Reviewerly/exHarmony huggingface dataset:
Cascadia 120 Slim Posthole: A Combined Strong and Weak Motion Sensor for Early Warning and Regional Networks

&amp;lt;p&amp;gt;Existing regional and EEW networks can be improved in data quality, providing more stations with unclipped continuous waveform observations and a uniform magnitude of completeness (Mc).&amp;amp;#160; This is possible using upgraded deeper sensor emplacements and new instrumentation, based on a current understanding of system noise and updated equipment available today.&amp;amp;#160; Station density is increasing with the EEW buildout, with a focus on minimizing latency, which also presents an opportunity to improve data quality for weak motion.&amp;amp;#160; Mc and signal-to-noise ratio across the seismic and geodetic spectrum will be important for future OEF challenges and for hazard and science efforts such as 4D studies and the high resolution geophysical imaging of deep Earth structure.&amp;amp;#160; Recent development of network Mc simulation code (Wilson et al., 2021) can be used to plan new networks, or infill stations to economically upgrade existing networks to these new best practices.&amp;amp;#160;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;An instrument system that provides high-gain weak motion data in precise alignment with strong motion data allows combined processing to create a seamless data set with maximum dynamic range.&amp;amp;#160; The Nanometrics Cascadia 120 Slim Posthole has been designed to meet these requirements. Both weak and strong motion sensors are integrated in a single case which enables lowest system noise, unclipped observations, and precise coherence of signals between the weak and strong motion channels.&amp;amp;#160; Cascadia 120 Slim and Cascadia Compact are deployed in networks now and we will present data showing the noise floor for weak motion, a seamless transition to strong motion, and high coherence between the two sensors for mid-sized events.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Authors: Valarie Hamilton, Geoffrey Bainbridge, Tim Parker, Daniela Wuthrich&amp;lt;/p&amp;gt;
Zaytun: Lattice Based PKE and KEM with Shorter Ciphertext Size

In this paper, we propose a lattice-based encryption scheme with a short ciphertext size. Our scheme is somewhat hybrid of the NTRU type encryptions and RLWE based encryptions. In particular, the ciphertext of the scheme is a ring element as NTRU type encryptions, yet it can be compressible as RLWE based encryption schemes. Furthermore, we present a key-encapsulation mechanism that is more efficient than a direct construction from our encryption scheme. The IND-CPA security of the schemes is based on the RLWE assumption and the NTRU assumption. Our parameterizations show that the schemes enjoy almost the same public key size as the NIST PQC finalist lattice-based candidates, yet the ciphertext size is only about $$37\%$$ of them.
TRAINING IN COMPUTER GRAPHICS AND CLOUDY TECHNOLOGIES

I. Ivanov1, A. Garkinin2, A. Kirkov2 1South-West University "Neofit Rilski" (BULGARIA) 2University of Library Studies and Information Technologies (BULGARIA)
FORMATION OF CONFLICTOLOGICAL COMPETENCE IN THE PROCESS OF PREPARATION OF STUDENTS-FUTURE TEACHERS

The publication analyzes the problem of forming conflictological competence in the process of training future teachers, in particular at the University of Ruse "Angel Kanchev". The modern professional and pedagogical training of future pedagogues in higher education is based on a change in education –from teaching knowledge to mastering key competencies and the development of problem-solving skills. The formation of conflictological competence undoubtedly plays an important role in the process of students' education, because the educational process is impossible without the presence of contradictions and conflicts between the main subjects. The problem of the emergence and management of conflicts is relevant, because they are present and have always been present in all social spheres and at all levels in society. This is especially true for the school institution, because it is in the educational environment that this phenomenon reveals its essence most fully and manifests itself in all its forms. The curricula and programs of the higher schools are being updated in order to introduce the competence approach in the education of the students in the pedagogical specialties. Conflict competence and the ability to maintain cooperative relationships with different actors in the educational process: children, parents, colleagues and administration, is very important for educators because it has a practical application in school and is the rational behavior of the teacher in different situations.The article presents the knowledge, skills and competencies that students acquire to deal with conflicts and which they will use in their professional activities, because they must know not only the mechanisms, development and patterns of conflict, but also the processes affecting the human psyche and influencing their cognitive abilities in critical situations.Expected results are the students to master the theoretical and methodological bases for the regularities and mechanisms for the emergence and development of conflicts, as well as for the principles and technologies of their management in certain cases in a socio-pedagogical environment.The knowledge they acquire is about the basic concepts, purpose, tasks, object, subject, research methods of conflictology, as well as about the current trends in the development of pedagogical conflictology as a science.The skills acquired in the course of study are: students to recognize the different types of conflicts in educational, upbringing, and interpersonal conflicts and ways to prevent and overcome them, including solving typical cases.
Zero-label Anaphora Resolution for Off-Script User Queries in Goal-Oriented Dialog Systems

