A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Federated learning leaks participant dataset quality even with secure aggregation.
The paper proposes a method to detect relevant model degradations without over-alerting.
Study identifies high-density anomalies in normal data regions.
New test ensures quality of shared data in machine learning.
EGFC learns from streaming data to classify power quality disturbances.
A key aspect of automating predictive machine learning entails the capability of properly triggering the update of the trained model. To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised. In …
Detecting earthquake events from seismic time series has proved itself a challenging task. Manual detection can be expensive and tedious due to the intensive labor and large scale data set. In recent years, automatic detection methods based on machine learning have been developed to improve accuracy and efficiency. How…
An Ensemble Anomaly Detection Framework for Risk Calculation Integrity
New index improves anomaly detection in correlated time series data.
Deep Autoencoder outperforms in anomaly detection for building energy data.
Unified AI system for data quality control and governance in regulated environments.
In this paper, we introduce DeepQuarantine (DQ), a cloud technology to detect and quarantine potential spam messages. Spam attacks are becoming more diverse and can potentially be harmful to email users. Despite the high quality and performance of spam filtering systems, detection of a spam campaign can take some time.…
Develops a framework for continual learning in anomaly detection.
Study improves data quality assessment for structural monitoring data.
Do-AIQ framework evaluates AI algorithms' quality using DOE.
Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focuses on the use of artificial neural networks for supervised and semi-supervised problems related to the identification of anomalies in the d…
The study reveals fundamental limits of fraud detection in card payment networks.
Big data analytics improves healthcare through early detection and quality life.
A new metric MSD detects bias in datasets efficiently.
It is crucial for the wine industry to have methods like electronic nose systems (E-Noses) for real-time monitoring thresholds of acetic acid in wines, preventing its spoilage or determining its quality. In this paper, we prove that the portable and compact self-developed E-Nose, based on thin film semiconductor (SnO2)…
Amazon SageMaker Model Monitor detects drift in deployed ML models.
We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and cau…
Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessment of these disorders, based on voice signals. However, these signals can be sensitive to the recording devices. Indeed, the channel effect …
Unsupervised model detects healthcare fraud from patient visit data.
SynthBH uses synthetic data to control FDR in multiple testing.
The paper proposes a method to detect and filter noisy or mislabeled data using pointwise mutual information.
A novel density-based approach QC detects outliers in data with high precision.
Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient amount of training data. In recent years, autoencoders have been shown to be effective anomaly detectors that train only on "normal" data. G…
Study improves accuracy of weather data for real-time building simulations.
TimeLAVA learns time series segment values without model dependence.
A new watermarking method corrects bias in language models using maximal coupling.
Despite tremendous progress in outlier detection research in recent years, the majority of existing methods are designed only to detect unconditional outliers that correspond to unusual data patterns expressed in the joint space of all data attributes. Such methods are not applicable when we seek to detect conditional …
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving. We here provide an easy-to-use benchmark to assess how object detection models perform when image quality degrades. The three resulting benchmark datasets,…
Survey of data augmentation methods for improving deep learning on time series data.
Thanks to the growing availability of spoofing databases and rapid advances in using them, systems for detecting voice spoofing attacks are becoming more and more capable, and error rates close to zero are being reached for the ASVspoof2015 database. However, speech synthesis and voice conversion paradigms that are not…
Machine Learning (ML) has seen massive progress in the last decade and as a result, there is a pressing need for validating ML-based systems. To this end, we propose, design and evaluate CALLISTO - a novel test generation and data quality assessment framework. To the best of our knowledge, CALLISTO is the first blackbo…
In recent years, as intrusion attacks on IoT networks have grown exponentially, there is an immediate need for sophisticated intrusion detection systems (IDSs). A vast majority of current IDSs are data-driven, which means that one of the most important aspects of this area of research is the quality of the data acquire…
Change detection in multivariate time series has applications in many domains, including health care and network monitoring. A common approach to detect changes is to compare the divergence between the distributions of a reference window and a test window. When the number of dimensions is very large, however, the naive…
The paper presents anomaly detection in time series data using InfluxDB and Python.
QA-Token improves tokenization for noisy data, boosting model performance.
Deep learning on an edge device requires energy efficient operation due to ever diminishing power budget. Intentional low quality data during the data acquisition for longer battery life, and natural noise from the low cost sensor degrade the quality of target output which hinders adoption of deep learning on an edge d…
Due to the growing amount of data from in-situ sensors in wastewater systems, it becomes necessary to automatically identify abnormal behaviours and ensure high data quality. This paper proposes an anomaly detection method based on a deep autoencoder for in-situ wastewater systems monitoring data. The autoencoder archi…
Improves spline quality and accuracy in computational microscopy.
Novel framework monitors cardiac image segmentation models in real-time.
Community detection is, at its core, an attempt to attach an interpretable function to an otherwise indecipherable form. The importance of labeling communities has obvious implications for identifying clusters in social networks, but it has a number of equally relevant applications in product recommendations, biologica…
Framework for automatically assessing and correcting data quality issues without domain knowledge.
Survival rates for colorectal cancer are higher when polyps are detected at an early stage and can be removed before they develop into malignant tumors. Automated polyp detection, which is dominated by deep learning based methods, seeks to improve early detection of polyps. However, current efforts rely heavily on the …
New methods detect objects in industrial settings with little training data.