Reviewerly/exHarmony huggingface dataset:
Problem-Based Questions in the Development of Theoretical Thinking

espanolLas preguntas basadas en problemas (PBQ) se dominan en el proceso de aprender el tema y desarrollar la capacidad de pensar. La construccion de PBQ permitio a los estudiantes desarrollar su capacidad para manejar conscientemente nociones, formular declaraciones, sacar conclusiones y revelar contradicciones. La capacidad de operar un sistema de nociones mientras se plantean preguntas y se buscan respuestas fomenta la comunicacion significativa. Los resultados se utilizaron para desarrollar e implementar un curso de formacion avanzada para profesores universitarios. El refuerzo del pensamiento teorico ayuda a pasar del conocimiento declarativo proporcionado por un profesor a su descubrimiento conjunto con los alumnos. EnglishProblem-based questions (PBQs) are mastered in the process of learning the subject matter and developing the ability to think. Constructing PBQs enabled students to develop their ability to consciously handle notions, formulate statements, make conclusions, and reveal contradictions. The ability to operate a system of notions while posing questions and looking for answers to them encourages meaningful communication.The findings were used to develop and implement an advanced training course for university teachers. The booster of theoretical thinking helps to switch from declarative knowledge given by a teacher to its joint discovery with learners.
Guides: Business Division Faculty LibGuide: Getting Started

Provides links to Western Library resources for students and faculty in the Business Division
Cloud Computing Security Framework Based on Shared Responsibility Models

In the present scenario, especially in the current pandemic situation, cloud computing enables new innovation in the IT industry and more than 90% of IT enterprises are migrating their services towards virtualization using cloud computing platforms. Organizations like manufacturing, automobiles, healthcare, and academic are also upgrading their infrastructure by using Internet of Things devices and sharing their valuable data and computing process with cloud computing service providers (CSPs). Although the cloud computing model is the most preferred for an internet-based computing platform and public domain deployment, it also increases cyber risk and the threat to cloud-hosted assets. This is because of outsourcing third-party cloud computing platforms to hosting by enterprising assets. In 2020, approximately 16 billion records were breached. However, according to researchers, 8.4 billion records had already been exposed in the first quarter of that year. As per the Varonis Report, on average only 5% of the folders of companies are protected properly. A recent study published by ETCISO in 2020 shows that the average data breach cost is $2 million in India. The security research firm recently revealed that more than 80% of the data breaches that occurred during the COVID-19 pandemic were either because of brute-force attacks or stolen credentials. In order to gain the maximum outcome from the advantages of a cloud computing platform, users rely on the cloud service providers for managing their data and IT services. So, for the smooth management of data and services of cloud users, CSPs are required for a strong cloud security framework. More than 50% of the market for cloud computing services is occupied by the top three CSPs: Amazon web services, Microsoft Azure, and Google cloud. This chapter presents a comprehensive analysis and the findings of a security framework related to top CSPs, and proposes a cloud security shared responsibility model and its application in various internet computing services like healthcare and e-governance.
IV-GNN : interval valued data handling using graph neural network

Interval-valued data is an effective way to represent complex information where uncertainty, inaccuracy etc. are involved in the data space and they are worthy of taking into account. Interval analysis together with neural network has proven to work well on Euclidean data. However, in real-life scenarios, data follows a much more complex structure and is often represented as graphs, which is non-Euclidean in nature. Graph Neural Network is a powerful tool to handle graph like data with countable feature space. So, there is a research gap between the interval-valued data handling approaches and existing GNN model. No model in GNN literature can handle a graph with interval-valued features and, on the other hand, Multi Layer Perceptron (MLP) based on interval mathematics can not process the same due to non-Euclidean structure behind the graph. This article proposes an Interval-Valued Graph Neural Network, a novel GNN model where, for the first time, we relax the restriction of the feature space being countable without compromising the time complexity of the best performing GNN model in the literature. Our model is much more general than existing models as any countable set is always a subset of the universal set $\mathbb {R}^{n}$ , which is uncountable. Here, to deal with interval-valued feature vectors, we propose a new aggregation scheme of intervals and show its expressive power to capture different interval structures. We validate our theoretical findings about our model for graph classification task by comparing its performance with those of the state-of-the-art models on several benchmark and synthetic network datasets.
Research on roof waterproof construction of agricultural construction engineering based on big data technology