Most of the prior work on goal-oriented dialog systems has concentrated on developing systems that heavily rely on the relevant domain APIs to generate a response. However, in the real world, users frequently make such requests that the provided APIs cannot handle, we call them “off-script” queries. Ideally, existing information retrieval approaches could have leveraged relevant enterprise's unstructured data sources to retrieve the appropriate information to synthesize responses for such queries. But, in multi-turn dialogs, these queries oftentimes are not self-contained, rendering most of the existing information retrieval methods ineffective, and the dialog systems end up responding “sorry I don't know this”. That is, off-script queries may mention entities from the previous dialog turns (often expressed through pronouns) or do not mention the referred entities at all. These two problems are known as coreference resolution and ellipsis, respectively; extensively studied research problems in the supervised settings. In this paper, we first build a dataset of off-script and contextual user queries for goal-oriented dialog systems. Then, we propose a zero-label approach to rewrite the contextual query as a self-contained one by leveraging the dialog's state. We propose two parallel coreference and ellipsis resolution pipelines to synthesize candidate queries, rank and select the candidates based on the pre-trained language model GPT-2, and refine the selected self-contained query with the pre-trained BERT. We show that our approach leads to higher quality expanded questions compared to state-of-the-art supervised methods, on our dataset and existing datasets. The key advantage of our novel zero-label approach is that it requires no labeled training data and can be applied to any domain seamlessly, in contrast to previous work that requires labeled training data for each new domain.
Spatial distribution of the b-value in Southern California based on Gauss process inference with a geologically defined prior

&amp;lt;p&amp;gt;As a population parameter, reliable estimation of the b-value is intrinsically complicated, particularly when spatial variability is considered. We approach this issue by treating the spatial b-value distribution as a non-stationary Gauss process for the underlying earthquake-realizing Poisson process. For Gauss process inference the covariance&amp;amp;#8212;which describes here the spatial correlation of the b-value&amp;amp;#8212;must be specified a priori. We base the covariance on the local fault structure, i.e. the covariance is anisotropic: elongated along the dominant fault strike and shortened when normal to the fault trace. This adaptive feature captures the geological structure better than an isotropic covariance or similarly defined and commonly used running-window estimates of the b-value.We demonstrate the Bayesian inference of the Gauss process b-value estimation for southern California based on the SCEDC earthquake catalog and with the covariance calibrated with the USGS fault model.Our model provides a continuous b-value estimate which reflects the local fault structure to a very high degree. Therefore, we are able to associate the b-value with the local seismicity distribution and can link it to the major Californian faults and geothermal areas. This technique, in its general formulation, can be applied to other non-stationary seismicity parameters, e.g. Omori&amp;amp;#8217;s law.&amp;lt;/p&amp;gt;
The Effects of Non-Face-to-Face Flipped Learning based Problem solving process, Self-directed learning ability, Academic Self-Efficacy of Nursing Students

Objectives This study develops an effective learner-centered, non-face-to-face flip learning program for nursing students. The purpose of this study non-face-to-face flipped learning, it is necessary to examine the effect on problem-solving process, self-directed learning ability, and academic self-efficacy for nursing college students&#x0D; Methods This study is a one group pretest-posttest design. It was consisted, conducted 2 March 2021 to 5 June 2021, 46 nursing students. It was applied for a total of 14 weeks in 2hours. This study was one-group pretest-posttest design. The data were collected using self-administered questionnaires. Data analyses paired t-test, and Pearson’ correlation coefficient.&#x0D; Results The result of this study nursing students problem-solving process (t=-5.68, p&lt;.000), self-directed learning (t=-4.64, p&lt;.000), academic self-efficacy (=-5.68) after application of non-face-to-face flip learning program practice p&lt;.000) showed a significant effect.&#x0D; Conclusions The results indicate that we proposed a non-face-to-face flipped learning design and application plan. And the implications of applying non-face-to-face flipped learning classes were presented. It is expected that the learning effect could be improved with the new teaching method.
Contatti linguistici lungo le coste tirreniche: storia della parola <i>marruffo</i>

Abstract The article intends to reconstruct the linguistic history of a particular fishing gear called marruffo , which is still used in the Gulf of Naples and in a few places of the North Tyrrhenian coasts. The marruffo is a fishing trap used by fishermen to preserve the catch while awaiting sale. Conical in shape, it looks like a large basket with a lid, in which fish or crustaceans of various types are placed. Starting from the 1871 work by the naturalist Achille Costa, entitled La pesca nel golfo di Napoli , the attestations of marruffo in Campania, Sardinia and Corsica are outlined. I put forward the hypothesis that the object arrived on the Tyrrhenian coasts as a consequence of migrations of fishermen from the Neapolitan area to the islands of Tuscany, Sardinia and Corsica between the 18th and 19th century. Since in southern Italy the name marruffo also indicates a jug for wine or water, I hypothesize that the word was extended from the lexicon of everyday life to the fishing lexicon throughout a process of resemantization, as it was demonstrated for other similar cases.
Pietro Bortone: Language and nationality: Social inferences, cultural differences, and linguistic misconceptions

