Correlation Of Events Research Articles

Model of combined application of intelligent methods of information security events correlation

Model of combined application of intelligent methods of information security events correlation

Multisource traffic incident reporting and evidence management in Internet of Vehicles using machine learning and blockchain

Intelligent transportation systems require efficient ways to manage traffic incidents like accidents and traffic rule violations. Secure logging of incident related data is important in case of accidents for forensic analysis, insurance, and legal settlements. Internet of vehicles can be enhanced using machine learning techniques to detect events automatically at the vehicle and road infrastructure level and blockchain can be used as a source for immutable evidence storage and management. The work proposes a framework addressing multiple challenges in traffic incident detection and evidence management like gathering of traffic event related evidence from multiple sources, addressing fault management in embedded vehicle on board units, resiliency from malicious event reporting and handling conflicting traffic incident event reports. The framework relies on a combination of cooperative event correlation and trust model to detect malicious and erroneous reporting of traffic incidents followed by Long Short term Memory (LSTM) and Bayesian model to resolve conflicting event reports. The events are correlated and verified before being transmitting onto the blockchain for evidence management and access control by various stakeholders. Analysis is presented highlighting the improvement in efficiency achieved by convergence of the multiple approaches proposed.

The impact of climate change on the storm surges of the Mediterranean Sea: Coastal sea level responses to deep depression atmospheric systems

The impact of climate change on the storm surges of the Mediterranean Sea: Coastal sea level responses to deep depression atmospheric systems

Early Miocene Sr-geochronology of Patagoniense marine units from southern Argentina: Implications for molluscan assemblages and interbasinal correlation

Early geological investigations in southern South America addressed the Oligocene-Miocene Patagoniense deposits, a fossiliferous marine succession distributed in several basins that had been flooded by three different transgressive events. Chronological calibration and correlation of these transgressive events, lithological units and fossil assemblages through the Patagonian basins still need further definitions in some areas. To tackle this problem, new Sr-isotope ages of pectinid shells from four early Miocene key sections located at the coastal sector of Patagonia, were obtained to improve correlations among the Magallanes-Austral (MAB), San Jorge (SJB) and Mazarredo (MB) basins. A bed in the middle part of the Punta Guanacos section (MAB) was dated in a mean age of 19.8 Ma allowing its correlation with the lower section of the Monte Leon Formation. In the SJB, the lowermost bed of the Chenque Formation at Punta Borja has an age of 19.6 Ma, while lower bed of Sequence 2 of this unit has an age of 17.7 Ma at Punta Delgada. Patagoniense deposits exposed along the cliffs of the southern San Jorge Gulf (MB), and overlying the continental Sarmiento Formation, are recognized herein as the Mazarredo Formation with a mean age of 20.9 Ma at Canadón El Lobo. It is younger than the San Julián Formation and correlates with the lower part of the Monte León Formation at Playa La Mina (MAB). The radiometric age of the Mazarredo Formation is of outstanding importance because the Mazarredo area constitutes a geographic link between the MAB and SJB and precludes the presence of the “Juliense” transgression in central Patagonia as had been proposed, supporting the early Miocene age previously provided by the authors for the Jorgechlamys centralis –Reticulochlamys borjasiensis Molluscan Assemblage. This assemblage is recorded in the Mazarredo Formation and in the lower section of the Chenque Formation (SJB). Moreover, faunal similarity with the Monte León Formation allows the identification of the Reticulochlamys zinsmeisteri - Struthiolarella patagoniensis - Pleuromeris cruzensis Assemblage at Punta Guanacos in the northeastern sector of the MAB.

Real-Time Cyber Analytics Data Collection Framework

In cyber security, it is critical that event data is collected in as near real time as possible to enable early detection and response to threats. Performing analytics from event logs stored in databases slows down the response time due to the time cost of database insertion and retrieval operations. The authors present a data collection framework that minimizes the need for long-term storage. Events are buffered in memory, up to a configurable threshold, before being streamed in real time using live streaming technologies. The framework deploys virtualized data collecting agents that ingest data from multiple sources including threat intelligence. The framework enables the correlation of events from various sources, improving detection precision. The authors have tested the framework in a real time, machine-learning-based threat detection system. The results show a time gain of 300 milliseconds in transmission time from event capture to analytics system, compared with storage-based collection frameworks. Threat detection was measured at 95%, which is comparable to the benchmark snort IDS.

