Artificial Intelligence. ECAI 2023 International Workshops
XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 – October 4, 2023, Proceedings, Part II
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Book and Conference Proceedings
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Chapter and Conference Paper
EcoShap: Save Computations by only Calculating Shapley Values for Relevant Features
One of the most widely adopted approaches for eXplainable Artificial Intelligence (XAI) involves employing of Shapley values (SVs) to determine the relative importance of input features. While based on a solid...
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Chapter and Conference Paper
Towards Explainable Deep Domain Adaptation
In many practical applications data used for training a machine learning model and the deployment data does not always preserve the same distribution. Transfer learning and, in particular, domain adaptation al...
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Book and Conference Proceedings
Artificial Intelligence. ECAI 2023 International Workshops
XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 – October 4, 2023, Proceedings, Part I
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Chapter and Conference Paper
AID4HAI: Automatic Idea Detection for Healthcare-Associated Infections from Twitter, a Framework Based on Active Learning and Transfer Learning
This research is an interdisciplinary work between data scientists, innovation management researchers and experts from Swedish academia and a hygiene and health company. Based on this collaboration, we have de...
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Chapter and Conference Paper
Data-Centric Perspective on Explainability Versus Performance Trade-Off
The performance versus interpretability trade-off has been well-established in the literature for many years in the context of machine learning models. This paper demonstrates its twin, namely the data-centric...
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Chapter and Conference Paper
Towards Geometry-Preserving Domain Adaptation for Fault Identification
In most industries, the working conditions of equipment vary significantly from one site to another, from one time of a year to another, and so on. This variation poses a severe challenge for data-driven fault...
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Chapter and Conference Paper
curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection
Recently and due to the advances in sensor technology and Internet-of-Things, the operation of machinery can be monitored, using a higher number of sources and modalities. In this study, we demonstrate that Mu...
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Chapter and Conference Paper
A Systematic Approach for Tracking the Evolution of XAI as a Field of Research
The increasing use of AI methods in various applications has raised concerns about their explainability and transparency. Many solutions have been developed within the last few years to either explain the mode...
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Chapter and Conference Paper
Incorporating Physics-Based Models into Data Driven Approaches for Air Leak Detection in City Buses
In this work-in-progress paper two types of physics-based models, for accessing elastic and non-elastic air leakage processes, were evaluated and compared with conventional statistical methods to detect air le...
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Article
Open AccessWisdom of the contexts: active ensemble learning for contextual anomaly detection
In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods use a single context based on a set of user-specified contextual features. However, iden...
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Chapter and Conference Paper
A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set
This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently de...
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Reference Work Entry In depth
Towards Autonomous Knowledge Creation from Big Data in Smart Cities
The notion of smart cities is inherently connected with the notion of Big Data. It is Big Data that allows more and more intelligence to be added to our existing urban systems. This intelligence then, at least...
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Chapter and Conference Paper
Forklift Truck Activity Recognition from CAN Data
Machine activity recognition is important for accurately estimating machine productivity and machine maintenance needs. In this paper, we present ongoing work on how to recognize activities of forklift trucks ...
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Living Reference Work Entry In depth
Towards Autonomous Knowledge Creation from Big Data in Smart Cities
The notion of smart cities is inherently connected with the notion of Big Data. It is Big Data that allows more and more intelligence to be added to our existing urban systems. This intelligence then, at least...
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Chapter and Conference Paper
Towards Understanding ICU Treatments Using Patient Health Trajectories
Overtreatment or mistreatment of patients is a phenomenon commonly encountered in health care and especially in the Intensive Care Unit (ICU) resulting in increased morbidity and mortality. We explore the MIMI...
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Article
Open AccessSelf-monitoring for maintenance of vehicle fleets
An approach for intelligent monitoring of mobile cyberphysical systems is described, based on consensus among distributed self-organised agents. Its usefulness is experimentally demonstrated over a long-time c...
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Chapter and Conference Paper
Monitoring Equipment Operation Through Model and Event Discovery
Monitoring the operation of complex systems in real-time is becoming both required and enabled by current IoT solutions. Predicting faults and optimising productivity requires autonomous methods that work with...
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Chapter and Conference Paper
Inductive Logic Programming Algorithm for Estimating Quality of Partial Plans
We study agents situated in partially observable environments, who do not have the resources to create conformant plans. Instead, they create conditional plans which are partial, and learn from experience to c...
