Reviewerly/exHarmony huggingface dataset:
Blockchain Based Intellectual Property Management

The electronic distribution of Intellectual Property (IP) poses multiple risks to innovators. It becomes difficult for them to maintain control of such property and track its usage; ownership can be difficult to prove, and the innovating party may not be able to curb or prevent infringements. By using blockchain as an IP registry, we can store IP as an encrypted document and represent it by a digital authenticity certificate, which guarantees tamper-proof evidence of ownership with timestamped transactions, thereby fostering transparency and decentralization. Through this project, we plan to conduct theoretical research to create a framework for a smart contract that covers this case. The approach would be to look at the problems in the Intellectual Property Rights (IPR) industry and come up with an on-paper solution with theoretical abstraction and design. We would consider various solutions and novel ideas that have already been proposed and try to optimize them with respect to local IP regulations. Eventually, the aim is to create a Proof-of- Concept of such a system that abides by modern patent laws in the Indian context.
Peningkatan Keterampilan Media Pembelajaran Interaktif Dengan Prezi Presenter Bagi Guru Dayah Modern Ihyaaussunnah Kota Lhokseumawe

Per the development of science and technology today, all people can get information anywhere and anytime quickly and easily from various sources and places in the world. The development of this science and technology is widely available tools that can use for the development of computer-based learning media are still few who use it. The problem encountered at the partner place is that there are still many teachers who deliver only lectures and sit in front of the class. This happens because of the lack of a teacher's ability to use instructional media. If this is left unchecked, the implications are for the success of the learning activities was achieved. This training activity aims to provide knowledge and skills and develop interactive learning media with presenter presenters. Training on developing interactive learning media with presenters using the Research and Development (R&D) method with 4D (Four-D) development procedures. The development of interactive learning media is carried out by training modern dayah ihyaaussunnah lhokseumawe city teachers as trial subjects totaling 15 people. Then the training instruments used were questionnaire validation of material, media, abilities, and teaches skills improvement. The products produced by the teachers are in the form of Prezi applications using the “exe” format. The results of the development of interactive learning media development based on the validation and reliability test which states that after training the ability of teachers there has been an increase in skills from the initial ability before training by 48.00% categorized as Validity (sufficient) with a reliability of 66% classified as moderate Reliability experienced significant change. After training the ability of teachers to be 87.90% categorized as Very High with 86% reliability categorized as very high Reliability. The conclusion that can a drawn from the test results is that there has been an increase in the skills of interactive learning media using the presenter Prezi is very appropriate to being used as a learning medium in school.
Mapping Vulnerable Populations with AI

Humanitarian actions require accurate information to efficiently delegate support operations. Such information can be maps of building footprints, building functions, and population densities. While the access to this information is comparably easy in industrialized countries thanks to reliable census data and national geo-data infrastructures, this is not the case for developing countries, where that data is often incomplete or outdated. Building maps derived from remote sensing images may partially remedy this challenge in such countries, but are not always accurate due to different landscape configurations and lack of validation data. Even when they exist, building footprint layers usually do not reveal more fine-grained building properties, such as the number of stories or the building's function (e.g., office, residential, school, etc.). In this project we aim to automate building footprint and function mapping using heterogeneous data sources. In a first step, we intend to delineate buildings from satellite data, using deep learning models for semantic image segmentation. Building functions shall be retrieved by parsing social media data like for instance tweets, as well as ground-based imagery, to automatically identify different buildings functions and retrieve further information such as the number of building stories. Building maps augmented with those additional attributes make it possible to derive more accurate population density maps, needed to support the targeted provision of humanitarian aid.
INTERACTIVE TEACHING AS AN EFFECTIVE TOOL FOR INCREASING STUDENTS' COMPETITIVENESS

L. Knapčíková , J. Husár , M. Trojanová , S. Hrehová Technical University of Košice, Faculty of Manufacturing Technologies with the seat in Prešov (SLOVAKIA)
THE POTENTIAL CONTRIBUTION OF E-LEARNING IN THE ENHANCEMENT OF ENGLISH READING COMPREHENSION

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 11224-11230ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.2332Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Individual Differences in Causal Learning

