Christian W. Omlin
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View article: Making Historical Consciousness Come Alive: Abstract Concepts, Artificial Intelligence, and Implicit Game-Based Learning
Making Historical Consciousness Come Alive: Abstract Concepts, Artificial Intelligence, and Implicit Game-Based Learning Open
As new technologies shape education, helping students develop historical consciousness remains a challenge. Building on Nordic curricula that emphasize students as both “history-made” and “history-making” citizens, this paper proposes an a…
View article: Sensing in Smart Cities: A Multimodal Machine Learning Perspective
Sensing in Smart Cities: A Multimodal Machine Learning Perspective Open
Smart cities rely on diverse sensing infrastructures, generating vast multimodal data from IoT devices, surveillance systems, health monitors and environmental sensors. The seamless integration and interpretation of such multimodal data is…
View article: Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection
Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection Open
The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, maki…
View article: Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions
Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions Open
Crowd anomaly detection is one of the most popular topics in computer vision in the context of smart cities. A plethora of deep learning methods have been proposed that generally outperform other machine learning solutions. Our review prim…
View article: Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. Performance
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. Performance Open
View article: Data-driven material removal rate estimation in bonnet polishing process
Data-driven material removal rate estimation in bonnet polishing process Open
View article: Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data
Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data Open
Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults. Identifying anomaly causes in large systems involves investigating a more extensive set of monitoring variables across multiple subsystems. …
View article: Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. Performance
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. Performance Open
Recent advancements in artificial intelligence promise ample potential in monitoring applications with surveillance cameras. However, concerns about privacy and model bias have made it challenging to utilize them in public. Although de-ide…
View article: Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection
Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection Open
The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, maki…
View article: An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection
An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection Open
Falls are a major risk factor for older adults, increasing morbidity and healthcare costs. Video-based fall-detection systems offer crucial real-time monitoring and assistance. Yet, their deployment faces challenges such as maintaining pri…
View article: Lightweight Multi-System Multivariate Interconnection and Divergence Discovery
Lightweight Multi-System Multivariate Interconnection and Divergence Discovery Open
Identifying outlier behavior among sensors and subsystems is essential for discovering faults and facilitating diagnostics in large systems. At the same time, exploring large systems with numerous multivariate data sets is challenging. Thi…
View article: An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition
An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition Open
This paper presents a human activity recognition framework tailored for healthcare applications, emphasizing the essential balance between accuracy and interpretability required for medical monitoring. The model utilizes MediaPipe to captu…
View article: Machine Learning Based Calibration of Force Sensors for Bonnet Polishing Process
Machine Learning Based Calibration of Force Sensors for Bonnet Polishing Process Open
View article: Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter Open
The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle…
View article: Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter Open
The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle…
View article: CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection
CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection Open
Video anomaly event detection (VAED) is one of the key technologies in computer vision for smart surveillance systems. With the advent of deep learning, contemporary advances in VAED have achieved substantial success. Recently, weakly supe…
View article: Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles
Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles Open
With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the effi…
View article: NLP-based Traffic Scene Retrieval via Representation Learning
NLP-based Traffic Scene Retrieval via Representation Learning Open
Many automated systems require the interpretation of visual information, i.e., images, videos, and natural language input, i.e., speech or text, to comprehend their surroundings and communicate with interacting humans.One such hybrid appli…
View article: SECAdvisor: a Tool for Cybersecurity Planning using Economic Models
SECAdvisor: a Tool for Cybersecurity Planning using Economic Models Open
Cybersecurity planning is challenging for digitized companies that want adequate protection without overspending money. Currently, the lack of investments and perverse economic incentives are the root cause of cyberattacks, which results i…
View article: Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks
Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks Open
Abnormal event detection is one of the most challenging tasks in computer vision. Many existing deep anomaly detection models are based on reconstruction errors, where the training phase is performed using only videos of normal events and …
View article: Online detrended fluctuation analysis and improved empirical wavelet transform for real-time oscillations detection in industrial control loops
Online detrended fluctuation analysis and improved empirical wavelet transform for real-time oscillations detection in industrial control loops Open
Detrended Fluctuation Analysis (DFA) is a reliable and assumption-free approach for gauging the complexity of a time series. In this paper, an online oscillations detection paradigm is presented, which integrates the potential of DFA in de…
View article: Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities Open
learning Deep learning Meta-survey Responsible AI a b s t r a c tThe past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems.However, thi…
View article: Stiction detection in industrial control valves using Poincaré plot-based convolutional neural networks
Stiction detection in industrial control valves using Poincaré plot-based convolutional neural networks Open
Valve stiction is one of the major causes of poorly performing industrial control loops. Stiction occurs when the static friction exceeds the dynamic friction during a direction change or when the stem is at rest. Recently, machine learnin…
View article: Towards artificial virtuous agents: games, dilemmas and machine learning
Towards artificial virtuous agents: games, dilemmas and machine learning Open
View article: SleepXAI: An explainable deep learning approach for multi-class sleep stage identification
SleepXAI: An explainable deep learning approach for multi-class sleep stage identification Open
Extensive research has been conducted on the automatic classification of sleep stages utilizing deep neural networks and other neurophysiological markers. However, for sleep specialists to employ models as an assistive solution, it is nece…
View article: Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions
Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions Open
Crowd anomaly detection is one of the most popular topics in computer vision in the context of smart cities. A plethora of deep learning methods have been proposed that generally outperform other machine learning solutions. Our review prim…
View article: Reinforcement learning with intrinsic affinity for personalized prosperity management
Reinforcement learning with intrinsic affinity for personalized prosperity management Open
The purpose of applying reinforcement learning (RL) to portfolio management is commonly the maximization of profit. The extrinsic reward function used to learn an optimal strategy typically does not take into account any other preferences …
View article: Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning
Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning Open
Machine ethics has received increasing attention over the past few years because of the need to ensure safe and reliable artificial intelligence (AI). The two dominantly used theories in machine ethics are deontological and utilitarian eth…
View article: Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models
Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models Open
The proliferation of artificial intelligence is increasingly dependent on model understanding. Understanding demands both an interpretation - a human reasoning about a model's behavior - and an explanation - a symbolic representation of th…
View article: Long Horizon Anomaly Prediction in Multivariate Time Series with Causal Autoencoders
Long Horizon Anomaly Prediction in Multivariate Time Series with Causal Autoencoders Open
Predictive maintenance is essential for complex industrial systems to foresee anomalies before major system faults or ultimate breakdown. However, the existing efforts on Industry 4.0 predictive monitoring are directed at semi-supervised a…