Roberto Corizzo
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View article: Development and Implementation of a Defect Detection Model for Microstructures Using Image Processing Methods
Development and Implementation of a Defect Detection Model for Microstructures Using Image Processing Methods Open
The aim of this research is to develop and implement artificial intelligence models for the automatic detection of defects in the microstructures of austempered ductile iron (ADI). Our research used three different approaches, representing…
View article: xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection
xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection Open
The recently proposed xLSTM is a powerful model that leverages expressive multiplicative gating and residual connections, providing the temporal capacity needed for long-horizon forecasting and representation learning. This architecture ha…
View article: Innovative artificial intelligence for practice management in medical healthcare
Innovative artificial intelligence for practice management in medical healthcare Open
View article: Distance-based change point detection for novelty detection in concept-agnostic continual anomaly detection
Distance-based change point detection for novelty detection in concept-agnostic continual anomaly detection Open
Anomaly detection provides an effective decision support capability in several real-world domains. One limitation of conventional approaches is their inability to preserve knowledge as models are constantly updated with recent data, leadin…
View article: ‘Slightly disappointing’ vs. ‘worst sh** ever’: tackling cultural differences in negative sentiment expressions in AI-based sentiment analysis
‘Slightly disappointing’ vs. ‘worst sh** ever’: tackling cultural differences in negative sentiment expressions in AI-based sentiment analysis Open
Advertisers, politicians, and social scientists alike have an interest in the current zeitgeist. With people expressing their sentiments in online comments, the Internet has become a great source for ‘reading’ the zeitgeist via artificial …
View article: TinySubNets: An Efficient and Low Capacity Continual Learning Strategy
TinySubNets: An Efficient and Low Capacity Continual Learning Strategy Open
Continual Learning (CL) is a highly relevant setting gaining traction in recent machine learning research. Among CL works, architectural and hybrid strategies are particularly effective due to their potential to adapt the model architectur…
View article: An end-to-end explainability framework for spatio-temporal predictive modeling
An end-to-end explainability framework for spatio-temporal predictive modeling Open
The rising adoption of AI models in real-world applications characterized by sensor data creates an urgent need for inference explanation mechanisms to support domain experts in making informed decisions. Explainable AI (XAI) opens up a ne…
View article: E-Catch: Event-Centric Cross-Modal Attention with Temporal Consistency and Class-Imbalance Handling for Misinformation Detection
E-Catch: Event-Centric Cross-Modal Attention with Temporal Consistency and Class-Imbalance Handling for Misinformation Detection Open
View article: pyCLAD: The universal framework for continual lifelong anomaly detection
pyCLAD: The universal framework for continual lifelong anomaly detection Open
Anomaly detection is a recognized problem with high significance and impact in many real-world settings. Continual anomaly detection is an emerging paradigm that allows for the design of anomaly detection methods capable of adapting to new…
View article: TinySubNets: An efficient and low capacity continual learning strategy
TinySubNets: An efficient and low capacity continual learning strategy Open
Continual Learning (CL) is a highly relevant setting gaining traction in recent machine learning research. Among CL works, architectural and hybrid strategies are particularly effective due to their potential to adapt the model architectur…
View article: Continual Semi-Supervised Malware Detection
Continual Semi-Supervised Malware Detection Open
Detecting malware has become extremely important with the increasing exposure of computational systems and mobile devices to online services. However, the rapidly evolving nature of malicious software makes this task particularly challengi…
View article: Mitigating social bias in sentiment classification via ethnicity-aware algorithmic design
Mitigating social bias in sentiment classification via ethnicity-aware algorithmic design Open
Sentiment analysis tools are frequently employed to analyze large amounts of natural language data gathered from social networks and generate valuable insights on public opinion. Research has discovered that these tools tend to be biased a…
View article: A deep fusion model for stock market prediction with news headlines and time series data
A deep fusion model for stock market prediction with news headlines and time series data Open
Time series forecasting models are essential decision support tools in real-world domains. Stock market is a remarkably complex domain, due to its quickly evolving temporal nature, as well as the multiple factors having an impact on stock …
View article: AD-NEv: A Scalable Multilevel Neuroevolution Framework for Multivariate Anomaly Detection
