Reviewerly/exHarmony huggingface dataset:
The Ability of Word Embeddings to Capture Word Similarities

Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embedding. Determining the most qualitative word embedding is of crucial importance for such models. However, selecting the appropriate word embedding is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
SITIO WEB PARA DESARROLLAR PROCEDIMIENTOS DE COSTURA MANUAL EN EL PROCESO DE ENSEÑANZA APRENDIZAJE DE EDUCACIÓN LABORAL: SITIO WEB PARA DESARROLLAR PROCEDIMIENTOS DE COSTURA MANUAL

In the basic secondary, the subject Labor Education is an important one half educational for the integral formation of the students, its use allows the acquisition of knowledge, as well as the development of abilities; it is for it that this investigation is guided to develop the procedures of carrying out stitches and to the improvement of abilities in textile Makings, modifying the pattern of the students' of ninth degree performance. This constitutes a fundamental element due to the educational and instructive character, as a result of the investigation we seek to implement a Place Web, where the procedures of stitches, videos and images and essential actions are shown that will allow him the interaction with this place. In the same one empiric and theoretical methods were used that allowed the search of bibliographies, elaboration of activities, study of informations, to determine the knowledge and the student’s difficulties.
SSE Tech Support: MATLAB: Matrices and arrays

Pick up the fundamentals of MATLAB programming, including installation and setup, and MATLAB syntax for metrics, loops, conditionals, and functions.
GEOSCIENCE WITH A PUBLIC PURPOSE: ADDING PEOPLE TO THE EQUATION

Geoscientists work on temporal and spatial scales that often seem far removed from daily life. Yet the processes and phenomena we study directly affect quality of life, such as availability of resources, environmental habitability, or natural hazards and public safety. The geosciences can contribute greatly to highly consequential societal decision making, but applying such contributions is challenging. A concerted effort is required to bridge the gap between those generating the science and those who put it to work. This in turn requires a commitment by geoscientists to engage in “participatory research,” where we actively collaborate with a full range of partners in the design, execution, and delivery of our science. For example, geoscience-based analyses of natural hazards can be coupled with analysis of human exposures and impacts on the built environment, so that the resulting information is conveyed in terms of issues that matter to people. This can be done within the framework of probabilistic assessments as well as through co-developed scenarios that explore the full range of societal consequences from a specific event. Either approach requires close partnering across disciplines: engineering, planning, emergency and land management, and especially social science. And it requires engagement with the end users. As a mission science agency, the U.S. Geological Survey (USGS) has a responsibility to ensure that the research and data collection we undertake and the resulting information that we deliver are useful as a public good for all. It is not enough to provide the science to decision makers who have sufficient technical capacity to make effective use of this information. We need to be doing more to ensure that the science can be used by those who need it the most. This recognition is reflected in recent executive orders on advancing racial equity (E.O. 13985) and tackling the climate crisis (E.O. 14008), both of which call for an increased focus on underserved communities. To address this challenge, the USGS has set a goal to enrich the value of our science through increased participatory research with our partners, provision of our science to underserved communities, and Tribal engagement.
Fracture network localization preceding catastrophic failure

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Frontiers in Earth Science. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Fracture network localization preceding catastrophic failureAuthorsJessicaMcBeckiDYehudaBen-ZioniDFrancoisRenardiDSee all authors Jessica McBeckiDCorresponding Author• Submitting AuthorUniversity of OsloiDhttps://orcid.org/0000-0002-4465-5522view email addressThe email was not providedcopy email addressYehuda Ben-ZioniDUniversity of Southern CaliforniaiDhttps://orcid.org/0000-0002-9602-2014view email addressThe email was not providedcopy email addressFrancois RenardiDUniversity Joseph Fourier GrenobleiDhttps://orcid.org/0000-0002-5125-5930view email addressThe email was not providedcopy email address
PEMBELAJARAN DARING MATERI MENULIS TEKS NEGOSIASI PADA SISWA KELAS X DENGAN MENGGUNAKAN METODE DISCOVERY LEARNING

