnad Dehradun248007 India Graphic Era Hill University Dehradun248002 India Dept. of Computer Science & Engg Visakhapatnam531162 India University of Diyala Department of Computer and Software Engineering Baquba Iraq

ISBN: (纸本)9798350337525

The advancement of agriculture in food production is an increasing concern. It is one of the major challenges in contemporary farming. The development methods of soil farming have increased the fabrication of crops and food. It emphasizes the vital role of sustainability in soil. Different technologies were used to grow and flourish in soil to supply the nutrition's. It buffers and delicate the plant roots from fluctuations in temperature. Hydroponically grown fruits can produce rich nutrition's like traditional grown fruits. Proposed research has focused on the IoT in Hydroponics for Growing Tomato Plants. The Nutrient Film Technique (NFT) is needed for the present farmers for monitor and managing the crop system. For managing and monitoring the crop to execute the method scientifically and take the decision about the crops in time. The integration of IoT has given the solutions to solve the contemporary problems of calcium deficiency, lack of airflow around plants and so on. This proposal has developed a smart hydroponic system with technology enabled by IoT system. © 2023 IEEE.

关键词： Crops

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Signal Processing Techniques from Accelerometers on Railway Transport Based on the Wavelet Transform

Signal Processing Techniques from Accelerometers on Railway ...

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2024 Conference of Young Researchers in Electrical and Electronic engineering, ElCon 2024

作者： Boronakhin, Alexander M. Bolshakova, Alexandra V. Klionskiy, Dmitry M. Larionov, Daniil Yu. Shalymov, Roman V. Saint Petersburg Electrotechnical University 'LETI' Faculty of Information Measurement and Biotechnical Systems Dept. of Laser Measurement and Navigational Systems Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Dept. of Laser Measurement and Navigational Systems Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Dept. of Information Systems Dept. of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9798350360646

Railway transport safety is directly connected with the condition of railway tracks and also the condition of railway wheels' surface. One of the main reasons for abnormal situations on railways are various defects such as irregularities of a railway track. Thus, we need to estimate the length of short and impulse irregularities. It is also important to carry out joint analysis of vibration acceleration signals recorded by accelerometers in order to study types and sizes of railway track irregularities. © 2024 IEEE.

关键词： continuous wavelet transform discrete wavelet transform irregularity of railway track vibration acceleration signal vibration movement wavelet scalogram

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Monitoring and Forecasting Quality Indicators of Predough Production in the Bakery Industry 5

Monitoring and Forecasting Quality Indicators of Predough Pr...

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Zhyltsov, Andrii Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350384499

The work demonstrates the process of developing a machine learning model for a system for monitoring and controlling the quality of predough at a bakery. This system will provide continuous analysis of the predough parameters, such as temperature, humidity, lifting force and titranic acidity, which will ensure timely detection of deviations in the quality of the predough and avoid production problems. In addition, it will help automate the quality control process and ensure the stability of production processes. Analysis and forecasting of dough parameters will improve the quality of produced bread or other bakery products. Management based also on the predicted values of technological indicators of the predough will allow the enterprise to avoid unnecessary costs of materials and energy and increase economic efficiency. As part of the study, seven machine learning models were created and reviewed, including three linear and four varieties of binary decision trees. An analysis of the best model - a regression decision tree - was carried out, confirming its effectiveness and the validity of the resulting hierarchical structure, in particular, the prediction of important indicators of predough with a high degree of reliability, which indicates its importance in practical applications in production. The created model can be used to monitor and control product quality in real time, allowing operators to quickly identify and correct any deviations in process performance. Also, by analyzing changes in the variables of the predough production process, it is possible to identify precursors of possible production accidents or deviations in product quality, which will allow timely measures to be taken to avoid them. ©2024 IEEE.

关键词： Bakeries

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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2023 International Multi-Disciplinary Conference in Emerging Research Trends, IMCERT 2023

作者： Muneer, Muhammad Shahbaz Nabeel Mustafa, Syed Muhammad Zehra, Syeda Sundus Maqsood, Haniya NED University of Engineering and Technology Dpertment of Computational Finance Karachi Pakistan Sir Syed Uuniversity of Engineering and Technology Dept. of Software Engineering Karachi Pakistan NED University of Engineering and Technology Department of Computer and Information Systems Engineering Karachi Pakistan

ISBN: (纸本)9798350347708

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast. © 2023 IEEE.