To improve the roof waterproof construction effect of agricultural construction projects, this paper applies big data technology to construction management and construction decision-making of construction projects. Moreover, this paper improves the traditional big data technology, and from the perspective of the roof waterproof construction requirements of agricultural construction projects, this paper improves data mining to make it a core intelligent algorithm that can be used for data recognition and analysis of construction projects. In addition, this paper simulates the construction process according to the roof waterproof construction process of agricultural construction projects, builds the corresponding intelligent construction simulation model and builds the agricultural construction engineering roof waterproof construction simulation through simulation research. Finally, after obtaining the data, this paper conducts experimental research and applies the data mining system of this paper to the analysis of agricultural construction engineering. From the experimental research, it can be known that the method proposed in this paper has a certain effect.
Identifiability in inverse reinforcement learning

Inverse reinforcement learning attempts to reconstruct the reward function in a Markov decision problem, using observations of agent actions. As already observed by Russell the problem is ill-posed, and the reward function is not identifiable, even under the presence of perfect information about optimal behavior. We provide a resolution to this non-identifiability for problems with entropy regularization. For a given environment, we fully characterize the reward functions leading to a given policy and demonstrate that, given demonstrations of actions for the same reward under two distinct discount factors, or under sufficiently different environments, the unobserved reward can be recovered up to a constant. Through a simple numerical experiment, we demonstrate the accurate reconstruction of the reward function through our proposed resolution.
Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics

Much of recent progress in NLU was shown to be due to models’ learning dataset-specific heuristics. We conduct a case study of generalization in NLI (from MNLI to the adversarially constructed HANS dataset) in a range of BERT-based architectures (adapters, Siamese Transformers, HEX debiasing), as well as with subsampling the data and increasing the model size. We report 2 successful and 3 unsuccessful strategies, all providing insights into how Transformer-based models learn to generalize.
Unknown Intent Detection Using Multi-Objective Optimization on Deep Learning Classifiers

Modelling and understanding dialogues in a conversation depends on identifying the user intent from the given text.Unknown or new intent detection is a critical task, as in a realistic scenario a user intent may frequently change over time and divert even to an intent previously not encountered.This task of separating the unknown intent samples from known intents one is challenging as the unknown user intent can range from intents similar to the predefined intents to something completely different.Prior research on intent discovery often consider it as a classification task where an unknown intent can belong to a predefined set of known intent classes.In this paper we tackle the problem of detecting a completely unknown intent without any prior hints about the kind of classes belonging to unknown intents.We propose an effective post-processing method using multi-objective optimization to tune an existing neural network based intent classifier and make it capable of detecting unknown intents.We perform experiments using existing state-of-the-art intent classifiers and use our method on top of them for unknown intent detection.Our experiments across different domains and real-world datasets show that our method yields significant improvements compared with the state-of-the-art methods for unknown intent detection.
ACTUALITY OF SCIENTIFIC RESEARCH OF YOUNG SCIENTISTS (on materials of the Lesya Ukrainka Volyn National University)

Third education level: quality control over scientific-professional training of a PhD: individual issues : Collective monograph / edited by Myroslava Filipovych. Riga, Latvia : Izdevnieciba “Baltija Publishing”, 2021. 227 p.
LibGuides: Library Hub Guide: Connect with Google Scholar

A guide to help you navigate the Library Hub. Connecting Google Scholar to the Univerisity of Divinity's Library Hub will automatically provide you access to full-text articles from the UD's databases.
Reading engagement during the pandemic: The Case of Csu-Sanchez Mira (Cagayan, Philippines) students

With the sudden move away from school in many areas of the world, it is unclear if online education will persist post-pandemic, and how such a shift will impact the global education market. This study is aimed to discover the reading engagement of students during pandemic. The study made use of the descriptive research design using purposive sampling technique in order to determine the respondents of the study. A researcher made questionnaire was used as the main instrument. Result of the study revealed smartphone is the common gadget used by the students to access the internet. Most of them surf the net for 4-6 hours in order to acquire general knowledge found on social media. Online reading materials are read in different number of hours. English is the most preferred language. There is an underpinning reading engagement among the respondents. Moreover, it is also evident that made use of varied means to connect to the internet as they immersed themselves to online reading. Though it may sound the other way around, the students perceived that online reading is still a bearable experience. Thus, parents and other stakeholders should find ways to boost student reading engagement in order to maximize learning even in this time of pandemic.
Searching for More Efficient Dynamic Programs.

