Reviewerly/exHarmony huggingface dataset:
Landform detection on Mars using image segmentation with a u-net convolutional neural network architecture

The detection and classification of landforms on planetary surfaces is a time-consuming task which deeply relies on expert knowledge. Such a process can be partially automated and optimized in a resource-efficient way using image processing algorithms. By classifying the surface into different landforms, such as volcanic craters, asteroid impacts, dunes, and more, several analyses can be performed, for instance the widely used crater counting age estimation method. In addition, by conducting these analyses, information about the characteristics and properties of a planet can be revealed. One of the major challenges for the implementation of these algorithms is to provide a generalized model. In many cases the generalization error tends to be very large and therefore a satisfactory accuracy on the test data set cannot be accomplished. This prevents reliable evaluation of new unseen data. In this work, a multi-class image segmentation algorithm is presented, which is based on a U-net convolutional neural network architecture. U-nets classify each pixel of a given input image and can thus produce segmentation masks for various landforms. Given that enough labeled data is available, such a classifier can replace manual detection and classification, thereby saving resources by providing a fast method for landform detection.
Origin and characteristics of the gold-bearing quartz carbonate veins in Şirindere area, Aydın (Western Turkiye): Implication for fluid inclusion data

Fluid inclusion analyses were performed on the gold-bearing quartz carbonate veins in order to pinpoint the source of the gold-bearing fluids and the temperature of the mineralizing systems to better comprehend the genesis of the gold mineralization at the &amp;#350;irindere area (Ayd&amp;#305;n, Western Turkiye). The &amp;#350;irindere area is geologically a part of the Menderes Massif metamorphic complex. It is made up of Neoproterozoic-Paleozoic mica schists of Selimiye Formation and metagranite/gneiss which are is overlain by Permo-Carboniferous phyllite, marble, and quartzite of G&amp;#246;ktepe Formation and Mesozoic marbles of Milas Marble. The gold-bearing quartz carbonate veins are hosted in the mica schists of Selimiye Formation occurred within the fault and shear zone trended N-S having locally boudinage forms parallel to the direction of foliation of the mica schist. Wall-rock alterations are mainly silicification and sericitization with carbonatization and subordinate amount of the chloritization. The ore minerals in the auriferous quartz veins include pyrite and hematite with free native gold which has Au contents up to 7.4 ppm. According to fluid inclusion data from the gold-bearing quartz carbonate vein, the &amp;#350;irindere gold mineralization formed from aqueous-carbonic ore fluids having a metamorphic source. These fluids are characterized by low CO2 content having low to medium salinity (0.88-23.89 wt.% NaCl equivalent) formed at temperatures ranging from 178.7 to 322.5&amp;#176;C with an average of 277.3&amp;#176;C. Therefore, the gold-forming processes in the &amp;#350;irindere area are nearly identical to those in mesozonal orogenic gold deposits.&amp;#160;Keywords: Fluid inclusion data, gold-bearing quartz carbonate vein, Menderes Massif, &amp;#350;irindere(Ayd&amp;#305;n,Turkiye)
A European Dashboard showcasing the State of the Environment

The European Open Science Cloud is a pan-European initiative that aims at developing a federated infrastructure that supports scientific research and Open Science. Having the FAIR principles at the core of Open Science practices, the Environmental Research Infrastructures (ENVRIs) that operate in Europe collaborate at cluster level (the ENVRI cluster) continuously improving the FAIRness of their services and working towards becoming more interoperable among them but also with other clusters. To showcase the benefits of an integration platform that supports scientific workflows, the ENVRIs develop the &amp;#8220;Dashboard for the State of the Environment&amp;#8221; as a cross-discipline service to address with scientific facts the environmental concerns. The project brings together three scientific domains (Atmosphere, Biodiversity, Ocean) that each have set up analytical workflows to provide environmental indicators in real-time, allowing the users to visualise the &amp;#8220;State of the Environment&amp;#8221; by interacting with the service interface.&amp;#160;The Dashboard is designed to be completely user configurable so that the users can select from a list the indicators to be shown and their order. Providers can add, remove and edit indicators through a standard REST API, that allows transferring all parameters, including the configuration of the indicators and how to provision data values and thumbnail interaction. The Dashboard is implemented and operated using engineering best practices, including YAML for the indicators&amp;#8217; descriptions and a robust and flexible container-based deployment. It builds on EOSC services like AAI, cloud services, and data storage, and the workflows that provide the indicators will also build on the EOSC and Research Infrastructure (RI) computing integration. As a proof of concept, a limited list of indicators is available, and we foresee that the participating RIs will provide many more indicator options in the near future. In addition, through the extension API it will be possible for new RIs to start providing indicators.&amp;#160;The Dashboard service is completely open source, and, as the whole concept, it is designed to be flexible and expansible. Therefore, we encourage other clusters that are part of EOSC to use the service as another basis for disseminating their relevant indicators to a wider audience.
Status of Canada&amp;#8217;s Geoscientific Site Evaluations for a Deep Geological Repository for Used Nuclear Fuel

