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Beyond Touch: A Comparative Study of Gesture and Tangible Interactions for Smart Tables Using Multi-Source Data 2

Beyond Touch: A Comparative Study of Gesture and Tangible In...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Ashraf, Mohamed Marzouk, Mustafa Amgad, Nadeen Atia, Ayman MSA University Faculty of Computer Science Giza Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligence Giza Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350362633

Understanding user interactions in smart restaurant environments is crucial for enhancing user experience and optimizing business operations. In this paper, a comparative analysis of two interactive systems was conducted: Gesture Recognition and Tangible User Interface (TUI), focusing on maximizing engagement and usability within smart dining contexts. Eye gaze tracking and facial expression analysis were integrated to provide deeper insights into user behavior and preferences during the interactive dining experience. The experimental study involves 10 participants performing specific tasks u sing both interaction methods in a controlled environment. Results indicate that gesture-based interaction outperforms TUI in terms of task completion time, with participants finding gestures more intuitive and enjoyable. The study revealed that participants using gesture-based interaction exhibited a faster performance compared to the Tangible User Interface, with an average completion time of 34.093 seconds. However, challenges such as hand strain were noted with gesture-based interaction. Emotion analysis revealed prevalent neutral expressions, with occasional misidentifications. Limitations include a small sample size and a focus on specific tasks, suggesting the need for further research with larger samples and diverse scenarios. Overall, the study highlights the effectiveness of gesture recognition in smart restaurant environments and underscores the importance of considering user preferences and comfort in interactive system design. © 2024 IEEE.

关键词： Gesture recognition

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AIMSM - A Mechanism to Optimize Systems with Multiple AI Models: A Case Study in computer Vision for Autonomous Mobile Robots 23rd

AIMSM - A Mechanism to Optimize Systems with Multiple AI M...

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23rd EPIA Conference on artificial intelligence, EPIA 2024

作者： Ferreira, Bruno Georgevich Sousa, Armando Jorge Reis, Luis Paulo Augusto de Sousa, António Rodrigues, Rui Rossetti, Rosaldo Edge Innovation Center Federal University of Alagoas Maceió Brazil FEUP - Faculty of Engineering of the University of Porto Porto Portugal LIACC - Member of LASI LA - Artificial Intelligence and Computer Science Laboratory Porto Portugal INESC TEC - INESC Technology and Science Porto Portugal

ISBN: (纸本)9783031735028

This article proposes the artificial intelligence Models Switching Mechanism (AIMSM), a novel approach to optimize system resource utilization by allowing systems to switch AI models during runtime in dynamic environments. Many real-world applications utilize multiple data sources and various AI models for different purposes. In many of those applications, every AI model doesn’t have to operate all the time. The AIMSM strategically allows the system to activate and deactivate these models, focusing on system resource optimization. The switching of each AI model can be based on any information, such as context or previous results. In the case study of an autonomous mobile robot performing computer vision tasks, the AIMSM helps the system to achieve a significant increment in performance, with a 50% average increase in frames per second (FPS) rate, for this specific case study, assuming that no erroneous switching occurred. Experimental results have demonstrated that the AIMSM can improve system resource utilization efficiency when properly implemented, optimize overall resource consumption, and enhance system performance. The AIMSM presented itself as a better alternative to permanently loading all the models simultaneously, improving the adaptability and functionality of the systems. It is expected that using the AIMSM will yield a performance improvement that is particularly relevant to systems with multiple AI models of a complex nature, where such models do not need to be all continuously executed or systems that will benefit from lower resource usage. Code is available at https://***/BrunoGeorgevich/AIMSM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models 31

FinDABench: Benchmarking Financial Data Analysis Ability of ...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Shu Zhao, Shangqing Jia, Chenghao Zhuang, Xinlin Long, ZhaoGuang Zhou, Jie Zhou, Aimin Lan, Man Chong, Yang Lab of Artificial Intelligence for Education East China Normal University China Shanghai Institute of Artificial Intelligence for Education East China Normal University China School of Computer Science and Technology East China Normal University China Bytedance

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks. However, their proficiency and reliability in the specialized domain of financial data analysis, particularly focusing on data-driven thinking, remain uncertain. To bridge this gap, we introduce FinDABench, a comprehensive benchmark designed to evaluate the financial data analysis capabilities of LLMs within this context. The benchmark comprises 15,200 training instances and 8,900 test instances, all meticulously crafted by human experts. FinDABench assesses LLMs across three dimensions: 1) Core Ability, evaluating the models' ability to perform financial indicator calculation and corporate sentiment risk assessment;2) Analytical Ability, determining the models' ability to quickly comprehend textual information and analyze abnormal financial reports;and 3) Technical Ability, examining the models' use of technical knowledge to address real-world data analysis challenges involving analysis generation and charts visualization from multiple perspectives. We will release FinDABench, and the evaluation scripts at https://***/cubenlp/FinDABench. FinDABench aims to provide a measure for in-depth analysis of LLM abilities and foster the advancement of LLMs in the field of financial data analysis. © 2025 Association for Computational Linguistics.