This reasearch was motivated by the researcher’s curiosity about the application of the method of discovery learning in online learning, especially in writing negotiation text in class X. The formulation of the problem of this study is how the results and responses of students to the application of discovery learning method in class X negotiation text material? The purpose of this study was to examine the results and responses of students to the application of the discovery learning method of writing negotiation text in class X. The research method used was descriptive qualitative. The sample used in this study were 33 students of class X IBB SMAN 1 Batujajar. The instruments used in this study were knowledge tests, skills tests and student quitionnaires. The results showed that the knowledge test based on the group was categorized as very good with an average value of 88,84. The knowledge test based on the individual was categorized as good with an average value of 75,90. The individual-based skill test was categorized as good with an average of 83,93. The The results of the student questionnaire with a total score of 1.949 were categorized as good with a percentage of 82,028%. So, it can be concluded that the discovery learning method is suitable for online learning in class X negotiation text material.
Exploring gaze entropy as a novel measure of visual scanning efficiency in problem solving

Gaze behaviour (i.e., the pattern of fixations and eye movements) is task dependent and tightly linked to underlying perceptual, cognitive and motor processes. Most research to date has examined the association between gaze behaviour characteristics and subsequent goal-directed actions using relatively simple problem-solving paradigms that do not match the complexity of those used in clinical or real-world applications. Therefore, the present investigation sought to examine gaze behaviour during a standardized version of the Tower of London (TOL) task in healthy adults, which is typically used to assess planning and problem-solving capabilities in clinical populations. The current study applied a novel method of gaze analysis to examine if changes in gaze entropy (i.e., gaze dispersion and predictability) were associated with the increasing demands placed on planning and working memory. Participants completed the Freiburg version of the TOL task. Gaze was recorded across 28 trials of increasing task difficulty (i.e., number of moves required to complete task goal). Preliminary analysis of two participants demonstrated that as task difficulty increased, stationary gaze entropy (i.e., spatial dispersion) increased and decreased in the initial planning stage and solution execution stage, respectively. Furthermore, gaze transition entropy (i.e., scan path predictability) during the planning stage demonstrated an inverted U-shaped trend as task difficulty increased, while the execution stage displayed a noticeable decrease in gaze transition entropy. Preliminary evidence suggests that task difficulty is linked to distinct changes in the spatial dispersion and predictability of gaze patterns across the different stages of planning and problem-solving.
Rendering and Tracking the Directional TSDF: Modeling Surface Orientation for Coherent Maps

Dense real-time tracking and mapping from RGB-D images is an important tool for many robotic applications, such as navigation or grasping. The recently presented Directional Truncated Signed Distance Function (DTSDF) is an augmentation of the regular TSDF and shows potential for more coherent maps and improved tracking performance. In this work, we present methods for rendering depth- and color maps from the DTSDF, making it a true drop-in replacement for the regular TSDF in established trackers. We evaluate and show, that our method increases re-usability of mapped scenes. Furthermore, we add color integration which notably improves color-correctness at adjacent surfaces.
TENSES PROBLEMS ENCOUNTERED BY ENGLISH FOR FOREIGN LANGUAGE STUDENTS IN INDONESIA

Learners often avoid a shift of tense since they have problems understanding and using it correctly and adequately. This research aimed to investigate and describe the tenses problems encountered by advanced students of the English Language Education Study Program in Lambung Mangkurat University and the causes of those tenses problems. This research was conducted using the descriptive-qualitative method. The subjects were the students who had passed all three structure courses in the English Language Education Study Program of Lambung Mangkurat University. The researcher used a questionnaire, tenses test, and interviews as the instrument to gather the data. The data were analyzed qualitatively and using the descriptive method for the tenses test. The research result revealed that the students often encounter problems on perfect tenses and tenses that have more than one aspect. They have structural problems mostly on irregular verbs and memorizing long formula; then the semantical problem mostly identifies the kind of tense to use semantically. The causes of the problems are mainly because of lack of input and practice result in feeling unfamiliar with certain tenses, also the students rarely use certain tenses such as perfect tense so that they prefer to use other tenses they are familiar with. It is suggested that there is more input and practice for the students in tenses that they rarely use in daily life.
CG2A: Conceptual Graphs Generation Algorithm.

