Reviewerly/exHarmony huggingface dataset:
ANALISIS SENTIMEN PADA ULASAN APLIKASI INVESTASI ONLINE AJAIB PADA GOOGLE PLAY MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN MAXIMUM ENTROPY

Investment is money or asset to earn profits in the future. Online investment applications are already available, one of which is Ajaib. A review of Ajaib’s application is needed to find out reviews given are positive or negative. Sentiment analysis in Ajaib is used to see the user's response to Ajaib’s performance which is divided into positive and negative classes. Sentiment analysis of the Ajaib’s reviews classification can be used with the Support Vector Machine and Maximum Entropy methods. Support Vector Machine on non-linear problems inserts the kernel into a high-dimensional space, to find a hyperplane that can maximize the distance between classes. The kernel used in SVM is the Radial Basis Function (RBF) kernel with gamma parameters of 0.002 and Cost (C) of 0.1; 1; 10. Maximum Entropy is a classification technique that uses the entropy value to classify data with the evaluation model used, namely 5-fold cross-validation. The algorithm which has the highest accuracy and kappa statistics is the best algorithm for classifying the sentiments of Ajaib users. The results using the Support Vector Machine algorithm show the overall accuracy is 85.75% and the kappa accuracy is 58.07%. The results using the Maximum Entropy algorithm show an overall accuracy of 83% and kappa accuracy of 50.5%. This shows that sentiment using the Support Vector Machine has a better performance than Maximum Entropy.
Development of Asset Administration Shell for the components of EBMR plant

Abstract The use of reference architectures and frameworks is an important prerequisite for dealing with the complexity, diversity and heterogeneity of cyber-physical systems. One of the most popular architectures is RAMI 4.0, suggested by the Industry 4.0 Working Group. According to RAMI 4.0, the Asset Administration Shell (AAS) is a digital copy of assets, an implementation of the digital twin for Industry 4.0. The paper represents a semantic approach for development of AAS, which presents the data model through ontology. The semantic model allows for the presentation of facts, data schemes and meta-data in a unified way, using a global identification scheme. An example from the development of AAS for I4.0 components of an EBMR plant illustrates the approach.
Pengembangan E-Modul Interaktif Instalasi Penerangan Listrik di Pendidikan Vokasi

This study aims to develop and produce valid and practical interactive e-modules for Class XII Electrical Lighting Installation subjects at SMK Negeri 2 Lubuk Basung. The model in this study is the 4D model with steps namely Define, Design, Development, and Dissemination. The validity test of the e-module product was carried out by 2 validators from lecturers and 1 education teacher. Media practicality test refers to the response of teachers and students. The research instrument was a media validation questionnaire and a practicality questionnaire. Based on the validation assessment by the e-module media validator, a very valid value was obtained. The practicality test by 1 subject teacher obtained a very practical value and the student practicality assessment of 25 students obtained a very practical value. Based on these results, the interactive e-module in the subject of Electrical Lighting Installation is practical and appropriate for teachers and students to use in the learning process.
PENGEMBANGAN MEDIA BAGAN POHON TERHADAP HASIL BELAJAR SISWA KELAS V DI SEKOLAH DASAR

This research is motivated by the lack of effective use of media in learning activities so that students are less effective in understanding the material, researchers offer the development of new media, namely tree chart media that is in accordance with the concrete operational stage in order to have a direct influence on students. The tree chart media is a complete medium that has branches, stems, and leaves, each of which facilitates or has meaning based on the learning material to be studied. The purpose of this study was to test the validity, practicality and effectiveness of the tree chart media on the learning outcomes of fifth grade students of SDN 17 Woja. This study uses research and development methods (Research and Development) which refers to the four D (4-D) model. Data collection techniques in this study were questionnaires, interviews, observations, tests, and documentation. Data analysis techniques in this study include media validity analysis, media practicality analysis and media effectiveness analysis. The results of the recapitulation of the validity of the learning media from two experts were 83% with the "Valid" category. The results of the recapitulation of the practicality of the tree chart media obtained a percentage of 95.5% with a very practical category. Recapitulation of students' cognitive learning outcomes with a classical completeness score of 83%. The test of students' affective learning outcomes, there are 3 students who fall into the "very good" category with a percentage of 13%, 14 students who fall into the "good" category with a percentage of 61%, 3 students who fall into the fairly good category with a percentage of 13%, and 3 students are included in the “Need Guidance” category with a percentage of 13%. Psychomotor learning outcomes, it is known that there are 16 students' psychomotor results in the "Good" category with a percentage of 70% and 7 students in the "good enough" category with a percentage of 30%.Keywords: Development, Tree Media, Learning Outcomes
The 3rd International Workshop on Machine Learning on Graphs (MLoG)

Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 3rd International Workshop on Machine Learning on Graphs (MLoG), held in conjunction with the 16th ACM Conference on Web Search and Data Mining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.
Detecting low-frequency earthquakes with deep learning

Subduction megathrusts are the largest earthquakes occuring worldwide. Yet the generation of large subduction earthquakes is still poorly understood. Recent research revealed that aseismic deformation in the form of slow slip events (SSEs) might play a key rule in the build-up of these events. However, SSEs are hard to observe directly, due to there slow nature. One way to identify and study aseismic deformation is through co-occuring signals, for example, low-frequency earthquakes (LFEs). Yet these events are again difficult to observe due to their low signal-to-noise ratio and emergent onsets.In this project, we build machine learning models to identify low-frequency earthquakes. These models are more flexible and transferable than the commonly employed template matching techniques for LFE detection. We focus on deep learning based models, building upon their excellent performance for the picking and detection of regular seismicity. To train and evaluate these models we have compiled a collection of LFE datasets from multiple world region in a format tailored for machine learning. We integrate our LFE detector into the SeisBench library to allow easy application of the model in future studies.
Cloud-driven patterns of surface solar irradiance as seen by a spatial network of radiometers

Surface solar irradiance varies on scales down to seconds or meters due to clouds. This highly variable nature of irradiance is not resolved by atmospheric models, yet heterogeneity in surface irradiance impacts the overlying cloud field. The inability to resolve irradiance variability, aside from insufficient model resolution, is explained by our limited understanding of cloud-driven solar irradiance variability at short spatiotemporal scales and the lack of high resolution spatial observational data. Cloud resolving models utilizing ray tracing techniques are a useful research tool, but ultimately require validation against observations.In 2021, we gathered new observational data with a network of radiometers, specifically designed to gather data on cloud-driven surface patterns of irradiance. I will present results on various kinds of surface patterns in relation to cloud type and atmospheric conditions, based on these observations. Our radiometers sample surface solar irradiance at 10 Hz for 18 wavelengths, which we deployed in different setups in the FESSTVaL (Germany) and LIAISE (Spain) field campaigns. Our results highlight the complexity and wide range of regimes in spatiotemporal irradiance variability, but also provide insights into its driving mechanisms. These insights help guide the development of improved radiative transfer calculations, in order to move towards models that can accurately resolve irradiance variability in an operational setting.
Random Forest Classification of Proterozoic and Paleozoic rock types of Tsagaan-uul area, Mongolia

The Tsagaan-uul area of the Khatanbulag ancient massif in the Central Asian Orogenic Belt is located in the southern part of Mongolia, which belongs to the Gobi Desert. It has a low vegetation cover, and because of this, remotely sensed data can be used without difficulty for geological investigations. Factors such as sparse population and underdeveloped infrastructure in the region further create a need for combining traditional geological mapping with remote sensing technologies. In existing geology maps of the area, the formations are lithologically very diverse and their boundaries were mapped variously, so a need for a more precise lithology-based map arouse.This study investigated combinations of fieldwork, multispectral data, and petrography for the rock type classification. A random forest classification method using multispectral Sentinel-2A data was employed in order to distinguish different rocks within Proterozoic Khulstai (NP1hl) metamorphic complex, which is dominated by gneiss, andesite, sandstone, limestone, amphibolite, as well as the Silurian terrigenous-carbonate Khukh morit (S1hm) formation, Tsagaan-uul area. Based on the ground samples collected from field surveys, ten kinds of rock units plus Quaternary sediments were chosen as training areas. In addition, morphometric parameters derived from SRTM data and band ratios used for iron-bearing minerals from Sentinel 2 bands are selected as variables in the accuracy of classification. The result showed that gneisses were recognized with the highest accuracy in the Khulstai complex, and limestones and Quaternary sediments were also well predicted. Moreover, the tectonic pattern was also well recognized from the results and compared to the existing maps provided a more detailed geological image of the area. This study emphasized the need for samples as baseline data to improve the machine learning methods, and the method provides an appropriate basis for fieldwork.&amp;#160;
Potentials and challenges of using Explainable AI for understanding atmospheric circulation