Quantitative assessment of the spatio-temporal correlations of seismic events induced by longwall coal mining

Rock failure process as a natural response to mining activities is associated with seismic events, which can pose a potential hazard to mine operators, equipment and infrastructures. Mining-induced seismicity has been found to be internally correlated in both time and space domains as a result of rock fracturing during progressive mining activities. Understanding the spatio-temporal (ST) correlation of mining-induced seismic events is an essential step to use seismic data for further analysis, such as rockburst prediction and caving assessment. However, there are no established methods to perform this critical task. Input parameters used for the prediction of seismic hazards, such as the time window of past data and effective prediction distance, are determined based on site-specific experience without statistical or physical reasons to support. Therefore, the accuracy of current seismic prediction methods is largely constrained, which can only be addressed by quantitively assessing the ST correlations of mining-induced seismicity. In this research, the ST correlation of seismic event energy collected from a study mine is quantitatively analysed using various statistical methods, including autocorrelation function (ACF), semivariogram and Moran's I analysis. In addition, based on the integrated ST correlation assessment, seismic events are further classified into seven clusters, so as to assess the correlations within individual clusters. The correlation of seismic events is found to be quantitatively assessable, and their correlations may vary throughout the mineral extraction process.

Records of chemical weathering and volcanism linked to paleoclimate transition during the Late Paleozoic Icehouse

The late Paleozoic ice age (LPIA) was the longest-lived glaciation of the Phanerozoic, and the demise of LPIA is the Earth's only recorded transition from an icehouse to a greenhouse state. In order to explore the records of chemical weathering and volcanism linked to paleoclimate, an integrated multi-proxy study from the Pennsylvanian to the earliest Cisuralian in the Western Shandong from the North China Craton (NCC) was conducted, including results of chemical weathering induces (CIA, CIW, PIA, WIP, CIX, and τ Na ), element indicators (Sr/Ba, Sr/Cu, and Rb/Sr), Hg concentration, total organic carbon (TOC), and organic carbon isotope (δ 13 C org ). The chemical weathering indies show a peak of chemical weathering intensity during the early Asselian (∼298 Ma), indicating a warming event occurred at that time. Hg/TOC shows one obvious peak of volcanic intensity, corresponding with the rapid increase of chemical weathering. Moreover, the peak in volcanic intensity coincides with negative excursions of δ 13 C org , hinting at volcanic drivers for the perturbations of carbon isotope and the change of paleoclimate. This phenomenon coincides with the change of conodont oxygen isotope (δ 18 O), a shift in atmospheric partial pressure of CO 2 ( p CO 2 ), sea-level variation, and late Paleozoic deglaciation records that together document the earliest Permian climate warming-cooling perturbation with a temperature maximum. We suggest that the climate warming in the early Asselian may be driven by volcanism through released greenhouse gas. Our result provides an important contribution to enabling the correlation of volcanic and climatic events during glacial and interglacial cycles. • Permo-Carboniferous weathering trends are reconstructed in North China Craton. • An obvious global warming event is verified at the Permo-Carboniferous transition. • Volcanic activity was a critical driver of climate warming in the early Asselian.

Adaptive clinical trial designs with blinded selection of binary composite endpoints and sample size reassessment.

For randomized clinical trials where a single, primary, binary endpoint would require unfeasibly large sample sizes, composite endpoints (CEs) are widely chosen as the primary endpoint. Despite being commonly used, CEs entail challenges in designing and interpreting results. Given that the components may be of different relevance and have different effect sizes, the choice of components must be made carefully. Especially, sample size calculations for composite binary endpoints depend not only on the anticipated effect sizes and event probabilities of the composite components but also on the correlation between them. However, information on the correlation between endpoints is usually not reported in the literature which can be an obstacle for designing future sound trials. We consider two-arm randomized controlled trials with a primary composite binary endpoint and an endpoint that consists only of the clinically more important component of the CE. We propose a trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. Especially, we consider a decision rule to select between a CE and its most relevant component as primary endpoint. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation, and the expected effect sizes of the composite components. We investigate the statistical power and significance level under the proposed design through simulations. We show that the adaptive design is equally or more powerful than designs without adaptive modification on the primary endpoint. Besides, the targeted power is achieved even if the correlation is misspecified at the planning stage while maintaining the type 1 error. All the computations are implemented in R and illustrated by means of a peritoneal dialysis trial.

