Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering Method for Mining Quantitative Association Rules

This paper presents a cluster method for discretization in the processing of mining quantitative association rules. Many efficient algorithms for mining association rules were presented in recent years. But association rules containing quantitative attributes is still a bottle neck in these works. Agrawal presented partial k completeness method for partitioning. Fukuda presented the equi depth partitioning and algorithm based on gaining optimized regions. Current method doesn't work very well on highly skewed data because it tends to split intervals with relative high support into several intervals, even if their behaviors are very similar. In order to solve this problem, this paper presents a Cluster Algorithm named PKCCA. It is different from traditional Cluster Algorithm on that PKCCA dynamically adjust the number of median in iterations, but it still fit for large sample that was the character of fast clustering. For efficiency consideration, sampling method is adopted in this paper.
Recent Advances In Relativistic Molecular Theory

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New Homogeneous Composite Of Energetic Electron Fluxes From POES: 2. Intercalibration of SEM‐1 and SEM‐2

One of the most popular long‐term data sets of energetic particles used in, for example, long‐term radiation belt studies and in atmospheric/climate studies is perhaps the NOAA/POES (Polar Orbiting Environmental Satellites) data set, which extends nearly continuously from 1979 to present. The present study aims to construct a new homogeneous long‐term composite record of daily latitude distributions of energetic electrons based on the MEPED (Medium Energy Proton and Electron Detector) data. Part 1 of this study corrected the data for temporally varying background noise related to cosmic rays and for the drift in the orientation of satellite orbital planes. The present paper addresses the final and most severe problem for the data homogeneity, caused by the difference of telescope pointing directions in older SEM‐1 and newer SEM‐2 versions of the MEPED instrument. Because the telescope pitch angles and the electron pitch angle distribution change with latitude, the difference in SEM‐1 and SEM‐2 fluxes depends on latitude and varies from time to time. The systematic flux differences between SEM‐1 and SEM‐2 can range between a factor of 1.5 to more than an order of magnitude. Novel statistical methodology based on principal components and canonical correlation mapping is presented here to robustly transform the daily SEM‐1 electron latitude distributions into SEM‐2 level. The data from different POES satellites are then combined into a spatially and temporally homogeneous composite series, which is well suited, for example, for long‐term studies of radiation belts and precipitation related atmospheric ionization and its chemical and dynamical effects in the atmosphere/climate system.
Certificate‐Based Signcryption Scheme without Pairing: Directly Verifying Signcrypted Messages Using a Public Key

To achieve confidentiality, integrity, authentication, and non‐repudiation simultaneously, the concept of signcryption was introduced by combining encryption and a signature in a single scheme. Certificate‐based encryption schemes are designed to resolve the key escrow problem of identity‐based encryption, as well as to simplify the certificate management problem in traditional public key cryptosystems. In this paper, we propose a new certificate‐based signcryption scheme that has been proved to be secure against adaptive chosen ciphertext attacks and existentially unforgeable against chosen‐message attacks in the random oracle model. Our scheme is not based on pairing and thus is efficient and practical. Furthermore, it allows a signcrypted message to be immediately verified by the public key of the sender. This means that verification and decryption of the signcrypted message are decoupled. To the best of our knowledge, this is the first signcryption scheme without pairing to have this feature.
Catchment-scale drought: capturing the whole drought cycle using multiple indicators

Abstract. Global agricultural drought policy has shifted towards promoting drought preparedness and climate resilience in favor of disaster-relief-based strategies. For this approach to be successful, drought predictability and methods for assessing the many aspects of drought need to be improved. Therefore, this study aims to bring together meteorological and hydrological measures of drought as well as vegetation and soil moisture data to assess how droughts begin, propagate and subsequently terminate for a catchment in eastern Australia. For the study area, 13 meteorological drought periods persisting more than 6 months were identified over the last 100 years. During these periods, vegetation health, soil moisture and streamflow declined; however, all of the indicators recovered quickly post-drought, with no evidence of extended impacts on the rainfall–runoff response, as has been observed elsewhere. Furthermore, drought initiation and propagation were found to be tightly coupled to the combined state of large-scale ocean–atmosphere climate drivers (e.g., the El Niño–Southern Oscillation, the Indian Ocean Dipole and the Southern Annular Mode), whereas termination was caused by persistent synoptic systems (e.g., low-pressure troughs). The combination of climatic factors, topography, soils and vegetation are believed to be what makes the study catchments more resilient to drought than others in eastern Australia. This study diversifies traditional approaches to studying droughts by quantifying the catchment response to drought using a range of measures that could also be applied in other catchments globally. This is a key step towards improved drought management.
Restoring a Natural Fire Regime Alters the Water Balance of a Sierra Nevada Catchment

