Reviewerly/exHarmony huggingface dataset:
STUDENTS’ PERCEPTION ON THE USE OF GOOGLE FORM IN ENGLISH ONLINE QUIZZES AT MA AT-THOHIRIYAH NGANTRU TULUNGAGUNG

ABSTRACTFitriah, Nurin Aini. Student Registered Number 17203163098. 2020. Students’ Perception on the Use of Google Form in English Online Quizzes at MA At-Thohiriyah Ngantru Tulungagung. Thesis. English Department. Faculty of Tarbiyah and Teacher Training State Islamic Institute (IAIN) of Tulungagung. Advisor: Dr. Hj. Nanik Sri Rahayu, M. Pd.Keyword: Student’s Perception, Google Form, and Online QuizzesAssessment plays an important role in the teaching and learning process. In this new decade, ICT supports the assessment process by which it enables teachers to conduct an online assessment by using mobile phones or computers by utilizing the internet network. One of the form of online assessment is the online quizzes. Considering that assessment is a crucial part to know the success of the teaching and learning process, therefore this study is intended to know the students’ perception on the use of Google Form in English online quizzes at MA At-Thohiriyah Ngantru Tulungagung.The formulation of research problem in this study : (1) How is the students’ perception on the use of Google Form in English online quizzes at MA At-Thohiriyah Ngantru Tulungagung? Based on the formulation of the research problem, the purpose of this research was to describe the students’ perception on the use of Google Form in English online quizzes at MA At-Thohiriyah Ngantru Tulungagung.This research used a descriptive quantitative approach with survey design The population of this study was all of the tenth grade students at MA At-Thohiriyah Ngantru Tulungagung that consist of 37 students. The sampling technique used in this study was purposive sampling and got X-MIA class that consist of 17 students as the sample. While the research instrument used was questionnaire. The researcher analyzed the data by using SPSS 16.0 to find out the descriptive statistic such as frequency, percentage, and the mean score.The results of this study showed that most of the students of the tenth grade MIA at MA At-Thohiriyah Ngantru Tulungagung had positive perception toward the use of Google Form in English online quizzes. It could be known from all of the statements of the questionnaire that had a positive response interpretation. Therefore, it could be concluded that Google Form could be applied as an alternative media used to conduct online assessment especially in making online quizzes at MA At-Thohiriyah Ngantru Tulungagung.
Real-Time Virtual Hair Try-on by 3D Face Tracking

Virtual hair try-on is becoming popular in many applications such as virtual social networking, try-on for online shopping, and hairstyle design.Most of existing systems are image-based where the user is not allowed to view the try-on effects from different perspectives and distances in real-time, which limits the usability of the system.A real-time virtual hair try-on system based on 3D face tracking is proposed, in this system, by using an ordinary web camera, the user is enabled to perform the virtual hair try-on in a way as s/he conducts the real hair try-on in front of a mirror.Firstly, the system realizes 3D face tracking, which combines the deep learning technique and the traditional optimization algorithm for determining the shape and pose of the 3D faces from the 2D video frames.Then, it automatically puts the 3D hair onto the 3D faces corresponding to the video frames with several pairs of user-specified points to wear hair.Finally, virtual-real rendering, which handles the occlusion of the 3D hair and renders it with the face in the 2D video frames, leads to the virtual-real effect of hair try-on.In addition, a simple but effective algorithm of recoloring the 3D hair is proposed so as to improve the effect of virtual hair try-on.In indoor and outdoor environments, extensive virtual try-on evaluations demonstrate the effectiveness and practicability of our system.
Multilingual Counter Narrative Type Classification

The growing interest in employing counter narratives for hatred intervention brings with it a focus on dataset creation and automation strategies. In this scenario, learning to recognize counter narrative types from natural text is expected to be useful for applications such as hate speech countering, where operators from non-governmental organizations are supposed to answer to hate with several and diverse arguments that can be mined from online sources. This paper presents the first multilingual work on counter narrative type classification, evaluating SoTA pre-trained language models in monolingual, multilingual and cross-lingual settings. When considering a fine-grained annotation of counter narrative classes, we report strong baseline classification results for the majority of the counter narrative types, especially if we translate every language to English before cross-lingual prediction. This suggests that knowledge about counter narratives can be successfully transferred across languages.
Paraphrase Generation: A Survey of the State of the Art.

