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Rethinking Machine Ethics - Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?

Rethinking Machine Ethics - Can LLMs Perform Moral Reasoning...

引用

2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Zhou, Jingyan Hu, Minda Li, Junan Zhang, Xiaoying Wu, Xixin King, Irwin Meng, Helen Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798891761193

Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. These approaches have been criticized for overgeneralizing the moral stances of a limited group of annotators and lacking explainability. This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research. The theory-guided top-down framework can incorporate various moral theories. Our experiments demonstrate the effectiveness of the proposed framework on datasets derived from moral theories. Furthermore, we show the alignment between different moral theories and existing morality datasets. Our analysis exhibits the potential and flaws in existing resources (models and datasets) in developing explainable moral judgment-making systems. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Supervised Learning Approaches for Vibration Analysis and Anomaly Detection in Rotating Machines 3

Supervised Learning Approaches for Vibration Analysis and An...

引用

3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Ali, Syed Zulfiqar Ali, Syed Hasnat Javeed, Anees Ullah, Ihsan Memon, Sufyan Ali Manzoor, Sajjad Dept. of Electrical and Computer Engineering CUI Abbottabad Campus Abbottabad Pakistan Dept. of Defence Systems Engineering Sejong University Seoul Korea Republic of Dept. of Electrical Engineering MUST AJK Mirpur Pakistan

ISBN: (纸本)9798331504861

Safety and reliability are crucial for modern systems and technologies. This paper presents an approach for real-time anomaly detection in rotating machines using vibration analysis and machine learning algorithms for predictive maintenance. Rotating machines such as motors, engines, gearboxes and turbines are widely used systems and faults in these systems can result in complete shutdowns. Preventing unexpected downtime while ensuring equipment reliability and safety is essential. In this context, anomaly detection plays a vital role in monitoring the healthy operation of rotating machines. A test-bed was designed to generate datasets for both balanced (healthy) and unbalanced (faulty) motor conditions. The experimental setup consists of a three-phase induction motor, a signal acquisition system (Raspberry Pi), a vibration sensor (MPU6050) and a coupling system capable of adjusting motor balance. Supervised machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF) were applied to classify and detect anomalies in the collected datasets. A grid search algorithm was employed for hyperparameter tuning of the SVM and RF models. The proposed technique achieved a maximum accuracy of approximately 98% using SVM and 100% using the RF algorithm leveraging nine optimally selected temporal features. ©2024 IEEE.

关键词： Rotating machinery

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Embracing the Future: Unveiling the Revolution of Human-AI Interaction in the Digital Education Era 13

Embracing the Future: Unveiling the Revolution of Human-AI I...

引用

13th International Conference on Virtual Campus, JICV 2023

作者： Cotino Arbelo, Andrea E. Gonzalez-Gonzalez, Carina S. Molina Gil, Jezabel M. University of La Laguna Dept. of Computer Engineering and Systems Tenerife Spain

ISBN: (纸本)9798350343441

The recent and emerging introduction of generative AI in all areas of society poses new challenges to be overcome, particularly in the educational setting. The intimate and affective relationship that may develop between students and generative AI devices raises critical questions about the psychological and emotional impact of such interactions. This issue becomes particularly significant when the interactions with AI-based systems begin at an early age, and these systems assume roles as digital tutors, digital secretaries, motivator agents, and/or mentors. The aim of this work in progress was to address a recent challenge within the digital educational context: the psychological and emotional impact that the introduction of AI-based systems in digital education may have on students. However, we have faced limitations in terms of data availability and resources, particularly when addressing the issue from early ages, where there is a notable lack of studies. Although the results may not have been significant, the research has provided a general overview and a solid theoretical foundation for future studies in this field. Therefore, we suggest conducting more rigorous studies with representative samples and complete data, alongside developing reliable and validated methodologies and assessment tools to address the issues involved. © 2023 IEEE.