Fire suppression in western U.S. mountains has caused dense forests with high water demands to grow. Restoring natural wildfire regimes to these forests could affect hydrology by changing vegetation composition and structure, but the specific effects on water balance are unknown. Mountain watersheds supply water to much of the western United States, so understanding the relationship between fire regime and water yield is essential to inform management. We used a distributed hydrological model to quantify hydrologic response to a restored fire regime in the Illilouette Creek Basin (ICB) within Yosemite National Park, California. Over the past 45 years, as successive fires reduced the ICB's forest cover approximately 25%, model results show that annual streamflow, subsurface water storage, and peak snowpack increased relative to a fire‐suppressed control, while evapotranspiration and climatic water deficit decreased. A second model experiment compared the water balance in the ICB under two vegetation cover scenarios: 2012 vegetation, representing a frequent‐fire landscape, and 1969 vegetation, representing fire suppression. These two model landscapes were run with observed weather data from 1972 to 2017 in order to capture natural variations in precipitation and temperature. This experiment showed that wet years experienced greater fire‐related reductions in evapotranspiration and increases in streamflow, while reductions in climatic water deficit were greater in dry years. Spring snowmelt runoff was higher under burned conditions, while summer baseflow was relatively unaffected. Restoring wildfire to the fire‐suppressed ICB likely increased downstream water availability, shifted streamflows slightly earlier, and reduced water stress to forests.
Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System

Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO2 gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards.
Determining ideal sites for a pilot experiment in Colombia to trial new forages in East Africa

The livelihood of approximately 37 million people of the East African (EA) region is highly dependent on livestock. As the pressure for livestock products heightens with growing populations, already harsh environmental conditions are predicted only to worsen (Fisher et al. 2005; Thornton et al. 2007). In Cali, Colombia, the International Center of Tropical Agriculture (CIAT) is leading a cutting edge new breeding programme by developing improved forage varieties specially designed to succeed in the East African
Online Shape Estimation of Icebergs at Sea

Offshore operations in the Arctic present several challenges, such as the threat of icebergs colliding with offshore structures.This thesis will investigate methods of estimating the shape and size of iceberg keel geometries. Accurate information of iceberg size and geometry is important for forecasting future iceberg trajectories, and for calculating local and global loads generated by an icebergs impact on offshore structures. The iceberg shape and size is also useful for determining its stability before a towing operation to avoid rope slippage or the iceberg rolling over. We have proposed a method on how iceberg surface data can be used in a towing operation for predicting its stability. This has been done by using surface lengths on icebergs to estimate their draft with a 95 % confidence interval based on previous surface length and keel draft data. The estimated draft has been used to scale trapezoidal shapes to represent iceberg keels, and then used to calculate the icebergs GZ curve. We discovered that the trapezoidal shapes gave poor shape estimations for stability calculations. This was because the volume distribution of a true iceberg was represented poorly, especially close to the water surface. We utilized an artificial neural network (ANN) in order to estimate the shape of an iceberg keel based on iceberg surface date. The (ANN) was made by 2D iceberg keel geometries from surface data, because 3D iceberg data is scarce. The original 2D iceberg continuous profile was reduced into 8 vertices for both sail and keel geometry. This created an input matrix representing the sail geometry, and a target matrix representing the keel geometry used to train the ANN with 42 2D iceberg samples. The ANN gave more accurate results than the trapezoidal method. We have also presented a case study on rebuilding reduced keel geometry data using shape factors from an ANN . The objection of the case study was to reduce iceberg data gathered by an AUV so that it can be transmitted real time to a surface vessel for processing. It was difficult to evaluate the performance of the ANN due to the low sample size, and because the shape factors used for estimation, were also used to measure the performance.
Intelligent Vehicle Identification System based on Traffic Video