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Linear Rank Intersection Types 28

Linear Rank Intersection Types

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28th International Conference on Types for Proofs and Programs, TYPES 2022

作者： Reis, Fábio Alves, Sandra Florido, Mário DCC-FCUP University of Porto Portugal LIACC – Artificial Intelligence and Computer Science Laboratory University of Porto Portugal CRACS INESC-TEC Centre for Research in Advanced Computing Systems Porto Portugal

ISBN: (纸本)9783959772853

Non-idempotent intersection types provide quantitative information about typed programs, and have been used to obtain time and space complexity measures. Intersection type systems characterize termination, so restrictions need to be made in order to make typability decidable. One such restriction consists in using a notion of finite rank for the idempotent intersection types. In this work, we define a new notion of rank for the non-idempotent intersection types. We then define a novel type system and a type inference algorithm for the λ-calculus, using the new notion of rank 2. In the second part of this work, we extend the type system and the type inference algorithm to use the quantitative properties of the non-idempotent intersection types to infer quantitative information related to resource usage. © Fábio Reis, Sandra Alves, and Mário Florido.

关键词： Differentiation (calculus)

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Explaining Deep Learning Time Series Classification Models using a Decision Tree-Based Post-Hoc XAI Method 1

Explaining Deep Learning Time Series Classification Models u...

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Joint 1st World Conference on eXplainable artificial intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Mekonnen, Ephrem T. Dondio, Pierpaolo Longo, Luca Artificial Intelligence and Cognitive Load Research Lab Ireland School of Computer Science Technological University Dublin Ireland

This preliminary study proposes a new post hoc method to explain deep learning-based time series classification models using a decision tree. Our approach generates a decision tree graph or rulesets as an explanation, improving interpretability compared to saliency map-based methods. The method involves two phases: training and evaluating the deep learning-based time series classification model and extracting prototypical events from the evaluation set to train the decision tree classifier. We conducted experiments on artificial and real datasets, evaluating the explanations based on accuracy, fidelity, number of nodes, and depth. Our preliminary findings suggest that our post-hoc method improves the interpretability and trust of complex time series classification models. © 2023 CEUR-WS. All rights reserved.

关键词： Decision trees

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An examination of the effect of the inconsistency budget in weighted argumentation frameworks and their impact on the interpretation of deep neural networks 1

An examination of the effect of the inconsistency budget in ...

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Joint 1st World Conference on eXplainable artificial intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Vilone, Giulia Longo, Luca Artificial Intelligence and Cognitive Load Research Lab Ireland School of Computer Science Technological University Dublin Ireland

Explaining the logic of a data-driven Machine Learning (ML) model can be seen as a defeasible reasoning process that is likely non-monotonic. This means a conclusion linked to a set of premises can be withdrawn when new information becomes available. Argumentation Theory (AT) formalises reasoning with a defeasible knowledge base. Abstract Argumentation Frameworks (AAF) organise conflicting arguments in a dialogical structure, allowing formal semantics to resolve conflicts. This study proposes an XAI method for automatically forming an AAF-based representation, using weighted attacks to model conflictual information. The concept of inconsistency budget is employed to eliminate the weakest attacks. Findings showed that the variation of the inconsistency budget could affect, albeit limited, the evaluation metrics computed over the resulting rulesets. © 2023 CEUR-WS. All rights reserved.

关键词： Deep neural networks

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Real-time Detection of Taikyoku Shodan Karate Kata Poses Using Classical Machine Learning and Deep Learning Models 3

Real-time Detection of Taikyoku Shodan Karate Kata Poses Usi...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Walid, Mazen Ameen, Mostafa Atia, Ayman Faculty of Computer Science Cairo Egypt Faculty of Computer Science Giza Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligence Egypt

ISBN: (纸本)9798350306231

The research discusses the necessity for a precise feedback karate training system utilizing machine learning (ML) and deep learning (DL) models to facilitate real-time pose detection by offering instantaneous and accurate feedback. This system has the potential to revolutionize training methodologies, improve performance, and make significant contributions to the field of martial arts *** ML & DL algorithms were explored to achieve optimal results. Among the classical ML methods, the Random Forest, attained an accuracy of 98%. Additionally, DL models were also examined, with the Long-Short term memory model achieving an accuracy of 97%. The research presents an overview of the problem, methods for data collection and preprocessing, model training, and evaluation. The results emphasize the system's potential for real-time pose detection among karate practitioners, ultimately benefiting their training and progress. © 2023 IEEE.