Chronostratigraphic correlation of the Upper Silurian Salina Group for the Michigan and Appalachian Basins through coupled (δ13Ccarb) chemostratigraphy and subsurface geophysical analyses

AbstractThe Upper Silurian Salina Group of eastern North America is well known for its thick evaporite successions and hydrocarbon resources. These strata have been assigned to numerous chronostratigraphic schemes within Ohio and Michigan and are currently identified by varying subsurface and outcrop nomenclatural schemes. These chronostratigraphic challenges have persisted for over 50 yr and dramatically inhibit the correlation of events recorded in the Silurian section of eastern North America with the global record of Silurian biogeochemical events. To help resolve the chronostratigraphic correlation of these units, we provide new high-resolution δ13Ccarb chemostratigraphic analyses of a core located in central Ohio for strata assigned to the Greenfield and Tymochtee Formations and integrate existing biostratigraphic, chemostratigraphic, and subsurface geophysical data in western, southern, and eastern Ohio. The new data presented here, integrated for the first time with basinwide subsurface geophysical data, demonstrate a mid-late Homerian Stage global sea-level lowstand, suggest a short interval of tectonic stability within the study area at the beginning of “Salina B–G” deposition, during which accommodation was occupied by the Greenfield Formation and laterally equivalent strata, and provide chronostratigraphic constraints for basin flexure and potential forebulge migration associated with renewed tectonic activity. The new chronostratigraphic correlation of these strata provides a broader picture of Silurian environmental change across the eastern half of the Laurentian paleocontinent.

Late Holocene earthquakes on the Papatea Fault and its role in past earthquake cycles, Marlborough, New Zealand

ABSTRACT The north-striking sinistral reverse Papatea Fault ruptured with a very large (up to 12 m) oblique slip as part of the 2016 M W 7.8 Kaikōura earthquake in the northeastern South Island. Paleoseismic studies were undertaken at three sites along the Papatea Fault, named Murray’s roadcut, Jacqui’s Gully (both on the main strand), and Wharekiri trench (western strand). These sites provide evidence for up to three Late Holocene paleoearthquakes prior to 2016 (=E0) on this previously unmapped active fault, with preferred OxCal-modelled timings of 98–149 (E1), 546–645 cal yr BP (E2), and >738 cal yr BP (E3). Event correlations between the sites are generally consistent across these past events, implying that the two strands of the Papatea Fault link at depth and rupture together co-seismically as in 2016. Comparisons of its paleoseismic record with the Kekerengu Fault and uplift data from Waipapa Bay and Kaikōura, suggest that the Papatea Fault may have three distinct rupture modes: (i) Kaikōura-type multi-fault ruptures with multi-metre, anelastic block displacements and associated major landscape change; (ii) multi-fault earthquake ruptures with other regional fault combinations; and (iii) single-fault Papatea ruptures with metre-scale displacement. OxCal models offer the possibility that the E1 fault rupture occurred in 1855 CE.

Automation of Asset Inventory for Cyber Security: Investigation of Event Correlation-Based Technique

Asset inventory is one of the essential steps in cyber security analysis and management. It is required for security risk identification. Current information systems are large-scale, heterogeneous, and dynamic. This complicates manual inventory of the assets as it requires a lot of time and human resources. At the same time, an asset inventory should be continuously repeated because continuous modifications of system objects and topology lead to changes in the cyber security situation. Thus, a technique for automated identification of system assets and connections between them is required. The paper proposes a technique for automated inventory of assets and connections between them in different organizations. The developed technique is constructed based on event correlation methods, namely linking the system events to each other. The essence of the technique consists of the investigation of event characteristics and identifying the characteristics that arise solely together. This allows determining system assets via assigning event characteristics to specific asset types. The security risks depend on the criticality of the assets; thus, a discussion of automated calculation of the outlined assets’ criticality is provided. Outlined system objects and topology can be further used for restoring possible attack paths and security assessment. The applicability of the developed technique to reveal object properties and types is demonstrated in the experiments.