This paper focuses on paraphrase generation,which is a widely studied natural language generation task in NLP. With the development of neural models, paraphrase generation research has exhibited a gradual shift to neural methods in the recent years. This has provided architectures for contextualized representation of an input text and generating fluent, diverseand human-like paraphrases. This paper surveys various approaches to paraphrase generation with a main focus on neural methods.
K대학교 출신 초등학교 초임교사들이 바라본 학교현장실습과 초등교사 전문성의 관계 분석 : 전문적 자본 관점을 중심으로

Despite the value and importance of school field practice, several problems have been raised for a long time, such as the continuous operation of the same program for several decades. Therefore, it is necessary to improve the education practice system so that school field practice can become a practical process for enhancing the professionalism of elementary school pre-service teachers and effective performance of their duties as teachers in their work sites. To this end, Hargreaves & Fullan(2012) analyzed the connection between school field practice and the professionalism of elementary school teacher in the school field, as perceived by elementary school first-time teachers from the perspective of professional capital, which is suggested as the driving force for the growth of teacher professionalism. From this point of view, the purpose of this study was to find ways to improve the school field practice system to enhance the professionalism of elementary school teachers. In order to achieve this research purpose, six elementary school first-time teachers who completed and graduated from the school field practice program of K University, a teacher training institution, were selected as the research participants, and an in-depth interviews were conducted. As a result of the in-depth interviews on the educational experience of elementary school first-time teachers from K University, they noted that they were given opportunities to learn a lot of human capital and social capital, but relatively few opportunities were afforded to experience decision-making capital. Based on these school field practice experiences, five ways to improve the school field practice system were suggested.
SISTEM INFORMASI GEOGRAFIS SUMBER DAYA ALAM INDONESIA BERBASIS WEB

Geographical information system is a technology for managing, analyzing and disseminating geographic information related to spatial layout and related information. With regard to natural resources, geographic information will contain the location of an area, and information about natural resources contained in the area. Changes that occur at any time in nature related to adding or subtracting data can be done up to date without having to create new ones. In building a geographic information system of Indonesia's natural resources, Indonesia uses Mapserver as a mapping server and PHP script as a web programming language in building web pages. The research was conducted in four stages, namely the Preparation, Design, Implementation, and Trial stages. The result of this research is that geographic information system that displays digital maps can be stored in the form of shape files that can be created using a desktop-based information system software, namely ArcView. In addition, the geographic information system of natural resources can be used as a source of information regarding the distribution of Indonesia's natural resources which can be accessed by the wider community both from within and outside the country.
БЎЛАЖАК БОШЛАНҒИЧ СИНФ ЎҚИТУВЧИЛАРИНИ ИНТЕЛЛЕКТУАЛ ФАОЛИЯТГА ТАЙЁРЛАШНИНГ МЕТОДОЛОГИК АСОСЛАРИ

This article highlights the methodological foundations of preparing future primary school teachers for intellectual activity. This article can be used by teachers dealing with the problem of training modern competitive developed personnel.
Pelatihan Pengembangan Model Pembelajaran Menulis Teks Deskripsi dengan Picture Word Inductive Model

Writing skills are rarely taught since many teachers encounter issues such as students' poor command of English vocabulary, inability to write in the correct English format, insufficient time allotted to aid each student, and a lack of teacher expertise regarding teaching strategies. For students, appropriate writing skills are required. Activities such as training for English teachers on approaches for teaching suitable and engaging writing skills to students are required. The Picture Word Inductive Model will be taught as a tool for practicing writing descriptive text abilities. This activity employs a participatory technique based on Kurt Lewin's theory, which stresses the target participants' active participation throughout the training process, from participating in training activities to completing training assignments. A total of 15 teachers from various agencies in Kabupaten Banjar participated in this community service project. Starting with the explanation, questions and answers session, small group discussions, reflection, and evaluation, the exercise went off without a hitch. The Picture Word Inductive Model is a learning paradigm that they have just acquired and will employ in teaching writing. Because there were steps of categorizing and refining it into phrases and paragraphs, participants concluded that utilizing this model would be better than using visual media. As a result, they can assist students with their writing. In a nutshell, English teachers in Kabupaten Banjar have a good conceptual understanding of how to teach descriptive text writing skills. Finally, the participants were able to create a learning plan for writing descriptive text skills using Picture Word Inductive Model.
CoAtNet: Marrying Convolution and Attention for All Data Sizes

