Automatic Event Research Articles

Automatic event detection in football using tracking data

One of the main shortcomings of event data in football, which has been extensively used for analytics in the recent years, is that it still requires manual collection, thus limiting its availability to a reduced number of tournaments. In this work, we propose a deterministic decision tree-based algorithm to automatically extract football events using tracking data, which consists of two steps: (1) a possession step that evaluates which player was in possession of the ball at each frame in the tracking data, as well as the distinct player configurations during the time intervals where the ball is not in play to inform set piece detection; (2) an event detection step that combines the changes in ball possession computed in the first step with the laws of football to determine in-game events and set pieces. The automatically generated events are benchmarked against manually annotated events and we show that in most event categories the proposed methodology achieves +90% detection rate across different tournaments and tracking data providers. Finally, we demonstrate how the contextual information offered by tracking data can be leveraged to increase the granularity of auto-detected events, and exhibit how the proposed framework may be used to conduct a myriad of data analyses in football.

Increasing the Lateral Resolution of 3D-GPR Datasets through 2D-FFT Interpolation with Application to a Case Study of the Roman Villa of Horta da Torre (Fronteira, Portugal)

The approach presented in this work uses an interpolation methodology to densify 3D-GPR datasets to sharpen the results obtained in GPR surveys carried out in an archaeological environment. It allows the estimation of missing data from the combined use of mathematical transforms, such as the Fourier and curvelet transforms, and predictive filters. This technique makes it possible to calculate the missing signal simply by meeting two requirements: the data in the frequency domain must be limited in a range of values and must be able to be represented by a distribution of Fourier coefficients (verified conditions). The INT-FFT algorithm uses an open-access routine (Suinterp, Seismic Unix) to interpolate the GPR B-scans based on seismic trace interpolation. This process uses automatic event identification routines by calculating spatial derivatives to identify discontinuities in space by detecting very subtle changes in the signal, thus allowing for more efficient interpolation without artifacts or signal deterioration. We successfully tested the approach using GPR datasets from the Roman villa of Horta da Torre (Fronteira, Portugal). The results showed an increase in the geometric sharpness of the GPR reflectors and did not produce any numerical artifacts. The tests performed to apply the methodology to GPR-3D data allowed for assessing the interpolation efficiency, the level of estimation of missing data, and the level of information lost when we chose to increase the distance between B-scans in the acquisition stage.

GeoClust: Feature engineering based framework for location-sensitive disaster event detection using AHP-TOPSIS

Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust, that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis.

Development of a frequency-domain based automatic event detection algorithm for EDA sleep signals and comparison with manual scorings

Development of a frequency-domain based automatic event detection algorithm for EDA sleep signals and comparison with manual scorings

A Nanopore‐Based Saccharide Sensor

AbstractSaccharides play critical roles in many forms of cellular activities. Saccharide structures are however complicated and similar, setting a technical hurdle for direct identification. Nanopores, which are emerging single molecule tools sensitive to minor structural differences between analytes, can be engineered to identity saccharides. A hetero‐octamericMycobacterium smegmatisporin A nanopore containing a phenylboronic acid was prepared, and was able to clearly identify nine monosaccharide types, including D‐fructose, D‐galactose, D‐mannose, D‐glucose, L‐sorbose, D‐ribose, D‐xylose, L‐rhamnose and N‐acetyl‐D‐galactosamine. Minor structural differences between saccharide epimers can also be distinguished. To assist automatic event classification, a machine learning algorithm was developed, with which a general accuracy score of 0.96 was achieved. This sensing strategy is generally suitable for other saccharide types and may bring new insights to nanopore saccharide sequencing.

A Nanopore-Based Saccharide Sensor.

Saccharides play critical roles in many forms of cellular activities. Saccharide structures are however complicated and similar, setting a technical hurdle for direct identification. Nanopores, which are emerging single molecule tools sensitive to minor structural differences between analytes, can be engineered to identity saccharides. A hetero-octameric Mycobacterium smegmatis porin A nanopore containing a phenylboronic acid was prepared, and was able to clearly identify nine monosaccharide types, including D-fructose, D-galactose, D-mannose, D-glucose, L-sorbose, D-ribose, D-xylose, L-rhamnose and N-acetyl-D-galactosamine. Minor structural differences between saccharide epimers can also be distinguished. To assist automatic event classification, a machine learning algorithm was developed, with which a general accuracy score of 0.96 was achieved. This sensing strategy is generally suitable for other saccharide types and may bring new insights to nanopore saccharide sequencing.

