Reviewerly/exHarmony huggingface dataset:
Investigating the use of YouTube as virtual teaching medium in ELT among non-English students

Since the COVID-19 pandemic came up suddenly to this world, everything takes a drastic turn. The Ministry of Education and Culture Republic of Indonesia reacted to this issue by making an online learning policy Number 3 of 2020 on Prevention of COVID-19 in the Education Unit. Many education institutions adopted this policy by creating online tools such as YouTube as a resource of transition from face-to-face learning to virtual learning since it is seen as a popular medium used in online classes. This research investigates the use of YouTube as a medium in the English 1 course for non-English students at one of State Islamic University in Salatiga. YouTube is an online learning platform that enables lecturers and students to upload, view, share, and even interact through the comments column in the learning videos. A descriptive qualitative method is used to achieve the objective of the research, which involved three classes from the Early Childhood Islamic Education study program, the Islamic Elementary School study program, and the Islamic Religious Education study program as the objects in this research. The researcher limits the content of YouTube from the channel "AR," while the materials are about Degree of Comparison and Collocation. Students' responses from those three classes in the YouTube column have been analyzed to investigate the findings. The finding shows that YouTube brings a good impact on non-English students in ELT. This finding is supported by the result of the analysis answer sheet of the quiz from students, the survey on students’ perception, and other comments about the materials on the YouTube comment column. Finally, it can be concluded that YouTube can be an effective virtual teaching medium in ELT among non-English students at IAIN Salatiga based on the result of survey by respondents.
Cloud Model Membership Degree of Rock Slope Stability Evaluation: Method and a Case Study

The ranking grade of rock engineering stability is generally determined by the maximum membership degree principle, and the value of membership degree is an exact numerical value.However, the membership degree of uncertainty with randomness and fuzziness has not been considered.The membership degree with uncertainty can describe the uncertainty effects of the evaluating factors on rock engineering stability and fully shows the degrees of one evaluating factor belonging to the ranking grades.In this study, a quantitative index representing the membership degree with uncertainty is proposed and obtained based on the membership degree of the cloud model.The membership degree of the cloud model is obtained based on normal cloud theory, the system and the ranking grade of evaluation factors and analytical hierarchy process (AHP) based on scale of the cloud model.An evaluation method for the membership degree of cloud model is conducted and applied in a rock slope project.The feasible and practical evaluation results is verified by comparing with that of AHP method, the extension theory and the numerical simulation.This proposed method can serve as a decision tool for stability evaluation in other similar projects.
Communication students’ anxiety in speaking skill practice

Students majoring in communication believe that their nervousness when speaking will affect their lives. It will either limit their employment opportunities or cause them to perform poorly at work. In the digital era, English speaking is very important to communicate with other members of an organization. There are limited discussions about speaking anxiety in ESP class and students majoring in communication studies are expected to convey their messages persuasively in an organization. So, descriptive qualitative research was utilized by the researcher to describe speaking anxiety in ESP class; Types of Speaking Anxiety, Factors of Speaking Anxiety, Reducing Speaking Anxiety. Semi-structured interviews and observations were the sources in this study with 19 students involved. Three natural phenomena in speaking anxiety or research problems in speaking anxiety were analyzed using data reduction, data display, and conclusion. The results showed that Communication Apprehension, Fear of Negative Acceptance, and Test Anxiety existed among the students in speaking class. They can overcome it using YouTube videos as references and community supports (teacher and close-friends). This study also found that students majoring in communication studies experience trait anxiety (one person), state anxiety (ten students), and situation-specific anxiety (eight students). Based on this result, further researchers related to speaking anxiety are to revise the approach using the mixed method in ESP class. These suggestions are to enrich speaking anxiety for English Specific Purposes, such as midwife students and economic students.
The linear characteristics of concept time: synchrony and diachrony, N-Gram Markov Language Model, new media education