Author(s): Johnston, Laila; Hillman, Noah; Danks, David | Abstract: Causal inference from observed cases is a central cognitive challenge. There has been some evidence for individual differences in causal learning strategies, but prior work has not examined fine-grained sequences of judgments. In this paper, we report a large-scale model-fitting effort to determine the best-fitting causal inference models for individual participants. We fit a range of different model-types against multiple judgment sequences from each participant, thereby enabling comparisons of learning strategy both between- and within-participant. The model-fitting effort revealed some diversity in learning strategy along both dimensions, though individuals did exhibit some stability. Overall, however, the model fits were worse than expected, particularly when compared to the high accuracy reported for many of the models when used to predict group-level causal judgments. These results thus call into question whether these models might accurately describe the average behavior without accurately describing many (or any) individual’s behaviors.
Design & Development of Data Security Enhancement Features Using IoT & Cloud Convergence

In this research paper, the design & development of data security enhancement features using cloud computance is presented. With the progression of the IoT and the variety of its application which currently goes from a basic framework to a total IoT based climate at an undertaking, the measure of information being created by these frameworks is huge. To manage that, distributed computing is presently being utilized to deal with and measure the information. In spite of the fact that it brings us numerous chances, it likewise makes difficulties and hazard managing security of the information. In this paper, we will introduce an investigation on such difficulties and the techniques that are by and by being utilized to manage those difficulties. We will be dealing with approaches to improve the effectiveness of these strategies and recommend changes or upgrades that can be inferred for upgrading the security. At last, we will be taking a case guide to think about how the recommended changes will improve the information security in genuine IoT. The simulation & experimental results show the efficiency & the power of the methodology that is being developed by the team
IDENTIFYING EFL STUDENTS’ MOTIVATION TOWARDS READING ACTIVITY TO ENRICH THE CLASSROOM INTERACTION IN ELT CONTEXT

Motivation is an inner condition that guides human behaviors towards a certain target. Motivation for learning is massively important, because the greater motivation that they have gained, the greater the learning outcomes of the learners. This research was done in the sense of low interaction in the classroom as learners seldom read a book, especially before attending to the lesson. The purpose of this research was therefore to discover the motivation of the students in reading activity to enrich the classroom interaction in learning English as a foreign language (EFL). The researcher used descriptive qualitative approach. Firstly, the study, first, the researcher looked for the questionnaire of motivation to read. Secondly, the researcher selected the constructs or in this term, the questions categories to be used in this study. Thirdly, the researcher spread the selected questions as a questionnaire to the students of fifth semester of English Department in Tidar University. Then, after the researcher had finished collecting the data, the researcher analyzed the data acquired by demonstrating the motivation of the students to read the literature review. Eventually, the researcher reported the results on the basis of the study evidence.
Naive Bayes for Thesis Labeling

The thesis preparation in the Department of Informatics Universitas Ahmad Dahlan is divided into two areas of interest, namely Intelligent Systems and Software and Data Engineering. Existing thesis title data is only used as an archive and has never been processed or classified to determine the trend of thesis topics based on student interest each year. The stages include data collection, the data is divided into two parts (training data and test data), manual labeling of training data, text preprocessing, and classification using Naive Bayes. The results show the trend of thesis title taking from 2013 to 2018 shows the thesis trend in the field of Intelligent Systems and Software. Accuracy testing uses Confusion Matrix and K-Fold Cross Validation with a k value is 10, has a value of 94.60%, precision of 97.30%, and a recall of 85.70%.
PEMODELAN PENYAKIT DIFTERI DI SUMATERA BARAT MENGGUNAKAN REGRESI ZERO INFLATED DAN REGRESI HURDLE

Data that states the number of events in a certain period of time is called count data. Poisson regression is one of the regression models included in the application of GLM that can be used to model the count data. In Poisson regression, there are assumptions that must be met, namely the mean and variance of the response variables must be the same (equidispersion). Several models that are able to overcome overdispersion due to excess zero are the Zero Inflated model and the Hurdle model. This study examines the characteristics of parameter estimation in the modeling of quantified data that is overdispersed due to excess zero using the Zero Inflated Poisson (ZIP), Zero Inflated Negative Binomial (ZINB), Hurdle Poisson (HP) model and the Hurdle Negative Binomial (HNB) model in cases of diphtheria. in West Sumatra in 2018. Based on individual parameter testing and AIC values, the HP model provides better performance than the ZIP, ZINB, and HNB models. So the Hurdle Poisson model is better used in this case than other models
Experimental multi-state quantum discrimination through a Quantum network