Data‐Driven Discovery of Fokker‐Planck Equation for the Earth's Radiation Belts Electrons Using Physics‐Informed Neural Networks

AbstractWe use the framework of Physics‐Informed Neural Network (PINN) to solve the inverse problem associated with the Fokker‐Planck equation for radiation belts' electron transport, using 4 years of Van Allen Probes data. Traditionally, reduced models have employed a diffusion equation based on the quasilinear approximation. We show that the dynamics of “killer electrons” is described more accurately by a drift‐diffusion equation, and that drift is as important as diffusion for nearly‐equatorially trapped ∼1 MeV electrons in the inner part of the belt. Moreover, we present a recipe for gleaning physical insight from solving the ill‐posed inverse problem of inferring model coefficients from data using PINNs. Furthermore, we derive a parameterization for the diffusion and drift coefficients as a function of L only, which is both simpler and more accurate than earlier models. Finally, we use the PINN technique to develop an automatic event identification method that allows identifying times at which the radial transport assumption is inadequate to describe all the physics of interest.

Fascia and Myofascial Pain Syndrome - An Overview

This paper synthesized the available published literature on myofascia, its structure, myofascial disorder, causative factors, scientific methods for evaluation and treatments, etc. The importance of myofascial in chronic and acute musculoskeletal pain is overlooked. According to Waller et al., mention in their article, up to 85% of people will experience myofascial pain at least once during their life. The myofascia is a musculoskeletal cell matrix that supports muscular connective tissue, it is essential for generating forces between muscles and also provides better flexibility. Myofascial pain syndrome is described as sensory, motor, and autonomic signs and symptoms that are produced by trigger points or taut bands of skeletal muscle or fascia. A myofascial trigger point is painful on compression and can give rise to referred pain, motor dysfunction, and automatic event, it restricts the range of motion and force generation capacity. Therefore, it is utmost importance to know about myofascial disorders, their prevention techniques, diagnosis, and treatment.

Event detection in surveillance videos: a review

Since 2008, a variety of systems have been designed to detect events in security cameras. There are also more than a hundred journal articles and conference papers published in this field. However, no survey has focused on recognizing events in the surveillance system. Thus, motivated us to provide a comprehensive review of the different developed event detection systems. We start our discussion with the pioneering methods that used the TRECVid-SED dataset and then developed methods using VIRAT dataset in TRECVid evaluation. To better understand the designed systems, we describe the components of each method and the modifications of the existing method separately. We have outlined the significant challenges related to untrimmed security video action detection. Suitable metrics are also presented for assessing the performance of the proposed models. Our study indicated that the majority of researchers classified events into two groups on the basis of the number of participants and the duration of the event for the TRECVid-SED Dataset. Depending on the group of events, one or more models to identify all the events were used. For the VIRAT dataset, object detection models to localize the first stage activities were used throughout the work. Except one study, a 3D convolutional neural network (3D-CNN) to extract Spatio-temporal features or classifying different activities were used. From the review that has been carried, it is possible to conclude that developing an automatic surveillance event detection system requires three factors: accurate and fast object detection in the first stage to localize the activities, and classification model to draw some conclusion from the input values.

End-to-end LSTM based estimation of volcano event epicenter localization

Locating sources of volcano-seismic event is very relevant to monitor and comprehend volcanic processes. Ordinary estimation of source seismic events is based on phase picking. The most accurate procedure of phase selection is the visual inspection of the records by experts, who employ local characteristics for phase detection and comparison with observed signals from other stations. This activity is highly time demanding, which in turn is a strong motivation to automatize the epicenter estimation process. However, automatic phase picking in volcano signals is highly inaccurate because of the short distances between the event epicenters and the seismograph stations. In this paper, an end-to-end based LSTM (Long-Short Term Memory) scheme is proposed to address the problem of volcano event localization without any a priori model relating phase picking with localization estimation. LSTM was chosen due to its capability to capture the dynamics of time varying signals, and to remove or add information within the memory cell state and model long-term dependencies. A brief insight into LSTM is also discussed here to justify the use of this neural network. The results presented in this paper show that the LSTM based architecture provided a success rate, i.e., an error smaller than 1.0 km, equal to 48.5%, which in turn is dramatically superior to the one delivered by automatic phase picking. Moreover, the proposed end-to-end LSTM based method gave a success rate (18%) higher than CNN (Convolutional Neural Network). The results presented suggest that the approach proposed here for automatic volcano event epicenter localization can be applied to other geophysics problems.

Single-Molecule Sensing of Acidic Catecholamine Metabolites Using a Programmable Nanopore.