&amp;lt;p&amp;gt;The Nuclear Waste Management Organization (NWMO) is responsible for implementing, collaboratively with Canadians and Indigenous people, Canada&amp;amp;#8217;s plan for the safe, long-term management of used nuclear fuel. The NWMO is working to implement Canada&amp;amp;#8217;s plan, in a manner that protects both people and the environment. Canada&amp;amp;#8217;s plan calls for containment and isolation of used nuclear fuel in a deep geological repository, which is made up of a series of natural and engineered barriers. This facility will be located in an area with informed, willing hosts within a suitable rock formation.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;In 2010, the NWMO initiated a nine-step site selection process to seek an informed and willing community to host Canada&amp;amp;#8217;s deep geological repository. The geoscience site evaluation process includes three main technical evaluation steps to assess the suitability of candidate areas in a stepwise manner. By the end of 2012, twenty-two communities had expressed interest in learning more about the project. As of the end of 2022, two areas remain in the site selection process.&amp;amp;#160; The NWMO is on track to identify a single, preferred location for this facility by approximately end of 2024. This paper describes the approach, methods and criteria being used to assess the geoscientific suitability of the sites, and lessons learnt to date. Activities to interweave Indigenous Traditional Knowledge with western science are also discussed.&amp;lt;/p&amp;gt;
Multidisciplinary, autonomous, Lagrangian floats for seismology, ocean acoustics and marine environmental science

We have developed autonomous, Lagrangian floats that make seismo-acoustic measurements in the oceans, with mission durations of 4+ years and running (http://earthscopeoceans.org). Earthquakes generate seismic waves that traverse the solid earth, convert to acoustic waves when they hit the seafloor from below, and are recorded by the hydrophone on our &amp;#8220;Mermaid&amp;#8221; floats drifting at ~1500m depth.In the long-term, we aim for dense and even global coverage of the oceans for seismology, following the model of oceanography&amp;#8217;s Argo initiative, or of internationally federated seismometer networks on land. In order to grow the network, we are exploring synergies with oceanography and the marine environmental sciences.We present technical developments towards the first multidisciplinary mission in 2024 in the Mediterranean, whose floats will run embedded applications in two frequency ranges: the seismic (~0.1-5 Hz) as well as the &amp;#8220;conventional&amp;#8221; ocean acoustics range (10 Hz to 30 kHz). It will feature detection and classification algorithms for earthquakes, rainfall, marine mammal vocalizations, and ship noise. While energy-limited, these seismological floats carry significantly larger batteries than Argo floats and allow for up to eight physical/chemical/other sensors and their analysis algorithms, whose concurrent needs are managed by a domain-specific language written for the purpose (Bonnieux 2020).&amp;#160;Reference: Bonnieux, S. (2020). Float for multidisciplinary monitoring of the marine environment. From business expertise to embedded codes (Doctoral dissertation, Universit&amp;#233; C&amp;#244;te d'Azur).
Seismic events relocation and velocity models for the Atacama seismic gap at Central-Northern Chile (24.5-29&amp;#176;S)

&amp;lt;p&amp;gt;Information about seismic velocities and the seismicity distribution in subduction zones can reveal the effect physical properties have over large ruptures. The ANILLO (Y6) Seismic Network spans between 24.5&amp;amp;#176;S - 29&amp;amp;#176;S in Chile. Composed of 104 seismic stations, it was operative between 2020 and 2022. Deep Learning algorithms (EQTransformer) enable the identification of more than 30,000 seismic events during the eighteen months of operation time, in an area with an important lack of moderate-to-large events during the last century since the nucleation of the 1922 M8.5 Atacama earthquake. Using a subset of events with a good spatial distribution, low residuals, and many P- and S-arrivals, we obtained a well-distributed subcatalog of 1000 earthquakes, corresponding to 26,570 P- and 22,109 S-wave arrival times. Using VELEST for the selected events, inverting for station corrections, we computed a novel 1-D velocity model representative of this region by minimizing the subset residuals. This enabled us to relocate the complete catalog, reducing both the residuals and the location errors of all events. Starting from this 1-D velocity model, staggered tomographic inversions were performed using SIMUL2000, simultaneously inverting for seismic velocity models and hypocentral parameters according to the iterative damped least squares method. The complexity in the tomographic model was gradually increased, from 2-D to ultimately a 3-D fine model with low node separation. Preliminary results show that earthquake arrival times picked using Deep Learning algorithms can provide an exceptional database for tomographic studies, with residuals similar to human-picked databases. We obtain a good resolution underneath the seismic network in the forearc, with a reduced resolution and smearing in off-shore areas where location uncertainties, especially in the vertical direction, are greater. We suggest that the incorporation of OBS data would significantly improve the off-shore model. We observe an east-dipping, high Vp velocity structure subducting in the expected slab position, in which the relocated seismic events, with a low vertical location error, follow the slab geometry in distinct seismicity bands. Additionally, observed shallow seismicity could be related to crustal faults, mining activity, or even dehydration processes near the slab&amp;#039;s upper surface. The event locations and velocity structure are interpreted in terms of the geological and geophysical characteristics of the subduction zone at these latitudes that may influence the behavior of large megathrust earthquakes.&amp;lt;/p&amp;gt;
Tectonic and volcano-tectonic seismicity below Tungurahua volcano (Ecuador) between 2013 and 2018