The need of discriminating between different quantum states is a fundamental issue in Quantum Information and Communication. The actual realization of generally optimal strategies in this task is often limited by the need of supplemental resources and very complex receivers. We have experimentally implemented two discrimination schemes in a minimum-error scenario based on a receiver featured by a network structure and a dynamical processing of information. The first protocol implemented in our experiment, directly inspired to a recent theoretical proposal, achieves binary optimal discrimination, while the second one provides a novel approach to multi-state quantum discrimination, relying on the dynamical features of the network-like receiver. This strategy exploits the arrival time degree of freedom as an encoding variable, achieving optimal results, without the need for supplemental systems or devices. Our results further reveal the potential of dynamical approaches to Quantum State Discrimination tasks, providing a possible starting point for efficient alternatives to current experimental strategies.
THE EFFECT OF AUTHENTIC MATERIALS ON THE NINTH GRADE STUDENT’S READING COMPREHENSION AT SMP NEGERI 4 KOTA TANGERANG

This research was conducted at SMPN 4 KOTA TANGERANG which took place from July to August. The sample consisted 76 students from ninth grade which devide into two classes, experimental class (IX.3) and controlled class (IX.4).Based on the observation, some of students feel that learning of English, especially in reading is difficult subject compared to other lessons. So, the researcherss conduct research with used the Authentic Material with the aim in this research is to find and know whether that authentic material’s effect or not on students’ reading comprehension. This research used a quantitative method with a quasiexperimental design. In collecting the data, the researcherss gave pre-test and post-test on both classes with mulitiple choice questions that had been tested for validity and reliability before and in analyzing the data the researchers used t-test. Hyphothesis test result as the degree of significant 5% indicates that pre-test done before treatment is Tcount (1,22982) &lt; Ttable 1,992543, so H0 is accepted and H1 is rejected or there is no significant effect from experimental class and the controlled class before giving a treatment. It was found that posttest done after treatment isTcount (2,24132) &gt; Ttable 1,992543, so H0 is rejected and H1 is accepted. The result of this research shows that there is significant difference on students’ reading comprehension by using authentic materials.Therefore, it can be interpreted that teaching reading by using authentic materials can increase the students’ comprehension.
Linguistic Search by C.S. Boosting Optimization Algorithm

The internet data developed incrementally and gathered as unstructured information like textual documents, pictures, videos and sound content etc. This information material is created daily from several social networking resources such as sites, Twitter news posts, community websites, even Face book, etc. Information sharing is occurring across programs of various businesses like healthcare, education, societal sites, and army forums, community-based Q.A. sessions, client attention, etc. Information retrieval methods are used for getting relevant data from acollection of tools to resolve consumer requirements anywhere at any moment. Users expect precise responses with a brief response time instead of a listing of files during internet searches. Data sources show up in various formats, and thus it's pretty tricky to recover data without processing for simple recovery. The construction and arrangement of information in a predetermined format are required, and it's accomplished by data preprocessing and clustering from its comparable classes. In this paper, a cuckoo searchoptimization algorithmis proposed to reduce the search space and time complexity. An experimental result demonstrates that the algorithm generated greater accuracy in contrast to state-of-art procedures.
3D4ALL: Toward an Inclusive Pipeline to Classify 3D Contents

Algorithmic content moderation manages an explosive number of user-created content shared online everyday. Despite a massive number of 3D designs that are free to be downloaded, shared, and 3D printed by the users, detecting sensitivity with transparency and fairness has been controversial. Although sensitive 3D content might have a greater impact than other media due to its possible reproducibility and replicability without restriction, prevailed unawareness resulted in proliferation of sensitive 3D models online and a lack of discussion on transparent and fair 3D content moderation. As the 3D content exists as a document on the web mainly consisting of text and images, we first study the existing algorithmic efforts based on text and images and the prior endeavors to encompass transparency and fairness in moderation, which can also be useful in a 3D printing domain. At the same time, we identify 3D specific features that should be addressed to advance a 3D specialized algorithmic moderation. As a potential solution, we suggest a human-in-the-loop pipeline using augmented learning, powered by various stakeholders with different backgrounds and perspectives in understanding the content. Our pipeline aims to minimize personal biases by enabling diverse stakeholders to be vocal in reflecting various factors to interpret the content. We add our initial proposal for redesigning metadata of open 3D repositories, to invoke users' responsible actions of being granted consent from the subject upon sharing contents for free in the public spaces.
RESEARCH DATA MANAGEMENT PRACTICES OF BULGARIAN UNIVERSITY RESEARCHERS: SURVEY FINDINGS

