Reviewerly/exHarmony huggingface dataset:
Pengaruh Pandangan ABH (Anak yang Berhadapan dengan Hukum) mengenai Pemenjaraan terhadap Konsep Diri, Pengungkapan Diri, dan Motivasi Belajar ABH

ABH is serving a sentence by being placed in LPKA to be given training so that their psychological conditions including self-concept, self-disclosure, and learning motivation remain good. Meanwhile individuals who are convicted or sent to prison tend to have poor psychological conditions. The research aims to determine whether there is an influence between the views regarding imprisonment on ABH’s self-concept, self-disclosure, and learning motivation. The research uses the Total Institution Concept which explains the experience of individuals in the total institution influencing the self-concept, self-disclosure, and prisoners' motivation to be productive. Simple linear regression statistical test is used to determine the effect of 1 independent variable on 1 dependent variable. The study was conducted in LPKA Kutoarjo by using a total sampling of 51 respondents. The results showed that there was a positive influence between ABH's views of imprisonment on self-concept by 9% with a significance value (0.0340,05) and learning motivation with a significance value of (0.256>0.05). It can be concluded that the hypothesis stating there is an influence between ABH's views regarding imprisonment on ABH's self-concept was accepted. While the hypothesis which states there is an influence between ABH's views regarding imprisonment on self-disclosure and ABH's learning motivation is rejected. The findings show ABH have a good self-concept, they often do self-disclosure and have high motivation to learn.
Kylo Data Lakes Configuration deployed in Public Cloud environments in Single Node Mode

The master thesis introduces the Kylo Data Lake which deployed in the public cloud environment,provides a perspective of datalake configuration and data ingestion experiment. This paper reveals the ...
From Tinkering to Engineering: Measurements in Tensorflow Playground.

In this article, we present an extension of the Tensorflow Playground, called Tensorflow Meter (short TFMeter). TFMeter is an interactive neural network architecting tool that allows the visual creation of different architectures of neural networks. In addition to its ancestor, the playground, our tool shows information-theoretic measurements while constructing, training, and testing the network. As a result, each change results in a change in at least one of the measurements, providing for a better engineering intuition of what different architectures are able to learn. The measurements are derived from various places in the literature. In this demo, we describe our web application that is available online at this http URL and argue that in the same way that the original Playground is meant to build an intuition about neural networks, our extension educates users on available measurements, which we hope will ultimately improve experimental design and reproducibility in the field.
온라인 수업에 대한 통번역대학원생들의 인식 및 선호도 사례연구

This case study looks into the perceptions and preferences of graduate students of interpretation and translation for online learning. Due to the Covid-19 pandemic, many offline classes had to be suddenly shifted to online classes. The purpose of this case study is to investigate students’ perceptions and preferences for online and offline classes in this unprecedented situation. A survey was developed focusing on three areas: 1) changes in perceptions and preferences for online classes, 2) perceptions on achievement in online and offline classes, and 3) suggestions for the improvement of online classes. The survey results show that students overall preferred offline classes to online classes and perceived that their performance improved more in offline classes. Also, students suggested that guidelines for online classes should be necessary if the pandemic situations persist in the future. It was also pointed that class design, changes in pedagogy, and mastery of the online equipment will greatly affect the quality of the online teaching and learning. This study reveals that development of a new pedagogy for online classes on interpretation and translation is needed in preparation for an era of worldwide pandemics.
Social Media effects on Emirates Airline

As people continue to spend more time online, it is important for social media to capture these interactions to influence buying behavior. This paper looks at how Emirates airline has utilized social media to effectively boost its online presence and create customer interactions. The Emirates brand has been successful because it has utilized the marketing communication mix effectively to reach out to its audience and customers effectively. For Emirates, social media has played an important role and the company has utilized various online platforms such as social networking sites, content communities, blogs and collaborative projects to engage with consumers.
From Semi-automated to Automated Methods of Ontology Learning from Twitter Data