Acidic catecholamine metabolites, which could serve as diagnostic markers for many diseases, demonstrate an importance of accurate sensing. However, they share a highly similar chemical structure, which is a challenge in the design of sensing strategies. A nanopore may be engineered to sense these metabolites in a single molecule manner. To achieve this, a recently developed programmable nano-reactor for stochastic sensing (PNRSS) technique adapted with a phenylboronic acid (PBA) adaptor was applied. Three acidic catecholamine metabolites, including 3,4-dihydroxyphenylacetic acid (DOPAC), 3,4-dihydroxymandelic acid (DHMA) and 3-methoxy-4-hydroxymandetic acid (VMA) were investigated by PNRSS. Specifically, DHMA, which contains an α-hydroxycarboxylate moiety and an adjacent cis-hydroxyl groups on its benzene ring, reports two binding modes simultaneously resolvable by PNRSS. Assisted with the high resolution of PNRSS, direct regulation of these two binding modes by pH can also be observed. A custom machine learning algorithm was also developed to achieve automatic event classification.

Automatic gait event detection in pathologic gait using an auto-selection approach among concurrent methods

BackgroundAccurate gait event detection is crucial to analyze pathological gait data. Existing methods relying on marker trajectories were reported to be sensitive to different gait patterns, which is an inherent characteristic of pathologic gait. Research questionWe propose a new approach based on auto-selection among different methods, original and taken from the literature. MethodsThe auto-selection approach evaluates the accuracy of the implemented methods for both foot-strike and foot-off on all available events detected by the force platforms, independently, and automatically selects the most accurate one to be used on the whole gait session. Pathological gait data from 272 patients with cerebral palsy and idiopathic toe walking were used retrospectively to evaluate the accuracy of this approach. Three methods previously reported in literature together with original methods developed based on auto-correlation were implemented and constituted our auto-selection approach. The accuracy and precision were compared to a recently reported method based on deep events as it is the method that showed the best performance in literature. ResultsResults showed that the proposed approach outperformed all implemented methods used alone, with an accuracy of − 2.0 ms and − 0.9 ms for foot strike and foot-off, respectively. Additionally, more than 99% and 93% of events detected were detected within 20 ms and 10 ms of accuracy, respectively. SignificanceThe proposed methodology has demonstrated to improve the accuracy and precision of gait event detection in gait analysis.

MALMI: An Automated Earthquake Detection and Location Workflow Based on Machine Learning and Waveform Migration

Abstract Robust automatic event detection and location is central to real-time earthquake monitoring. With the increase of computing power and data availability, automated workflows that utilize machine learning (ML) techniques have become increasingly popular; however, ML-based classical workflows still face challenges when applied to the analysis of microseismic data. These seismic sequences are often characterized by short interevent times and/or low signal-to-noise ratio (SNR). Full waveform methods that do not rely on phase picking and association are suitable for processing such datasets, but are computationally costly and lack clear event identification criteria, which is not ideal for real-time processing. To leverage the advantages of both the methods, we propose a new workflow—MAchine Learning aided earthquake MIgration location (MALMI), which integrates ML and waveform migration to perform automated event detection and location. The new workflow uses a pretrained ML model to generate continuous phase probabilities that are then backprojected and stacked to locate seismic sources using migration. We applied the workflow to one month of continuous data collected in the Hengill geothermal area of Iceland to monitor induced earthquakes around two geothermal production sites. With a ML model (EQ-Transformer) pretrained using a global distribution of earthquakes, the proposed workflow automatically detects and locates 250 additional seismic events (accounting for 36% events in the obtained catalog) compared to a reference catalog generated using the SeisComP software. Most of the new events are microseismic events with a magnitude less than 0. Visual inspection of the waveforms of the newly detected events indicates that they are real seismic events of low SNR and are only reliably recorded by very few stations in the array. Further comparison with the conventional migration method based on short-term average over long-term average confirms that MALMI can produce much clearer stacked images with higher resolution and reliability, especially for events with low SNR. The workflow is freely available on GitHub, providing an automated tool for simultaneous event detection and location from continuous seismic data.

Automatic Events Recognition in Low SNR Microseismic Signals of Coal Mine Based on Wavelet Scattering Transform and SVM

The technology of microseismic monitoring, the first step of which is event recognition, provides an effective method for giving early warning of dynamic disasters in coal mines, especially mining water hazards, while signals with a low signal-to-noise ratio (SNR) usually cannot be recognized effectively by systematic methods. This paper proposes a wavelet scattering decomposition (WSD) transform and support vector machine (SVM) algorithm for discriminating events of microseismic signals with a low SNR. Firstly, a method of signal feature extraction based on WSD transform is presented by studying the matrix constructed by the scattering decomposition coefficients. Secondly, the microseismic events intelligent recognition model built by operating a WSD coefficients calculation for the acquired raw vibration signals, shaping a feature vector matrix of them, is outlined. Finally, a comparative analysis of the microseismic events and noise signals in the experiment verifies that the discriminative features of the two can accurately be expressed by using wavelet scattering coefficients. The artificial intelligence recognition model developed based on both SVM and WSD not only provides a fast method with a high classification accuracy rate, but it also fits the online feature extraction of microseismic monitoring signals. We establish that the proposed method improves the efficiency and the accuracy of microseismic signals processing for monitoring rock instability and seismicity.