The aim of this study is to examine the data literacy (DL) and research data management (RDM) awareness of Bulgarian university researchers. In 2016 the international research group for implementation of the Research Data Management Survey (RDMS) were established [1]. RDMS was conducted in three Bulgarian universities, namely: University of Library Studies and Information Technologies (ULSIT) in March 2017, University of Chemical Technology and Metallurgy (UCTM) in March 2018 and Konstantin Preslavsky University of Shumen (SHU) in May 2018. The general aggregation consists of 108 effectively surveyed lecturers and doctoral students.We aimed our analysis at obtaining information on the differences in practices and perceptions across the subject disciplines of respondents – between representatives of the Social Science and Humanities (S/H Sciences) (ULSIT and SHU) and those from Natural and Engineering Sciences (N/Е Sciences) (UCTM). For the goals of this paper we interpreted the findings from purposely selected questions about data management literacy and familiarity with data management practices. They examine what are the typical behaviours of university researchers from S/H Sciences and N/Е Sciences in the context of their preferences for: • the file type and volume of data use for research; • how they usually get and use the data for research;• what type and volume of data they produced from research;• data storage practices;• and assign of additional information to the own research data.In previous analyses, we examined the current state of data sharing and reuse perceptions, open access practices, metadata of datasets and the knowledge level about data management plans among the researchers [2]. The study, which found gaps in awareness and understanding of data management best practices, revealed the need for more research and training in all aspects of RDM for the surveyed population. The survey results find out some differences in practices and perceptions across the subject disciplines of respondents. These findings will be useful for preparation of learning content and methodology of training, according to the scientific field and specific requirements of the researchers. In connection of implementation of the National Plan for Development of the Initiative for Open Access in Bulgaria (2021-2025) they will be useful to design appropriate programs for data literacy, research data management and culture of sharing in the higher education system.
PERANCANGAN VIRTUAL TOUR UNIVERSITAS BUDI LUHUR SEBAGAI MEDIA PENUNJANG PROMOSI

Tour is a simulated media that can be used as information media. Teenagers today have a high sense of interest in virtual. With this approach, the creators made this work to introduce Budi Luhur University as a promotional media. This Tour introduces the units and rooms on the campus as a whole. This work is aimed at prospective students who are interested in continuing to pursue college, so that illustrations and gameplay are made adapted to the original look of Budi Luhur University in order to attract the attention of the target audience. With the design of the application Virtual Tour Universitas Budi Luhur, the creators hope this work will be a solution to introduce the campus environment with an interesting and informative media.
INFORMATION TECHNOLOGY COMPETENCE OF THE HEADS OF A MODERN SMALL SCHOOL IN REMOTE AREA

G. Yalamov 1, M. Kozlov 1, N. Zimniukova 1, A. Voronov2 1The Federal State Budget Scientific Institution «Institute of Education Management of the Russian Academy of Education» (RUSSIAN FEDERATION) 2MIREA - Russian Technological University (RUSSIAN FEDERATION)
Traffic Density Based Signal Control System

The venture is planned for structuring a thickness based powerful traffic signal framework where the planning of sign will change naturally on detecting the traffic thickness at any intersection. Traffic clog is a serious issue in many urban communities over the world and hence it is time to move increasingly manual mode or fixed clock mode to a robotized framework with dynamic abilities. Present day traffic flagging framework is fixed time based which may render wasteful on the off chance that one path is operational than the others. To streamline this difficult this paper has made a structure for an insightful traffic control framework. Now and again higher traffic thickness at one side of the intersection requests longer green time when contrasted with standard allocated time We, in this way propose here a component in which the timeframe of green light and red light is allocated based on the thickness of the traffic present around then. This is accomplished by utilizing PIR (proximity Infrared sensors). When the thickness is determined, the gleaming time of green light is allotted by the assistance of the microcontroller (Arduino). The sensors which are available on roadsides will identify the nearness of the vehicles and sends the data to the microcontroller where it will choose to what extent a flank will be open or when to change over the sign lights. In ensuing segments, this paper has expounded the technique of this structure.
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification

Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the exploitation of lexical features and their distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. In this paper, we propose a novel meta-learning framework integrated with an adversarial domain adaptation network, aiming to improve the adaptive ability of the model and generate high-quality text embedding for new classes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, the accuracy of 1-shot and 5-shot classification on the dataset of 20 Newsgroups is boosted from 52.1% to 59.6%, and from 68.3% to 77.8%, respectively.
LibGuides: LI 802 XU - Fall 2020 - Freemasonry: Conspiracy Theories

Freemasonry’s long history has provided an abundance of resources and materials, and it can be overwhelming to know where to start. This Pathfinder will help get you started on your research.