FAA identifies rodent disease carriers for human health.
problem Identifying rodent species that carry zoonotic diseases.
method Applied Formal Concept Analysis to rodent trait data.
result Identified concepts linking rodent traits to zoonotic disease carrier status.
Deep learning models outperform traditional methods in automated chief complaint classification for syndromic surveillance.
problem Improving accuracy and speed of automated classification of emergency department records for outbreak detection.
method Implemented two LSTM and GRU models compared to MNB and SVM classifiers trained on 3.6 million de-identified records.
result RNN models outperform bag-of-words classifiers, especially for chief complaints.
Paper uses LSTM autoencoder for ADS-B data to detect surveillance aircraft.
problem Detecting surveillance aircraft from ADS-B flight data.
method LSTM-based sequence autoencoder for ADS-B data.
result LSTM autoencoder effectively learns features for detecting surveillance aircraft.
New method tackles anomaly detection in video surveillance using continual learning.
problem Challenges in continual learning for high-dimensional applications like video surveillance.
method Transfer learning and continual learning for online anomaly detection.
result Significantly reduces training complexity and continual learning from recent data.
Improved bio-surveillance through automated document classification.
problem Tracking infectious diseases across global news alerts.
method Recurrent neural networks, TF-IDF, Naive Bayes, logistic regression.
result 97% recall and 93.3% accuracy in bio-surveillance event classification.
Classifiers and beamforming algorithms improved audio surveillance detection accuracy.
problem Detecting surveillance sound events with high accuracy and efficiency.
method Evaluated seven classifiers and two beamforming algorithms; used data augmentation and tested with varying SNR levels.
result SVM and Delay-and-Sum (DaS) combination achieved the highest accuracy (86.0%), but had high computational cost.
New algorithm rSVDdpd improves robustness and scalability for video surveillance background modeling.
problem Camera tampering and noisy videos make background separation challenging.
method Introduces rSVDdpd, a robust singular value decomposition technique for scalable video surveillance.
result Demonstrates superior performance on benchmark and real-life datasets.
Online anomaly detection in surveillance videos with false alarm rate bounds.
problem Lack of theoretical performance analysis and online decision making in anomaly detection.
method Proposes an online anomaly detection method with asymptotic bounds on false alarm rate.
result Demonstrates effectiveness on publicly available data sets, outperforming state-of-the-art algorithms.
Smart city surveillance benefits from sound event recognition.
problem Improving monitoring capabilities in smart cities.
method Exploration of several classifiers on the SESA dataset.
result SGD achieved 72.13% accuracy in sound event recognition.
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial time algorit…
New method optimizes searchers' allocation on perimeters over time.
problem Optimizing searchers' allocation on perimeters to detect intrusions.
method Combinatorial multi-armed bandit (CMAB) with upper confidence bound approach.
result Upper and lower bounds on expected performance of the method.
Online surveillance detects systemic risk in financial markets.
problem Detecting and monitoring systemic risk in financial markets.
method Online monitoring procedures for multiple series, controlling for false rejections.
result Procedures allow timely detection of financial distress.
Supervised learning improves disease outbreak detection accuracy.
problem Early detection of infectious disease outbreaks to protect public health.
method Developed a supervised learning approach based on hidden Markov models for disease outbreak detection.
result Reduces false positive rate by up to 50% while maintaining sensitivity.
Proposes a method to identify key components for predicting epidemic dynamics with limited resources.
problem Predicting epidemic dynamics with limited surveillance resources.
method Developed a group sparse Bayesian learning algorithm to identify sentinel components for monitoring.
result The proposed algorithm effectively predicts epidemic dynamics using partial data from sentinel components.
Paper describes a video surveillance system for highway traffic events.
problem Detecting specific sequences of situations in highway traffic videos.
method Compares RNN and CNN architectures for analyzing video frames and sequences.
result Best architecture performs well in real conditions.
Word2vec model captures disease attributes from unstructured text.
problem Augmenting disease surveillance with unstructured data requires accurate taxonomical correlations and trace mapping.
method Developed a disease vocabulary driven word2vec model (Dis2Vec) to model diseases and their attributes.
result Dis2Vec outperforms traditional word2vec methods in capturing taxonomical attributes across different disease classes.
Cloud-based health records predict flu outbreaks.
problem Real-time monitoring of influenza outbreaks.
method Electronic health records, machine learning, historical epidemiology.
result Accurate regional predictions of flu outbreaks.
Machine learning algorithms improve autism surveillance accuracy.
problem Efficiently predicting autism from evaluations to reduce human labor.
method 8 supervised learning algorithms tested on CDC ASD data.
result Random forest and NB-SVM perform similarly, with NB-SVM having more false negatives.
Algorithm predicts zoonotic virus emergence with high accuracy.
problem Precise spatio-temporal prediction of zoonotic virus emergence.
method Machine inference using protein sequence databases.
result Quantitative indicators of jump risk from genotypic changes.
Anomaly detection identifies unusual malaria transmission patterns in Ghana.
problem Identifying atypical malaria transmission patterns in Ghana's spatiotemporal surveillance data.
method Consensus-based anomaly detection framework applied to monthly malaria surveillance data.
result High-burden areas are not necessarily those with the most frequent anomalous transmission.
Study uses Instagram data to predict flu-like illnesses.
problem Influenza surveillance challenges due to resource limitations.
method Machine learning models trained on Instagram data, including visual content.
result Best nowcasting model had an MAE of 11.33 and correlation of 0.963.
