Reviewerly/exHarmony huggingface dataset:
Beyond Hard Negatives in Product Search: Semantic Matching Using One-Class Classification (SMOCC)

Semantic matching is an important component of a product search pipeline. Its goal is to capture the semantic intent of the search query as opposed to the syntactic matching performed by a lexical matching system. A semantic matching model captures relationships like synonyms, and also captures common behavioral patterns to retrieve relevant results by generalizing from purchase data. They however suffer from lack of availability of informative negative examples for model training. Various methods have been proposed in the past to address this issue based upon hard-negative mining and contrastive learning.
International Workshop on Learning with Knowledge Graphs: Construction, Embedding, and Reasoning

A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering. During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. Moreover, graph neural networks have also brought new opportunities to KG learning. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning.
Classifying topoi in synthetic guarded domain theory

Several different topoi have played an important role in the development and applications of synthetic guarded domain theory (SGDT), a new kind of synthetic domain theory that abstracts the concept of guarded recursion frequently employed in the semantics of programming languages. In order to unify the accounts of guarded recursion and coinduction, several authors have enriched SGDT with multiple "clocks" parameterizing different time-streams, leading to more complex and difficult to understand topos models. Until now these topoi have been understood very concretely qua categories of presheaves, and the logico-geometrical question of what theories these topoi classify has remained open. We show that several important topos models of SGDT classify very simple geometric theories, and that the passage to various forms of multi-clock guarded recursion can be rephrased more compositionally in terms of the lower bagtopos construction of Vickers and variations thereon due to Johnstone. We contribute to the consolidation of SGDT by isolating the universal property of multi-clock guarded recursion as a modular construction that applies to any topos model of single-clock guarded recursion.
New CNN based tool to discriminate anthropogenic from natural low magnitude seismic events

Over the past 15 years, the deployment of dense permanent seismic networks leads to a dramatic increase in the amount of data to process. The seismic coverage and the station quality pave the way toward a comprehensive catalogue of natural seismicity. This means to i) detect the lowest magnitudes as possible and ii) to discriminate natural from anthropogenic events. To achieve this discrimination, we present a new convolutional neural network (CNN) trained from 60 s long three component spectrograms between 1 and 50 Hz. This CNN is trained using a reliable database of labelled events located in Metropolitan France between January 2020 and June 2021. The application of our trained model on the detected events in Metropolitan France between June and November 2021 gives a high discrimination accuracy of 98.18%. To demonstrate the versatility of our approach, this trained model is applied to different catalogues: from a post-seismic campaign in NW France (48 events) and from University of Utah Seismograph Stations, Utah, USA, (396 events between January and March 2016). We reach an accuracy of 100.00% and 96.72%, respectively, for the discrimination between natural and anthropogenic events. Since each discrimination comes with a level of confidence, our approach can be seen as a decision making tool for the analysts. It also allows to build reliable seismic event catalogues and to reduce the number of mislabelled events in the databases.
Estimation of soil CO2 flux emissions at Fogo Volcano (São Miguel Island, Azores)

Secondary manifestations of volcanism found out at Fogo Volcano (S&amp;#227;o Miguel Island, Azores) comprise essentially hydrothermal fumaroles, thermal and cold-CO2 rich springs, as well as diffuse degassing areas. Visible emissions are essentially located on the north flank of the volcano and are associated to the general NW-SE faults of the Ribeira Grande graben. Diffuse degassing surveys aiming at measuring the soil CO2 concentration at about 50 cm depth were carried out in the north flank of the volcano in the last 20 years and anomalous CO2 was associated to NW-SE trends, coincident with the graben faults. Soil CO2 flux surveys, through the accumulation chamber method, were carried out during summer-autumn 2022 in the areas surrounding the three main fumarolic fields: Caldeira Velha, Caldeiras da Ribeira Grande, and Pico Vermelho. A total of 1207 sites were sampled in an area with about 1 km2. Interpolated data (sequential Gaussian simulation) together with the Graphical Statistical Approach estimated a value around 95.5 t/d for the CO2 emitted to the atmosphere. Main anomalous CO2 fluxes are correlated with temperature anomalies and DDS (Diffuse Degassing Structures) show general NW-SE orientations, suggesting the correlation between degassing and tectonic structures in the study site, similarly to the observed in previous studies. This work contributes not only to the estimation on the total carbon-budget, but may also be a valuable tool to identify potential anomalies related with unrest of the volcanic system.This work was partially funded by FCT &amp;#8211; Funda&amp;#231;&amp;#227;o para a Ci&amp;#234;ncia e Tecnologia, under project MAGAT - From MAGma to the ATmosphere - uma contribui&amp;#231;&amp;#227;o para desenvolver a pr&amp;#243;xima gera&amp;#231;&amp;#227;o de sensores geoqu&amp;#237;micos para a monitoriza&amp;#231;&amp;#227;o em tempo real do movimento do magma em profundidade (CIRCNA/OCT/0016/2019).
Machine learning based approach for zircon classification and origin

