Reviewerly/exHarmony huggingface dataset:
THE IMPLEMENTATION OF PICTURE SERIES TO IMPROVE STUDENTS’ ABILITY IN WRITING NARRATIVE TEXTS AT THE SEVENTH GRADE OF SMP SWASTA BAHAL BATU IN ACADEMIC YEAR 2019/2020

This study presents a research report on the implication of picture series to improve students' narrative writing skills. The aim is to find out whether the students' narrative writing skills have improved by using picture series. It was done using the classroom action research method. The subjects of this study were seventh grade students of SMP Bahalbatu. The instruments used were test descriptions, questionnaires, diaries, and observations. The data analysis techniques were qualitative and quantitative. In analyzing the data, it is known that the mean increase from 63.3 to 80.13. That is, using picture series significantly improves students' writing skills.In conclusion, the implementation of Picture series improves students' narrative writing skills. It is recommended that the teacher implementation of picture series figures as a strategy to improve students' narrative writing skills.
An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces.

Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that maps embeddings to a common space. Alternatively, word embeddings may be understood as nodes in a weighted graph. This framing allows us to examine a node’s graph neighborhood without assuming a linear transform, and exploits new techniques from the graph matching optimization literature. These contrasting approaches have not been compared in BLI so far. In this work, we study the behavior of Euclidean versus graph-based approaches to BLI under differing data conditions and show that they complement each other when combined. We release our code at https://github.com/kellymarchisio/euc-v-graph-bli.
Students’ Perspectives on the use of The Atomic Game-Based Learning for Secondary School Chemistry

Technology has become part of our daily life, where it helps to ease our work, to communicate, get entertainment and even to learn.In education, technology has become essential as it creates a new approach of delivery in teaching and learning.Various approaches were invented or developed by educators to assist learning process.Nowadays, integrating game elements in the educational content is crucial and beneficial not only to the educators but also to the students.Game is one of the alternative methods that offers an interactive teaching and learning.It can increase student's motivation and excitement to learn on the subject and at the same time encouraging active learning among them.They can access the learning materials at the tip of their finger using smartphones, tablets, mobile computers, and other digital devices.They can create their own study plans, where and when to study at their convenience and it is more effective as compared to the traditional approach.The aim of this study is to examine students' perspective towards the use of Atomic Game-Based Learning (AGBL) app for secondary school chemistry in assisting their learning activities to the topic of structure on atom.AGBL is an Android based app that was designed and developed by integrating game element in the subject contents to support teaching and learning process.A total of 32 Form 4 students were involved in this study.The findings showed that the app does assists the student to score the in-game quiz as compared to the group of students who learned through hardcopy notes and flash cards.Besides, it also shows that 53% of the students strongly agreed that this app could be used as a tool of delivering the topic to the students.Therefore, this app can be counted as one of the effective approaches in teaching and learning of the chemistry subject.
The effect of photocopier types on line quality features of handwriting in multi-generational photocopies

The present research was aimed at finding out the relative effect of advancement in photocopier technology on the analysis of handwriting line quality features in multi-generational photocopies. Five subsequent multi-generational photocopies of 50 signature samples from different individuals which were produced by 75 black and white photocopiers were used for line quality assessments. Variations in line quality features in different photocopy generations, such as: smooth edges or ragged edges, broken or continuous stroke, fine tapering changes, and some identification details of features like pen lifts, retouching, and hesitation were carefully observed in sample photocopies, which were recorded according to the brand and model of photocopier machines, as well as their size and speed. The results of the study revealed that raggedness in line was often observed in third generation (G3) and higher generations, in samples produced by some low speed copiers (copying speed &lt; 30 cpm) or very high speed copiers (copying speed ≤ 80 cpm). However, their line quality was still found to be good enough for handwriting analysis up to the fifth generation (G5) in photocopy samples produced by some medium speed copiers (copying speed 30 to 50 cpm). It was, however, observed that, usually, continuity of line and smoothness of line were appreciably lost in G3 and higher generations in photocopies which were produced by portable desktop printers. Exceptionally, certain artifacts that are usually found in some higher generation copies were conspicuously absent in all photocopy generations (G1–G5) which were produced by some advanced third generation photocopiers. Hopefully, the information obtained from the study will be found useful to document examiners when examining certain cases involving multi-generations of photocopier reproductions.
Knowledge Base Completion Meets Transfer Learning