Conceptual Graphs (CGs) are a formalism to represent knowledge. However producing a CG database is complex. To the best of our knowledge, existing methods do not fully use the expressivity of CGs. It is particularly troublesome as it is necessary to have CG databases to test and validate algorithms running on CGs. This paper proposes CG2A, an algorithm to build synthetic CGs exploiting most of their expressivity. CG2A takes as input constraints that constitute ontological knowledge including a vocabulary and a set of CGs with some label variables, called $\gamma$-CGs, as components of the generated CGs. Extensions also enable the automatic generation of the set of $\gamma$-CGs and vocabulary to ease the database generation and increase variability.
Evaluation of economics journals based on structural equation dimension reduction method

Evaluation of economic journals is helpful to improve the quality of these journals. In this study, we adopt a new structural equation dimension reduction method to evaluate economics journals. We classify evaluation indexes based on cluster analysis and factor analysis to establish a structural equation model, normalize the regression coefficients to obtain the weight, and then weigh and summarize the first-level evaluation indexes for the ultimate dimension reduction and evaluation. The evaluation based on JCR 2019 Economics journals shows that the structural equation dimension reduction method overcomes the randomness of manual classification of evaluation indicators. The linear dimension reduction method is conducive to preserving a large amount of information of the original indicators; evaluating the first-level indicators facilitates the comprehensive evaluation of journals; the first-level indicators evaluation approach is more objective and reflects systematic academic evaluation. It should be noted that the stability of the structural equation has a significant impact on evaluation, which is generally suitable for a relatively mature academic evaluation.
Conceptual study of smart machines for business development

The Business organizations are improving their performance by using advance technology in performing the business functions. A smart machine is a device embedded with machine-to-machine (M2M) and/or cognitive computing technologies such as artificial intelligence (AI), machine learning or deep learning, all of which it uses to reason, problem-solve, make decisions and even, ultimately, take action. Smart machines include robots, self-driving cars and other cognitive computing systems that are designed to work through tasks without human intervention. Smart machines are digital disruptors because of the positive and negative impact they have, and will continue to have, on society. In business, the competitive advantages these technologies are capable of providing are expected to bring higher profit margins and lead to more efficient manufacturing processes. However, smart machines are also expected to displace workers and dramatically change the nature of work and other societal norms. According to Tech Target, a smart machine is an intelligent device that uses machine-to-machine technology. It includes Smart machines include robots, self-driving cars and other cognitive computing systems that are able to make decisions and solve problems without human intervention. The smart machines are very common in society now days. Also many companies are using smart machines for their functional processes in business functions. It has lot of benefits in terms of efficiency, saving time of service, improve various tasks in the companies.
DETEKSI TINGGI RENDAH GELOMBANG AIR LAUT DENGAN SISTEM MULTI SENSOR BERBASIS MOBILE APPLICATION MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE

Indonesia is an island nation that has 17,499 islands with a total area of Indonesia is about 7.81 million km2 and only about 2.01 km2 in the form of land and the rest are about 3.1 million km2 of territorial sea and 2.7 million km2 of exclusive economic zones. The ocean is widely used by people for marine activities, but in the region of the ocean there are often high waves. High waves are a very important factor for marine activities. If there are often high waves, it can cause disruption of marine activities that impact the community. For that, knowing the condition of the wave is very important. By knowing the condition of the waves, people can plan marine activities better to anticipate high waves or to avoid them. From the problem, this system was made to be able to monitor the condition of ocean waves in realtime. This system uses Support Vector Machine algorithm in determining the classification of seawater wave conditions. The result of the classification has an accuracy of 55%. keywords—Mobile Application, Sea Waves, Support Vector Machine
Metric Learning for Structured Data

Distance measures form a backbone of machine learning and information retrieval in many application fields such as computer vision, natural language processing, and biology. However, general-purpose distances may fail to capture semantic particularities of a domain, leading to wrong inferences downstream. Motivated by such failures, the field of metric learning has emerged. Metric learning is concerned with learning a distance measure from data which pulls semantically similar data closer together and pushes semantically dissimilar data further apart. Over the past decades, metric learning approaches have yielded state-of-the-art results in many applications. Unfortunately, these successes are mostly limited to vectorial data, while metric learning for structured data remains a challenge.In this thesis, I present a metric learning scheme for a broad class of sequence edit distances which is compatible with any differentiable cost function, and a scalable, interpretable, and effective tree edit distance learning scheme, thus pushing the boundaries of metric learning for structured data.Furthermore, I make learned distances more useful by providing a novel algorithm to perform time series prediction solely based on distances, a novel algorithm to infer a structured datum from edit distances, and a novel algorithm to transfer a learned distance to a new domain using only little data and computation time.Finally, I apply these novel algorithms to two challenging application domains. First, I support students in intelligent tutoring systems. If a student gets stuck before completing a learning task, I predict how capable students would proceed in their situation and guide the student in that direction via edit hints. Second, I use transfer learning to counteract disturbances for bionic hand prostheses to make these prostheses more robust in patients' everyday lives.
Socialization of The Application of Language Skills for SD Teachers in Kecamatan Deket