Gender difference in association between diabetes mellitus and all-cause mortality in atrial fibrillation patients

ObjectiveThere may be gender difference in correlation of diabetes mellitus (DM) and cardiovascular events. We attempt to investigate whether there is gender-heterogeneity in one-year outcomes of atrial fibrillation (AF) patients with DM or not. MethodsPatients who were diagnosed with AF admitted to the emergency departments in the Chinese AF Multicenter Registry study were enrolled. Basic demographics information, initial Blood Pressure and heart rate, medical histories, and treatments of each patient were collected. Follow-up was carried out with a mean duration of one year. The primary endpoint was all-cause mortality and systemic embolism. ResultsA total of 2016 patients were selected from September 2008 and April 2011. All-cause mortality was significantly higher in male AF patients with DM than those without (21.8 % & 13.6 %, P = 0.014). Cox regression analysis showed that there was an interaction between gender and DM for one-year all-cause mortality (P = 0.049). DM was significantly associated with one-year all-cause mortality regardless of univariate analysis (HR = 1.436, 95%CI:1.079–1.911, P = 0.013) or multivariate analysis (HR = 1.418, 95%CI: 1.059–1.899, P = 0.019). For male patients with AF, DM was significantly associated with one-year all-cause mortality (P = 0.048), but not for female patients with AF (P = 0.362). ConclusionDM was independently associated with one-year all-cause mortality in the entire cohort of AF patients. This association was found mainly in male patients with AF, but not in female patients. DM management programs may need to reflect gender difference.

(Invited) Atom Probe Tomography: A New Tool For the Analysis of Solid Liquid Interfaces?

Atom probe tomography is an established method for the microsocopic analysis of hard materials, which stands out by combining single-atom sensitivity with a 3D volume reconstruction. The method is based on laser-assisted field evaporation from nanometric emitter tips. In solid state reactions it delivered decisive information to understand the detailed conditions of nucleation and growth of product phases. In recent efforts, we desire to break the materials limitations aiming for application to liquids, softmatter and polymeric materials. To this end, a new instrument has been constructed that joins cryogenic ion beam preparation with a flexible transfer into an atom probe chamber. Meanwhile, we are able to produce nanometric needles from frozen liquids, even from pure water [1]. Studies are performed with aqueous solutions [2], liquids of short alkanes [3], self-assembling monolayers [4]. The talk discusses the field desorption of complex materials and presents time of flight mass spectra and their dependence on field or laser intensity. The complex mechanisms of field desorption from frozen water surfaces are further elucidated by DFT and Monte-Carlo simulation. Finally, by evaluating evaporation probabilities and event correlations, even insight in bonding strength and the stability of molecules in strong electrical fields may be provided.[1] T. M. Schwarz, E. M. Weikum, K. Meng, E. Hadjixenophontos, C. A. Dietrich, J. Kästner, P. Stender, G. Schmitz, Field evaporation and atom probe tomography of pure water tips, Nature Scientific Reports (2020) 10:20271 | (DOI: 10.1038/s41598-020-77130-x[2] T. M. Schwarz, C. A. Dietrich, J. Ott, E. M. Weikum, R. Lawitzki, H. Solodenko, E. Hadjixenophontos, B. Gault, J. Kästner, G. Schmitz, P. Stender, 3D Sub-Nanometer Analysis of Glucose in an Aqueous Solution by Cryo-Atom Probe Tomography, Scientific reports 11 (2021) 11607 (DOI: 10.1038/s41598-021-90862-8)[3] Kuan Meng, Tim Schwarz, Eric Weikum, Patrick Stender, Guido Schmitz, Frozen n-Tetradecane investigated by cryo-Atom Probe Tomography, J. Microsc. Microanal. (2021), 1-11; (DOI:10.1017/S14319276210125)[4] Helena Solodenko, Patrick Stender, Guido Schmitz, “Atom Probe Study of 1-octadecanethiol self-assembled Monolayers on Platinum (111) and (200) surfaces”, J. Microsc. Microanal. (2021), 1–10. (DOI: 10.1017/S1431927621012654)