Article Pietro Bortone: Language and nationality: Social inferences, cultural differences, and linguistic misconceptions was published on April 1, 2022 in the journal Folia Linguistica (volume 56, issue 1).
Comparing machine-learning based picking algorithms in a subduction setting

&amp;lt;p&amp;gt;As the number of seismic stations and experiments greatly increases due to ever greater availability of instrumentation, automated data processing becomes more and more necessary and important. Machine Learning (ML)methods are becoming widespread in seismology, with programsthat identify signals and patterns or extract features that can eventually improve our understanding of ongoing physical processes. We here focus on comparing and testing a selection of currently available methods for machine-learning-based seismic event detection and arrival time picking, performing a comparative study of the two autopickers EQTransformer and GPD with seismic data from the IPOC deployment in Northern Chile within the open-source Seisbench framework.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;As a small benchmark dataset, we chose a random day for which we handpicked all visually discernible events on the 16 IPOC stations, which led to 200 events from 450 extracted, comprising 1493 P and 1163 S-phases. These events cover a large range of hypocentral depths (surface to &amp;gt;200 km) as well as magnitudes (&amp;lt;1.5 to 4.5).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present first results from the application of the two autopickers EQTransformer and GPD, which have been shown to be most suitable for our type of dataset in a recent study by M&amp;amp;#252;nchmeyer et al. (2021), to IPOC data. We use our small benchmark dataset to evaluate detection rate (missed events, false detections) as well as picking accuracy (residuals to handpicks), and also investigate the effect of using different training datasets.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The present study is the first step towards the design of an automated workflow that comprises event detection and phase picking, phase association and event location and will be used to evaluate subduction zone microseismicity in different locations.&amp;lt;/p&amp;gt;
Elements of Probability and Graph Theory

This chapter introduces required theoretical concepts for the definition of Bayes and Markov networks. After important elements of probability theory—especially (conditional) independences—are discussed, we present relevant graph-theoretic notions with emphasis on so-called separation criteria. These criteria will later allow us to capture probabilistic independences with an undirected or directed graph.
A Study on PE Teachers" Perceptions according to COVID19: focused on High School PE classes

The purposes of this study were to explore how PE classes had changed due to the COVID -19 and to understand how the high school PE teachers perceived the PE classes. Seven high school PE teachers recruited and six students selected by the teachers were participated as the subjects. For the purpose of this study, in depth interviews were conducted and their experiences before and after the COVID-19 were explored.<BR> According to the interviews, class environments have changed in comparison to previous physical education classes due to online environments and quarantine guidelines of COVID-19 and it was shown that changed environments have had limiting effects on physical education classes, causing teachers to have difficulties operating proper physical education classes with diversity. In such conditions, teachers have used processes of overcoming difficulties as opportunities to increase physical education class expertise and they perceive this time to be an opportunity to further develop physical education for the future.
The use of infrared remote sensing to prospect ore deposits on Mars. Preliminary results from a planetary field analog in the Rio Tinto mining area in Spain

&amp;lt;p&amp;gt;Data from martian rovers and martian meteorites suggest the presence of ore minerals on Mars (eg. pyrite, chalcopyrite, pentlandite). Three spectrometers: CRISM (The Compact Reconnaissance Imaging Spectrometer for Mars; spectral range 0.4-3.9 &amp;amp;#181;m) onboard Mars Reconnaissance Orbiter (MRO), OMEGA (Observatoire pour la Mineralogie, l'Eau, les Glaces et l; Activit&amp;amp;#233;, 0.4 - 5.1 &amp;amp;#181;m ) and PFS (Planetary Fourier Spectrometer, 1.3-45.0 &amp;amp;#181;m) onboard Mars Express (MEX) operate in near infrared (NIR) spectrum and provide information on the mineral composition of Mars but none of them is yet capable to efficiently identify sulfides. Detecting sulfide ore deposits is difficult in NIR due to spectral interferences with silicates. Due to the limited &amp;lt;em&amp;gt;in-situ&amp;lt;/em&amp;gt; measurements by the Opportunity, Spirit, Curiosity, and Perseverance rovers, Mars mineralogical studies must be supported by studies of terrestrial analogs. One example is the Rio Tinto area in Andalusia, Spain, which hosts the largest known volcanogenic massive sulfide deposits on Earth. In this area, we analyzed satellite images in the infrared spectrum (ASTER, Landsat 8). We will compare these results to mineralogical data we will retrieve in the field during envisaged geological mapping in Spring 2022. By establishing our test field for remote sensing of sulfide deposits in a PFA site on Earth, we will be able to determine abundance thresholds for the detection of major sulfide phases on Mars and identify their key spectral features. Our results will help in 1) more efficient use of the current NIR Martian spectrometers to detect ore minerals, 2) designing new space instruments optimized for ore detection to include in future missions to Mars such as one developed at the Institute of Geological Sciences and the Space Research Centre of the Polish Academy of Sciences called MIRORES (Martian far-IR ORE Spectrometer).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Acknowledgments: The presented research are supported by National Science Centre of Poland project OPUS19 no. 2020/37/B/ST10/01420 and Europlanet2024-research infrastructure grant no. 20-EPN2-020.&amp;lt;/p&amp;gt;
Hybrid Systems for Tuning and Learning Fuzzy Systems