关键词： Rain

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Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

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Informatica (Slovenia) 2024年第14期48卷 27-42页

作者： Alabbasi, Awab Aljabery, Maalim A. Bölük, Pınar Sarısaray Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Bahcesehir University Istanbul34353 Turkey Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah61004 Iraq Faculty of Engineering and Natural Sciences Software Engineering Dept. Bahçeşehir University Istanbul34353 Turkey

VGIS (Virtual Geographic Information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelligence & Internet of Things), GIS (Geographic Information System), Digital Twin, and AR (Augmented Reality)/ VR (Virtual Reality). The VGIS platform connects, collects, integrates, analyses, and acts on disparate data sources to provide the oilfield with a centralized view to help make informed decisions fast, bring operational visibility across all assets to improve safety and operational efficiency, reduce costs and improve margins, provide comprehensive analytics and decision support, increase business agility, and reduce management complexity. This is a case study of the application of the VGIS platform Majnoon oilfield, Basrah, Iraq. VGIS digital transformation, production process, and KPIs (Key performance indicators) are highly managed taking oilfield management to a higher level globally, in this study, the objective is to address and resolve the issue of high costs associated with using cloud services by utilising a local Data Center (a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data). This approach involves directly uploading 3D maps to ensure faster service on the intranet, eliminating the need to pay for cloud *** aims providing a platform for researching and implementing monitoring and controlling processes in a more convenient and remote manner. The vGIS platform has emerged as a crucial instrument in the realm of disaster response and crisis management. This technology enables responders to rapidly evaluate, analyse, and respond to crucial information in real-time. This research examined the application of vGIS technology in disaster response and analyse the potential advantages and obstacles associated with its utilisation. © 2

关键词： Information management

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Interpretable Pulmonary Disease Diagnosis with Graph Neural Network and Counterfactual Explanations 2

Interpretable Pulmonary Disease Diagnosis with Graph Neural ...

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2nd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies, SMC-IoT 2023

作者： Li, Jiahong Chen, Yiyuan Wang, Yichi Ye, Yiqiang Sun, Min Ren, Hao Cheng, Weibin Zhang, Haodi College of Computer and Software Engineering Guangzhou China Cornell University Dept. Computer Science New York United States Shenzhen University College of Computer and Software Engineering Shenzhen China China Mobile Information Technology Co. Limited Shenzhen China Guangdong Second Provincial General Hospital Institute for Healthcare Artificial Intelligence Guangzhou China

ISBN: (纸本)9798350373257

With recent fast development of artificial intelligence and related techniques, computer-aided diagnosis is increasingly emerging. During the process of automated diagnosis, the importance of explainable decision making has grown significantly. Traditional black-box models have often hindered the practical implementation of automated diagnosis algorithms. To address these challenges, we propose a novel framework that seamlessly integrates prototype learning with Graph Neural Networks (GNN) while also providing post-hoc explanations. On one hand, our model achieves intrinsic interpretability by reasoning based on the similarity calculations with prototypes for each disease. On the other hand, it employs counterfactual reasoning on graphs to pinpoint the most significant features for post-hoc explanations, supporting the diagnosis process. Building on these strengths, we integrate vision-language models to effectively harness multimodal patient information, thereby capturing a more detailed understanding of their medical condition. Our experiments on real Chinese EMRs of Pulmonary diseases demonstrate that our method not only delivers precise diagnoses but also accurately identifies medical findings substantiating the diagnoses. © 2023 IEEE.

关键词： Pulmonary diseases

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A Task Scheduler for Mobile Edge Computing Using Priority-based Reinforcement Learning

A Task Scheduler for Mobile Edge Computing Using Priority-ba...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Avan, Amin Kheiri, Farnaz Mahmoud, Qusay H. Azim, Akramul Makrehchi, Masoud Rahnamayan, Shahryar Computer and Software Engineering Ontario Tech University Dept. of Electrical OshawaONL1G 0C5 Canada Brock University Dept. of Engineering St. CatharinesONL2S 3A1 Canada

ISBN: (纸本)9781665430654

Edge computing offers cloud-like services closer to users and IoT devices, providing high speed and accessibility for network users. Edge computing, often called Mobile Edge Computing (MEC), is a distributed paradigm that utilizes heterogeneous computational and storage resources with well-provisioned capabilities rather than relying on the ample resources of the cloud. In addition, edge users usually refer to portable and mobile devices that connect to and disconnect from the network at will. Therefore, scheduling tasks at the appropriate time and allocating the right resources can be modeled as a multi-objective optimization problem in MEC. Moreover, each task has specific requirements, further adding to the complexity of the optimization problem. In this study, we formulate the scheduling problem as a Markov Decision Process (MDP) to schedule the tasks. The learning time of the task scheduler is minimized when it faces new users and edge servers. Subsequently, we employ the Q-learning (QL) algorithm from the Reinforcement Learning (RL) paradigm to address the optimization problem and effectively adapt the proposed scheduler to the dynamic nature of MEC. Accordingly, we designed the valid state space, action space, and reward function with appropriate conditions and proper rewards for the proposed QL-based technique. We conducted comprehensive experiments to validate the results of the proposed solution, taking into account the inherent randomness of the QL-based technique. The experimental results demonstrate that the proposed technique achieves the lowest learning time compared to Deep learning-based and Deep RL-based approaches. Furthermore, on average, the proposed technique obtains a 72% faster runtime compared to previous works, using 58% fewer computation cycles and 50% less memory. These improvements make the proposed approach an efficient and lightweight task scheduler for MEC. © 2023 IEEE.

关键词： Edge Computing Machine Learning Markov Decision Process Mobile Edge Computing Reinforcement Learning Task Scheduling

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