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21st International Conference on Principles of Knowledge Representation and Reasoning, KR 2024

作者： Ostropolski-Nalewaja, Piotr Rudolph, Sebastian TU Dresden Germany University of Wroclaw Poland Center for Scalable Data Analytics and Artificial Intelligence Dresden Germany Center for Scalable Data Analytics and Artificial Intelligence Leipzig Germany

ISBN: (纸本)9781956792058

Extensive research in the field of ontology-based query answering has led to the identification of numerous fragments of existential rules (also known as tuple-generating dependencies) that exhibit decidable answering of atomic and conjunctive queries. Motivated by the increased theoretical and practical interest in navigational queries, this paper considers the question for which of these fragments decidability of querying extends to regular path queries (RPQs). In fact, decidability of RPQs has recently been shown to generally hold for the comprehensive family of all fragments that come with the guarantee of universal models being reasonably well-shaped (that is, being of finite cliquewidth). Yet, for the second major family of fragments, known as finite unification sets (short: fus), which are based on first-order-rewritability, corresponding results have been largely elusive so far. We complete the picture by showing that RPQ answering over arbitrary fus rulesets is undecidable. On the positive side, we establish that the problem is decidable for the prominent fus subclass of sticky rulesets, with the caveat that a very mild extension of the RPQ formalism turns the problem undecidable again. © 2024 Proceedings of the International Conference on Knowledge Representation and Reasoning. All rights reserved.

关键词： Question answering

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Unraveling Neurophysiological Attentional Habituation Dynamics of Food and Non-Food Paradigms: ERP Correlates with BMI in Response to Repetitive Visual Stimuli 5

Unraveling Neurophysiological Attentional Habituation Dynami...

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5th International Conference on data intelligence and Cognitive Informatics, ICDICI 2024

作者： Duraisingam, Aruna Soria, Daniele Palaniappan, Ramaswamy University of Kent Artificial Intelligence and Data Analytics Research Group Canterbury United Kingdom

ISBN: (纸本)9798350389609

Previous research indicates that human salivary responses habituate to repeated exposure to visual, olfactory, or gustatory food cues in adults and children. This study aims to delve into the neurophysiological dynamics of within-session attentional habituation and its correlation with Body Mass Index (BMI) concerning the repetition of high and low-calorie food, as well as non-food images. Event-related potentials (ERPs) were measured for both high and low BMI groups, as they repeatedly viewed the same food and non-food images. Analysis of ERPs on trial groups within each session revealed distinct effects of repeated image exposure on the brain's attentional responses to food and non-food stimuli for both BMI groups. The result indicates that habituation effects and significance were observed in the parietal area of the brain for both BMI groups. Specifically, ERP responses to non-food image declined in motivation and attention over time, with significant changes observed within the 170-310 ms and 180-320 ms onset time windows for low and high BMI groups, respectively. However, no significant habituation effect was observed for both low/high caloric images in either BMI group. Additionally, ERP results indicated that high-calorie images exhibited a slower habituation rate, followed by low-calorie and non-food images across both BMI groups. In conclusion, this ERP study revealed that a habituation-like mechanism modulates attention towards repeated non-food images, while food images had a negligible effect in both groups. This suggests that the enhanced reward value associated with high-energy foods might lead to prolonged attention and diminished habituation, potentially contributing to obesity risks in individuals across different BMI groups. © 2024 IEEE.

关键词： Body Mass Index (BMI) Electroencephalogram (EEG) Signal Processing Event-Related Potential (ERP) Food Habituation Repetition Visual Stimuli

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Traffic Speed Forecasting in Congested Conditions: Evaluation and Improvements 8

Traffic Speed Forecasting in Congested Conditions: Evaluatio...

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8th International Conference on Intelligent Traffic and Transportation, ICITT 2024 - incorporating the 8th International Conference on Communication and Network Technology, ICCNT 2024

作者： Jagadeesh, George Rosario Thia, Kai Xin Artificial Intelligence - Data Analytics Strategic Technology Centre ST Engineering Ltd. Singapore

ISBN: (纸本)9781643685601

Traffic forecasts are especially valuable in congested conditions for travelers and traffic managers. While deep learning-based forecasting models perform impressively on data from mostly normal traffic conditions, their performance in congested conditions has not been adequately examined and improved upon. Towards addressing this gap, we first present and apply a method for determining the spatial and temporal extents of congestion episodes in a traffic speed dataset. We evaluate two deep learning models and a baseline model on data within the congestion extents and analyze the results. Subsequently, we propose and evaluate two simple modifications related to the input data features, which improve the prediction accuracy of a state-of-the-art model in congested conditions. Overall, this study indicates the need for more targeted research efforts towards developing robust prediction models that perform well in congested conditions. © 2024 The Authors.