With the development of information technology, in particular, to improve the video and image processing technology, intelligent video surveillance system collected data can be used to extract the relevant information on the vehicle for life and related investigations provided for convenience. Since the acquisition by the light, angle, and the impact of weather, so that the video data is difficult to retrieve, for this problem, this paper presents a background model fusion method can effectively eliminate the adverse weather light and other factors. This algorithm can quickly and efficiently and with high accuracy to detect vehicle information.
Airflow design and source control strategies for reducing airborne contaminant exposure in passenger aircraft cabins during the climb leg

The climb leg is one of the most acceleration-intensive periods in a passenger aircraft flight. It was previously found that the passenger exposure to cough-released airborne contaminants during a climb may reach 2.8 to 3.0 times that compared to other legs (Elmaghraby et al., Science and Technology for the Built Environment, 2019, accepted. DOI:10.1080/23744731.2019.1576457). In the current study, airflow design and source control strategies are researched numerically for their ability to reduce cough-released airborne contaminant dispersion in the cabin of a Boeing 767-300 aircraft during a climb. Sulfur hexafluoride (SF6) was used to mimic the contaminant, which mainly includes cough-released particles in the size range 1.6 to 3 µm in diameter. The airflow design strategies involved altering the supply airflow direction and changing the supply airflow rate. The source control strategies involved changing the cough direction, varying the cough velocity or flow rate, and moving the cougher to different locations in the cabin. Among all cases, relocation of the cougher, changing the airflow direction, and modest increases in airflow rate exhibited the highest reduction in passenger exposure to contaminant compared to the baseline climb case. The exposure reductions were 0.5–0.7 times for the first case, 0.5–0.7 times for the second case, and 0.6–0.7 times for the third case.
Detection of a Thick and Weak Low‐Velocity Layer atop the Mantle Transition Zone beneath the Northeastern South China Sea from Triplicated P‐Wave Waveform Modeling

Triplicated P waveforms related to the 410‐km discontinuity from five intermediate‐depth earthquakes in the central Philippines are clearly recorded by the Chinese Digital Seismic Network, but some branches of the S‐wave triplications are obscure. Matching the observed P‐wave triplications with synthetics through a grid‐search technique, we obtain the best‐fit 1D P‐wave velocity model near the 410‐km discontinuity beneath the northeastern South China Sea. In such a model, a low‐velocity layer (LVL) is found to reside atop the mantle transition zone, and it is characterized by a thickness of 92.5±11.5 km and a P‐wave velocity decrement of 1.5%±0.1% compared with the IASP91 model. The relatively thick and weak LVL is possibly a response of a small amount of remnant hydrous partial melts after plume‐like upwelling.
Experimental validation of unsteady models for wind / sails / rigging fluid structure interaction

To cite this version : Benoit AUGIER, Patrick BOT, Frédéric HAUVILLE, Mathieu DURAND Experimental validation of unsteady models for wind / sails / rigging fluid structure interaction In: 2nd INNOV'SAIL International Conference on Innovation in High Performance Sailing Yachts · INNOV'SAIL 2010, France, 2010-06-30 The Second International Conference on Innovation in High Performance Sailing Yachts, Lorient, France 2010
Controls on suspended particle properties and water clarity along a partially-mixed estuary, York River, Virginia, USA

The Chesapeake Bay and its associated tidal tributaries, which together form one of the United States’ largest and most important estuaries, are among the many coastal systems where degraded water clarity is a major concern. Despite long-term decreases in sediment input, water clarity over the last 20 years has continued to deteriorate in the Bay and its tributaries (Williams et al., 2010). Analysis of long-term monitoring data (Wang et al., 2010) found that the concentration of total suspended solids (TSS), the regulatory indicator of ‘suspended sediment pollution’, is about as likely to be positively or negatively correlated to sediment loads from the watershed. At the same time, locally suspended sediments are related to water clarity given that the diffuse light attenuation coefficient (K d ) is always positively correlated to TSS. This suggests that even though the suspended sediment in the water column is degrading water clarity, recent sediment input is not determining its concentration.
A four layer of abstraction communication framework Supporting design dialogue