Computational models of human language often involve combinatorial problems. For instance, a probabilistic parser may marginalize over exponentially many trees to make predictions. Algorithms for such problems often employ dynamic programming and are not always unique. Finding one with optimal asymptotic runtime can be unintuitive, time-consuming, and error-prone. Our work aims to automate this laborious process. Given an initial correct declarative program, we search for a sequence of semantics-preserving transformations to improve its running time as much as possible. To this end, we describe a set of program transformations, a simple metric for assessing the efficiency of a transformed program, and a heuristic search procedure to improve this metric. We show that in practice, automated search—like the mental search performed by human programmers—can find substantial improvements to the initial program. Empirically, we show that many speed-ups described in the NLP literature could have been discovered automatically by our system.
What Augmentations are Sensitive to Hyper-Parameters and Why?

We apply augmentations to our dataset to enhance the quality of our predictions and make our final models more resilient to noisy data and domain drifts. Yet the question remains, how are these augmentations going to perform with different hyper-parameters? In this study we evaluate the sensitivity of augmentations with regards to the model’s hyper parameters along with their consistency and influence by performing a Local Surrogate (LIME) interpretation on the impact of hyper-parameters when different augmentations are applied to a machine learning model. The methodology consists of creating lime vectors, training machine learning models with different combinations of hyper-parameters and augmentation, training a linear regression model on the accuracy of the models, and utilizing the Linear regression coefficients for weighing each augmentation. Our research has proved that there are some augmentations which are highly sensitive to hyper-parameters and others which are more resilient and reliable.
THE EFFECTIVENESS OF USING SCAVENGER HUNT GAME ON STUDENTS' VOCABULARY MASTERY OF FIFTH GRADE AT SDN 1 SUKODONO KARANGREJO

ABSTRACTSavira, Veliya. Student Registered Number. 17203163005. 2020. The Effectiveness of Using Scavenger Hunt Game on Students’ Vocabulary Mastery of Fifth Grade at SDN 1 Sukodono Karangrejo. Sarjana Thesis. English Education Department. Faculty of Tarbiyah and Teacher Training. State Islamic Institute (IAIN) of Tulungagung. Advisor: Dr. H. Nursamsu, S.Pd.I., M.Pd.Keywords: effectiveness, scavenger hunt game, vocabularyVocabulary is important component for every learner who wants to master English. Without an extensive vocabulary and strategies for acquiring new vocabulary, students cannot achieve one of the skill of English well. However, the students get difficulties in understanding the meaning of new vocabulary. Therefore, the researcher found scavenger hunt game as an effective and fun method to teach vocabulary. In this method, the students look for the list of items in a period of time.The formulation of the research problem was: Is there any significant different scores of students’ vocabulary mastery before and after taught by using scavenger hunt game?The objective of this study is to identify whether there is significant different scores of students’ vocabulary mastery before and after taught by using scavenger hunt game.Research method: 1) the research design in this study was pre-experimental design with quantitative approach, 2) the population of this study was the students of the fifth grade at SDN 1 Sukodono, 3) the sample was fifth grade students which consisted of 16 students, 4) the research instrument in this study is test, which consists of pre-test and post-test, 5) the data analysis used Paired Sample Test through IBM SPSS Statistics 22.The result showed that the students’ score before being taught using scavenger hunt game was 64.38. While, the students’ score after being taught by using scavenger hunt game was 78.75. Then, the result of paired sample test showed that the significant value is lower than the level of significant (0.000 < 0.05). It means that Ha which stated that there is significant difference scores of students’ vocabulary mastery before and after taught by using scavenger hunt game for the fifth graders at SDN 1 Sukodono was accepted Whereas, H0 which stated that there is no significant difference scores of students’ vocabulary mastery before and after taught by using scavenger hunt game for the fifth graders at SDN 1 Sukodono was rejected. In conclusion, scavenger hunt game can be used as an alternative method to teach vocabulary to the students at elementary school level.
THE EFFECTIVENESS OF USING INQUIRY METHOD TOWARD STUDENTS’ SPEAKING ACHIEVEMENT AT THE FIRST GRADE OF MTsN 2 TULUNGAGUNG

