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2023 learning. Knowledge. Data. Analytics, LWDA 2023

作者： Haritz, Pierre Liebig, Thomas Faculty of Computer Science TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany

Training acting agents for the goal of controlling complex live systems on the system itself is often an unfeasible task, either due to the high cost or the potential dangers that might arise. In this paper, we take a step towards identifying ways to evaluate the transferability of models for the class of constrained Reinforcement learning problems. Furthermore, we present an approach based on free-energy advantage functions to improve adaptability and in turn transferability for constrained Reinforcement learning problems and subsequently manage to increase the performance of a baseline algorithm, CPO, with regard to safety constraints in noisy environments. © 2023 CEUR-WS. All rights reserved.

关键词： Reinforcement learning

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GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text 24

GPT Assisted Annotation of Rhetorical and Linguistic Feature...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Hamilton, Kyle Longo, Luca Božić, Bojan School of Computer Science and SFI Centre for Research Training in Machine Learning Technological University Dublin Dublin Ireland Artificial Intelligence and Cognitive Load Research Lab School of Computer Science SFI Centre for Research Training in Machine Learning Technological University Dublin Dublin Ireland

ISBN: (纸本)9798400701726

While the use of machine learning for the detection of propaganda techniques in text has garnered considerable attention, most approaches focus on "black-box" solutions with opaque inner workings. Interpretable approaches provide a solution, however, they depend on careful feature engineering and costly expert annotated data. Additionally, language features specific to propagandistic text are generally the focus of rhetoricians or linguists, and there is no data set labeled with such features suitable for machine learning. This study codifies 22 rhetorical and linguistic features identified in literature related to the language of persuasion for the purpose of annotating an existing data set labeled with propaganda techniques. To help human experts annotate natural language sentences with these features, RhetAnn, a web application, was specifically designed to minimize an otherwise considerable mental effort. Finally, a small set of annotated data was used to fine-tune GPT-3.5, a generative large language model (LLM), to annotate the remaining data while optimizing for financial cost and classification accuracy. This study demonstrates how combining a small number of human annotated examples with GPT can be an effective strategy for scaling the annotation process at a fraction of the cost of traditional annotation relying solely on human experts. The results are on par with the best performing model at the time of writing, namely GPT-4, at 10x less the cost. Our contribution is a set of features, their properties, definitions, and examples in a machine-readable format, along with the code for RhetAnn and the GPT prompts and fine-tuning procedures for advancing state-of-the-art interpretable propaganda technique detection. © 2024 Copyright held by the owner/author(s).

关键词： machine learning

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Multilingual Facial Emotion Decoding and Posture Recognition using Random Forest Classifier

Multilingual Facial Emotion Decoding and Posture Recognition...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Sangeetha, K. Balaji, V.S. Manju, M. Renuka Devi, S. Velayutham, P. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India

ISBN: (纸本)9798350374933

Effective communication is crucial for success in various interactions, including personal and online interviews. The work proposed is to refine the communication effectiveness and extend the understanding of interactions by incorporating new system functions of real-time emotion and body language capture. Facial and body landmarks are analyzed using Google’s Mediapipe to ensure accurate identification of the frequently performed emotions and frequently posed postures. The proposed system uses three classifiers, namely Random Forest, Gradient Boosting, and Ridge Classifier, all integrated in one pipeline;this makes our work distinct. In the testing phase the classifiers are checked to avoid misidentification of emotions or incorrect posture in different situations. One of the major novelties of this work is its ability to address users of different languages, thus it is relevant anywhere in the world. This dynamic and robust system provides accurate emotion analysis achieving the accuracy of 92%, enhancing understanding in various communication contexts and setting a new standard for interview and interaction analysis. © 2024 IEEE.

