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.
AI systems that explain their decisions can be monitored for harmful intentions.
Paper optimizes a big data and ML risk monitoring system for financial markets.
Process Monitoring involves tracking a system's behaviors, evaluating the current state of the system, and discovering interesting events that require immediate actions. In this paper, we consider monitoring temporal system state sequences to help detect the changes of dynamic systems, check the divergence of the syste…
Neural system optimizes glucose levels in diabetics.
Online surveillance detects systemic risk in financial markets.
Amazon SageMaker Model Monitor detects drift in deployed ML models.
Dividing deep learning models for consistent anomaly detection in changing log data.
Proposes real-time risk monitoring for machine learning systems under unknown shifts.
MLDemon monitors ML systems post-deployment, improving reliability with real-time performance estimates and expert labels.
This research tackles monitoring machine learning algorithms post-deployment, addressing performativity issues.
AnomalyCD discovers anomaly causes in large systems with binary flags, reducing computational burden.
Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is …
Deep learning improves material recognition in construction monitoring.
Adaptive monitoring for AI systems detects and diagnoses shifts in data distribution.
Nowadays, when crashes and crises are rather frequent events, an effective monitoring system for the international financial market is needed. Modern nonlinear methods, such as Recurrence Quantification Analysis (RQA), demonstrate the ability to reveal the regularities of the system behavior. Thus, they can be useful f…
Paper improves ETF tail-risk monitoring reliability.
AIMM-X monitors markets for suspicious behavior using transparent scoring.
Optimizes state monitoring in Markovian systems with cost constraints.
We present a test platform for visual in-cabin scene analysis and occupant monitoring functions. The test platform is based on a driving simulator developed at the DFKI, consisting of a realistic in-cabin mock-up and a wide-angle projection system for a realistic driving experience. The platform has been equipped with …
The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents and detecting illegal activities. In this work, we describe research gaps and challenges in machine learning for vessel behavior change and …
Database activity monitoring (DAM) systems are commonly used by organizations to protect the organizational data, knowledge and intellectual properties. In order to protect organizations database DAM systems have two main roles, monitoring (documenting activity) and alerting to anomalous activity. Due to high-velocity …
Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining …
Work addresses long-term accuracy issues in IoT air quality sensors.
AI model enhances grid monitoring with synchro-waveform tech.
Monitoring means to observe a system for any changes which may occur over time, using a monitor or measuring device of some sort. In this paper we formulate a problem of monitoring dates of maximal risk of a financial position. Thus, the systems we are going to observe arise from situations in finance. The measuring de…
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…
We introduce a methodology for efficient monitoring of processes running on hosts in a corporate network. The methodology is based on collecting streams of system calls produced by all or selected processes on the hosts, and sending them over the network to a monitoring server, where machine learning algorithms are use…
This paper discusses challenges and opportunities in vessel behavior detection using machine and deep learning.
Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, they may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, t…
Optimal probing framework for scalable network monitoring.
Study compares semi-supervised learning methods for anomaly detection in hydraulic systems.
Risk-based active learning improves SHM decision-making.
SafeML monitors ML systems for safety and security risks.
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator c…
The paper develops methods for monitoring TPL machine health.
One approach to monitoring a dynamic system relies on decomposition of the system into weakly interacting subsystems. An earlier paper introduced a notion of weak interaction called separability, and showed that it leads to exact propagation of marginals for prediction. This paper addresses two questions left open by t…
Real time large scale streaming data pose major challenges to forecasting, in particular defying the presence of human experts to perform the corresponding analysis. We present here a class of models and methods used to develop an automated, scalable and versatile system for large scale forecasting oriented towards saf…
The failure of a complex and safety critical industrial asset can have extremely high consequences. Close monitoring for early detection of abnormal system conditions is therefore required. Data-driven solutions to this problem have been limited for two reasons: First, safety critical assets are designed and maintained…
Personalized deep learning reduces inappropriate shocks in VA detection.
Infrastructure monitors AI/ML radiology models across multiple sites.
While current machine learning models have impressive performance over a wide range of applications, their large size and complexity render them unsuitable for tasks such as remote monitoring on edge devices with limited storage and computational power. A naive approach to resolve this on the model level is to use simp…
Two new approaches improve decision-making in asset monitoring systems.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
The high-dimensionality and volume of large scale multistream data has inhibited significant research progress in developing an integrated monitoring and diagnostics (M&D) approach. This data, also categorized as big data, is becoming common in manufacturing plants. In this paper, we propose an integrated M\&D approach…
Paper develops a robust federated recommendation system against poisoning attacks.
Study reviews machine learning techniques for stress monitoring.
The paper improves neural network predictions by integrating process knowledge.
Modified PCA algorithm with continual learning preserves features of previous modes for multimode process monitoring.