关键词： Human computer interaction

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Applying Deep Learning Methods on Spam Review Detection 7

Applying Deep Learning Methods on Spam Review Detection

引用

7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ramu, M. Raj, Chinnakotla Jayanth Nithish, Apthiri Boggula, Chandhu Ganesh Babu, G. Srikanth Goud, K.I. Dept. of Computer Science and Systems Engineering SVEC Andhrapradesh Tirupati India

ISBN: (纸本)9781665464086

In today's environment, a reliable and effective technique for identifying spam reviews is essential if you want to purchase things online without being taken advantage of. There are possibilities for publishing reviews in many internet locations, which opens the door for sponsored or deceptive fake reviews. These fabricated evaluations may mislead the general audience and leave them unsure of whether or not to believe them. The issue of spam review finding has been solved by the introduction of prominent deep literacy methods. The focus of recent research has been on supervised literacy practices that contain labelled data, which is inadequate for online review. This initiative aims to expose any dishonest textbook reviews. To do this, we've used both labelled and unlabeled data and suggested deep learning techniques for spam review detection, including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and a Long Short-Term Memory (LSTM) variation of Recurrent Neural Networks (RNN). We also used standard machine learning classifiers to identify spam reviews, including Naive Bayes (NB), K Nearest Neighbor (KNN), and Support Vector Machine (SVM). Finally, we compared the effectiveness of traditional and deep literacy classifiers. We'll use deep literacy classifiers to boost the finesse and efficiency. © 2023 IEEE.

关键词： Long short-term memory

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Applying LSTM for River's Water Level Forecasting as Potential Flood Indication: A Case Study in Greece 15

Applying LSTM for River's Water Level Forecasting as Potenti...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Tziolas, Theodoros Papageorgiou, Elpiniki Papageorgiou, Konstantinos Zardas, Georgios Lekidis, Alexios Panagiotis, Papazoglou University of Thessaly Dept. of Energy Systems Gaiopolis Campus Larisa41500 Greece University of Thessaly Dept. of Electrical and Computer Engineering Volos Greece National and Kapodistrian University of Athens Dept. of Architecture and Computer and Communication Simulation Systems Chalkida Greece

ISBN: (纸本)9798350368833

Floods can have significant life-threatening and economic impacts on human societies. Flood forecasting with data-driven methods is a crucial aspect of disaster risk management, providing valuable insights into potential flood events and allowing for timely mitigation measures. This work illustrates the application of a Long Short-Term Memory (LSTM) neural network for short-term forecasting of a river's water level. The water treatment data used for training the LSTM models were gathered from Sperchios river, located in central Greece. Historical time-series data in a half-hour interval of the river's water level, water velocity, and area precipitation were utilized for the development of the proposed scheme. The developed model is deployed in the backend of a web application for half-hour ahead predictions of the river's water level that are visualized in the frontend. The post-analysis of the deployed forecasting model indicates that it consistently delivers forecasts with minimal error and demonstrates high accuracy in recent flood events. © 2024 IEEE.

关键词： Rivers

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Exploring the Impact of Robotics in STEM Education Activities and Competitive Challenges 2

Exploring the Impact of Robotics in STEM Education Activitie...

引用

2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Farooq, Adil Zukhraf, Syeda Zillay Nain Maryam, Hafsa Attard, Conrad Kamal, Mohsin University of Cyprus Dept. of Electrical and Computer Engineering Cyprus National University of Computer and Emerging Sciences Dept. of Electrical Engineering Pakistan University of Malta Dept. of Computer Information Systems Msida Malta

ISBN: (纸本)9798350336573

As robotics and technology become increasingly integrated into education and daily life, it is important to recognize their essential role. Even young children have access to electronic toys and gadgets, which can facilitate their learning and interaction. This study highlights the significance of STEM (science, technology, engineering, and mathematics) education at the elementary school level, emphasizing how university students from a variety of disciplines can design and construct robots capable of carrying out challenging tasks for humanitarian purposes. The study also emphasizes the need to encourage women's participation in robotics to showcase their skills and innovative ideas. Furthermore, the study demonstrates how robotics can effectively be used in both STEM education and competitive challenges, showcasing its effectiveness in teaching and promoting technological advancement. By encouraging early engagement in STEM education and promoting diversity in the field of robotics, we can empower future generations to lead the way in creating innovative solutions to global challenges. © 2023 IEEE.