A DAG-free Method to Event Correlation for Revealing Abnormal Connections of Cloud Instances

We proposes an event correlation method with rule evaluation(ECRE) to reveal abnormal connections from cloud Instances. We combine some characteristics of status-pair to categorize all abnormal status-connections as far as possible. Our method can classify abnormal connections of instances but doesn’t require the DAG of tasks. Those labels that unmatched the supervision rules are removed to remain more and better samples for training. The results that our method achieved show high accuracy, and a significant correlation between the accuracy and the usage count of supervision-rules.

Dependency multi-weight-view graphs for event detection with label co-occurrence

Event detection is an extremely important step for extracting events, and its goal is to detect the occurrences of events and categorize them. Prior works use either a fine-grained approach or a coarse-grained approach to detect events. However, both approaches encounter two problems: (1) Enriched feature information obtained from external tools probably introduces unnecessary errors. (2) Ignoring the phenomenon of event co-occurrence in a sentence degrades the performance of event detection. To address these issues, a novel framework called Dependency Multi-Weight-View Graph (DMWVG) is proposed to detect events without trigger tagging and external feature engineering. Specifically, DMWVG leverages the tree transformer to automatically generate syntactic relations with different weights to mitigate the errors generated by external syntactic parsing tools. In addition, an event correlation matrix is constructed with the help of the event label co-occurrence to deliver essential information on triggers and to promote the performance of event detection. Finally, the experimental results indicate that our approach is competitive with existing SOTA methods on four datasets (75.77% F1-score on ACE 2005, 82.11% F1-score on MLEE, 75.61% F1-score on BioNLP 09, and 72.20% F1-score on BioNLP 11).

CORE

Complex Event Recognition (CER) systems are a prominent technology for finding user-defined query patterns over large data streams in real time. CER query evaluation is known to be computationally challenging, since it requires maintaining a set of partial matches, and this set quickly grows super-linearly in the number of processed events. We present CORE, a novel COmplex event Recognition Engine that focuses on the efficient evaluation of a large class of complex event queries, including time windows as well as the partition-by event correlation operator. This engine uses a novel automaton-based evaluation algorithm that circumvents the super-linear partial match problem: under data complexity, it takes constant time per input event to maintain a data structure that compactly represents the set of partial matches and, once a match is found, the query results may be enumerated from the data structure with output-linear delay. We experimentally compare CORE against state-of-the-art CER systems on real-world data. We show that (1) CORE's performance is stable with respect to both query and time window size, and (2) CORE outperforms the other systems by up to five orders of magnitude on different workloads.

To Bring an End to Global Warming -Make it Rain

Over the last 40 years, the average global temperature has risen by 1°C and the catastrophic storm risk has tripled, as the latent heating power of the atmosphere grew, driven by the 15% increase in the average global concentration of the primary greenhouse gas, water vapor. Global warming and the catastrophic storm risk only worsen as the average global concentration of water vapor continues to increase at 0.4% yr.-1driving the average global temperature up at 0.2°C per decade. As the latent heating power of the atmosphere rose, the annual number of catastrophic, weather-related events increased to over 750, by 2019, 525 above the 1980 baseline of 225 annual events. Since 1980, these weather-related catastrophic events have taken tens of thousands of lives, wiped out whole communities while wreaking4.6 trillion dollars in cumulative worldwide weather-related destruction, of which 2.4 trillion dollars is the result of global warming driven increasing atmospheric latent heating power, as shown by the close correlation of major weather-related events with the average global temperature record (correlation coefficient 0.84).