The aim of this paper is to describe and discuss a proposed framework that enables and encourages a robust communication context for the design process. Sustainability can be seen as a wicked problem, therefore creating, maintaining and managing a consensus during the design process is a necessary requirement. Consequently dialogue is central to this. Dialogue, when successful, triggers the co-evolution of these high level meanings towards a shared consensus, a given for a successful co-design process. The framework proposes the use of four different layers of abstraction of information in order to facilitate the creation of shared meaning through dialogue. The four levels selected for this model are: the meaning level, the semantic level, the syntactic level and the artifact level. Communication modalities and the types of concepts are different at each level. Given that “the whole is larger than the sum of its parts”, deconstruction into layers of abstraction can lead to the fragmentation of information and the loss of its richness. In order to avoid loss of meaning, or grounding, the mechanisms of reframing and emergence have been adopted when switching layers. The necessity for this framework stems from the wide range of emerging approaches to design and their myriad connections to the notion of sustainability. The proposed framework fosters the creation of a platform that allows exchange, evolution and synthesis. That enables designers to access a wider range of understandings and design in a more holistic way utilizing the different possibilities present and widening their solution spaces.
Leveraging Overlapping Communities Detection Improve Personalized Recommendation in Folksonomy Networks

In folksonomy based networks,users are allowed to annotate resources with personalized tags,which can facilitate users conveying the user's interest and preference information.However,this flexibility and loosening method of classification brings with it certain costs: redundant,ambiguous and polysemy,which can render resource discovery difficult.So,in tag-based recommendation,the recommended result of precision and diversity is low and has a poor user experience.Communities detection(clustering) provides a means to remedy these problems.Starting from a tagging co-occurrence network,we leverage overlapping communities detection method in tagging co-occurrence network to comprehend the proper meaning of the tags and reduce tagging noise.Based on overlapping communities detection,a complete scheme of personalized recommendation was presented.We validate this approach through evaluation of proposed personalization algorithm using data from a real collaborative tagging Web site,the result demonstrates that overlapping communities detection could considerably improve the precision and diversity of recommendations.
A systematic approach to testing uml designs

In Model Driven Engineering (MDE) approaches, developers create and refine design models from which substantial portions of implementations are generated. During refinement, undetected faults in an abstract model can traverse into the refined models, and eventually into code. Hence, finding and removing faults in design models is essential for MDE approaches to succeed. This dissertation describes a testing approach to finding faults in design models created using the Unified Modeling Language (UML). Executable forms of UML design models are exercised using generated test inputs that provide coverage with respect to UML-based coverage criteria. The UML designs that are tested consist of class diagrams, sequence diagrams and activity diagrams. The contribution of the dissertation includes (1) a test input generation technique, (2) an approach to execute design models describing sequential behavior with test inputs in order to detect faults, and (3) a set of pilot studies that are carried out to explore the fault detection capability of our testing approach. The test input generation technique involves analyzing design models under test to produce test inputs that satisfy UML sequence diagram coverage criteria. We defined a directed graph structure, named Variable Assignment Graph (VAG), to generate test inputs. The VAG combines information from class and sequence diagrams. Paths are selected from the VAG and constraints are identified to traverse the paths. The constraints are then solved with a constraint solver. The model execution technique involves transforming each design under test into an executable form, which is exercised with the generated inputs. Failures are reported if the observed behavior differs from the expected behavior. We proposed an action language, named Java-like Action Language (JAL), that supports the UML action semantics. We developed a prototype tool, named UMLAnT, that performs test execution and animation of design models. We performed pilot studies to evaluate the fault detection effectiveness of our approach. Mutation faults and commonly occurring faults in UML models created by students in our software engineering courses were seeded in three design models. Ninety percent of the seeded faults were detected using our approach.