关键词： Intelligent systems

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Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction 19

Integrating Temporal Analysis with Hybrid Machine Learning a...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed M. Le, Duc-Tai Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

Air pollution remains a critical issue, adversely affecting public health and the environment. In this study, we utilize the Air Quality index dataset from Kaggle to analyze temporal and seasonal variations of key pollutants, specifically Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Benzene (C6H6 ). Building upon this analysis, we predict Absolute Humidity (AH), a vital meteorological factor influencing pollutant dispersion, using Machine Learning (ML) and Deep Learning (DL) techniques. Three ML techniques, Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR), and three DL techniques, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are employed for predicting AH. The results indicate that while the RF model achieved the lowest Mean Absolute Error (MAE) among ML methods (0.02), the CNN model, despite having an MAE of 0.04, demonstrated statistical superiority through paired t-tests and Wilcoxon signed-rank tests (p © 2025 IEEE.

关键词： Convolutional neural networks

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Driver Assistance System, based on the Convolutional Neural Network 16

Driver Assistance System, based on the Convolutional Neural ...

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16th Electrical engineering Faculty Conference, BulEF 2024

作者： Filipova-Petrakieva, Simona Yanev, Petar Dept. Fundamentals of Electrical Engineering Technical University of Sofia Sofia Bulgaria Dept. Computer Systems Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9798350391671

The continuously rising number of deaths due to car accidents on the roads is a critical problem nowadays. To reduce the number of risky situations on the road, it is necessary to increase the attention of drivers when driving motor vehicles. In this context, an advisory system to assist driving is proposed in this paper. The mathematical model uses a convolutional neural network to recognize traffic signs and detect real road situations. It is implemented using Python in the Jupyter Notebook environment. The following libraries were used: Tensorflow (Tensors and Keras libraries, for machine learning and deep learning, respectively), Tkinter (GUI library), Pillow (image processing library), matplotlib (data visualization library), NumPy (library for performing complex computations), and Pandas (data analysis and processing library). The developed application is installed on the onboard vehicle computer. The detected sign is displayed in an appropriate form on the automobile dashboard. Simulations of real traffic situations are implemented in the system. All this will facilitate driving and help drivers with less experience acquire the necessary driving reactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Movement Attentive Graph Embedding for Improving Next Point of Access Prediction 18

Movement Attentive Graph Embedding for Improving Next Point ...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Kim, Taesoo Yang, Huigyu Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Superintelligence Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Next Point-of-Attachment (PoA) prediction is a deep learning-based approach to provide seamless connectivity in the mobile networks from heterogenous and dense user access. Existing recurrent neural models utilize time series characteristics to extract and learn the sequential association changes occurs between all Base Stations (BSs). Since the user moves from certain BS to its adjacent BS, the deployments of these BSs are a crucial feature for improving prediction accuracy. To this end, we propose a Graph Embedding - Long Short-Term Memory (GE-LSTM) model using Recurrent Neural Network (RNN) and Graph Neural Network (GNN) jointly. The time-series pattern of the user movement is extracted by LSTM, and the movement between adjacent BSs is emphasized as GNN through GE. The joint property of the proposed model is implemented through mixture-of-experts approach from GNN and LSTM. The performance is evaluated along with the RNN families and Transformer models, furthermore, it explores the significance of the graph-based mobility embeddings. In our experiment, the proposed model outperforms other SOTA models by achieving the maximum accuracy 94%. © 2024 IEEE.

关键词： Forecasting

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Design of Array Microstrip Antenna with Circular Polarization for WBAN/WIMAX 26

Design of Array Microstrip Antenna with Circular Polarizatio...

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2024 IEEE International Conference on Automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Ahumada, Cristian Kaschel, Hector Osorio-Comparan, Roman Cordero, Sergio Universidad Técnica Federico Santa María Dept. Electrotechnics and Computer Valparaiso Chile Dept. of Electrical Engieneering Santiago Chile Dept. of Computer Systems Engineering Ciudad de México Mexico

ISBN: (纸本)9798350378115

This article presents the design of a array microstrip antenna with circular polarization for the evaluation of the specific absorption rate (SAR) in human tissues. The analysis focuses on a single element and a two element microstrip patch, each designed at 2.5 GHz. Both antennas were crafted on a microstrip substrate, sporting a thickness of 1.6 mm and a dielectric constant of 4.3. An evaluation was conducted that contrasted the efficiency of the engineered antenna array with that of the standalone antenna, with particular emphasis on VSWR (Voltage Standing Wave Radio), gain, and directionality, was designed, simulated, and optimized using CST Microwave Studio (MWS) software. Furthermore, the SAR level was scrutinized under flat conditions within the human tissue representation. © 2024 IEEE.

关键词： Circular polarization

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