Reviewerly/exHarmony huggingface dataset:
Different Types of Internally Headed Relative Clauses in Korean: A Corpus-based Analysis

Internally Headed Relative Clauses have received much attention since the early days of generative grammar. This study primarily addresses whether the IHRCs in Korean are homogeneous or not. This study utilized the Sejong Spoken Corpus (i.e., transcription of naturally occurring speeches) to understand IHRCs with crucial reference to the discourse context (exploring 60,378 instances of relativization). The corpus exploration subclassifies the Korean IHRCs into three subtypes, using the replacement test (whether a common noun can be substituted for kes) and the among test (whether the internal head can co-occur with cwungey ‘among’). The first one is such that kes serves to link the internal head directly to the main predicate as a semantic dependent. The second one conveys a partitive interpretation so that kes means the subset or subpart of the entity associated with the internal head. The third one involves a change-of-status of the internal head and thereby kes refers to something resulting from the event described in IHRCs.
Comments on &amp;quot;Lithology and climate controlled soil aggregate size distribution and organic carbon stability in the Peruvian Andes&amp;quot;

Soil organic matter storage and sequestration is a function of multiple factors from climate over parent material to plant traits and soil biology.The experimental differentiation between these factors in order to assess what drives SOC sequestration at a given soil ecosystem is always a tricky thing as it includes often multiple assumptions and or possible artefacts.The authors were up for the endeavour and aimed to elucidate how lithology and precipitation affect SOC stability in the Peruvian Andes.On top of this, the authors tried to measure if aggregation is the key factor that determines SOC stability at the study sites.This work in general is of special interest to a wider community working on the stabilization and destabilization of SOC.As the authors used a manipulation of the aggregation of the soils to compare intact and disrupted soils, this study
Research and Application of Computer Artificial Intelligence Technology in Online Learning System and Mobile Internet Terminal

In order to overcome the poor application effect of the traditional English-led intelligent online learning system, a PIMS-based English-led intelligent online learning system is proposed. Based on the analysis of the current situation of the intelligent teaching system, the article designs an intelligent English learning platform system based on PIMS. This has further improved the English teaching model and completed the construction of the overall framework structure. The research also solicited the user's evaluation and feedback information on the expected function improvement, and the results showed that the function improvement design has considerable value for the new concept college English network teaching system.
Fractal Dimension of Seismicity at Cangar, Arjuno-Welirang Complex, East Java

This study aimed to determine the value of fractal dimensions in the Cangar region, Arjuno-Welirang Volcano Hosted Geothermal System (VHG) based on seismic time series data. The determination of fractal dimension values is done using the delay embedding theorem method. The process starts from determining the value of the delay time by using the autocorrelation function and also determining the value of embedding dimensions using the False Nearest Neighbors (FNN) method. These two parameters are used to reconstruct the attractor diagram. Quantization of the attractor diagram provides a correlation dimension curve that is directly related to the fractal dimension value. The fractal dimension value in the study area tends to be high, ranging from 5.43 to 6.29. This high value is associated with the amount of energy needed by hot water to make their way out to the surface through rock with very little permeability. The emergence of fractures due to the continuous heat discharge process in Cangar hot water and the gas release process triggers the appearance of seismic signals.
Quantum Gates

Abstract As discussed in Chap. 10.1007/978-3-030-61601-4_2 , information in classical computers is represented by bits. However, if the bits did not change, then the computer would remain the same forever and would not be very useful! Therefore, it is necessary to change the values of bits depending on what you want the computer to do. For example, if you want a computer to multiply the number 2 and the number 3 together to produce the number 6, then you need to put each of the numbers 2 and 3 into an 8-bit binary representation, and then have a computational operation to multiply the two 8-bit values together to produce 6. The operation of changing bits in a classical computer is performed by classical logic gates.
Cumulative author index Volume 29 (2020)

International Journal of Modern Physics DVol. 29, No. 16, 2099001 (2020) No AccessCumulative author index Volume 29 (2020)https://doi.org/10.1142/S0218271820990011Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail You currently do not have access to the full text article. Recommend the journal to your library today! FiguresReferencesRelatedDetails Recommended Vol. 29, No. 16 Metrics History PDF download
The Web as Medium for Data Visualization

Exploring the types of graphics made possible by the web, including interactive dataviz, games and virtual reality (VR).
Conference reports

In this installment, Jamie Vicary (University of Cambridge, UK) reports on OWLS, the Online Worldwide Seminar in Logic and Semantics, 1 which he organizes together with Alexandra Silva (University College London) and Pawel Sobocinski (Tallinn University of Technology) since last April.
Adversarial Learning-Based Automatic Evaluator for Image Captioning