Transformers have attracted increasing interests in computer vision, but they still fall behind state-of-the-art convolutional networks. In this work, we show that while Transformers tend to have larger model capacity, their generalization can be worse than convolutional networks due to the lack of the right inductive bias. To effectively combine the strengths from both architectures, we present CoAtNets(pronounced coat nets), a family of hybrid models built from two key insights:(1) depthwise Convolution and self-Attention can be naturally unified via simple relative attention; (2) vertically stacking convolution layers and attention layers in a principled way is surprisingly effective in improving generalization, capacity and efficiency. Experiments show that our CoAtNets achieve state-of-the-art performance under different resource constraints across various datasets. For example, CoAtNet achieves 86.0% ImageNet top-1 accuracy without extra data, and 89.77% with extra JFT data, outperforming prior arts of both convolutional networks and Transformers. Notably, when pre-trained with 13M images fromImageNet-21K, our CoAtNet achieves 88.56% top-1 accuracy, matching ViT-huge pre-trained with 300M images from JFT while using 23x less data.
LibGuides: Theory of Knowledge: RSS News Feeds

Resources related to the Theory of Knowledge course, including internal and external assessments
Jurnal Educatio FKIP UNMA

Jurnal Educatio, mainly focuses on major issues in teaching, Teaching Assessment, Learning Media, Development Subject of Education, and Management of Education. This Journal received All topics about the results of studies and research of lecturers, students, teachers, practitioners, and scientists in the field of elementary education.
Multi-Objective Meta Learning

Meta learning with multiple objectives can be formulated as a Multi-Objective Bi-Level optimization Problem (MOBLP) where the upper-level subproblem is to solve several possible conflicting targets for the meta learner. However, existing studies either apply an inefficient evolutionary algorithm or linearly combine multiple objectives as a single-objective problem with the need to tune combination weights. In this paper, we propose a unified gradient-based Multi-Objective Meta Learning (MOML) framework and devise the first gradient-based optimization algorithm to solve the MOBLP by alternatively solving the lower-level and upper-level subproblems via the gradient descent method and the gradient-based multi-objective optimization method, respectively. Theoretically, we prove the convergence properties of the proposed gradient-based optimization algorithm. Empirically, we show the effectiveness of the proposed MOML framework in several meta learning problems, including few-shot learning, neural architecture search, domain adaptation, and multi-task learning.
Study On Impact Of Virtual Employee Performance Management On Employee Productivity During Covid-19 Pandemic With Reference To Higher Education Sector In Kerala

The recent changes brought by Covid-19 to this world have imposed higher education field to transit to a virtual or digital operations scenario. Teachers  has a vital role to play on the hand to help the students  in using digital platforms to access  the theory classes, labs, researches etc…  and on other hand to support institution in their efforts to continue the routine processes, audits, job placements etc. Virtual employee performance management brought new opportunities to the institutions for extending their opportunity worldwide. Students all around the globe are getting affected by the pandemic and the institutions are closed at the moment. This study will focus on different virtual employee performance management practices that the institutions used in the current context of Covid-19 and the impact of those strategies on teachers’ productivity.Higher-Education Institutes are modernizing teaching as well as learning through ICT enabled service (Markowitz and al. 2018). ICT promises to improve teaching and learning. However, ICT will increase the demand for university teachers to have the skills and knowledge to use ICT effectively. University teachers are subject to increasing stress due to changes in teaching and learning, instructor roles, teaching methods, and work requirements (Jena ,2015; Syvanen, 2016).The study was conducted within the higher education institutes in Kerala state. Data was collected by distributing questionnaires to all teachers of selected higher education institutions. The purpose is to collect responses from employees of these education institutes is to determine, evaluate, assess, and examine the effect of virtual performance management on employee productivity in Higher education institutes in Kerala during the covid-19 pandemic.The study greatly contributed a simple Virtual employee performance management strategy adopted in the evaluation of the Teacher’s during pandemic which can be used by college deans to determine the level of Teacher’s performance at the time of the covid-19 or while education is still using online modalities in delivering lessons.
Multi-density peaks clustering superpixel