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
problem Enhancing vehicle tracking accuracy in WSNs without increasing energy consumption.
method Decentralized strategy with dynamic reinforcement learning to adjust sensing areas.
result Simulation results demonstrate superior performance of DRL-aided design.
Zero-shot understanding of accidents from surveillance videos using vision-language models
problem Accident understanding from surveillance videos
method Three-stage pipeline with vision-language similarity, metadata-driven multi-prompt reasoning, and entropy-gated pairwise adjudicator
result Substantial improvement in harmonic-mean score over baseline
New model maps malaria prevalence across Kenya's changing administrative boundaries.
problem Mapping disease prevalence with changing administrative boundaries.
method Combines deep learning and MCMC with aggVAE for disease mapping.
result Solves the change-of-support problem in disease surveillance.
According to the definition of the London Interbank Offered Rate (LIBOR), contributing banks should give fair estimates of their own borrowing costs in the interbank market. Between 2007 and 2009, several banks made inappropriate submissions of LIBOR, sometimes motivated by profit-seeking from their trading positions. …
Flusion combines multiple data sources to improve flu forecasts.
problem Accurate flu predictions to improve public health actions.
method Ensemble model combining gradient boosting quantile regression and Bayesian autoregressive models.
result Flusion was the top-performing model in the CDC's influenza prediction challenge.
Mobile apps and machine learning improve malaria prevention and treatment.
problem High malaria cases and deaths in low-income countries.
method Adaptive interventions using mobile health apps and machine learning.
result Increased malaria testing, adherence, and provider skills.
Deep learning model for AIS data improves vessel monitoring.
problem Improving maritime safety and efficiency with AIS data.
method Combining RNNs and latent variable modeling for multi-task learning.
result Demonstrated relevance on real AIS datasets for trajectory reconstruction, anomaly detection, and vessel type identification.
Develops an efficient online robust PCA method for big data.
problem Efficiency and robustness in processing big data with changing subspaces.
method Online moving window robust principal component analysis (OMWRPCA) with change point detection.
result Successfully tracks both slowly and abruptly changing subspaces and detects change points.
In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interact…
Improved outbreak detection using machine learning fusion of statistical algorithms.
problem Balancing detection of outbreaks with false alarms.
method Train a fusion classifier using p-values and additional features.
result Fusion classifier using p-values and additional features improves outbreak detection.
New feature representation improves HAR model accuracy.
problem Improving human activity recognition model performance.
method Proposed new feature representation and comparison with existing methods.
result Techniques based on the proposed representation outperform baselines.
Unsupervised fire and smoke segmentation from IR videos.
problem Early detection of fire from infrared videos.
method Spatial, temporal, and motion information fusion using optical flow, divergence, and intensity values.
result Markov Random Field outperforms other clustering algorithms in fire and smoke segmentation.
WoodScape dataset for fisheye cameras in autonomous driving.
problem Lack of public datasets for evaluating computer vision on fisheye images.
method Release WoodScape dataset with four surround view cameras and nine tasks.
result Encourages adaptation of computer vision models for fisheye cameras.
Method detects insider trading using trading data and dimensionality reduction.
problem Identifying insider trading in large datasets.
method Unsupervised machine learning, principal component analysis, autoencoders.
result Identifies suspicious trading behavior based on reconstruction errors.
The paper classifies vehicle shapes and colors using deep neural networks.
problem Vehicle reidentification and classification challenges.
method Used deeper neural networks for classification accuracy.
result Good classification accuracy on make/model and color.
Person re-identification (re-id), an emerging problem in visual surveillance, deals with maintaining entities of individuals whilst they traverse various locations surveilled by a camera network. From a visual perspective re-id is challenging due to significant changes in visual appearance of individuals in cameras wit…
In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a collusive clique and collaborate to manipulate some instruments, thus mislead other …
It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveil…
Adaptive sequential testing optimizes epidemic control by learning optimal test strategies.
problem Optimizing test allocation in epidemics with network and temporal dependence.
method Adaptive sequential design with Online Super Learner for optimal test strategies.
result Superior performance in simulated university COVID-19 pandemic.
Two machine learning methods detect insider trading from investor activity data.
problem Detecting insider trading from trading activity data is challenging.
method Two unsupervised machine learning methods: clustering and group identification.
result Identifies potential insider trading rings around price sensitive events.
ASTPN improves video-based person re-identification by jointly attending to spatial and temporal features.
problem Video-based person re-identification in surveillance and HCI.
method Joint Spatial and Temporal Attention Pooling Network (ASTPN).
result ASTPN outperforms state-of-the-art methods on multiple datasets.
Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance. Traditional approaches to this problem typically rely on supervised lea…
Framework extracts symptoms from EHRs for rapid disease outbreak detection.
problem Extracting relevant data from unstructured medical texts.
method Conformal active learning for efficient data mining.
result Framework achieves strong performance with minimal manual labeling.
Efficiently detects anomalies in videos with reduced computation.
problem Anomaly detection in videos for robust and efficient systems.
method Powers-of-two weights and denoising for CNN simplification and robustness.
result 10% faster detection with comparable accuracy and robustness.
Classi-Fly uses machine learning to infer aircraft categories from open data.
problem Lack of metadata for aircraft in open data sources.
method Machine learning approach based on aircraft movement patterns.
result Correct aircraft category inference with over 88% accuracy.
The detection of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks. Beyond its wide applicability, graph structured anomaly detection serves as a case study in the difficulty of bal…
A new ML framework for RF fingerprinting across various applications.
problem Extracting unique RF fingerprints for specific emitter identification.
method Generic machine learning framework for automatic RF fingerprinting.
result Framework achieves superior performance compared to traditional methods.