Image captioning imitates the process of humans describing the visual world using natural language. Evaluating image captioning remains a challenging task. Current methods mainly focus on the similarity between the generated description and reference texts, despite the direct relevance of the caption and the corresponding image. In this paper, we propose an adversarial learning-based evaluator. The evaluator is designed on Conditional Generative Adversarial Networks and trained with Proximal Policy Optimization. Since it directly works on the relationship between images and descriptions, our model takes advantage of references of other similar images in the dataset. It gives more reliable judgment than state-of-the-art methods, taking variability of natural language into consideration for better evaluation.
Prediction and Analysis of Extracting Relations using Spacy Model

Text is an extremely rich resources of information. Each and every second, minutes, peoples are sending or receiving hundreds of millions of data. There are various tasks involved in NLP are machine learning, information extraction, information retrieval, automatic text summarization, question-answered system, parsing, sentiment analysis, natural language understanding and natural language generation. The information extraction is an important task which is used to find the structured information from unstructured or semi-structured text. The paper presents a methodology for extracting the relations of biomedical entities using spacy. The framework consists of following phases such as data creation, load and converting the data into spacy object, preprocessing, define the pattern and extract the relations. The dataset is downloaded from NCBI database which contains only the sentences. The created model evaluated with performance measures like precision, recall and f-measure. The model achieved 87% of accuracy in retrieving of entities relation.
Assessment and development of global competence in the structure of the functional literacy of schoolboys

Analyzes the organizational, substantive and methodological, evaluation and measurement approaches to assessing the functional literacy of schoolchildren in the context of assessing the global competence index of the international standard for comparing world education systems OECD - PISA and the implementation of the benchmark for Russia's entry into the top 10 countries in the world. education as a result of the PISA study. The goal is to characterize the organizational, substantive, methodical, assessment and measurement tools for assessing the functional literacy of schoolchildren and the possibility of its institutionalization in the educational system on the example of the Arctic zone of the Russian Federation. The results of the study present educational project objectives for the development of global competences of schoolchildren, integrated into the content of the educational program of basic general education, as well as the results of the assessment of global competences of schoolchildren using the PISA global skills indicators.
Yoshio Fukao Receives 2018 Inge Lehmann Medal

Yoshio Fukao was awarded the 2018 Inge Lehmann Medal at the AGU Fall Meeting Honors Ceremony, held on 12 December 2018 in Washington, D. C. The medal is for “contributions to the understanding of the structure, composition, and dynamics of the Earth’s mantle and core.”
Superhuman Intelligence

Chapter 12 is the story of AlphaGo – the first computer program to defeat a top human player at the board game Go. On March 19, 2016, grandmaster Lee Sedol took on AlphaGo for a US$1 million prize in a best of five match. Experts expected that it would be easy money for Sedol. To most observers surprise, AlphaGo swept the first three games to win the match. AlphaGo was based on deep artificial neural networks (ANNs). The networks were trained with 30 million example moves followed 1.2 million games played against itself. AlphaGo was the creation of a London based company named Deep Mind Technologies. Founded in 2010 and acquired by Google 2014, DeepMind’s made a succession of high profile breakthroughs in artificial intelligence. Recently, their AlphaZero ANN displayed signs of general-purpose intelligence. It learned to play Chess, Shogi, and Go to world champion level in a few days.
A Narravite Cloud Storage Management using Multi-level Semantic Bonding Technique

The problem of cloud storage management has been well studied. The growing size and types of data increases the challenge in storage and retrieval. Number of approaches has been discussed for the problem of storage management. Text based clustering algorithms and Semantic based approaches are defined to improve the performance of storage management. However, the methods suffer to achieve higher performance in indexing and retrieval in terms of storage management. To solve this issue, an efficient semantic bonding measure based clustering and data management algorithm is presented. The method maintains ontology of various classes where each class has been mentioned in multiple levels. Each level of a class has specific properties and values. Using the semantic ontology, the method estimates MSB (Multi-level semantic Bonding) measure for different class of data. The same has been estimated for different level of semantic classes. Indexing of document class is performed based on MSB where the documents similarity has been measured using Topical Closure Measure (TCM). According to the value of TCM, the documents which are similar are identified and merge. The proposed algorithm improves the performance of document clustering and storage management in cloud environment
Decision Trees