Saussure introduced us to synchronicity and diachronism; indicators and meanings in general linguistics courses. This basic set of opposites can study any human phenomenon, which is based on a system of contrasts and combinatorial relations between regularized indicative elements. In this article, the author is committed to analyzing and summarizing the concept of N-Gram Markov language Model and specific steps, and using this language model to analyze the manifestation of time linear characteristics in language variants. We try to find out the role of the concept of time in the change of youth are in different eras, and the cause of youth ar. The research method used in this article is based on Saussure&#x2019;s theory- synchronicity and diachronicity. And the author use the N-Gram Markov language model to try to explore practical problems in new media education. The academic research results can be used in foreign language teaching, social media and educational psychology.
Development of an Interpretable, Multivariable, Machine Learning Model for Clinical Decision Support on Mortality Prediction of People Admitted to Intensive Care Units

Purpose: Early mortality probability prediction in intensive care units using clinical decision support approaches is important for timely patient risk stratification, improving prognosis, and optimizing hospital resources. We developed an all-cause mortality risk prediction model based on the Machine Learning (ML) algorithm eXtreme Gradient Boosting (XGBoost). Additionally, we included an explainability layer based on Shapley Additive exPlanations (SHAP) that allow the user to understand the model's predictions and individual feature contribution. Material and Methods:We trained and tested our model with a curated database assembled after performing a retrospective study of ICU admissions from the public Medical Information Mart for Intensive Care (MIMIC-III, version 1.4) and eICU Collaborative Research Database (eICU-CRD, version 2.0) databases.Results and Conclusions: We showed that our XGBoost-based ML model accurately predicts mortality better than the most commonly used scores in ICUs and other ML-based models. Additionally, we showed that our model could predict all-cause mortality using just 20 or eight clinical variables measured within the first 24 hours of ICU admission. Finally, we demonstrated that race/ethnicity is an important factor influencing the performance of our ML-based models and that cohorts used to do so need to be diverse and representative to increase their global applicability.
Sociodynamic Approach to Evaluation of Efficiency and Formation of Russian University Rankings

The article discusses the issues of assessing the activities of higher educational institutions based on rankings. It is proved that rankings, like any other instruments of state educational policy, should have a solid theoretical basis, since unreasonable political decisions in higher education are able to cause a deterioration in the quality of life of individual members of society and society as a whole. It is proposed to use a sociodynamic approach to measuring the results of universities, which allows to take into account the social and individual usefulness of higher education services. Within the framework of the proposed approach, the authors formed and substantiated the system of indicators for assessing the contribution of Russian universities in the formation of the effects of individual and public consumption of higher education services. For calculations of consolidated indicators of such a contribution, according to the developed methodology, a group of educational organizations of higher education subordinate to the Ministry of Agriculture of the Russian Federation (N = 53) was chosen. The proposed approach allows, when making political decisions in the field of higher education, classify Russian universities according to the degree of achievement of goals and the performance of the functions that were entrusted with the state and society
A Game Theoretic Approach for Managing Multi-Modal Urban Mobility Systems

Collective adaptive systems provide secure and robust collaboration between heterogeneous entities such as humans and computer systems. Such entities have potentially conflicting goals that attempt to satisfy by interacting with each other. Understanding and analyzing their behavior and evolution requires technical, social and economic aspects of modeling. In this paper, we develop a new design principle to describe an integrated and multimodal urban mobility system and model the interactions of various entities by means of game theoretic techniques.
PENGEMBANGAN APPLICATION SERVICE DATA MAHASISWA PADA WEBSITE JURUSAN ILMU KOMPUTER

