the proceedings contain 92 papers. the special focus in this conference is on intelligentdataengineering and automatedlearning. the topics include: Model-Based Meta-reinforcement learning for Hyperparameter Op...
ISBN:
(纸本)9783031777370
the proceedings contain 92 papers. the special focus in this conference is on intelligentdataengineering and automatedlearning. the topics include: Model-Based Meta-reinforcement learning for Hyperparameter Optimization;towards Sustainable Precision: Machine learning for Laser Micromachining Optimization;association Rules Mining with Auto-encoders;using Contrastive learning to Map Stylistic Similarities in Narrative Writers;automatic Classification of Signal and Noise in Functional Magnetic Resonance Imaging Scans Using Convolutional Neural Networks;how Resilient are Language Models to Text Perturbations?;emotional Sequential Influence Modeling on False Information;CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care data Interoperability;padel Two-Dimensional Tracking Extraction from Monocular Video Recordings;drowsiness Detection Using Vital Sign Sensors and Deep learning on Smartwatches;Benchmarking Out of the Box Open-Source LLMs for Malware Detection Based on API Calls Sequences;multimodal Visio-Lingual Content Analysis to Detect Fake Content on Reddit;MetaLIRS: Meta-learning for Imputation and Regression Selection;pipeline for Semantic Segmentation of Large Railway Point Clouds;preliminary Investigation on Machine learning and Deep learning Models for Change of Direction Classification in Running;efficient Radar Scheduling Using Genetic Algorithms and Stochastic Heuristic Initialization;towards a Communication Specification Language for Heterogeneous Service Orchestration Based on Process Calculus and Holonic Multi-agent Systems;counterfactual Explanations for Sustainable Tourism Indicators;Tracking Healthy Organs in Medical Scans to Improve Cancer Treatment by Using UW-Madison GI Tract Image Segmentation;low Consumption Models for Disease Diagnosis in Isolated Farms;fast and Scalable Recommendation Retrieval Model with Mixed Attention and Knowledge Distillation;Federated learning for Vietnamese SMS Spam Detection Using Pre
the proceedings contain 92 papers. the special focus in this conference is on intelligentdataengineering and automatedlearning. the topics include: Model-Based Meta-reinforcement learning for Hyperparameter Op...
ISBN:
(纸本)9783031777301
the proceedings contain 92 papers. the special focus in this conference is on intelligentdataengineering and automatedlearning. the topics include: Model-Based Meta-reinforcement learning for Hyperparameter Optimization;towards Sustainable Precision: Machine learning for Laser Micromachining Optimization;association Rules Mining with Auto-encoders;using Contrastive learning to Map Stylistic Similarities in Narrative Writers;automatic Classification of Signal and Noise in Functional Magnetic Resonance Imaging Scans Using Convolutional Neural Networks;how Resilient are Language Models to Text Perturbations?;emotional Sequential Influence Modeling on False Information;CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care data Interoperability;padel Two-Dimensional Tracking Extraction from Monocular Video Recordings;drowsiness Detection Using Vital Sign Sensors and Deep learning on Smartwatches;Benchmarking Out of the Box Open-Source LLMs for Malware Detection Based on API Calls Sequences;multimodal Visio-Lingual Content Analysis to Detect Fake Content on Reddit;MetaLIRS: Meta-learning for Imputation and Regression Selection;pipeline for Semantic Segmentation of Large Railway Point Clouds;preliminary Investigation on Machine learning and Deep learning Models for Change of Direction Classification in Running;efficient Radar Scheduling Using Genetic Algorithms and Stochastic Heuristic Initialization;towards a Communication Specification Language for Heterogeneous Service Orchestration Based on Process Calculus and Holonic Multi-agent Systems;counterfactual Explanations for Sustainable Tourism Indicators;Tracking Healthy Organs in Medical Scans to Improve Cancer Treatment by Using UW-Madison GI Tract Image Segmentation;low Consumption Models for Disease Diagnosis in Isolated Farms;fast and Scalable Recommendation Retrieval Model with Mixed Attention and Knowledge Distillation;Federated learning for Vietnamese SMS Spam Detection Using Pre
Federated learning is one of the main research lines in the last years about distributed learning, where participating nodes share their models but maintain the privacy of the data used to learn such models. Consensus...
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ISBN:
(纸本)9783031777370;9783031777387
Federated learning is one of the main research lines in the last years about distributed learning, where participating nodes share their models but maintain the privacy of the data used to learn such models. Consensus is a way of calculating a mean value between a set of agents using only information from the local neighbors. this paper presents a new approach based on Asynchronous Consensus, called Multi-layered Asynchronous Consensus-based Federated learning (MACoFL). It randomly chooses a neighbor and a layer from the neural model and interchanges it with him. this new algorithm is presented and tested using the MNIST dataset.
作者：
Mitic, PeterUCL
Dept Comp Sci Gower St London WC1E 6BT England
We present a quantitative definition of reputation risk, formulated in terms of a reputation time series comprising daily sentiment measurements. Self Supported learning is used to quantify reputation risk by progress...