Correlation of plastic events with local structure in jammed packings across spatial dimensions

In frictionless jammed packings, existing evidence suggests a picture in which localized physics dominates in low spatial dimensions, d = 2, 3, but quickly loses relevance as d rises, replaced by spatially extended mean-field behavior. For example, quasilocalized low-energy vibrational modes and low-coordination particles associated with deviation from mean-field behavior (rattlers and bucklers) all vanish rapidly with increasing d. These results suggest that localized rearrangements, which are associated with low-energy vibrational modes, correlated with local structure, and dominant in low dimensions, should give way in higher d to extended rearrangements uncorrelated with local structure. Here, we use machine learning to analyze simulations of jammed packings under athermal, quasistatic shear, identifying a local structural variable, softness, that correlates with rearrangements in dimensions d = 2 to d = 5. We find that softness—and even just the local coordination number Z—is essentially equally predictive of rearrangements in all d studied. This result provides direct evidence that local structure plays an important role in higher d, suggesting a modified picture for the dimensional cross-over to mean-field theory.

Environmental changes since 14 ka BP in the southernmost Kuril islands (North-Western Pacific) and regional correlation of events

• New data on climatic changes at the late Pleistocene-Holocene boundary for Kurils. • Climate fluctuations and volcanic ash-falls were mail drivers in lake-mire evolution. • The lake level was highest during warmer and humid climate of the Allerød. • Younger Dryas cooling in the Kuril Islands was less distinct than on the continent. • Pollen record vegetation changes on the land bridge at Late Glacial–early Holocene. Despite considerable progress in understanding Late Pleistocene and Holocene palaeoenvironmental changes worldwide, there are still very few such data for the Far East, and Kuril islands, in particular. Here, the sea level rise during the Late Glacial–Holocene transition was accompanied by a loss of the land connectivity culminating into the development of isolated islands. The ecosystems evolution and importance of isolation for vegetation dynamics on islands are still insufficiently understood. We investigated a unique natural record of environmental change since 14 ka has been recently discovered in lake–mire complex on Kunashir Island. The sediments include 12 tephra layers from Kunashir and Hokkaido volcanoes. The lake level was highest during warmer and humid climate of the Allerød. Diatom assemblages indicated the higher lake level between 12 730–12 160 cal yr BP. The first half of Younger Dryas was characterized by heavy snowfalls. Cooling attributed to the Younger Dryas in the Southern Kurils was less distinct than on the continent. The pollen assemblages recorded changes in vegetation on the land bridge. At the end of the Pleistocene large areas were occupied by birch forests and shrub pine. The broadleaf species first appeared at the end of Allerød. Starting from the Pleistocene-Holocene transition and during the early Holocene , the role of conifers sharply increased. In the middle Holocene the proportion of broadleaf trees in forests increased. Coniferous forests became abundant during the Little Ice Age. Short-tern climatic fluctuations correlate well with regional pattern, particularly with fluctuations of the moisture supply.

Online correlation for unlabeled process events: A flexible CEP-based approach

Process mining is a sub-field of data mining that focuses on analyzing timestamped and partially ordered data. This type of data is commonly called event logs. Each event is required to have at least three attributes: case ID, task ID/name, and timestamp to apply process mining techniques. Thus, any missing information need to be supplied first. Traditionally, events collected from different sources are manually correlated. While this might be acceptable in an offline setting, this is infeasible in an online setting. Recently, several use cases have emerged that call for applying process mining in an online setting. In such scenarios, a stream of high-speed and high-volume events continuously flow, e.g. IoT applications, with stringent latency requirements to have insights about the ongoing process. Thus, event correlation must be automated and occur as the data is being received.We introduce an approach that correlates unlabeled events received on a stream. Given a set of start activities, our approach correlates unlabeled events to a case identifier. Our approach is probabilistic. That implies a single uncorrelated event can be assigned to zero or more case identifiers with different probabilities. Moreover, our approach is flexible. That is, the user can supply domain knowledge in the form of constraints that reduce the correlation space. This knowledge can be supplied while the application is running. We realize our approach using complex event processing (CEP) technologies. We implemented a prototype on top of Esper, a state of the art industrial CEP engine. We compare our approach to baseline approaches. The experimental evaluation shows that our approach outperforms the throughput and latency of the baseline approaches. It also shows that using real-life logs, the accuracy of our approach can compete with the baseline approaches.