The information system at the Department of Computer Science, University of Lampung has some data such as data about students, lecturers, courses schedule and final assignments or theses. The information system contained in the Computer Science Department, University of Lampung is still not integrated between one system and another. This makes the existing data inconsistent so that there are some differences in data between systems. To solve this problem, a student data Application Service Interface (API) program with Representational Transfer State (REST) architectural style was created. Framework CodeIgniter (CI) will be used in the development of this application using the waterfall method. This research has succeeded in building thirteen APIs and tested using Postman software. System testing is also carried out using the equivalence partitioning method and system functional testing. Based on testing with the equivalence partitioning method and system functional testing, overall success was obtained at each API access point. The results of the API access will be sent in the Javascript Object Notation (JSON) data format.
Aspect-based Sentiment Analysis using BERT with Disentangled Attention

Aspect-Based Sentiment Analysis (ABSA) tasks aim to identify consumers’ opinions about different aspects of products or services. BERT-based language models have been used successfully in applications that require a deep understanding of the language, such as sentiment analysis. This paper investigates the use of disentangled learning to improve BERT-based textual representations in ABSA tasks. Motivated by the success of disentangled representation learning in the field of computer vision, which aims to obtain explanatory factors of the data representations, we explored the recent DeBERTa model (Decoding-enhanced BERT with Disentangled Attention) to disentangle the syntactic and semantics features from a BERT architecture. Experimental results show that incorporating disentangled attention and a simple fine-tuning strategy for downstream tasks outperforms state-of-the-art models in ABSA’s benchmark datasets.
Vehicle-counting with automatic Region-of-Interest and Driving-Trajectory detection

Vehicle counting systems can help with vehicle analysis and traffic incident detection. Unfortunately, most existing methods require some level of human input to identify the Region of interest (ROI), movements of interest, or to establish a reference point or line to count vehicles from traffic cameras. This work introduces a method to count vehicles from traffic videos that automatically identifies the ROI for the camera, as well as the driving trajectories of the vehicles. This makes the method feasible to use with Pan-Tilt-Zoom cameras, which are frequently used in developing countries. Preliminary results indicate that the proposed method achieves an average intersection over the union of 57.05% for the ROI and a mean absolute error of just 17.44% at counting vehicles of the traffic video cameras tested.
Fast and Precise Binary Instance Segmentation of 2D Objects for Automotive Applications

n this paper, we focus on improving binary 2D instance segmentation to assist humans in labeling ground truth datasets with polygons. Humans labeler just have to draw boxes around objects, and polygons are generated automatically. To be useful, our system has to run on CPUs in real-time. The most usual approach for binary instance segmentation involves encoder-decoder networks. This report evaluates state-of-the-art encoder-decoder networks and proposes a method for improving instance segmentation quality using these networks. Alongside network architecture improvements, our proposed method relies upon providing extra information to the network input, so-called “extreme points”, i.e. the outermost points on the object silhouette. The user can label them instead of a bounding box almost as quickly. The bounding box can be deduced from the extreme points as well. This method produces better IoU compared to other state-of-the-art encoder-decoder networks and also runs fast enough when it is deployed on a CPU.
ColocationSim: Simulate Colocation Datacenter with Microservices and Performance Interference

Colocating Latency-Critical (LC) jobs and Best-Effort (BE) jobs on one host is the best practice to improve datacenter resource efficiency and the Colocated Job Scheduling algorithms play a key role in this scenario. But validating a new Colocated Job Scheduling algorithm at scale is usually resource-, energy- and time-consuming in the real-datacenter, therefore simulation is the first choice. Unfortunately, existing simulators are not suitable for colocation simulation because: 1) they cannot simulate microservices, which is widely adopted in LC/BE jobs; 2) they cannot simulate performance interference of colocated jobs. In this paper, we present a new simulator, ColocationSim, which 1) uses REG(Request Execution Graph) model and queue theory to simulate the behavior of microservices; 2) uses pre-trained models to simulate performance interference. Experiments show that ColocationSim can simulate performance interference of both sequential and fan-out structure microservices with high accuracy and it can also simulate more than 8,000 colocated hosts in a short time.
How elementary students experience the use of immersive technology