Zircon, a stable paragenetic mineral in various geological environments, has been recognized as a great tool to study the ages of primary rocks. Trace elements of zircons thus can record the geological evolution processes. Zircon-associated trace elements have been long studied for zircon classification and formation traditionally using binary diagram technique, classical examples including Th-U and LaN-(Sm/La)N diagrams. However, with the massive increase of zircon research, the traditional binary diagrams currently cannot precisely classify zircon types because the binary plot cannot demonstrate the higher dimensional information. It therefore significantly restricts a clear understanding of zircon formation. To address the research gap, we performed the machine-learning-based approaches on 3498 zircon trace-element data of different zircon genetic types, producing high-dimensional zircon-classification diagram plots. We applied and tested four machine learning methods (random forest, support vector machine, artificial neural network, and k-nearest neighbor) and proposed that support vector machine can best contribute to zircon genetic classification study, with an 86.8% accuracy in the prediction of zircon type and formation. In addition to the high-dimensional zircon classification diagram, this work massively improves the accuracy of zircon formation analyses by trace elements, which benefit future studies in zircons. Using the machine learning approach on zircon trace element big data is an effective multidisciplinary exploration of the modern data science technique in the geochemistry study.
Stochastic Modeling of the Eruption History of Nyiragongo Volcano in the Virunga Volcanic Province, Western Branch of the East African Rift System

The modeling of statistical distribution of the eruptive frequency provides basic information to quantitatively assess the volcanic hazard and constrain the physics of the eruptive process. Here, we discuss the statistics of the time series of lateral eruptions of the Nyiragongo volcano in the Virunga Volcanic Province, western branch of the East African Rift System. We examine eruption data with a volcanic explosivity index of at least 1 listed in the Global Volcanism Network Bulletins. After investigating the completeness, stationarity, and independence of the eruption time series, we employ five distribution models (Brownian passage time, gamma, log-logistic, lognormal, and Weibull) to fit the repose time. First, we identify a clear tendency for events to cluster in time. We hypothesize two clusters, the ‘pre-1927’ cluster related to the intracraterial and volcanic activity of the lava lake, and the ‘post-1977’ cluster mainly related to lateral eruptions (i.e. those potentially generating lava flows). Using the maximum likelihood estimations, we evaluate the model parameters with a 95% confidence interval. Next, we use the Akaike Information Criterion to determine the most suitable distribution and we perform the Bayesian Model Averaging approach to assess uncertainty issues in model selection process. The results suggest that the BPT distribution provides the best fit for data of lateral eruptions (post-1977). Then we estimate the time-dependent probability of the occurrence of a lateral eruption for the 50-year period between 2022 and 2072. The estimates reach 50.79%, 88.61%, 97.59%, 99.50%, and 99.89% for 2032, 2042, 2052, 2062, and 2072 years, respectively.
Core Processes of Intuition

Abstract The main idea of this chapter is that there are multiple brain systems which are the neural bases for intuitive cognition. ‘Two minds in one brain’ can be thought of as a metaphor for two different types of thinking. There is no one part of the brain where System 1 (including intuition) is located exclusively, and the idea that intuition is in the right brain is considered a neuromyth. The limitations of the traditional methods used by business researchers are acknowledged and the methods of neuroscience are discussed as an alternative way to ‘capture’ the biological bases of intuition. Five of the ‘core processes’ of intuition that have been identified and studied using the methods of neuroscience are proposed. These core processes are: ‘perceptions of coherence’; ‘pattern recognition’; ‘somatic markers’; ‘interoception’; and the ‘mirror mechanism’. The neural correlates of these core processes are discussed. The relevance of these five core processes of intuition for problem-solving and decision-making is business is explored.
Comparing Different Grasping Visualizations for Object Manipulation in VR using Controllers