关键词： Emotion Recognition

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Recent trends in computational intelligence for educational big data analysis

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Iran Journal of computer science 2024年第1期7卷 103-129页

作者： Ikegwu, Anayo Chukwu Nweke, Henry Friday Anikwe, Chioma Virginia Computer Science Department Alex Ekwueme Federal University Ndufu-Alike Ikwo Ebonyi Abakaliki Nigeria Centre for Research in Machine Learning Artificial Intelligence and Network Systems Computer Science Department Ebonyi State University Abakaliki Nigeria

Educational big data analytics and computational intelligence have transformed our understanding of learning ability and computing power, catalyzing the emergence of Education 4.0. However, educators and researchers still struggle to identify appropriate methods to analyze the diverse data generated within educational environments. The complexity and uncertainty inherent in heterogeneous and homogeneous data often compound these challenges. This study aims to explore the potential applications of computational intelligence methods to support educational big data analysis. We begin by discussing the processes involved in educational big data analytics (EDA), including data collection, data preprocessing, feature extraction, modeling, and evaluation. We then provided an extensive review of computational intelligence and its methods, including artificial intelligence approaches, machine learning methods, deep learning methods, meta-heuristic optimization approaches, ensemble techniques, and the Markov model, as applied to educational big data analysis. Furthermore, we discussed novel application areas for computational intelligence in education, including predicting academic performance, social network analysis, detecting undesirable student behaviors, adaptive curriculum sequencing and personalization, courseware development, and decision support systems. We also mapped various computational intelligence methods to these novel application areas. Despite the progress made in educational big data analytics implementation, challenging research areas still require further investigation. These research areas include enhanced academic performance prediction, data-driven intelligent tutoring systems, adversarial machine learning, student engagement, personalized learning, and more. In this paper, we briefly discussed these ten important research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： artificial intelligence Big data analytics Computational intelligence Deep learning Educational data mining machine learning

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics 3

Enhancing Interpretability: The Role of Explainable AI in He...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Zade, Nikita Langote, Meher Verma, Prateek Faculty of Engineering & Technology Department of Artificial Intelligence & Data Science Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Artificial Intelligence & Machine Learning Maharashtra Sawangi442001 India

ISBN: (纸本)9798331509675

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

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Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors

Person Segmentation and Action Classification for Multi-Chan...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Seliunina, Svetlana Otelepko, Artem Memmesheimer, Raphael Behnke, Sven University of Bonn Autonomous Intelligent Systems Group Computer Science Institute VI - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics Germany

ISBN: (纸本)9798331531614

Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different actions and annotated it. We propose a method based on a MaskDINO model to detect and segment persons and to recognize their actions from combined spherical projected multi-channel representations of the LiDAR data with an additional positional encoding. Our approach demonstrates good performance for the person segmentation task and further performs well for the estimation of the person action states walking, waving, and sitting. An ablation study provides insights about the individual channel contributions for the person segmentation task. The trained models, code and dataset are made publicly available. © 2025 IEEE.

关键词： Signal encoding

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Solar Tracker with Obstacle Detection Vehicle

Solar Tracker with Obstacle Detection Vehicle

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2023 IEEE International Conference on Electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Singh, Yashdeep Patidar, Umesh Gaur, Vivek Thakur, Shivendra Singh Bilthare, Harikant Bansal, Kritika Madhav Institute of Technology and Science Artificial Intelligence and Machine Learning Gwalior India Centre of Artificial Intelligence Madhav Institute of Technology and Science Gwalior India

ISBN: (纸本)9798350305111

The aim of this paper is to develop a system that combines solar tracking technology with obstacle detection. This improves the safety and energy efficiency of a solar-powered vehicle. Solar powered vehicles face several challenges such as limited range and the need for efficient energy harvesting. This is done by adjusting the solar panel position based on the location of the sun to maximize energy harvesting. This ensures that the vehicle can operate for longer distances and remain charged. The system is designed to be reliable and efficient, and it was able to perform well in various testing environments. © 2023 IEEE.