This paper presents four different mechanisms for ontology learning from Twitter data. The learning process involves the identification of entities and relations from a specified Twitter data set, which is then used to produce an ontology. The initial two methods considered, the Stanford and GATE based ontology learning frameworks, are both semi-automated methods for identifying the relations in the desired ontology. Although the two frameworks effectively create an ontology supported knowledge resource, the frameworks feature a particular disadvantage; the time-consuming and cumbersome task of manually annotating a relation extraction training data sets. As a result two other ontology learning frameworks are proposed, one using regular expressions which reduces the required resource, and one that combines Shortest Path Dependency parsing and Word Mover’s Distance to fully automate the process of creating relation extraction training data. All four are analysed and discussed in this paper.
WMU Research Guides: Library Collections Budget Reduction: Databases

This guide provides information about the University Libraries strategies for absorbing the budget reduction and rebalancing our collections in order to address the financial contraints affecting our university.
LibGuides: Basic Academic Research: The Research Process

This LibGuide covers skills, strategies, and materials employed in basic academic research: the sorts of assignments often given to lower division (freshman and sophomore) students in the first two years of college.
Waukesha company's 'fully autonomous' robots used to kill COVID

Surfacide's fully autonomous COVID-killing robots utilize UV-C energy to sanitize workspaces.
PEMBELAJARAN MENULIS CERITA PENDEK MELALUI METODE MIND MAPPING

This research is to study the learning of writing short stories through mind mapping methods in class XI students of SMK HAMKA Cikande. Learning to write short stories in fact reap various problems and are still less attractive to students, among them, the difficulty of students to start the writing process, the difficulties of students to develop ideas thinking, and writing as difficult and less interesting things. This study aims to describe learning to write short stories through mind mapping methods that will overcome students 'difficulties in writing, help students find ideas, and to open students' minds in mapping ideas. This research method uses descriptive qualitative which aims to make a description naturally through objects accurately, factually and systematically. The results showed students get an average score of 49 initial tests and final tests get an average score of 79. For the sake of learning, learning to write short stories through the mind mapping method has increased very well.
PISA: A measure of Preference In Selection of Arguments to model verb argument recoverability

Our paper offers a computational model of the semantic recoverability of verb arguments, tested in particular on direct objects and Instruments. Our fully distributional model is intended to improve on older taxonomy-based models, which require a lexicon in addition to the training corpus. We computed the selectional preferences of 99 transitive verbs and 173 Instrument verbs as the mean value of the pairwise cosines between their arguments (a weighted mean between all the arguments, or an unweighted mean with the topmost k arguments). Results show that our model can predict the recoverability of objects and Instruments, providing a similar result to that of taxonomy-based models but at a much cheaper computational cost.
SIMBox bypass frauds in cellular networks: Strategies, evolution, and detection survey

Due to their complexity and opaqueness, cellular networks have been subject to numerous attacks over the past few decades. These attacks are a real problem to telecom operators and cost them about USD 28.3 Billion annually, as reported by the Communications Fraud Control Association. SIMBox fraud, which is one of the most prevalent of these telephone frauds, is the main focus of this paper. SIMBox fraud consists of diverting international calls on the VoIP network and terminating them as local calls using an off-the-shelf device, referred to as SIMBox. This paper surveys both the existing literature and the major SIMBox manufacturers to provide comprehensive and analytical knowledge on SIMBox fraud, fraud strategies, fraud evolution, and fraud detection methods. We provide the necessary background on the telephone ecosystem while extensively exploring the SIMBox architecture required to understand fraud strategies. Our goal is to provide a complete introductory guide for research on SIMBox fraud and stimulate interest for SIMBox fraud detection, which remains little investigated. In this vein, we conclude the paper by presenting insights into tomorrow's SIMBox fraud detection challenges.
The Revolutionary Conservatism of Normal Science

The article analyzes the functioning of normal science. It has conservative fea­tures and implies the restriction of research practices to solving puzzle, rarely reflects on the ontological assumptions of its own paradigm. Such functioning of normal science allows it to solve a large number of various scientific and sci­entific-technical problems. As a result, normal science is developing quite rapidly. At the same time, revolutionary features can be distinguished in its func­tioning. They are associated with the struggle of each specific normal science for its position in the field of science, in the desire, through the dissemination of the results of its research beyond the scientific community and active participa­tion in the examination, to enlist the support of extra-scientific actors. Alliances with extra-scientific actors allow normal sciences to actively participate in the struggle for the redistribution of public attention and the financing of scientific research. With the help of such alliances, they are trying to introduce the results of their research into industry and public life. The success of such implementa­tions leads to an active redistribution of positions in the field of science. In this case, the revolutionary element of normal science is associated not with the de­sire to reconsider the fundamental ideas underlying it, but in the desire to rebuild the system of relations within the field of science, to take a leading position in it. As a result, it is concluded that the successful functioning of normal science is associated with the desire to make permanent micro-revolutions in the field of science, subject to a conservatively protective attitude to the fundamental as­sumptions of its own paradigm.
Arbitrary Spearman’s Rank Correlations in Maximum Entropy Bootstrap and Improved Monte Carlo Simulations