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ISBN:
(纸本)9783031777301;9783031777318
We present a quantitative definition of reputation risk, formulated in terms of a reputation time series comprising daily sentiment measurements. Self Supported learning is used to quantify reputation risk by progressively refining an initial proposal for a Minimum Acceptable Sentiment, calculated from descriptive statistics of the reputation data. the derived values are validated using a "sense test" based on a Loess quantile. the results show that the Minimum Acceptable Sentiment value is given approximately by a two standard deviation lower tail of the observed data.
Systems based on artificial intelligence have become prominent in nearly all domains. However, knowledge of the inner workings of these intelligent systems is not as widespread, partly because the associated issues ha...
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ISBN:
(纸本)9783031777370;9783031777387
Systems based on artificial intelligence have become prominent in nearly all domains. However, knowledge of the inner workings of these intelligent systems is not as widespread, partly because the associated issues have been discussed only to a limited extent in computer science education. In order to gain an overview of AI in curricula and to see what competencies teachers need to teach this content, the AIrelated content of the computer science curricula of the German federal states was analysed and compared with existing approaches. Proposals for further training courses are derived from this to enable teachers to teach AI competently.
Online hotel booking became increasingly popular as time passed, and with its popularity, the datathat can be collected based on customer actions has increased. this data can serve to build intelligent systems that c...
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ISBN:
(纸本)9783031777370;9783031777387
Online hotel booking became increasingly popular as time passed, and with its popularity, the datathat can be collected based on customer actions has increased. this data can serve to build intelligent systems that can provide knowledge for both customers and hotel owners. In this paper, we focus on hotel owners who can benefit from the collected data by adjusting the prices to optimise the profit of their accommodations. To accomplish this, we built a system that collected the data from *** and gathered a helpful dataset for price prediction. We used five regression algorithms and an optimization technique to obtain the best results, leading us to a 9% error for price prediction. this result allows accommodation owners to predict the room price to keep the rooms fully occupied.
Sign language recognition and understanding are challenging tasks for many people who are not familiar with it, which limits communication between deaf-mute people and others. the system presented in this paper lowers...
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ISBN:
(纸本)9783031777370;9783031777387
Sign language recognition and understanding are challenging tasks for many people who are not familiar with it, which limits communication between deaf-mute people and others. the system presented in this paper lowers the communication barrier, introducing an automatic translation layer that facilitates sign language understanding. the system uses a deep-learning model for sign language detection and a separate library for hand joint mapping. the application's architecture was designed to allow users to access the system from desktop and mobile devices. the model's results revealed an 82% accuracy, and after several tweaks on the activation function in our tests, we achieved perfect classification in our real word tests. the results of the system offered excellent accuracy, and its usability lowers the communication barrier between people, providing flexibility as the application is available for any device with a browser.
When delivered to the market, machine learning models face new data which are possibly subject to novel characteristics - a phenomenon known as concept drift. As this might lead to performance degradation, it is neces...
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ISBN:
(纸本)9783031777301;9783031777318
When delivered to the market, machine learning models face new data which are possibly subject to novel characteristics - a phenomenon known as concept drift. As this might lead to performance degradation, it is necessary to detect such drift and, if required, adapt the model accordingly. While a variety of drift detection and adaptation methods exists for standard vectorial data, a suitable treatment of text data is less researched. In this work we present a novel approach which detects and explains drift in text data based on their representation via transformer embeddings. In a nutshell, the method generates suitable statistical features from the original distribution and the possibly shifted variation. Based on these representations, drift scores can be assigned to individual data points, allowing a visualization and human-readable characterization of the type of drift. We demonstrate the approach's effectiveness in reliably detecting drift in several experiments.
the high number of sentiment analysis systems and applications developed over the last few years provided companies with very sophisticated analysis tools, allowing them to establish preferences, trends and patterns o...
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ISBN:
(纸本)9783031777370;9783031777387
the high number of sentiment analysis systems and applications developed over the last few years provided companies with very sophisticated analysis tools, allowing them to establish preferences, trends and patterns of customer behavior. this is quite important for companies intending to change their way of being, promoting work actions aimed at specific customer segments, to obtain business advantages and improve their image and performance in the market in which they work. In this paper, we present and describe a sentiment analysis system that combine techniques based on ontologies and domain lexicons, to provide relevant indicators to support the evaluation of the degree of user satisfaction and know the influence of each ontological element incorporated in opinion texts in sentiment classification.
Hyperparameter Optimization (HPO) plays a significant role in enhancing the performance of machine learning models. However, as the size and complexity of (deep) neural architectures continue to increase, conducting H...
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ISBN:
(纸本)9783031777301;9783031777318
Hyperparameter Optimization (HPO) plays a significant role in enhancing the performance of machine learning models. However, as the size and complexity of (deep) neural architectures continue to increase, conducting HPO has become very expensive in terms of time and computational resources. Existing methods that automate this process still demand numerous evaluations to find the optimal hyperparameter configurations. In this paper, we present a novel approach based on model-based reinforcement learning to effectively improve sample efficiency while minimizing resource consumption. We formulate the HPO task as a Markov decision process and develop a predictive dynamics model for efficient policy optimization. Additionally, we employ the Deep Sets framework to encode the state space, which is then leveraged in meta-learning for transfer of knowledge across multiple datasets, enabling the model to quickly adapt to new datasets. Empirical studies demonstrate that our approach outperforms alternative techniques on publicly available datasets in terms of sample efficiency and accuracy.
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