Machine Learning (ML) and AI techniques, especially methods based on Deep Learning, have long been considered as black boxes that might be good at predicting, but not explaining predictions. This has changed recently, with more techniques becoming available that explain predictions by ML models &amp;#8211; known as Explainable AI (XAI). These have seen adaptation also in climate science, because they could have the potential to help us in understanding the physics behind phenomena in geoscience. It is, however, unclear, how large that potential really is, and how these methods can be incorporated into the scientific process. In our study, we use the exemplary research question of which aspects of the large-scale atmospheric circulation affect specific local conditions. We compare the different answers to this question obtained with a range of different methods, from the traditional approach of targeted data analysis based on physical knowledge (such as using dimensionality reduction based on physical reasoning) to purely data-driven and physics-unaware methods using Deep Learning with XAI techniques. Based on these insights, we discuss the usefulness and potential pitfalls of XAI for understanding and explaining phenomena in geosciences.&amp;#160;
Non-stationarity of volcanic tremor signals revealed by blind source separation and manifold learning

Volcanic tremors are one of many seismic signals recorded on volcanoes and are associated with different pre- and co-eruptive processes. Therefore, they are widely used in volcano monitoring. The properties of the tremor signals such as duration, spectral content, or intermittency are very variable, reflecting the possible different tremor source mechanisms. In many cases, several tremor-generating processes can act simultaneously resulting in overlapping signals in the seismogram. Despite their complex signal characteristics and different source mechanisms, volcanic tremors are either treated as one seismic signal class or as a set of seismic signal classes. With a scattering network, we can access the information conveyed by volcanic tremors, even in the presence of short-term impulsive signals. We apply blind source separation methods and manifold learning techniques to continuous seismograms recorded at the Klyuchevskoy Volcanic Group (Kamchatka, Russia) and reveal the underlying patterns in the time series data dominated by volcanic tremors. The data-driven descriptors of the year-long seismogram reveal an ever-changing tremor signal, challenging the division of the observed volcanic tremors into a few distinct classes. The results highlight the complexity and non-stationarity of the volcanic tremors, suggesting a non-stationary volcanic system. Relating the data-driven patterns to the different underlying processes is the next step to understanding better the inner workings of a volcano.
Natural and social patterns in the distribution of Bronze Age Nuragic sites (Sardinia, Italy): using the Widom-Rowlinson penetrable sphere model to understand past human occupation

When looking into land use and human agency in the modification of the landscape, the concepts of socio-economic opportunities vs. natural constraints is one of the key issues. In comparison with the modern world, past human communities relied much more upon local resources and a tight societal structure to better adapt to the conditions and changes in the surrounding environment. Therefore, in the study of prehistorical cultures land use is both a strong source of information about sustenance strategies and community behaviours and a subject potentially easier to model within a set of natural and social parameters. To this purpose, we investigated the settlement distribution patterns of Bronze Age structures of the Nuragic culture on the island of Sardinia (Italy) using spatial point pattern analysis. We investigated different covariates divided into natural (topography, water and geological resources) and cultural (type of structure, settlement hierarchy), alone and in combination, and looked at how each could explain the distribution of Nuragic sites.Several covariates from both natural and cultural groups show significant values, with the best representing models of pattern distribution coming from the combination of covariates from both groups. Aside from topographic parameters, distance from known ore deposits seems to have an impact on structure density. Among cultural covariates, there is a clear association between simple and complex megalithic structures (nuraghes). This pattern suggests the collation of smaller structures around larger settlements, either by the former emerging from the presence of the latter or vice versa. These findings offer new insight on the development and ways of life of the Nuragic society in their geographical context, and highlight how the relationship between the physical and the social aspects of human-landscape interactions is fundamentally interdependent. This approach could also represent a potential tool to compare to other Bronze Age and prehistorical communities.
TREAD daTa and pRocesses in sEismic hAzarD: a MSCA-Doctoral Networks project 2023-2027

TREAD is a new project funded by the European Commission in the framework of Marie Sklodowska-Curie actions, Horizon Europe Doctoral Networks. The aim of TREAD is to train a new generation of researchers to tackle the challenges of earthquake forecasting in complex tectonic settings using integrated observations and physics. The TREAD objectives are: (i) to develop a novel integrative approach to seismic hazard analysis in Europe and the Mediterranean from small-scale laboratory experiments to large-scale observations. (ii) to establish physics-based earthquake modelling bridging time scales from millions of years to fractions of a second in complex tectonic settings. (iii) to improve the link between earthquake geology, computational modelling and hazard and risk assessment with a focus on the needs of governments, industry and scientific stakeholders. To reach these objectives the TREAD consortium comprises 14 academic and 8 non-academic institutions, of which 8 private partners, of high scientific level, from 7 European countries, covering cutting-edge knowledge and expertise in observational, experimental and modelling fields. 11 PhD positions will be available soon.
Development of an earthquake early warning and shakemaps system using low-cost sensors in Taiwan