The R package for maximum entropy bootstrap (meboot) is widely used for numerous applications involving statistical inference for time series data without having to do differencing or de-trending. We report some simulations confirming its effectiveness. It has been used for simulating time series, especially the function "flexMeboot," in the package, which uses non-overlapping blocks while avoiding allowing for random trend reversals. This paper describes a newer extension called "mebootSpear," which permits the user to specify arbitrary Spearman's rank correlation coefficient rho between original data xt and resampled series. We use simulations to study the properties of the new option and extensions to the traditional Monte Carlo simulations.
인용명사의 분포적 특성 연구

This paper used newspaper corpus and the novel corpus as a comparative group to examine the distributional features of the ''shell nouns'' which has a dependent characteristic among korean nouns. The main purpose of this article is to describe the distributional characteristics of the shell nouns by genre based on the quantitative results of corpus. As a result, in the case of newspaper texts, the shell noun is mainly combined with the ‘-daneun’ clause to form the shell noun phrase. In the case of fiction, the combination with the ''-daneun'' clause is significantly lower than that of the newspaper, but the use of other shell noun phrases is high. The difference between these aspects is that there are shell noun phrases that a particular genre prefers and can be concluded that the functional part of the shell noun phrases is in contract with the nature of the genre. Therefore, this paper is meaningful in that it has further revealed that there is a preferred shell noun phrase by genre, and its function also varies depending on the genre.
A Composite Likelihood Approach for Inference under Photometric Redshift Uncertainty

Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We combine the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least $0.002(1+z)$, consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.
Virtual communication and emotions in social networks

The author made a theoretical analysis of the impact of communication and expression of emotions in social networks on the individual. The conditions of origin, distribution and use of social networks in the formation of a new social environment for the implementation of social ties are considered. The properties of social networks and their impact on modern society are analyzed. In recent decades, attention to emotions has grown again among scientists from various disciplines: psychology, psychiatry, neurobiology, cognitive sciences, computer science, sociology, economics and many others. It is noted that social networks have helped us become closer to our friends, even at a long distance. The urgency of the topic is due to the urgent need to study the features of social networks as a type of social structures formed as a result of the development of electronic social communications.
Mathematical Models of Human Learning

Although learning was a key focus during the early years of mathematical psychology, the cognitive revolution of the 1960s caused the field to languish for several decades. Two breakthroughs in neuroscience resurrected the field. The first was the discovery of long-term potentiation and long-term depression, which served as promising models of learning at the cellular level. The second was the discovery that humans have multiple learning and memory systems that each require a qualitatively different kind of model. Currently, the field is well represented at all of Marr’s three levels of analysis. Descriptive and process models of human learning are dominated by two different, but converging, approaches – one rooted in Bayesian statistics and one based on popular machine-learning algorithms. Implementational models are in the form of neural networks that mimic known neuroanatomy and account for learning via biologically plausible models of synaptic plasticity. Models of all these types are reviewed, and advantages and disadvantages of the different approaches are considered.
IMPLEMENTING A SECURE CLOUD ENVIRONMENT: AN EXPLORATIVE STUDY

Cloud enables the business to reduce IT cost while increasing the efficiency, utilization and flexibility of their existing computer hardware. It is a boon for IT industry that can achieve more by paying less cost to infrastructure, keeping low maintenance cost of software and hardware that save the manpower from monotonous upkeep of infrastructure. Further, it gives a platform for high-level computation leading to innovation. Cloud has been born from the concept of many but mainly virtualizations that enables a virtual environment constituting virtual resources and devices including storage, server, network and operating system. Further, it aids in segregating the resources corresponding to multiple execution environments. Though it is emerging at an amazing pace still faces rejection due to its various security issues. Cloud has to ensure data security and privacy. This paper describes a detailed analysis of all the security issues faced by both user and developer and their prevention and mitigation strategies. It has been concluded that single security solution is not sufficient for ensuring security thus, hybridization of various technologies and strategies must be implemented to make cloud sound and secure.
First Steps towards Universal Dependencies for Laz