A novel hybrid-based approach of snort automatic rule generator and security event correlation (SARG-SEC).

The rapid advanced technological development alongside the Internet with its cutting-edge applications has positively impacted human society in many aspects. Nevertheless, it equally comes with the escalating privacy and critical cybersecurity concerns that can lead to catastrophic consequences, such as overwhelming the current network security frameworks. Consequently, both the industry and academia have been tirelessly harnessing various approaches to design, implement and deploy intrusion detection systems (IDSs) with event correlation frameworks to help mitigate some of these contemporary challenges. There are two common types of IDS: signature and anomaly-based IDS. Signature-based IDS, specifically, Snort works on the concepts of rules. However, the conventional way of creating Snort rules can be very costly and error-prone. Also, the massively generated alerts from heterogeneous anomaly-based IDSs is a significant research challenge yet to be addressed. Therefore, this paper proposed a novel Snort Automatic Rule Generator (SARG) that exploits the network packet contents to automatically generate efficient and reliable Snort rules with less human intervention. Furthermore, we evaluated the effectiveness and reliability of the generated Snort rules, which produced promising results. In addition, this paper proposed a novel Security Event Correlator (SEC) that effectively accepts raw events (alerts) without prior knowledge and produces a much more manageable set of alerts for easy analysis and interpretation. As a result, alleviating the massive false alarm rate (FAR) challenges of existing IDSs. Lastly, we have performed a series of experiments to test the proposed systems. It is evident from the experimental results that SARG-SEC has demonstrated impressive performance and could significantly mitigate the existing challenges of dealing with the vast generated alerts and the labor-intensive creation of Snort rules.

Deep long-period earthquakes near Mount Meager, British Columbia

Deep long-period earthquakes (DLPs) are a class of seismicity that has been documented in volcanic fields worldwide and are thought to be related to the motion of fluids (magmas and volatiles) associated with volcanic processes. We applied an automatic event detection algorithm to stations of the Canadian National Seismograph Network in southwestern British Columbia and documented the first DLPs observed within the Garibaldi Volcanic Belt. A total of 42 events satisfying the spectral criteria of DLPs were detected at station PMB between 1993 and 1998, of which 26 events were located using three-component polarization analysis and 1-D ray tracing. An additional six events were identified and located from station MGMB between 2016 and 2019. The events are small ( ML ∼ 0) and the majority of the epicenters are clustered at ∼45 km ENE of Mount Meager, the site of Canada’s most recent large eruption at 2.4 ka. They lie in closer proximity and may be related to Quaternary volcanism associated with the Bridge River Cones.

Characterizing and Modeling Smoking Behavior Using Automatic Smoking Event Detection and Mobile Surveys in Naturalistic Environments: Observational Study.

BackgroundThere are 1.1 billion smokers worldwide, and each year, more than 8 million die prematurely because of cigarette smoking. More than half of current smokers make a serious quit every year. Nonetheless, 90% of unaided quitters relapse within the first 4 weeks of quitting due to the lack of limited access to cost-effective and efficient smoking cessation tools in their daily lives.ObjectiveThis study aims to enable quantified monitoring of ambulatory smoking behavior 24/7 in real life by using continuous and automatic measurement techniques and identifying and characterizing smoking patterns using longitudinal contextual signals. This work also intends to provide guidance and insights into the design and deployment of technology-enabled smoking cessation applications in naturalistic environments.MethodsA 4-week observational study consisting of 46 smokers was conducted in both working and personal life environments. An electric lighter and a smartphone with an experimental app were used to track smoking events and acquire concurrent contextual signals. In addition, the app was used to prompt smoking-contingent ecological momentary assessment (EMA) surveys. The smoking rate was assessed based on the timestamps of smoking and linked statistically to demographics, time, and EMA surveys. A Poisson mixed-effects model to predict smoking rate in 1-hour windows was developed to assess the contribution of each predictor.ResultsIn total, 8639 cigarettes and 1839 EMA surveys were tracked over 902 participant days. Most smokers were found to have an inaccurate and often biased estimate of their daily smoking rate compared with the measured smoking rate. Specifically, 74% (34/46) of the smokers made more than one (mean 4.7, SD 4.2 cigarettes per day) wrong estimate, and 70% (32/46) of the smokers overestimated it. On the basis of the timestamp of the tracked smoking events, smoking rates were visualized at different hours and were found to gradually increase and peak at 6 PM in the day. In addition, a 1- to 2-hour shift in smoking patterns was observed between weekdays and weekends. When moderate and heavy smokers were compared with light smokers, their ages (P<.05), Fagerström Test of Nicotine Dependence (P=.01), craving level (P<.001), enjoyment of cigarettes (P<.001), difficulty resisting smoking (P<.001), emotional valence (P<.001), and arousal (P<.001) were all found to be significantly different. In the Poisson mixed-effects model, the number of cigarettes smoked in a 1-hour time window was highly dependent on the smoking status of an individual (P<.001) and was explained by hour (P=.02) and age (P=.005).ConclusionsThis study reported the high potential and challenges of using an electronic lighter for smoking annotation and smoking-triggered EMAs in an ambulant environment. These results also validate the techniques for smoking behavior monitoring and pave the way for the design and deployment of technology-enabled smoking cessation applications.International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2018-028284