关键词： Solar energy

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Polyhouse Agriculture with AI: Strategies for Climate Control, Energy Efficiency and Yield Improvement

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SN computer science 2024年第8期5卷 1119页

作者： Mayuri, K.P. Kathavate, Sheela Niranjanamurthy, M. Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management (Affiliated to VTU -Belgaum) Karnataka Bengaluru India Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Karnataka Bengaluru India

artificial intelligence is applied to an extensive array of climate change responses, from data collection and classification to conservation decision-making and rule enforcement, leading to improved understanding and mitigation efforts. Greenhouse and polyhouse farming practices could significantly add to the carbon footprint, posing a challenge to be addressed if sustainable and environmentally friendly solutions are to be assured. This study describes the use of artificial intelligence in polyhouse agriculture to achieve better yields, save energy and advocate for sustainable farming. This study further elucidates the impact of AI-driven climate control systems on the quality of crops, resource use efficiency and productivity. The findings demonstrated the enormous potential of AI technology to improve plant growth, lower greenhouse gas emissions and increase sustainability in polyhouse agriculture. AI-driven climate control systems optimize the growing environment and reduce energy use with reduced emissions of GHGs, thus contributing to sustainable farming. However, this study also emphasizes the need for detailed research into the deployment and consequences of AI technology within polyhouse operations. The key bottlenecks are availability, security and regulatory compliance related to the generation of data. Therefore, need for a holistic approach to the design phase of AI-driven climate control systems in polyhouses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： AI-driven climate control Crop yield Greenhouse gas emissions Polyhouse agriculture Resource efficiency Sustainable farming

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A Comparative Study of DenseNet121 and InceptionResNetV2 model for DeepFake Image Detection 3

A Comparative Study of DenseNet121 and InceptionResNetV2 mod...

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3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Manoranjitham, R. Swaroop, Somu Santhi Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The rise of deep fake technology poses a significant challenge to societal integrity, necessitating robust detection methods. This research study investigates the effectiveness of DenseNet121 and InceptionResNetV2 deep learning models in detecting deep fake images. The study starts with the curation of the dataset and then applies different preprocessing methods to improve the quality of the dataset like scaling, normalization, and data augmentation. The DenseNet121 and InceptionResNetV2 models are trained to differentiate authentic and manipulated images based on distinctive features. DenseNet121's dense connectivity facilitates feature reuse, enabling effective extraction of discriminative features crucial for identifying manipulated content. In contrast, InceptionResNetV2's sophisticated architecture exploits both inception and residual connections, enhancing its ability to capture intricate patterns indicative of deep fake manipulation. The comparative analysis of performance metrics reveals InceptionResNetV2's superiority, achieving an impressive accuracy rate of 99.78% compared to DenseNet121. These findings underscore the efficacy of leveraging existing architectures for identifying deep fake images, thereby contributing to the ongoing efforts to combat misinformation and safeguard digital content integrity. © 2024 IEEE.

关键词： Deep learning

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RoboCup@Home 2024 OPL Winner NimbRo: Anthropomorphic Service Robots Using Foundation Models for Perception and Planning

RoboCup@Home 2024 OPL Winner NimbRo: Anthropomorphic Service...

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27th RoboCup International Symposium, 2024

作者： Memmesheimer, Raphael Nogga, Jan Pätzold, Bastian Kruzhkov, Evgenii Bultmann, Simon Schreiber, Michael Bode, Jonas Karacora, Bertan Park, Juhui Savinykh, Alena Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn Bonn Germany

ISBN: (纸本)9783031858581

We present the approaches and contributions of the winning team NimbRo@Home at the RoboCup@Home 2024 competition in the Open Platform League held in Eindhoven, NL. Further, we describe our hardware setup and give an overview of the results for the task stages and the final demonstration. For this year’s competition, we put a special emphasis on open-vocabulary object segmentation and grasping approaches that overcome the labeling overhead of supervised vision approaches, commonly used in RoboCup@Home. We successfully demonstrated that we can segment and grasp non-labeled objects by text descriptions. Further, we extensively employed Large Language Model (LLMs) for natural language understanding and task planning. Throughout the competition, our approaches showed robustness and generalization capabilities. A video of our performance can be found online (https://***/videos/RoboCup_2024). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robot programming

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