Tungurahua (5023 m a.s.l.) is an andesitic stratovolcano located in Central Ecuador. The more recent eruptive cycle started in September 1999 and lasted until March 2016 with repeated phases of enhanced activity. Its activity included the occurrence of distinct eruptive phases separated by periods of quiescence, both lasting from few weeks to months. From October 2013 until March 2018, we operated at Tungurahua a temporary seismic network including up to 13 broadband stations. It complemented the permanent monitoring network operated by the Instituto Geof&amp;#237;sico de la Escuela Polit&amp;#233;cnica Nacional (IG-EPN) and included stations up to 4275 m a.s.l. as well as stations on the remote Eastern flank. Using IG-EPN catalogs and cross-correlation techniques, we identified several clusters of shallow and deep (volcano-)tectonic earthquakes. For these clusters, we manually picked a selection of larger events and used them to pick automatically other similar events. A visual inspection of the pickings was performed to confirm the absence of major biases. The comparison of P-phase times shows differences less than 0.1 s. Regarding S-phases, the cross correlation technique detected by far more S-phases per event, providing a general improvement in the location of events. Additionally we used seismic amplitudes and their decay as a function of distance to locate tremor and explosion quake sources during eruptive phases.The seismicity below sea level defines 4 main clusters spread around the volcano between 2 and 10 km b.s.l.. The temporal evolution of these clusters displays a rather steady behavior for 3 of them and a swarm-type behavior for the fourth. Their relation with the eruptive phases is, however, unclear. Above sea level a single cluster of small volcano-tectonic events is observed about 2-3 km below the summit. This cluster displays a rather clear relationship with the eruptive phases and often preceded phases with strong explosive onsets. Most of tremor and explosion quake sources are found just above this cluster.This study emphasizes the importance of dense, geographically well distributed networks, to identify seismic precursors and decipher volcanic plumbing systems.
Predicting Email Opens with Domain-Sensitive Affect Detection

The content and style of the text constitutes the semantic context of its words—a property that is important for many downstream tasks in natural language processing. We demonstrate the advantages of incorporating domain information for affect analysis, and subsequently for the prediction of user responses to marketing emails. Emails are a primary form of marketing communication, and email subject lines are the only indicators of whether the receiver will open an email especially in the case of bulk communication. We analyze the performance of affective features in predicting email opens, on a dataset of 60,000 unique promotion emails from 3 different industries. Our results show that the use of domain-specific affect words is strongly correlated with email opens and outperforms words from the standard ANEW lexicon and other state of the art affective lexica. Implications of this findings can be incorporated into writing tools to improve the productivity of marketing campaigns.
Evaluating Terminology Translation in MT

Terminology translation plays a crucial role in domain-specific machine translation (MT). Preservation of domain knowledge from source to target is arguably the most concerning factor for clients in translation industry, especially for critical domains such as medical, transportation, military, legal and aerospace. Evaluation of terminology translation, despite its huge importance in the translation industry, has been a less examined area in MT research. Term translation quality in MT is usually measured with domain experts, either in academia or industry. To the best of our knowledge, as of yet there is no publicly available solution to automatically evaluate terminology translation in MT. In particular, manual intervention is often needed to evaluate terminology translation in MT, which, by nature, is a time-consuming and highly expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems are often needed to be updated for many reasons (e.g. availability of new training data or leading MT techniques). Hence, there is a genuine need to have a faster and less expensive solution to this problem, which could aid the end-users to instantly identify term translation problems in MT. In this study, we propose an automatic evaluation metric, TermEval, for evaluating terminology translation in MT. To the best of our knowledge, there is no gold-standard dataset available for measuring terminology translation quality in MT. In the absence of gold-standard evaluation test set, we semi-automatically create a gold-standard dataset from English–Hindi judicial domain parallel corpus. We trained state-of-the-art phrase-based SMT (PB-SMT) and neural MT (NMT) models on two translation directions: English-to-Hindi and Hindi-to-English, and use TermEval to evaluate their performance on terminology translation over the created gold-standard test set. In order to measure the correlation between TermEval scores and human judgments, translations of each source terms (of the gold-standard test set) is validated with human evaluator. High correlation between TermEval and human judgements manifests the effectiveness of the proposed terminology translation evaluation metric. We also carry out comprehensive manual evaluation on terminology translation and present our observations.
Intelligent gloves: An IT intervention for deaf-mute people