关键词： Prediction models

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Comparative Analysis of Visual Attribute Tagging Models for Upper-Body Clothing Products 9

Comparative Analysis of Visual Attribute Tagging Models for ...

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9th International Conference on Computer Science and Engineering, UBMK 2024

作者： Kaya, Engin Yanik, Mert Lc Waikiki Meǧazacilik Hizmetleri A.Ş. Department of Data Analytics and Artificial Intelligence Istanbul Turkey

ISBN: (纸本)9798350365887

Accurate classification of fashion attributes is crucial for improving product recommendations and customer experience in e-commerce. In this study, we evaluate the performance of three advanced classification models, EfficientNet, Vision Transformer, and CatBoost Classifier with FashionClip embeddings, on various fashion attribute classification tasks, which including sleeve length, pattern type, neckline, and fit. We use upper-body clothing products from LC Waikiki catalogue. Our experimental results indicate that all models achieve high recognition rate in sleeve length classification, with accuracy of 0.97. The CatBoost model demonstrates superior performance in pattern type classifications with accuracy score of 0.88, highlighting its robustness in capturing intricate patterns. EfficientNet excels in neckline classification with an accuracy score of 0.91, particularly for round neck and shirt collar types, while Vision Transformer shows competitive but slightly lower performance. Fit type proved to be the most challenging attribute to classify accurately from product images. These findings highlight the strengths and limitations of each model, offering valuable insights for future enhancements in fashion attribute classification. © 2024 IEEE.

关键词： Clothes

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Reasoning in SHIQ with Axiom- and Concept-Level Standpoint Modalities 21

Reasoning in SHIQ with Axiom- and Concept-Level Standpoint M...

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21st International Conference on Principles of Knowledge Representation and Reasoning, KR 2024

作者： Álvarez, Lucía Gómez Rudolph, Sebastian Université Grenoble Alpes Inria CNRS Grenoble INP LIG France Computational Logic Group TU Dresden Germany Center for Scalable Data Analytics and Artificial Intelligence Dresden Germany Center for Scalable Data Analytics and Artificial Intelligence Leipzig Germany

ISBN: (纸本)9781956792058

Standpoint logic is a recently proposed modal logic framework that is well-suited for multiperspective reasoning and ontology integration. For this reason, combinations of standpoint logic with description logics (DLs), a popular family of logic-based ontology languages, are of special interest. Prior work has shown that it is possible to add standpoints to numerous decidable fragments of first-order logics – including very expressive DLs up to SROIQbs – while preserving their reasoning complexity, so long as standpoint modalities are limited to the axiom level. A more expressive tighter modal integration, where standpoint modalities are also allowed to occur in concept expressions, has so far only been investigated for the much less expressive DL EL+. In this paper, we push this line of research showing that the DL SHIQ allows for a tight modal integration with standpoints without compromising its EXPTIME reasoning complexity. The core insight toward this result is that any satisfiable knowledge base admits a model with only polynomially many worlds, an argument which requires a rather elaborate model-theoretic construction. This allows us to establish a polynomial equisatisfiable translation into plain SHIQ which, beyond showing the theoretical result, enables us to use highly optimised OWL reasoners to provide practical reasoning support for ontology languages extended by standpoint modelling. We complement our findings with the observation that our techniques would fail upon adding the modeling feature of nominals to the underlying DL. © 2024 Proceedings of the International Conference on Knowledge Representation and Reasoning. All rights reserved.

关键词： Knowledge representation

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Advanced Notebook: A tool for enhanced Management of Machine Learning models and procedures in the Healthcare Domain 3

Advanced Notebook: A tool for enhanced Management of Machine...

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3rd International Conference on Applied Mathematics and Computer Science, ICAMCS 2023

作者： Danciu, Gabriel Nicolae, Irina E. Ilie, Iulia Septimiu Nechifor, C. Data Analytics and Artificial Intelligence Siemens Technology Configuration Technologies Braov Romania

ISBN: (纸本)9798350324266

The significant improvements in machine learning today necessitate procedures and frameworks that are simple to configure by data scientists, ML researchers, and non-DevOps engineers, allowing them to rapidly develop ML models and address real-world challenges. This article provides an in-depth analysis of the architectural design of a holistic system that addresses the management of machine learning processes and deployment procedures. The proposed system has been designed to provide seamless configuration options and smooth integration with other solutions by enabling the rapid development of ML models. Its versatility makes it applicable in various scenarios, enhancing its usability across different domains. The effectiveness of the proposed solution has been validated through practical implementation in two distinct machine ML scenarios, both of which were integrated into European Union (EU) projects centered around the healthcare domain. The findings from these specific use cases have been documented and are presented as part of the empirical evidence supporting the viability and success of the solution. © 2023 IEEE.