This paper presents the first treebank for the Laz language, which is also the first Universal Dependencies Treebank for a South Caucasian language. This treebank aims to create a syntactically and morphologically annotated resource for further research. We also aim to document an endangered language in a systematic fashion within an inherently cross-linguistic framework: the Universal Dependencies Project (UD). As of now, our treebank consists of 576 sentences and 2,306 tokens annotated in light with the UD guidelines. We evaluated the treebank on the dependency parsing task using a pretrained multilingual parsing model, and the results are comparable with other low-resourced treebanks with no training set. We aim to expand our treebank in the near future to include 1,500 sentences. The bigger goal for our project is to create a set of treebanks for minority languages in Anatolia.
상호운용성과 저작권법적 보호 - Oracle v. Google 사건을 중심으로 -

Interoperability between systems has been growing in importance as more and more people collaborate and exchange ideas with one another. As interoperability became a necessity in the software market, late movers often attempt to achieve interoperability with the existing system even if to do so may risk copyright infringement. In the 80’s, US courts did not excuse the late movers’ effort to catch up, ruling such attempts did constitute copyright infringement. However, since the 90’s, the courts took a turn and made exceptions when alleged act of infringement was committed to achieve interoperability. Some argue that this turn made a significant contribution toward the US computer industry’s transition from a manufacturing industry to a interoperable software industry in the internet era. However, recently, the US courts seem to have taken a new turn from their standings established since the 90’s. In the famous Oracle v. Google case, the US Federal Circuit ruled that Google’s use of Java API SSO was not fair use even though Google’s copying of the Java code was to achieve a degree of interoperability. Therefore, a further investigation was necessary to examine whether the Federal Circuit’s stance is inconsistent with its precedents. Copyright law seeks to promote progress of useful arts and science by finding a balance between providing protection and allowing for fair use. And fair use doctrine is a crucial tool that keeps the balance. Fair use doctrine could better serve its purpose as a balancing tool if interoperability factors into account in the fair use analysis. This article investigated other legislations to examine whether considering argument for interoperability in the fair use analysis would be consistent with the legal system as a whole. Furthermore, cases from other legal systems such as EU were analyzed in order to compare current US court’s standing to international norm.
Automated Localized Approach for Airway Segmentation in 3D Chest CT Volume

Bronchial airway structure and morphology identification is very useful for analysis of many lung diseases. Since, the human tracheo-bronchial tree is a dyadic non-symmetric branching network which is very complex and its manual tracing is quite tedious and unwieldy. Moreover, automatic detection techniques for airway are quite challenging. This is due to its complexity and fading off the airway intensity because of the smaller asynchronous branching and noise in the image reconstruction. In this paper, an unsupervised approach for segmentation of localized airway has been proposed after segmenting the lung region. Firstly, airways are segmented out by using 3D region growing techniques with intensity constrained to prevent leakages. This results in limited segmentation of airways due to partial volume effect and leakage risk. Further, deeper bronchial branches are segmented by applying adaptive morphological techniques on 3D segmented lungs. Then, these two results are combined followed by 3D region growing to get complete segmentation of airway. The proposed technique is tested on Exact’09 20 test cases and evaluated by Exact’09 team. The performance of the proposed approach is quite reliable in segmenting distal branches with reasonable leakages. The advantage of this scheme is that it is easy to implement, fully automated, and time efficient.
Using Multiple Subwords to Improve English-Esperanto Automated Literary Translation Quality

Building Machine Translation (MT) systems for low-resource languages remains challenging. For many language pairs, parallel data are not widely available, and in such cases MT models do not achieve results comparable to those seen with high-resource languages. When data are scarce, it is of paramount importance to make optimal use of the limited material available. To that end, in this paper we propose employing the same parallel sentences multiple times, only changing the way the words are split each time. For this purpose we use several Byte Pair Encoding models, with various merge operations used in their configuration. In our experiments, we use this technique to expand the available data and improve an MT system involving a low-resource language pair, namely English-Esperanto. As an additional contribution, we made available a set of English-Esperanto parallel data in the literary domain.