The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases. In this work, we introduce the first approach for transfer of knowledge from one collection of facts to another without the need for entity or relation matching. The method works for both canonicalized knowledge bases and uncanonicalized or open knowledge bases, i.e., knowledge bases where more than one copy of a real-world entity or relation may exist. Such knowledge bases are a natural output of automated information extraction tools that extract structured data from unstructured text. Our main contribution is a method that can make use of a large-scale pretraining on facts, collected from unstructured text, to improve predictions on structured data from a specific domain. The introduced method is the most impactful on small datasets such as ReVerb20K, where we obtained a 6% absolute increase of mean reciprocal rank and 65% relative decrease of mean rank over the previously best method, despite not relying on large pre-trained models like BERT.
New Approach For Creating Secure Clouds By Using Auditing Techniques

Cloud computing, an arising innovation is in effect generally utilized for re-appropriating the information into the cloud as opposed to putting away it in the nearby actual stockpiling. It tends to be gotten to by either singular client or gathering of clients. Cloud specialist co-ops should worry about the protection, security and information inspecting of the rethought information. Administration disappointment and the information misfortune is conceivable is because of the malevolent gate crashers in the cloud area. As clients at this point don't have actual ownership of the information, information honesty and security have become the essential worry in distributed computing. Checking the honesty of information put away on the cloud is a significant security concern. This is paper recommended that different standards ought to be persistently examined. In any case, unveil that the vast majority of current approaches are not material for outsider review purposes. Hence to propose a theoretical design for ceaseless review, featuring the significant segments and cycles that should be executed. At long last, talk about advantages and provokes that should be addressed to send the CSA idea. Additionally add to information and practice by giving pertinent inward and outsider review strategies to evaluators as well as suppliers.
Explaining The Impact Of Information Technology Capabilities And Organizational Acceleration On Performance Of Banking Sector

This study aimed to explain the effect of information technology capabilities and organizational acceleration on performance with the role of innovation capacity moderator in the banking sector of Mazandaran province of Iran during 2020-21. The statistical population of the study includes 1400 people. In order to determine the sample size based on Cochran's limited formula, the number of samples in this study was set at 302, which were selected by simple random sampling. We collect the primary data through fieldwork, and the instrument of primary data collection was five-choice Likert questionnaires, and also library method was used to collect secondary data. Both content and formal methods were used for the validity of this questionnaire, and we analyzed its reliability with Cronbach's alpha that the overall reliability is equal to 0.906. Structural equation modeling using smart PLS software was also used to test the research hypotheses. The results showed that information technology capabilities and innovation capacity have a significant relationship with organizational acceleration. In addition, Organizational acceleration has a significant relationship with organizational performance, and innovation capacity in the relationship between IT capabilities also has a moderating role on organizational acceleration.
Can predicate-argument relationships be extracted from UD trees?

In this paper we investigate the possibility of extracting predicate-argument relations from UD trees (and enhanced UD graphs).Concretely, we apply UD parsers on an English question answering/semantic-role labeling data set (FitzGerald et al., 2018) and check if the annotations reflect the relations in the resulting parse trees, using a small number of rules to extract this information.We find that 79.1% of the argument-predicate pairs can be found in this way, on the basis of Udify (Kondratyuk and Straka, 2019).Error analysis reveals that half of the error cases are attributable to shortcomings in the dataset.The remaining errors are mostly due to predicateargument relations not being extractible algorithmically from the UD trees (requiring semantic reasoning to be resolved).The parser itself is only responsible for a small portion of errors.Our analysis suggests a number of improvements to the UD annotation schema: we propose to enhance the schema in four ways, in order to capture argument-predicate relations.Additionally, we propose improvements regarding data collection for question answering/semantic-role labeling data.
Unified Word Sense Disambiguation Inventory for Polish

An updated inventory for Polish Word Sense Disambiguation corpora. Includes an updated version of old sense-annotated treebank - Skladnica (and, in the next version, an updated KPWr annotation), PolEval’s 2020 sense-annotated corpora named SH (Sherlock Holmes - The Adventure of the speckled band) and KPWr-100 (a new subset of 100 documents from KPWr), and Wikiselect which is a part of Wikipedia-based sense definitions from plWordNet 4.2. The sense annotations are compatible with plWordNet 4.2. The work is on progress and will be published officially soon.
NEW COMPOSITE MAGNETITE NANOPARTICLES FOR USE IN IMMUNOCHROMATOGRAPHIC