A superpixel segmentation algorithm called multi-density peaks clustering (MDPC) is proposed. By selecting a sufficient number of local density maximum pixels from the image as cluster centers to depict the image texture, the boundary of the object can be captured very accurately. The algorithm framework of MDPC is divided into three steps. First, the local density of pixel is defined, and the local density maximum pixels are calculated. Then, the local density maximum pixels are used as cluster centers, and the global optimal search, which is based on the path-to-point idea, is used to complete the clustering of the remaining non-cluster center pixels to realize the initial segmentation. Finally, superpixels are obtained by merging the initial segments according to the size of the segments and the distance between adjacent segments. In quantitative comparisons, MDPC is compared with 13 state-of-the-art superpixel segmentation algorithms in three image segmentation datasets. The experimental results show that MDPC achieves better performance in terms of boundary recall, boundary precision, achievable segmentation accuracy, undersegmentation error, and explained variation. And the qualitative comparisons show that the proposed algorithm has obvious advantages over other superpixel segmentation algorithms in image detail description and boundary adherence. Finally, the practicability and stability of MDPC are further demonstrated by the application of image segmentation. The source code of MDPC will be available at https://github.com/zhaojianaaa.
Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs

We tackle the problem of weakly-supervised conversational Question Answering over large Knowledge Graphs using a neural semantic parsing approach. We introduce a new Logical Form (LF) grammar that can model a wide range of queries on the graph while remaining sufficiently simple to generate supervision data efficiently. Our Transformer-based model takes a JSON-like structure as input, allowing us to easily incorporate both Knowledge Graph and conversational contexts. This structured input is transformed to lists of embeddings and then fed to standard attention layers. We validate our approach, both in terms of grammar coverage and LF execution accuracy, on two publicly available datasets, CSQA and ConvQuestions, both grounded in Wikidata. On CSQA, our approach increases the coverage from 80% to 96.2%, and the LF execution accuracy from 70.6% to 75.6%, with respect to previous state-of-the-art results. On ConvQuestions, we achieve competitive results with respect to the state-of-the-art.
GraphAttackerA General Multi-Task Graph Attack Framework

Graph neural networks (GNNs) have been successfully exploited in graph analysis tasks in many real-world applications. The competition between attack and defense methods also enhances the robustness of GNNs. In this competition, the development of adversarial training methods put forward higher requirement for the diversity of attack examples. By contrast, most attack methods with specific attack strategies are difficult to satisfy such a requirement. To address this problem, we propose GraphAttacker, a novel generic graph attack framework that can flexibly adjust the structures and the attack strategies according to the graph analysis tasks. GraphAttacker generates adversarial examples through alternate training on three key components: the multi-strategy attack generator (MAG), the similarity discriminator (SD), and the attack discriminator (AD), based on the generative adversarial network (GAN). Furthermore, we introduce a novel similarity modification rate SMR to conduct a stealthier attack considering the change of node similarity distribution. Experiments on various benchmark datasets demonstrate that GraphAttacker can achieve state-of-the-art attack performance on graph analysis tasks of node classification, graph classification, and link prediction, no matter the adversarial training is conducted or not. Moreover, we also analyze the unique characteristics of each task and their specific response in the unified attack framework.
Spears and shields: attacking and defending deep model co-inference in vehicular crowdsensing networks

Abstract Vehicular CrowdSensing (VCS) network is one of the key scenarios for future 6G ubiquitous artificial intelligence. In a VCS network, vehicles are recruited for collecting urban data and performing deep model inference. Due to the limited computing power of vehicles, we deploy a device-edge co-inference paradigm to improve the inference efficiency in the VCS network. Specifically, the vehicular device and the edge server keep a part of the deep model separately, but work together to perform the inference through sharing intermediate results. Although vehicles keep the raw data locally, privacy issues still exist once attackers obtain the shared intermediate results and recover the raw data in some way. In this paper, we validate the possibility by conducting a systematic study on the privacy attack and defense in the co-inference of VCS network. The main contributions are threefold: (1) We take the road sign classification task as an example to demonstrate how an attacker reconstructs the raw data without any knowledge of deep models. (2) We propose a model-perturbation defense to defend against such attacks by injecting some random Laplace noise into the deep model. A theoretical analysis is given to show that the proposed defense mechanism achieves $$\epsilon$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ϵ</mml:mi></mml:math> -differential privacy. (3) We further propose a Stackelberg game-based incentive mechanism to attract the vehicles to participate in the co-inference by compensating their privacy loss in a satisfactory way. The simulation results show that our proposed defense mechanism can significantly reduce the effects of the attacks and the proposed incentive mechanism is very effective.