Abstract Deaf-mute people have much potential to contribute to society. However, communication between deaf-mutes and non-deaf-mutes is a problem that isolates deaf-mutes from society and prevents them from interacting with others. In this study, an information technology intervention, intelligent gloves (IG), a prototype of a two-way communication glove, was developed to facilitate communication between deaf-mutes and non-deaf-mutes. IG consists of a pair of gloves, flex sensors, an Arduino nano, a screen with a built-in microphone, a speaker, and an SD card module. To facilitate communication from the deaf-mutes to the non-deaf-mutes, the flex sensors sense the hand gestures and connected wires, and then transmit the hand movement signals to the Arduino nano where they are translated into words and sentences. The output is displayed on a small screen attached to the gloves, and it is also issued as voice from the speakers attached to the gloves. For communication from the non-deaf-mutes to the deaf-mute, the built-in microphone in the screen senses the voice, which is then transmitted to the Arduino nano to translate it to sentences and sign language, which are displayed on the screen using a 3D avatar. A unit testing of IG has shown that it performed as expected without errors. In addition, IG was tested on ten participants, and it has been shown to be both usable and accepted by the target users.
Russian Language Datasets in the Digital Humanities Domain and Their Evaluation with Word Embeddings

In this paper, we present Russian language datasets in the digital humanities domain for the evaluation of word embedding techniques or similar language modeling and feature learning algorithms. The datasets are split into two task types, word intrusion and word analogy, and contain 31362 task units in total. The characteristics of the tasks and datasets are that they build upon small, domain-specific corpora, and that the datasets contain a high number of named entities. The datasets were created manually for two fantasy novel book series (“A Song of Ice and Fire” and “Harry Potter”). We provide baseline evaluations with popular word embedding models trained on the book corpora for the given tasks, both for the Russian and English language versions of the datasets. Finally, we compare and analyze the results and discuss specifics of Russian language with regards to the problem setting.
Learning and Transforming General Representations to Break Down Stability-Plasticity Dilemma

In the Class Incremental Learning (CIL) setup, a learning model must have the ability to incrementally update its knowledge to recognize newly appeared classes (plasticity) while maintaining the knowledge to recognize the classes it has already learned (stability). Such conflicting requirements are known as the stability-plasticity dilemma, and most existing studies attempt to achieve a good balance between them by stability improvements. Unlike those attempts, we focus on the generality of representations. The basic idea is that a model does not need to change if it has already learned such general representations that they contain enough information to recognize new classes. However, the general representations are not optimal for recognizing the classes a model has already learned because the representations must contain unrelated and noisy information for recognizing them. To acquire representations suitable for recognizing known classes while leveraging general representations, in this paper, we propose a new CIL framework that learns general representations and transforms them into suitable ones for the target classification tasks. In our framework, we achieve the acquisition of general representations and their transformation by self-supervised learning and attention techniques, respectively. In addition, we introduce a novel knowledge distillation loss to make the transformation mechanism stable. Using benchmark datasets, we empirically confirm that our framework can improve the average incremental accuracy of four types of CIL methods that employ knowledge distillation in the CIL setting.
Visualizing and Analyzing Networks of Named Entities in Biographical Dictionaries for Digital Humanities Research

This paper shows how named entity extraction and network analysis can be used to examine biographies individually and in groups to aid historians in biographical and prosopographical research. For this purpose a reference network of 13 100 biographies in the collections of the Biographical Centre of the Finnish Literature Society was created, based on links between the biographies as well as automatically extracted named entities found in the texts. The data was published in a SPARQL endpoint as a Linked Data knowledge graph on top of which network analytic tools were created and analysis were done showing the usefulness of the approach in Digital Humanities. The reference graph has been utilized for network analysis to examine egocentric networks of individual persons as well as networks among groups of people in prosopography. The data and tools presented are in use since autumn 2018 in the semantic portal BiographySampo that has had tens of thousands of users.