ABSTRACTMahendra, IhdA Fahmi. NIM. 17203153112. 2019. The Effectiveness of UsingInquiry Method Toward Students’ Speaking Achievement at The FirstGrade of MTsN 2 Tulungagung. Thesis. English EducationDepartment. Faculty of Tarbiyah and Teacher Training. State IslamicInstitute (IAIN) of Tulungagung. Advisor: Dr. Erna Iftanti, S.S.,M.Pd.Keywords: Effectiveness, Inquiry Method, Teaching SpeakingSpeaking is one of the language elements that has important role inincreasing four skills in English; they are Vocabulary, listening, reading, andwriting. As we know, speaking becomes an obstacle for students to learn English.One of the reasons, they are shy to express the ideas. It can be a solution by usingInquiry Method. The researcher uses inquiry method because the student feelsmore comfortable when they are discussed or solve the problem with the otherfriends cause in Inquiry Method students are formed into groups. It trains thestudents to critical thinking and solve problem and the students be able to moreadult solve problem in around areas. . For that reason, the writer tries to applymethod in teaching English, especially in teaching speaking by using InquiryMethod to overcome the problem.The formulation of the research problem is: “Is there any significantdifference on the student’s speaking achievement before and after being taught byusing Inquiry Method?”. The purpose of this research is to find out whether theuse of Inquiry method is effective to use in teaching speaking or not.This research used pre-experimental design with quantitative approach.The population of this research was first grade students at MTsN 2 Tulungagung.The sample of this research was seven F class which consisted of 36 studentschosen by purposive sampling technique. Technique of data collection used tests.To analyze the collected data, the researcher used IBM SPSS Statistics 25.0.The finding of this study showed that there is a significant differencebetween the result of pre-test and post-test. The mean of post-test was 55.28 and itwas bigger than the mean of pre-test 43.75. It means there was increasing score inthe students’ achievement. From statistical calculation showed that the significantvalue is 0.000 which was smaller than the significant level 0.05. It means that thenull hypothesis (H0) was rejected and the alternative hypothesis (Ha) wasaccepted.Based on the result above, it can be concluded that the students at the firstgrade of MTsN 2 Tulungagung had better score in speaking after being taught byusing Inquiry method. It is suggested that Inquiry method can be used to teachspeaking for Junior High School students.
Development of ‘School Nocturnble’: A Sensitive Game with Eye Trackers

This paper presents “School Nocturnble,” a new type of eye-tracking game, and explains about the development process.Recently, the game industry has been actively applying various new technologies into games. It’s because gamers, who want more realistic and new experience through game, have been reacting positively towards the industry’s new attempts. One of the field that the industry tries to innovate through new technology is a gaming controller. Eye-tracking technology is one of many technological innovations in development of gaming controllers.“School Nocturnble” is a 2D side-scrolling puzzle adventure game that uses eye-tracker as an assisting gaming controller. A single player can control the protagonist with a keyboard while they can also control the assist character with the eye-tracker, solving puzzles and participating turn-based battles to save the magic school “School Nocturnble” from its curse. The game was developed with Unity 2019, Visual Studio 2019(C# code), and Tobii Unity SDK Version4. This game can be played in Windows PC and requires Tobii Eye Tracker5.The development of “School Nocturnble” focuses on providing new game mechanics for gamers who prefers various experience and contents. At last, it’s a meaningful attempt in both the gaming and eye-tracking field to use the Eye-tracker as an assiting gaming controller, since it’s not as commonly used as its potential.
Dynamic virtual camera based three-dimensional imaging in a synthetic field of view

To achieve spatial retrieval with compact flexible setup, we present a three-dimensional imaging technique using a dynamic virtual camera to combine multiview acquisition with overlap prelocation, enabling stereo reconstruction across a synthetic field of view.
A Data-Centric Approach to Real-World Custom NMT for Arabic

In this presentation, we will present our approach to taking Custom NMT to the next level by building tailor-made NMT to fit the needs of businesses seeking to scale in the Arabic-speaking world. In close collaboration with customers in the MENA region and with a deep understanding of their data, we work on building a variety of NMT models that accommodate to the unique challenges of the Arabic language. This session will provide insights into the challenges of acquiring, analyzing, and processing customer data in various sectors, as well as insights into how to best make use of this data to build high-quality Custom NMT models in English-Arabic. Feedback from usage of these models in production will be provided. Furthermore, we will show how to use our translation management system to make the most of the custom NMT, by leveraging the models, fine-tuning and continuing to improve them over time.
Blokzincir Teknolojisinin Dış Ticaretteki Uygulama Alanları ve Hukukî Durumu Blokzincir Dış Ticaret Finansman Uygulama Örneği

Trade has become one of the earning forms which humanity has almost from all eternity. “Foreign trade” has become more important since the period where the boundaries are clear and the human is qualified as a citizen of a country.  In present, blockchain technology has also been implemented in foreign trade field which takes from according to the interest, desire, need and institutions of the period.The legal matters which are caused by blockchain technology satisfying many questions and problems arising from foreign trade applications considered as old, are being discussed seriously and solutions are tried to be produced. The purpose of the study is to explain and criticize the implementations of blockchain technology in foreign trade and its legal consequences within the econo social philosophical framework over the first Isbank blockchain transaction example in Turkey