The integration of immersive technology is one of the main trends in education. The purpose of this study is to understand how elementary school students experience the use of immersive technology, considering three properties: interactivity, presence, and flow. A natural science pedagogical intervention that incorporated the use of virtual reality and augmented reality was elaborated. 231 students from four schools in Concepción, Chile, participated. After the intervention, an instrument was applied to learn about the immersive experience. Findings indicate a high degree of agreement with the instrument's statements, which allows us to determine favourable levels of interactivity, presence, and flow, as well as the significant and positive association of these technological properties. Guidelines are proposed to maximise the capacity of immersive technologies in learning environments.
Review on String-Matching Algorithm

String-matching algorithm is one of the most researched algorithms in computer science which has become an important factor in many technologies. This field aims at utilizing the least time and resources to find desired sequence of character in complex data content. The most classical and famous string-search algorithms are Knuth-Morris-Pratt (KMP) algorithm and Boyer-Moore (DM) algorithm. These two algorithms provide efficient heuristic jump rules by prefix or suffix. Bitap algorithm was the first to introduce bit-parallelism into string-matching field. Backward Non-Deterministic DAWG Matching (BNDM) algorithm is a modern practical algorithm that is an outstanding combination of theoretical research and practical application. Those meaningful algorithms play a guiding role in future research in string-search algorithm to improve the average performance of the algorithm and reduce resource consumption.
TAXChe: An Online Taxonomy for Che’s Marginalias: A Proposal from the Digital Humanities

This book explores the latest advances in how knowledge organisation can both draw upon and inform diverse disciplines and technological developments. It explores how best to combine theory and practice, with its contents considering practical solutions as well as the theory behind the design, development and implementation of knowledge organising systems, ranging from controlled vocabularies, classification systems and metadata schemas to ontologies and taxonomies. With contributions by Carmen Agustín-Lacruz, Mayukh Bagchi, Mario Barité, Maria Teresa Biagetti, Briony Birdi, Lynne Bowker, Thiago Henrique Bragato Barros, Ana Inés Brozia, Gema Bueno-de-la-Fuente, Julia Bullard, Rafael Cacciolari Dalessandro, José Augusto Chaves Guimarães, Christian Cole, Stephanie Colombo, Luciana Corts Mendes, Luis Corujo, Niamh Nic Daeid, Patrícia de Almeida, Hadassa De Zen Itepan, Anthony Dunbar, Jonathan Furner, Francisco-Javier García-Marco, Fausto Giunchiglia, Claudio Gnoli, Míriam Gontijo de Moraes, Hazlem Granados-Amaya, Hugo Alberto Guadarrama-Sánchez, Carlos Guardado da Silva, David Haynes, Ania Hernández Quintana, Philip Hider, Chris Holstrom, Eva Hourihan Jansen, Fidelia Ibekwe, Mat Kelly, Michael Kleineberg, Nair Yumiko Kobashi, Deborah Lee, Maël Le Gall, Joaquín López del Ramo, Luís Miguel Oliveira Machado, Hugo Moquet, Catalina Naumis-Peña, Mirtha Rauch, Mariana Ramos Crivelente, Raissa Resende de Moraes, Jorge Revez, Riccardo Ridi, Somayeh Roshan, Clarissa Schmidt, Negin Shokrzadeh Hashtroudi, Richard P. Smiraglia, Mariângela Spotti Lopes Fujita, Adriana Suárez-Sánchez, Erik Stubkjwær, Rick Szostak, Seyed Mahdi Taheri, Marc Tanti, Ana Lúcia Terra, Amanda Terrero Trinquete, Natalia Bolfarini Tognoli, Jéssica Beatriz Tolare, Raquel Torrão Valentim, Hyung Wook Choi and Mohsen Haji Zeinolabedini.
Developing a Digital Forensics Terminology Using Natural Language Processing