In applications, often a combination of different methods is used. In this Chapter, we study such hybrid systems. In the first part of the chapter, we combine the advantages of fuzzy systems (its interpretability) with one of the neural networks (its ability to learn from data). These systems are called neuro-fuzzy systems. There are lots of different cooperative and integrative architectures, especially for control, classification, and function approximation.
Approximate Reasoning

Approximate reasoningApproximate Reasoning ReasoningApproximate is based on the idea of sacrificing soundness or completeness for a better understandability of the results and for a significant speed-up of the reasoning. When pursuing such approximate reasoning approaches, however, it is important to be critical not only about appropriate application domains, but also about the quality of the resulting approximate reasoning procedures. Fuzzy Control is introduced as a “model-free contro” approach based on expert’s experience and knowledge.
Systemic Approach to Risk Estimation Using DSS

A concept is proposed for solving the problem of adaptive risk estimation that is based on the system analysis methodology and combined use of preliminary data processing techniques, mathematical and statistical modeling, optimal state estimation of the nonlinear nonstationary processes widely met in risk analysis and forecasting. The cyclical adaptation of a model structure and its parameters on the basis of a set of statistical criteria of the process being studied provides a possibility for computing high quality estimates of conditional variance forecasts under condition that data available is informative. To identify and take into consideration possible stochastic, structural and parametric uncertainties it is proposed to use optimal and digital filtering and the methods of intellectual data analysis such as Bayesian networks, adaptive Bayesian networks, various approaches to Kalman filtering, particle filters and other necessary instruments. Possible parametric uncertainties of the model being developed are minimized with application of several alternative parameter estimation techniques such as LS, RLS, ML and Markov chains Monte Carlo sampling. The models constructed are used for short term volatility (conditional variance) forecasting to be further used for market risk estimation. Bayesian networks allow for the formal description of other risk types such as operational, credit and actuarial risks, etc. The system proposed has wide possibilities for risk analysis application and further enhancement of its functional possibilities with new methods and computational procedures directed towards refining risk estimation and forecasting procedures.
Skeletonization Based on K-Nearest-Neighbors on Binary Image

Skeletonization on binary image is a process for reducing foreground regions to a skeletal remnant, which largely preserves the original region’s connectivity. An algorithm for skeletonization based on k-nearest-neighbors is proposed in this paper. Instead of the fixed 8-neighborhood approach, which is the most common in the thinning field, our algorithm implements skeletonization based on k-nearest-neighbors. The method mainly consists of two stages: raw skeleton extraction and a novel thinning for post-processing. Extensive experiments are conducted and results show that the skeleton extracted by our method is precise, clean, and much smoother than the previous works.
Optical Letter Recognition for Roman-Text

The reading abilities of computers are still way behind that of human beings. It is a too difficult task to build OCR for human use. Optical letter recognition is a scanned image which is converted into editable text. In this process, the image pictures are encoded with the help of Unicode. OCR system helps to add a volume or a journal product directly into an electronic PC document and alter the record utilizing a word processor. After removing the noise from the image and normalizing the image, the next step is to segment the image. Several segmentation techniques are used, such as line segmentation, word segmentation, and letter segmentation. It will be helpful to recognize the letter in the document image. After the letters are classified, the identified letters are grouped to reproduce the first symbol strings. The proposed method for developing high-performance OCR software for Roman-text includes the extraction of image, preprocessing highlights, and the creation of classification algorithms.
Deep Learning for Inverse Problems

Machine learning and in particular deep learning offer several data-driven methods to amend the typical shortcomings of purely analytical approaches. The mathematical research on these combined models is presently exploding on the experimental side but still lacking on the theoretical point of view. This workshop addresses the challenge of developing a solid mathematical theory for analyzing deep neural networks for inverse problems.