关键词： Learning systems

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Latent space interpretation and visualisation for understanding the decisions of convolutional variational autoencoders trained with EEG topographic maps 1

Latent space interpretation and visualisation for understand...

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Joint 1st World Conference on eXplainable artificial intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Ahmed, Taufique Longo, Luca Artificial Intelligence and Cognitive Load Research Lab Ireland School of Computer Science Technological University Dublin Ireland

Learning essential features and forming simple representations of electroencephalography (EEG) signals are difficult problems. Variational autoencoders (VAEs) can be used with EEG signals to learn the salient features of EEG data. But explainability should disclose knowledge of how the model makes its decision. The key contribution of this research is the combining of known components in a pipeline that allows us to give meaningful visualisations that help us understand which component of latent space is responsible for capturing which region of brain activation in EEG topographic maps. The results reveal that each component in the latent space contributes to capturing at least two generating factors in topographic maps. This pipeline can be used to produce EEG topographic maps of any scale. Furthermore, assist us in understanding each component of latent space responsible for activating a portion of the brain. © 2023 CEUR-WS. All rights reserved.

关键词： Electroencephalography

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Optimizing Deep Q-Learning Experience Replay with SHAP Explanations: Exploring Minimum Experience Replay Buffer Sizes in Reinforcement Learning 1

Optimizing Deep Q-Learning Experience Replay with SHAP Expla...

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Joint 1st World Conference on eXplainable artificial intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Sullivan, Robert S. Longo, Luca Artificial Intelligence and Cognitive Load Research Lab Ireland School of Computer Science Technological University Dublin Ireland

Explainable Reinforcement Learning (xRL) faces challenges in debugging and interpreting Deep Reinforcement Learning (DRL) models. A lack of understanding for internal components like Experience Replay, which samples and stores data from the environment, risks burdening resources. This paper presents an xRL-based Deep Q-Learning (DQL) system using SHAP (SHapley Additive exPlanations) to explain input feature contributions. Data is sampled from Experience Replay, creating SHAP Heatmaps to understand how it influences the neural network Q-value approximator's actions. The xRL-based system aids in determining the smallest Experience Replay size for 23 simulations of varying complexities. It contributes an xRL optimization method, alongside traditional approaches, for tuning the Experience Replay size hyperparameter. This visual and creative approach achieves over 40% reduction in Experience Replay size for 18 of the 23 tested simulations, smaller than the commonly used sizes of 1 million transitions or 90% of total environment transitions. © 2023 CEUR-WS. All rights reserved.

关键词： Reinforcement learning

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Framework for Crack Detection using Deep Learning 1

Framework for Crack Detection using Deep Learning

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Atta, Omar Atia, Ayman Faculty of Computer Science Giza Egypt Faculty of Computers and Artificial Intelligence HCI-LAB Helwan University Egypt

ISBN: (纸本)9798350335569

This study presents an innovative hierarchical classification approach aimed at detecting and classifying various types of road cracks. The methodology consists of three main stages: image preprocessing, binary classification, and hierarchical classification. The first experiment incorporates a binary classification model based on the OLenet architecture, where after preprocessing, the model is trained and tested on the Metu and ELpv datasets, achieving impressive accuracies of 97% and 92%, respectively. In the second experiment, the method amalgamates the Road Crack and Solar Cells datasets into a diverse dataset of 4,000 images. A Convolutional Neural Network (CNN) model is trained on this combined dataset using transfer learning techniques and architectures like VGG16, VGG19, and MobileNet, where the VGG19 model demonstrates superior performance with a classification accuracy of 98.69%. The final experiment involves a Mask RCNN model trained on the Road Crack dataset with 7,048 images of 13 distinct types of road cracks, offering the unique capability to classify multiple types of cracks within a single image and outperforming traditional CNN models with an accuracy of 90.42%. This novel approach holds significant potential for effective road crack detection and classification, contributing to enhanced road maintenance and safety. Future work will expand the hierarchical classifier with more crack types and further optimize the accuracy of all models. The main contributions of this study lie in its unique methodological approach, the effective use of diverse datasets, and the successful application of transfer learning and the Mask RCNN model. © 2023 IEEE.

关键词： computer vision

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