This book explores the latest advances in how knowledge organisation can both draw upon and inform diverse disciplines and technological developments. It explores how best to combine theory and practice, with its contents considering practical solutions as well as the theory behind the design, development and implementation of knowledge organising systems, ranging from controlled vocabularies, classification systems and metadata schemas to ontologies and taxonomies. With contributions by Carmen Agustín-Lacruz, Mayukh Bagchi, Mario Barité, Maria Teresa Biagetti, Briony Birdi, Lynne Bowker, Thiago Henrique Bragato Barros, Ana Inés Brozia, Gema Bueno-de-la-Fuente, Julia Bullard, Rafael Cacciolari Dalessandro, José Augusto Chaves Guimarães, Christian Cole, Stephanie Colombo, Luciana Corts Mendes, Luis Corujo, Niamh Nic Daeid, Patrícia de Almeida, Hadassa De Zen Itepan, Anthony Dunbar, Jonathan Furner, Francisco-Javier García-Marco, Fausto Giunchiglia, Claudio Gnoli, Míriam Gontijo de Moraes, Hazlem Granados-Amaya, Hugo Alberto Guadarrama-Sánchez, Carlos Guardado da Silva, David Haynes, Ania Hernández Quintana, Philip Hider, Chris Holstrom, Eva Hourihan Jansen, Fidelia Ibekwe, Mat Kelly, Michael Kleineberg, Nair Yumiko Kobashi, Deborah Lee, Maël Le Gall, Joaquín López del Ramo, Luís Miguel Oliveira Machado, Hugo Moquet, Catalina Naumis-Peña, Mirtha Rauch, Mariana Ramos Crivelente, Raissa Resende de Moraes, Jorge Revez, Riccardo Ridi, Somayeh Roshan, Clarissa Schmidt, Negin Shokrzadeh Hashtroudi, Richard P. Smiraglia, Mariângela Spotti Lopes Fujita, Adriana Suárez-Sánchez, Erik Stubkjwær, Rick Szostak, Seyed Mahdi Taheri, Marc Tanti, Ana Lúcia Terra, Amanda Terrero Trinquete, Natalia Bolfarini Tognoli, Jéssica Beatriz Tolare, Raquel Torrão Valentim, Hyung Wook Choi and Mohsen Haji Zeinolabedini.
On the benefits and problems related to using Definition of Done — A survey study

Context: Definition of Done (DoD) is one of the fundamental concepts of Scrum. It expresses a shared view of a Scrum Team on what makes an increment of their product complete. DoDs are often defined as checklists with items being requirements towards software (e.g., quality requirements) or towards activities performed to make the increment shippable (e.g., code reviews, testing). Unfortunately, the knowledge about the usefulness of DoD is still very limited. Objective: The goal is to study what benefits using the DoD practice can bring to an agile project, what problems it may trigger, and how it is created and maintained. Method: In the survey among members of agile software development projects, 137 practitioners from all over the globe shared their experience with us. Results: 93% of the respondents perceive DoD as at least valuable for their ventures. It helps them to make work items complete, assure product quality, and ensure the needed activities are executed. However, they indicated that every second project struggles with infeasible, incorrect, unavailable, or creeping DoD. Conclusions: It follows from the study that DoD is important but not easy to use and more empirical studies are needed to identify best practices in this area.
Exploring The Role of Web Crawler and Anti-Crawler Technology in Big Data Era

In the era of big data, with lower costs and higher efficiency, web crawlers access resources and information from the Internet, bringing a lot of convenience to businesses and individuals. Nevertheless, there are two sides to everything, as malicious crawlers bring incalculable threats and losses to websites. In order to prevent web crawlers from being abused or even developing into malicious crawlers, web sites usually perform anti-crawler based on techniques such as ip access frequency, browsing page speed, account login, input captcha, js encryption, ajax obfuscation, etc. Anti-crawlers cannot completely block crawlers with a particular technique, but only find ways to increase the cost of crawling for attackers, forcing the catching party to make the right choice after weighing the cost-benefit.