Composite nanoparticles based on magnetite with various coatings have been synthesized and characterized. Their effectiveness as markers for the immunochromatographic determination of troponin T, a diagnostic indicator of acute myocardial infarction, was compared.
Generation of time-frequency grid state with integrated biphoton frequency combs

Encoding quantum information in continuous variables is intrinsically faulty. Nevertheless, redundant qubits can be used for error correction, as proposed in [1]. We show how to experimentally implement this encoding using time-frequency continuous de- grees of freedom of photon pairs produced by spontaneous parametric down conversion. We illustrate our results using an integrated AlGaAs photon-pair source. We show how sin- gle qubit gates can be implemented and finally propose a theoretical scheme for correcting errors in a circuit-like and in a measurement-based architecture.
A Socio-Culturally Based Didactic Strategy To Improve Students’ Reading Skills

To design a Socio-Culturally based didactic strategy to improve students’ reading skills, a mixed investigation of a descriptive scope was carried out in the year 2020. The population of the study was formed by a hundred English teachers from different schools in Esmeraldas, and the sample consisted of 28 of them, who were chosen at random. The methods of investigation used were Analysis, Synthesis, Hermeneutics, and the techniques used were a survey and a documentary review. The results revealed that the main difficulties concerning reading skills students present are the insufficient critical thinking and low analysis about the reading content; most of the students develop reading comprehension identifying main ideas; the principal reading strategies used by teachers in class occasionally involve social interaction, they mainly develop individual activities. Based on the diagnosis made, a Socio-Culturally Based didactic strategy was designed, with three stages: Preparatory Socio-Interactional Reading, which consists of getting the students be prepared before the reading process start, by means of socio-interactive activities; Socio-Interactional Reading, which deals with creating an interactive environment where the students can interchange ideas about the reading content, and Post Socio-Interactional Reading, which focuses on doing activities where the students can apply what they have learned.
The Hype Machine. SinanAral. New York, NY: Penguin Random House, 2020. 416 pp. $28.00 (hardcover). (ISBN 9780525574514)

Journal of the Association for Information Science and TechnologyEarly View BOOK REVIEW The Hype Machine. Aral, Sinan. New York, NY: Penguin Random House, 2020. 416 pp. $28.00 (hardcover). (ISBN 9780525574514) Waseem Afzal, Corresponding Author Waseem Afzal wafzal@csu.edu.au School of Information and Communication Studies, Charles Sturt University, Wagga Wagga, New South Wales, Australia Correspondence Waseem Afzal, School of Information and Communication Studies, Charles Sturt University, Wagga Wagga, NSW, Australia. Email: wafzal@csu.edu.auSearch for more papers by this author Waseem Afzal, Corresponding Author Waseem Afzal wafzal@csu.edu.au School of Information and Communication Studies, Charles Sturt University, Wagga Wagga, New South Wales, Australia Correspondence Waseem Afzal, School of Information and Communication Studies, Charles Sturt University, Wagga Wagga, NSW, Australia. Email: wafzal@csu.edu.auSearch for more papers by this author First published: 09 November 2021 https://doi.org/10.1002/asi.24604Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Information Sharing of Medical Resources for Emergency Rescue Based on Blockchain

A digital emergency rescue system is an effective means to achieve accurate rescue, minimize casualties and property losses. When large emergencies occur, medical institutions need to jointly participate in the emergency rescue coordination. But it is hard to share medical resource and injured information, which reduces the efficiency of recue and affects the treatment of injured person. Therefore, this paper proposes a rescue information sharing architecture based on blockchain and distributed storage, which shares data for many medical institutions. Moreover, we propose a new consensus algorithm ERBFT based on reputation models and Verifiable Random Function (VRF) to reduce communication complexity, and maintain the security of blockchain information sharing system.
A Survey of Online Hate Speech through the Causal Lens

The societal issue of digital hostility has previously attracted a lot of attention. The topic counts an ample body of literature, yet remains prominent and challenging as ever due to its subjective nature. We posit that a better understanding of this problem will require the use of causal inference frameworks. This survey summarises the relevant research that revolves around estimations of causal effects related to online hate speech. Initially, we provide an argumentation as to why re-establishing the exploration of hate speech in causal terms is of the essence. Following that, we give an overview of the leading studies classified with respect to the direction of their outcomes, as well as an outline of all related research, and a summary of open research problems that can influence future work on the topic.
Substantiation of selection and dimension reduction of non-centered informative features of signals in information systems of short-range location