Building an earthquake early warning (EEW) network requires the installation of seismic instruments around the seismogenic zone. Using low-cost sensors to build a seismic network for EEW and to generate shakemaps is a cost-effective way in the field of seismology. The National Taiwan University (NTU) network employing 770 P-Alert low-cost sensors based on micro-electro-mechanical systems (MEMS) technology is operational for almost the last 10 years in Taiwan. This instrumentation is capable of recording the strong ground motions of up to &amp;#177; 2g and is dense enough to record the near-field ground motion. The NTU system has shown its importance during various earthquakes that caused damage in Taiwan. Although the system is capable of acting as a regional as well as an on-site warning system, it is particularly useful for onsite warning. Using real-time seismic signals, each P-Alert device provided a 2&amp;#8211;8 s warning time for the near-source earthquake regions situated in the blind zone of the Central Weather Bureau (CWB) regional EEW system, during the 2016 Mw6.4 Meinong and 2018 Mw6.4 Hualien earthquakes. The shakemaps plotted by the P-Alert dense network help to assess the damage pattern and act as key features in the risk mitigation process. These shakemaps are delivered to the intended users, including the disaster mitigation authorities, for possible relief purposes. Currently, the P-Alert network can provide peak ground acceleration (PGA), peak ground velocity (PGV), spectral acceleration (Sa) at different periods, and CWB intensity shakemaps. Using shakemaps it is found that PGV is a better indicator of damage detection than PGA. Encouraged by the performance of the P-Alert network, more instruments are installed in Asia-Pacific countries.
A simple semantic ranking approach for entity linking

Entity Linking is the task of mapping entity mentions in text to real entity in a given knowledge base. One of the main obstacles to this task is the variations of name and the ambiguity of the entity. Prior studies have explored various methods for modeling local semantic relatedness between each entity-mention pair and global topical mention consistency to address these issues. Nevertheless, most of these existing methods are relatively complex so that they are impractical in some situation, e.g. on-line search-engine. This paper proposes a simple approach that combines the merits of the local and global paradigm, which aims at simplifying the entity linking process and achieving considerable performance. This proposed approach benefits from a semantic relatedness measure for local modeling and a collective score for global coherence guarantees. Our approach is evaluated on different types of datasets and the results show that, despite its simplicity, our model can achieve comparable performance compared to other similar metrics.
Modelling inter-event times from central volcanoes at São Miguel Island (Azores)

Central volcanoes can produce a wide spectrum of volcanic eruptions, with different magmatic compositions, styles, sizes, and recurrence periods. On volcanic islands, the eruptive record of central volcanoes is often incomplete, due to the small subaerial area, irregular topography, and high erosion rates typical of these islands, generating large uncertainties about the past eruptive activity and making the estimation of eruptive parameters, recurrence times and probabilities of future eruptions very challenging.S&amp;#227;o Miguel Island (Azores archipelago) is one of these cases, where most eruptions of the three active central volcanoes (Sete Cidades, Fogo, and Furnas) are undated or poorly reconstructed. Based on the known stratigraphy, Sete Cidades volcano erupted at least 36 times in the last 15 ky, producing 24 trachytic events, almost all explosive, and 12 basaltic flank eruptions; Fogo volcano erupted at least 21 times also in the last 15 ky, producing 16 trachytic explosive eruptions and 5 basaltic flank eruptions; while Furnas volcano erupted at least 22 times over the last 17 ky, all trachytic explosive events.Here, we model eruptive event times based on the generation of synthetic catalogues that follow the known stratigraphic sequence and include the uncertainty of eruption ages. The completeness of the eruptive records of each volcano was assessed by plotting the cumulative number of eruptions in time and identifying breaks in slope, which may indicate changes in the recording rate of events, as well as possible changes in the eruptive behaviour. In the parts of catalogues after the first break-in-slope we also checked the stationarity to identify the portions of the catalogues that could be modelled by renewal models. Fitting the stationary portion of data with several renewal models allowed to identify which statistical model best describes how eruptive events occur in time.&amp;#160;This study presents a statistical analysis where data uncertainties are accounted for to model eruptive inter-event times, estimate recurrence periods and probabilities of future eruptions. This approach is crucial for a more robust long-term assessment of volcanic hazard, providing important clues to forecast the eruptive behaviour of central volcanoes, even in cases of low-activity systems or volcanic islands, where eruptive catalogues are frequently incomplete. In the case of S&amp;#227;o Miguel this approach allowed to estimate recurrence periods and the probability of a future event for each of the three active central volcanoes of the island.&amp;#160;