Virtual grasping is one of the most common and important interactions performed in a Virtual Environment (VE). Even though there has been substantial research using hand tracking methods exploring different ways of visualizing grasping, there are only a few studies that focus on handheld controllers. This gap in research is particularly crucial, since controllers remain the most used input modality in commercial Virtual Reality (VR). Extending existing research, we designed an experiment comparing three different grasping visualizations when users are interacting with virtual objects in immersive VR using controllers. We examine the following visualizations: the Auto-Pose (AP), where the hand is automatically adjusted to the object upon grasping; the Simple-Pose (SP), where the hand closes fully when selecting the object; and the Disappearing-Hand (DH), where the hand becomes invisible after selecting an object, and turns visible again after positioning it on the target. We recruited 38 participants in order to measure if and how their performance, sense of embodiment, and preference are affected. Our results show that while in terms of performance there is almost no significant difference in any of the visualizations, the perceived sense of embodiment is stronger with the AP, and is generally preferred by the users. Thus, this study incentivizes the inclusion of similar visualizations in relevant future research and VR experiences.
Privacy Data Protection Scheme of Industrial Field Equipment Based on Fully Homomorphic Encryption

With the increasing value of industrial data, it has become an important demand to effectively protect the security of industrial network data. In the edge cloud collaborative environment of industrial Internet, how to transmit and process private data to avoid exposure and tampering caused by attacks is an urgent problem to be solved. The privacy data security of industrial field equipment is the basis of creating a secure environment for industrial edge computing. Therefore, how to ensure the security of private data of industrial field equipment, resist attacks of attackers and avoid privacy data leakage has become a research hotspot. This paper designs a privacy data protection method for industrial field equipment based on fully homomorphic encryption, and builds a test simulation platform to verify the proposed method. The results show that this method can effectively protect the privacy data of field equipment according to the sensitivity of the industrial field equipment data to the edge gateway and industrial cloud platform.
Hybrid generation based on machine learning to enhance numerical simulation for earthquake

In the last decades, geophysicists have developed numerical simulators to predict earthquakes and other natural catastrophes. However, the more precise the model is, the higher the computational burden and the time to results. In addition, even if we could reproduce the phenomenon with more complex and more representative models, the underlying uncertainty would remain significantly high, affecting the reliability of the final prediction. In response to this challenge, we adopted a hybrid strategy, consisting into mixing physics-based numerical simulations and machine-learning. The goal is to transform synthetic earthquake ground motion, obtained via physics-based simulation, accurate up to a frequency of 5 Hz, into a broader-band prediction that mimics the recorded seismographs. In doing so, we factorize the latent representation of the seismic signal, by forcing an encoding that splits features into two parts: a low frequency one (0-1 Hz) and a high frequency one (1-20 Hz). In the following, we train a convolutional U-Net neural network and apply two different signal-to-signal translation techniques: pix2pix and BiCycleGAN. The latter strategies are compared with the prior work of Gatti et al., 2020, on the Stanford Earthquake Dataset (STEAD) showing their capability of mimicking recorded seismographs. We finally tested the two strategies on the synthetic time-histories obtained for the 2019 Le Teil earthquake (France).&amp;#160;&amp;#160;&amp;#160;
Remote Sensing based Evapotranspiration Estimation and Sensitivity Analysis

Evapotranspiration (ET) is an important factor to calculate the water loss to the atmosphere and water demand for crops. Global and regional estimates of daily evapotranspiration are essential for our understanding of the hydrologic cycle. Remote sensing methods have many advantages in estimating daily ET for a large heterogeneous area.&amp;#160; In the present study, the sensitivity of ET with respect to different remote sensing-derived variables has been quantified while using the energy balance algorithm for land (SEBAL) method to estimate daily ET. The sensitivity of SEBAL-based ET has been determined for NDVI, LST, albedo, and SAVI using Extended Fourier Amplitude Sensitivity Test (eFAST) method. Relative changes in ET estimates for a range &amp;#177; 20% of important parameters i.e., NDVI, albedo, SAVI, and LST have been determined and the sensitivity coefficient was estimated. Further, the sensitivity of SEBAL estimated ET has been investigated for different land cover and land use classes i.e., cropland, barren land, settlement, forest, and sparse vegetation. Results show that ET is significantly sensitive to the albedo and LST, however, other LULC classes have a different level of sensitivity. For cropland, ET is sensitive to NDVI. The sensitivity coefficient also indicates a significant effect of albedo and LST on the SEBAL estimated ET. For cropland, a 20% decrease in albedo and LST resulted in a 4.24% and 4.19% reduction in ET, and a 20% increase leads to an increase in ET by 13% and 5.53%, respectively. For sparse vegetation, a 20% reduction in albedo leads to an increase in ET by 7.46% while a 20% increase in albedo may reduce the ET by 15.70%. SAVI has an inverse relationship with ET for forest, barren land, settlement, and sparse vegetation as compared to other variables. The study concludes that SEBAL estimated ET is sensitive to albedo and LST significantly. The study helps in understanding the scope of uncertainty in remote sensing-based ET estimation.
Multi-tenancy in Cloud-native Architecture: A Systematic Mapping Study