In this chapter, we construct decision trees by estimating the relationship between the covariates and the response from observed data. Starting from the root, each vertex traces to either the left or right at each branch, depending on whether a condition w.r.t. the covariates is met, and finally reaches a terminal node to obtain the response. Compared with the methods we have considered thus far, since it is expressed as a simple structure, the estimation accuracy of a decision tree is poor, but since it is expressed visually, it is easy to understand the relationship between the covariates and the response. Decision trees are often used to understand relationships rather than to predict the future, and decision trees can be used for regression and classification. The decision tree has the problem that the estimated tree shapes differ greatly even if observation data that follow the same distribution are used. Therefore, similar to the bootstrap discussed in Chap. 4 , by sampling data of the same size from the original data multiple times, we reduce the variation in the obtained decision tree and this improvement can be considered. Finally, we introduce a method (boosting) that produces many small decision trees in the same way as the backfitting method learned in Chap. 7 to make highly accurate predictions.
Debate Methods to Improve Speaking Skills in Students of Grade X Language 1 MA Al Mahrusiyah Lirboyo

One of the factors of the ineffectiveness of speaking skills in students is caused when learning is too dominated by the teacher and does not provide access for students to develop independently through discovery in their thinking processes, students are just silent and do not dare to give their arguments against the material provided. Therefore, the author tries to make efforts to create a more effective teaching and learning process by applying speaking skills using the debate method. The aims of this research are (1) To find out the teacher's activities in improving students' speaking skills using the debate method. (2) To find out students' activities in improving speaking skills using the debate method. (3) To find out the application of the debate method in improving the speaking ability of students of class X Language 1 MA Al Mahrusiyah Lirboyo. Practically speaking, the results of this study can be used as input and methods for teachers to develop learning to speak in class X Language I MA Al Mahrusiyah through the discussion method, then can be an alternative way of learning to speak that is effective and appropriate for students, and can be a contribution of ideas to improve the system. better speaking learning for schools.
Konsep Diri, Kecerdasan Emosi dan Perilaku Asertif pada Siswa SMA Kelas X

This assertive behavior becomes an important thing to pay attention to generally for each individual and especially for individuals who are studying, namely students to be more able to optimize their potential. This study want to inform the impact of : (1) self-concept and assertive behavior in high school students in class X, (2) emotional intelligence and assertive behavior in high school students in class X, (3) self-concept and emotional intelligence in students' assertive behavior in high school class X. This study uses a correlational quantitative method. Researchers used as many as 100 high school students in class X as the study sample selected using random sampling techniques. The data analysis technique used in this research is multiple linear regression analysis test. The results of this study indicate that: (1) self-concept has a significant effect on assertive behavior with a positive relationship direction that can be seen from the significance value of 0.005 and the regression coefficient of 0.315. (2) emotional intelligence has a significant effect on assertive behavior with a positive relationship direction that can be seen from the significance value of 0,000 and a regression coefficient of 0.414. RSquare value of 0.464; that is, 46.4% of the variation in assertive behavior is influenced by self-concept variables and emotional intelligence and the remaining 53.6% is caused by other variables not measured by researchers. The relationship between self-concept and emotional intelligence with assertive behavior is known to have a significant relationship with a significance value of 0,000.
EU update

Saudi Arabia grants nationality to an AI robot; the first “clash of robots” took place in Japan; and, Bill Gates suggests that robots start paying taxes. We believe that these developments justify new legal fiction interventions. Software has long now exceeded the intellectual property boundaries. It is no longer merely property; it has assumed life of its own. It does not matter that such life is imaginary today. Legal persons were brought to life through legal fiction intervention that was based on much less motivation – merely the human incentive for profit. Software is certainly connected today with profit, given that the world's most valued corporations are software companies. However, it has moved much further than that, to assume in many ways artificial life of its own. We think that it is time that the dichotomy between natural and legal persons, that has served humanity so well over the past centuries, now be trisected: A new, digital person, ought to be added to it.
Naïve Bayes Filter for Communication &amp; Enhancing Semantic in Email

Due tothe current pandemic of COVID-19, the world has turned into ONLINE modeand an increase in online communication thereby information exchange, sharing useful data through emails and other social Medias. So addressing the security issues places a vital role in computer security and shouldhave thepriorities. We need a security check to enhance the inbox so that the important information or emails should not reach to the spam box. In this paper to improve the filtering techniques, wehave adopted the Naïve Bayes approach in implementation and enhancing the spam filter in the email. Bayes’s approach is efficient, accurate, and simple in implementing the proposed algorithm. Bayes algorithm is used to verify correct semantic information of the email and avoidsthe pass to pass approach if the incoming mail is important. The Python language is used to develop the proposed algorithm.
Image stitching algorithm based on fast feature extraction and perceptual hash