关键词： Machine learning

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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Can Unlabelled data Improve AI Applications A Comparative Study on Self-Supervised Learning in Computer Vision 18

Can Unlabelled Data Improve AI Applications A Comparative St...

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18th Conference on Computer Science and intelligence Systems, FedCSIS 2023

作者： Bauer, Markus Augenstein, Christoph Center for Scalable Data Analytics and Artificial Intelligence Humboldtstraszlige 25 Leipzig04105 Germany

ISBN: (纸本)9788396744784

artificial intelligence (AI) represents a highly investigated area of study at present and has already become an indispensable component within an extensive range of business models and applications. One major downside of current supervised AI approaches lies in the need of numerous annotated data points to train the models. Self-supervised learning (SSL) circumvents the need for annotation, by creating supervision signals such as labels from the data itself, rather than requiring experts for this task. Current approaches mainly include the use of generative methods such as autoencoders and joint embedding architectures to fulfil this task. Recent works present comparable results to supervised learning in downstream scenarios such as classification after SSL-pretraining. To achieve this, typically modifications are required to suit the approach for the exact downstream task. Yet, current review works haven't paid too much attention to the practical implications of using SSL. Thus, we investigated and implemented popular SSL approaches, suitable for downstream tasks such as classification, from an initial collection of more than 400 papers. We evaluate a selection of these approaches under real-world dataset conditions, and in direct comparison to the supervised learning scenario. We discuss SSL's potential to take up with supervised learning, as well as the influence of the right training methods. Furthermore, we also introduce future directions for SSL research, as well as current limitations in real-world applications. © 2023 Polish Information Processing Society.

关键词： Supervised learning

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SWJEPA: Improving Prostate Cancer Lesion Detection with Shear Wave Elastography and Joint Embedding Predictive Architectures 27th

SWJEPA: Improving Prostate Cancer Lesion Detection with Shea...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Bauer, Markus Gurwin, Adam Augenstein, Christoph Franczyk, Bogdan Malkiewicz, Bartosz Center for Scalable Data Analytics and Artificial Intelligence Leipzig Germany Leipzig University Leipzig Germany Wroclaw Medical University Wroclaw Poland

ISBN: (纸本)9783031783975

Detecting and localising lesions is a key task in the staging phase of diagnosing and treating prostate cancer (PCa). After a positive digital rectal examination or rise in prostate-specific antigen, pinpointing lesion positions for biopsy using multiparametric magnetic resonance imaging (mpMRI) is crucial. mpMRI and ultrasound (US) imaging already aid in collecting cores for biopsy accurately, a procedure called FBx: mpMRI-targeted US-guided prostate fusion biopsy. Yet, physicians face challenges, e.g., due to limited resolutions of both mpMRI and US. This affects patients’ therapy choices, as malignancy assessment accuracy depends on FBx. Recent research aims to improve lesion detection in both mpMRI and US using more objective markers, such as shear wave elastography (SWE). AI can improve FBx using both mpMRI or US data, which has been demonstrated in various studies. However, in the case of mpMRI, labelled lesion examples are still limited, which hinders the performance of state-of-the-art models. Self-supervised learning (SSL) provides a solution by utilising large unannotated databases to create robust feature extractors, enabling the training of case-specific AI models with limited data. Thus, in this paper, we investigate how to improve the models for PCa lesion detection by combining mpMRI and US. We show that recent joint embedding predictive architectures may be a good choice for mpMRI-SSL pretraining. Moreover, we present a false-positive-filtering approach based on real and AI-based SWE, that further improves the mpMRI-model’s specificity. Our model achieves state-of-the-art performance of 0.626 average precision in mpMRI-based segmentation and carries the potential to significantly improve lesion detection and localisation accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biopsy

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Comprehensive Review and Analysis on Facial Emotion Recognition:Performance Insights into Deep and Traditional Learning with Current Updates and Challenges

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Computers, Materials & Continua 2025年第1期82卷 41-72页

作者： Amjad Rehman Muhammad Mujahid Alex Elyassih Bayan AlGhofaily Saeed Ali Omer Bahaj Artificial Intelligence&Data Analytics Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia MIS Department College of Business Administration Prince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia

In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry *** demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial *** study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each *** immense achievement of deep learning has resulted in a growing use of its much architecture to enhance *** review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of *** following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics *** this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further *** information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.

关键词： Face emotion recognition deep learning hybrid learning CK+ facial images machine learning technological development

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