AD-NEv: A Scalable Multilevel Neuroevolution Framework for Multivariate Anomaly Detection Open
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given da…
View article: Correction to: Exploiting sparsity and statistical dependence in multivariate data fusion: an application to misinformation detection for high-impact events
Correction to: Exploiting sparsity and statistical dependence in multivariate data fusion: an application to misinformation detection for high-impact events Open
View article: GAP-LSTM: Graph-Based Autocorrelation Preserving Networks for Geo-Distributed Forecasting
GAP-LSTM: Graph-Based Autocorrelation Preserving Networks for Geo-Distributed Forecasting Open
Forecasting methods are important decision support tools in geo-distributed sensor networks. However, challenges such as the multivariate nature of data, the existence of multiple nodes, and the presence of spatio-temporal autocorrelation …
View article: From MNIST to ImageNet and back: benchmarking continual curriculum learning
From MNIST to ImageNet and back: benchmarking continual curriculum learning Open
Continual learning (CL) is one of the most promising trends in recent machine learning research. Its goal is to go beyond classical assumptions in machine learning and develop models and learning strategies that present high robustness in …
View article: AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework
AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework Open
Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a give…
View article: Towards efficient deep autoencoders for multivariate time series anomaly detection
Towards efficient deep autoencoders for multivariate time series anomaly detection Open
Multivariate time series anomaly detection is a crucial problem in many industrial and research applications. Timely detection of anomalies allows, for instance, to prevent defects in manufacturing processes and failures in cyberphysical s…
View article: Distributed and explainable GHSOM for anomaly detection in sensor networks
Distributed and explainable GHSOM for anomaly detection in sensor networks Open
The identification of anomalous activities is a challenging and crucially important task in sensor networks. This task is becoming increasingly complex with the increasing volume of data generated in real-world domains, and greatly benefit…
View article: Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media
Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media Open
Online hate speech proliferation has created a difficult problem for social media platforms. A particular challenge relates to the use of coded language by groups interested in both creating a sense of belonging for its users and evading d…
View article: Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights Open
Anomaly detection is of paramount importance in many real-world domains characterized by evolving behavior, such as monitoring cyber-physical systems, human conditions and network traffic. Current research in anomaly detection leverages of…
View article: Ada-QPacknet – Multi-Task Forget-Free Continual Learning with Quantization Driven Adaptive Pruning
Ada-QPacknet – Multi-Task Forget-Free Continual Learning with Quantization Driven Adaptive Pruning Open
Continual learning (CL) is a challenging machine learning setting that is attracting the interest of an increasing number of researchers. Among recent CL works, architectural strategies appear particularly promising due to their potential …
View article: Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting
Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting Open
Continual Learning (CL) is a process in which there is still huge gap between human and deep learning model efficiency. Recently, many CL algorithms were designed. Most of them have many problems with learning in dynamic and complex enviro…
View article: HURI: Hybrid user risk identification in social networks
HURI: Hybrid user risk identification in social networks Open
View article: One-Class Learning for AI-Generated Essay Detection
One-Class Learning for AI-Generated Essay Detection Open
Detection of AI-generated content is a crucially important task considering the increasing attention towards AI tools, such as ChatGPT, and the raised concerns with regard to academic integrity. Existing text classification approaches, inc…
View article: AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection Open
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given da…
View article: From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning
From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning Open
Continual learning (CL) is one of the most promising trends in recent machine learning research. Its goal is to go beyond classical assumptions in machine learning and develop models and learning strategies that present high robustness in …
View article: Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights Open
Anomaly detection is of paramount importance in many real-world domains, characterized by evolving behavior. Lifelong learning represents an emerging trend, answering the need for machine learning models that continuously adapt to new chal…
View article: Lifelong Learning for Anomaly Detection: New Challenges, Perspectives, And Insights
Lifelong Learning for Anomaly Detection: New Challenges, Perspectives, And Insights Open