The classic feature point image stitching algorithm is time-consuming in feature extraction, and an image sparse matrix method is proposed to determine the feature points. This method first uses Laplace operator to extract the image gradient and set the threshold to obtain the sparse matrix of image segmentation, then using features from accelerated segment test (FAST) detection algorithm for feature point. Finally, the speeded-up robust features (SURF) descriptor is given to increase the stability of the feature points. This method solves the problem of time-consuming feature extraction, making the real-time image mosaic possible. In the process of image mosaic, this paper presents a method using Kalman filter to predict the overlap region, thus reducing the computational complexity. In addition, because there is always a mismatch between feature points matching, this paper proposes a method using perceptual hashing to refine the matching pair, which solves the error caused by mismatching for the next image registration and improves the registration accuracy. Experimental results show that the proposed algorithm improves the speed and precision of image mosaic.
Supplemental Material for Identifying the Dimensions of Secrets to Reduce Their Harms

The harms of secrecy: Integrating data-driven dimension identification with experimental intervention to improve coping with secrecy Supplemental Study: Content Ratings of Personal SecretsStudy 1 (across seven participant samples) found that people naturally see secrets as existing along three dimensions: how immoral they are, how relational they are, and how profession/goal-oriented they are.In so doing, we obtained coordinates for where each of the common categories of secrets were seen to consensually fall in the data-driven space spanned by the three dimensions.Studies 2a-2c and 3 then validated the model, examining how people experience their real-world personal secrets.That is, from the secrets' coordinates on the three dimensions, we could predict how people who have such secrets experience those secrets, predicting: 1) reports of shame from the secret, uniquely from where secrets fall along the immoral dimension of the model; 2) reports of insight into the secret, uniquely from where those secrets fall along the profession/goal-oriented dimension; 3) how much participants feel their secrets connect them to others, uniquely from where those secrets fall along the relational dimension.We posited that the relationship between the three data-driven dimensions (immoral, profession/goal-oriented, relational) and these three experiences (shame, insight, and social connection) are based in the content of the secrets.A Supplemental Study examined this hypothesis. MethodParticipants (89 men, 111 women; Mage = 35.10years, SD = 12.23) recruited on MTurk were provided with the list of the common categories of secrets and per each current secret they had, participants completed a series of measures.
Development of a Scalable Coding for the Encryption of Images using Min-Max Block Truncation Code

In today’s world, security of data from intruders and hackers during transmission and reception needs image encryption, and to reduce space requirement and faster transmission needs image compression, which tend to be the emerging research arenas. Especially for lossy compression, rebuilding of image equivalent to the transmitted original image is highly unachievable. So far many papers are reported for scalable coding on unencrypted images. We propose a scalable coding for encrypted images by Min-Max Block Truncation Coding Technique(MMBTC). The Min-Max Block Truncation Coding Technique compress the raw image and later encrypted by pseudorandom number, and the encoded bit streams are transmitted. The secret key is encryption key and communicated between encoder and decoder. In the decoding process, the compressed image is recovered with secret key and the raw image is rebuilded by using Min-Max Block Truncation Coding Technique.
Retraction Note to: Potential of support vector regression for solar radiation prediction in Nigeria

The Editor-in-Chief has retracted this article [1] because the validity of the content of this article cannot be verified.
Chapter 3: Scale Conversion

In many applications, the variables describing the objects to be clustered will not be measured in the same scales. They may often be variables of completely different types, some interval, others categorical. There are three approaches to cluster objects described by variables of different types. One is to use a similarity coefficient that can incorporate information from different types of variables, such as the general coefficients described in Section 6.5. The second is to carry out separate analyses of the same set of objects, each analysis involving variables of a single type only, and then to synthesize the results from different analyses. The third is to convert the variables of different types to variables of the same type, such as converting all variables describing the objects to be clustered into categorical variables. In this section, we describe some methods for converting variables between different types.
Investigation on large vocabulary continuous Kannada speech recognition

The proposed work carried out to convert the Kannada speech signal to a text document using Sphinx tools. The system is implemented for the large vocabulary of the Kannada speech signal by using the acoustic model (AM) and language model (LM) with the decoder. The AM extracted the Mel frequency cepstral coefficients (MFCC) of the speech signal successfully and trained these coefficients using hidden Markov model (HMM) and tweak the estimation of the AM using Baum-Welch method. The required language model (LM) format is built for the decoder using n-gram count. The decoder is configured to create a text file to the corresponding input speech signal using Sphinx3. The proposed automatic speech recognition (ASR) system achieves a better recognition rate with less word error rate (WER) with AM and LM adaptation using Sphinx3 for large vocabulary and Pocket Sphinx implemented on RasberryPi3 results better accuracy than the other ASR system.