In this paper, we consider the issues of selection of informative features, dimension reduction of feature vectors in regression algorithms of detection and recognition of signals and interference, as well as the issues of obtaining informative features using neural network algorithms with ill-conditioned data. The problem is considered in relation to the short-range location, with large dynamic ranges of informative features and small decision intervals, when it is impossible to estimate mathematical expectations, that is, it is impossible to use adaptive algorithms. Regression algorithms for processing non-centered random signals are presented, with a priori unknown mathematical expectations of informative parameters, which consider the specificity of short-range location and use a priori information about the initial regression characteristics of informative features – multiple initial regression coefficients. Unlike it is in traditional regression analysis, the coefficients are determined through the elements of the matrices that are inverse to the matrices of the initial correlation moments. In regression algorithms, it is necessary to calculate the square error of multiple initial regression representations. The residual mean of squares of the initial regression representations are used to justify the methods for selection and dimension reduction of informative features of signals in the problems of detection and recognition of signals and interference. We give examples of application of the proposed methods for the problems of detection and recognition of a helicopter and airplane by acoustic signals when processing histograms of the distributions of the durations of intervals between zeros, samples of envelopes and samples of local extrema of the power spectral density. Good separability of the {airplane} and {helicopter} classes in the space of non-centered parameters of signals (features) is shown. The issue of obtaining regression statistical characteristics with illconditioned data is considered. If the matrices of the correlation moments of the informative features of signals and noise are illconditioned, it becomes impossible to obtain a priori information about the multiple initial regression coefficients. The possibility of using neural network algorithms to obtain estimates of the residual mean squares of regression representations and multiple initial regression coefficients through the weight coefficients on the inputs of neurons with ill-conditioned data is shown. The results can be used in short-range location systems with a large dynamic range of non-centered informative parameters, when it is not possible to estimate the mathematical expectations of the signal parameters due to the limited observation interval.
DEVELOPMENT OF THE PUGUNG RAHARJO ARCHAEOLOGICAL PARK IN LAMPUNG PROVINCE WITH GEOPARK AND ASTROTOURISM CONCEPT

Pugung Raharjo Archaeological Park is an archaeological site covering a 30 hectares area in East Lampung Regency, Lampung Province. There is some evidence of the megalithic era, also called the stepped pyramid of Pugung Raharjo discovered in 1957. There are other megalithic buildings from the 12th to 16th centuries AD, including menhirs and dolmen and prehistoric remains dating back to 2500 BC. From a geological perspective, Pugung Raharjo Archaeological Park was built on the extent of vesicular basalt lava of the Sukadana Formation, which is situated above the top of the Sukadana Basalt Plateau. These stones even have a local designation referred to as 'curly stones' because of their rough texture and vesicular holes. Due to its good strength and adhe-sion, the people of East Lampung use this stone as their house foundation. These stone is also used to make pundan berundak, menhirs, dolmen and others. The use of local stone for buildings and daily life tools shows a strong connection between geological and cultural aspects. The Pugung Raharjo Site area is also a very suitable location for astronomical observations because of the low level of light pollution. Moreover, according to history, people have been familiar with astronomy in their daily lives, such as wor-ship, time markers, navigation, or directions. This gives reach to a hypothesis about the possibility of this ancient society's astro-nomical knowledge manifested in determining each object's position in an area with specific constellation patterns. Based on its potential, Pugung Raharjo Archaeological Park is very suitable for developing the Geopark concept, which integrates geological, biological, and cultural aspects.
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings

Natural language processing (NLP) models often require a massive number of parameters for word embeddings, which limits their application on mobile devices.Researchers have employed many approaches, e.g.adaptive inputs, to reduce the parameters of word embeddings.However, existing methods rarely pay attention to semantic information.In this paper, we propose a novel method called Unique and Class Embeddings (UnClE), which explicitly leverages semantic similarity with weight sharing to reduce the dimensionality of word embeddings.Inspired by the fact that words with similar semantic can share a part of weights, we divide the embeddings of words into two parts: unique embedding and class embedding.The former is one-to-one mapping like traditional embedding, while the latter is many-toone mapping and learn the representation of class information.Our method is suitable for both word-level and sub-word level models and can be used to reduce both input and output embeddings.Experimental results on the standard WMT 2014 English-German dataset show that our method is able to reduce the parameters of word embeddings by more than 11x, with about 93% performance retaining in BLEU metrics.For language modeling task, our model can reduce word embeddings by 6x or 11x on PTB/WT2 dataset at the cost of a certain degree of performance degradation.