New particle formation event detection with Mask R-CNN

Abstract. Atmospheric new particle formation (NPF) is an important source of climate-relevant aerosol particles which has been observed at many locations globally. To study this phenomenon, the first step is to identify whether an NPF event occurs or not on a given day. In practice, NPF event identification is performed visually by classifying the NPF event or non-event days from the particle number size distribution surface plots. Unfortunately, this day-by-day visual classification is time-consuming and labor-intensive, and the identification process renders subjective results. To detect NPF events automatically, we regard the visual signature (banana shape) which has been observed all over the world in NPF surface plots as a special kind of object, and a deep learning model called Mask R-CNN is applied to localize the spatial layouts of NPF events in their surface plots. Utilizing only 358 human-annotated masks on data from the Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II station (Hyytiälä, Finland), the Mask R-CNN model was successfully generalized for three SMEAR stations in Finland and the San Pietro Capofiume (SPC) station in Italy. In addition to the detection of NPF events (especially the strongest events), the presented method can determine the growth rates, start times, and end times for NPF events automatically. The automatically determined growth rates agree with the manually determined growth rates. The statistical results validate the potential of applying the proposed method to different sites, which will improve the automatic level for NPF event detection and analysis. Furthermore, the proposed automatic NPF event analysis method can minimize subjectivity compared with human-made analysis, especially when long-term data series are analyzed and statistical comparisons between different sites are needed for event characteristics such as the start and end times, thereby saving time and effort for scientists studying NPF events.

Stance Phase Detection by Inertial Measurement Unit Placed on the Metacarpus of Horses Trotting on Hard and Soft Straight Lines and Circles.

The development of on-board technologies has enabled the development of quantification systems to monitor equine locomotion parameters. Their relevance among others relies on their ability to determine specific locomotor events such as foot-on and heel-off events. The objective of this study was to compare the accuracy of different methods for an automatic gait events detection from inertial measurement units (IMUs). IMUs were positioned on the cannon bone, hooves, and withers of seven horses trotting on hard and soft straight lines and circles. Longitudinal acceleration and angular velocity around the latero-medial axis of the cannon bone, and withers dorso-ventral displacement data were identified to tag the foot-on and a heel-off events. The results were compared with a reference method based on hoof-mounted-IMU data. The developed method showed bias less than 1.79%, 1.46%, 3.45% and −1.94% of stride duration, respectively, for forelimb foot-on and heel-off, and for hindlimb foot-on and heel-off detection, compared to our reference method. The results of this study showed that the developed gait-events detection method had a similar accuracy to other methods developed for straight line analysis and extended this validation to other types of exercise (circles) and ground surface (soft surface).

A forensic-driven data model for automatic vehicles events analysis

Digital vision technologies emerged exponentially in all living areas to watch, play, control, or track events. Security checkpoints have benefited also from those technologies by integrating dedicated cameras in studied locations. The aim is to manage the vehicles accessing the inspection security point and fetching for any suspected ones. However, the gathered data volume continuously increases each day, making their analysis very hard and time-consuming. This paper uses semantic-based techniques to model the data flow between the cameras, checkpoints, and administrators. It uses ontologies to deal with the increased data size and its automatic analysis. It considers forensics requirements throughout the creation of the ontology modules to ensure the records’ admissibility for any possible investigation purposes. Ontology-based data modeling will help in the automatic events search and correlation to track suspicious vehicles efficiently.