Cloud-native architectures has become an essential part of the cloud computing paradigm with the capacity of improved horizontal and vertical scalability, automation, usability and multi-tenancy. However, there are parts that are yet to be fully discovered like multi-tenancy. Multi-tenancy an essential part of the cloud computing, has not been fully. The purpose of this study is to survey existing research on multi-tenancy in cloud-native architecture in order to identify useful trends, opportunity, challenges and finally the needs for further researches. A systematic mapping method was used to systematically compare, classify, analyse, evaluate and appraise existing works of literature on multi-tenancy in cloud-native. We started from over 921 potentially relevant peer reviewed publications. We applied a selection procedure resulting in 64 peer reviewed publications over the last six years between 2015 to 2022 and the selected studies were classified through the characterisation framework. The review shows the emerging challenges and trending concepts in multi-tenancy within cloud native architecture, but also discusses the improvement in multi-tenancy while considering cloud native architecture in the recent years.
Research on the Automatic Subject-Indexing Method of Academic Papers Based on Climate Change Domain Ontology

It is important to classify academic papers in a fine-grained manner to uncover deeper implicit themes and semantics in papers for better semantic retrieval, paper recommendation, research trend prediction, topic analysis, and a series of other functions. Based on the ontology of the climate change domain, this study used an unsupervised approach to combine two methods, syntactic structure and semantic modeling, to build a framework of subject-indexing techniques for academic papers in the climate change domain. The framework automatically indexes a set of conceptual terms as research topics from the domain ontology by inputting the titles, abstracts and keywords of the papers using natural language processing techniques such as syntactic dependencies, text similarity calculation, pre-trained language models, semantic similarity calculation, and weighting factors such as word frequency statistics and graph path calculation. Finally, we evaluated the proposed method using the gold standard of manually annotated articles and demonstrated significant improvements over the other five alternative methods in terms of precision, recall and F1-score. Overall, the method proposed in this study is able to identify the research topics of academic papers more accurately, and also provides useful references for the application of domain ontologies and unsupervised data annotation.
Digitalisasi Museum Cakraningrat sebagai Sumber Literasi Edukasi pada Siswa Di Era Disrupsi 5.0

Museums are a source of knowledge and literacy for their visitors. However, so far, museum visitors only stop by and don't discuss comprehensive data related to objects seen by visitors. Others create interest in museums by digitizing the objects in them. So this research is a preliminary study before developing barcodes. This research is a preliminary study prior to the development of learning media for literary language and its barcode-based teaching for reading skills to facilitate the learning process in museums and educate local knowledge about relics that are full of information and knowledge. Understanding the contents of the object description utilizes barcode-based technology. The development design used is Research and Development Borg and Gall. There are seven research stages: problems, information collection, product design, product testing, design revision, design validation, and product revision. The barcode application developed can be used on Android phones or the like by utilizing barcodes, while its development is on the use of technology in objects in museums in Cakraningrat Bangkalan Madura of Ajhar Museum. Keywords: Museum, learning resources, reading literacy, barcode application.
Research on the application of enabling technology for river basin hydro-junctions operation safety monitoring system

To meet the informatization and intelligence needs of river basin hydro-junctions operation safety management, we study the application of innovative technologies such as Internet of Things, artificial intelligence, big data, and digital twin as enabling technologies for the construction of the operation safety monitoring system from the dimensions of intelligent sensing, data transmission, computational analysis, and interactive services. These applications greatly enhance the intelligence level of the monitoring system, thus laying the foundation for achieving the goal of informatization and intelligence of hydro-junctions operation safety management.
Regional phase picking on single stations using deep learning

Automatic detection of seismic events in processing pipelines at the IDC and many NDCs is mostly done using beamforming on arrays; however, extensive use of single stations can improve the detection capability and accuracy of event location. Advances in deep learning methods enable faster and more accurate processing of large quantities of single station data not seen previously. We use event catalogues including phase picks on a range of arrays in Scandinavia at regional distances (200-2000km), i.e., up to 3min separation between P and S arrivals, to train several deep learning models (PhaseNet and EQTransformer variants) using single stations within the arrays. The models are trained on clips of 324s to capture the multiple arrivals. The models are then applied to various single stations in Norway to assess their generalization. We can detect events at a variety of back-azimuths and distances.&amp;#160;&amp;#160; Furthermore, we expand the existing deep learning models to provide predictions for back-azimuth and distance. This imposes physical restrictions on the models, leading to increased picking accuracy for the predicted phase arrivals. Moreover, this enables us to use the vast number of single stations available to efficiently detect and locate distant events.&amp;#160;&amp;#160;