This study aims to describe: the socialization of the application of oral language skills for elementary school teachers and the role in improving written language skills for elementary school teachers in Deket District. This study used a qualitative research design. The main object of this research is the socialization of the application of language skills for elementary school teachers in Deket District. The location of this research is in the UPT Office of the Education Office, Deket District. The data in this study is a socialization of the application of oral language skills for elementary school teachers in Deket District and the role in improving written language skills for elementary school teachers in Deket District. The data collection techniques used were observation, interview, and note-taking techniques. The technique used is descriptive analysis techniques. The results of the research on the socialization of the application of skills in oral language for elementary school teachers in Deket sub-district are that elementary school teachers in Deket sub-district are an important means for their students in good and correct language through oral and written to be applied in their respective schools. Implementation of spoken language in everyday life, teachers admit that they are not able to speak or use spoken language with Indonesian in full because they tend to use the language that is widely used by the environment in which they live. The role in improving skills in written language for elementary school teachers in Deket sub-district is by means of the teacher applying a contextual approach in the form of modeling components that are expected to make students find it easier to write because they already have a picture that has been given by the teacher through an example, and it is hoped that students can develop ideas , their thoughts, and ideas so that learning objectives can be achieved
Optimizing Spark Applications

Chapter 4 covered major capabilities in Spark SQL to perform simple to complex data processing. When you use Spark to process large datasets in hundreds of gigabytes or terabytes, you encounter interesting and challenging performance issues; therefore, it is important to know how to deal with them. Mastering Spark application performance issues is a very interesting, challenging, and broad topic. It requires a lot of research and a deep understanding of some of the key areas of Spark related to memory management and data movement.
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains

A common approach to prediction and planning in partially observable domains is to use recurrent neural networks (RNNs), which ideally develop and maintain a latent memory about hidden, task-relevant factors. We hypothesize that many of these hidden factors in the physical world are constant over time, changing only sparsely. Accordingly, we propose Gated $L_0$ Regularized Dynamics (GateL0RD), a novel recurrent architecture that incorporates the inductive bias to maintain stable, sparsely changing latent states. The bias is implemented by means of a novel internal gating function and a penalty on the $L_0$ norm of latent state changes. We demonstrate that GateL0RD can compete with or outperform state-of-the-art RNNs in a variety of partially observable prediction and control tasks. GateL0RD tends to encode the underlying generative factors of the environment, ignores spurious temporal dependencies, and generalizes better, improving sampling efficiency and prediction accuracy as well as behavior in model-based planning and reinforcement learning tasks. Moreover, we show that the developing latent states can be easily interpreted, which is a step towards better explainability in RNNs.
Accurate Determination of Fractional Retardations inPhotoelasticity through Digital Fringe Multiplication

Digital fringe multiplication is a useful technique for obtaining fractional fringe orders in photoelasticity at regions with very feeble retardations. In this paper, the effectiveness of the existing fringe multiplication techniques is investigated theoretically and experimentally. A new approach for fringe multiplication, using background nullified fringes, is developed to overcome the prevailing issues. Three options for obtaining background nullified fringe patterns are explained. Further, quadrature transformation of these fringe patterns is carried out to fetch uniform modulation. Moreover, a simplified fringe multiplication method is worked out to obtain fractional fringes from the uniformly modulated fringe patterns. The proposed procedures have been demonstrated through simulated as well as experimental images. A parametric study is carried out to understand the influence of pixel resolution and bit depth of the images on fringe multiplication. A criterion is established for finding out the maximum possible fringe multiplication for a given initial pixel/fringe resolution. It is observed that, for higher levels of fringe multiplication, larger bit depth of the image in conjunction with sufficient fringe resolution would be essential. The same also holds for fringe multiplication at areas with stress concentrations.