Reviewerly/exHarmony huggingface dataset:
Now, It’s Personal : The Need for Personalized Word Sense Disambiguation

Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors.This might not be captured with an author-agnostic word sense disambiguation (WSD) model that was trained on multiple authors.Our work finds that WordNet's first senses, the predominant senses of our dataset's genre, and the predominant senses of an author can all be different and therefore, authoragnostic models could perform well over the entire dataset, but poorly on individual authors.In this work, we explore methods for personalizing WSD models by tailoring existing stateof-the-art models toward an individual by exploiting the author's sense distributions.We propose a novel WSD dataset and show that personalizing a WSD system with knowledge of an author's sense distributions or predominant senses can greatly increase its performance.
THE USING OF THINK-PAIR-SHARE (TPS) STRATEGY THROUGH DESCRIPTIVE TEXT TO IMPROVE STUDENTS’ READING COMPREHENSION AT GRADE TEN STUDENTS OF SMA SW DHARMA BHAKTI SIBORONGBORONG

This study aimed to improve students’ reading comprehension through descriptive text by using Think-Pair-Share (TPS) strategy. This study was conducted by using classroom action research (CAR). This sample of this research was class X IPA SMA SW Dharma Bhakti Siborongborong which consists 52 students. The research was conducted in two cycles and every cycle consisted of three meeting. The instruments for collecting data were taken from reading test (multiple choice test consisted of 20 questions of two different texts). The qualitative data were taken from diary notes, interview, and observation. Based on reading scores, students score kept improving in every test. In the orientation test the mean was 55.28, in the cycle I test the mean was 65.67, and in the cycle II test the mean was 79.23. Based on diary notes, interview and observation, it was found that teaching learning process run well. Students were active, enthusiastic and interested in reading. The result of the research showed that the using Think-Pair-Share (TPS) strategy significantly improved students’ reading comprehension through descriptive text.
Pengembangan Lembar Kerja Peserta Didik Berbasis Inkuiri Terbimbing pada Mata Pelajaran Dasar Listrik dan Elektronika

The student worksheet (LKPD) is one of the tools used by educators to deliver learning materials. The background of this research is that the learning process in schools has not been carried out properly, the results of student learning activities are low, so that the objectives of learning are not achieved, this learning outcome depends on the learning process. Things that affect learning activities are LKPD as a tool in practical learning activities. The purpose of this research is to develop LKPD that is suitable for use so that it will improve the quality of the practicum learning process so that student learning outcomes are better. This type of research is Research and Development (R&amp;D) with a 4D development model consisting of Define, Design, Develop and Disseminate. Valid assessment of LKPD is carried out by experts consisting of media experts and material experts, while practicality is assessed by subject teachers and students, then for effectiveness it is seen from the results of practical learning when using guided inquiry-based worksheets. The instrument collects data using a questionnaire. The results of this study obtained that guided inquiry-based worksheets based on media experts were categorized as valid, while the feasibility of the material based on material experts obtained valid categories. Practicality assessments assessed by subject teachers are categorized as very practical, then practicality assessments from students are categorized as very practical. Assessment of the effectiveness of student learning outcomes is categorized as effective. So that the developed LKPD can be used by teachers to convey material in practical learning activities.
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection

Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. However, modeling human language at higher-levels of context (i.e., sequences of messages) is under-explored. In stance detection and other social media tasks where the goal is to predict an attribute of a message, we have contextual data that is loosely semantically connected by authorship. Here, we introduce Message-Level Transformer (MeLT) – a hierarchical message-encoder pre-trained over Twitter and applied to the task of stance prediction. We focus on stance prediction as a task benefiting from knowing the context of the message (i.e., the sequence of previous messages). The model is trained using a variant of masked-language modeling; where instead of predicting tokens, it seeks to generate an entire masked (aggregated) message vector via reconstruction loss. We find that applying this pre-trained masked message-level transformer to the downstream task of stance detection achieves F1 performance of 67%.
GreynirTranslate - mBART25 NMT models for Translations between Icelandic and English

Provided are a general domain IS-EN and EN-IS translation models developed by Miðeind ehf. They are based on a multilingual BART model (https://arxiv.org/pdf/2001.08210.pdf) and finetuned for translation on parallel and backtranslated data. The model is trained using the Fairseq sequence modeling toolkit by PyTorch. Provided here are a model files, sentencepiece subword-tokenizing model and dictionary files for running the model locally. You can run the scripts infer-enis.sh and infer-isen.sh to test the model by translating sentences command-line. For translating documents and evaluating results you will need to binarize the data using fairseq-preprocess and use fairseq-generate for translating. Please refer to the Fairseq documentation for further information on running a pre-trained model: https://fairseq.readthedocs.io/en/latest/-Pakkinn inniheldur almenn þýðingarlikon fyrir attirnar IS-EN og EN-IS þrouð af Miðeind ehf. Þau eru byggð a margmala BART likani (https://arxiv.org/pdf/2001.08210.pdf) og finþjalfuð fyrir þýðingar. Likonin eru þjalfað með Fairseq og PyTorch.Likonin sjalf og asamt sentencepiece tilreiðingarlikani eru gerð aðgengileg. Skripturnar infer-enis.sh og infer-isen.sh gefa daemi um hvernig er haegt að keyra likonin a skipanalinu. Til að þýða stor skjol og meta niðurstoður þarf að nota fairseq-preprocess skipunina asamt fairseq-generate. Frekari upplýsingar er að finna i Fairseq leiðbeiningunum: https://fairseq.readthedocs.io/en/latest/
A Deep Learning System for Automatic Extraction of Typological Linguistic Information from Descriptive Grammars

Linguistic typology is an area of linguistics concerned with analysis of and comparison between natural languages of the world based on their certain linguistic features.For that purpose, historically, the area has relied on manual extraction of linguistic feature values from textural descriptions of languages.This makes it a laborious and time expensive task and is also bound by human brain capacity.In this study, we present a deep learning system for the task of automatic extraction of linguistic features from textual descriptions of natural languages.First, textual descriptions are manually annotated with special structures called semantic frames.Those annotations are learned by a recurrent neural network, which is then used to annotate un-annotated text.Finally, the annotations are converted to linguistic feature values using a separate rule based module.Word embeddings, learned from general purpose text, are used as a major source of knowledge by the recurrent neural network.We compare the proposed deep learning system to a previously reported machine learning based system for the same task, and the deep learning system wins in terms of F1 scores with a fair margin.Such a system is expected to be a useful contribution for the automatic curation of typological databases, which otherwise are manually developed.
Сучасні тенденції розвитку креативності інтелектуально обдарованих учнів у закладах загальної середньої освіти

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
Promoting Students' Reading Comprehension Through Explicit Reading Strategies

Penelitian ini dilakukan untuk mengetahui aspek membaca apakah yang paling dipengaruhi oleh pembelajaran strategi membaca secara eksplisit and persepsi siswa tentang strategi tersebut. Desain penelitian ini adalah desain kelompok control pre-test post –test dua kelas; eksperimen dan kontrol. Instrumen pada  penelitian ini berupa tes membaca dan kuesioner. Validitas instrumen diukur menggunakan validitas isi dan konstruk dan reliabilitas instrumen diukur menggunakan iteman. Subjek penelitian yaitu 2 kelas XI di SMA. Hasil analisis data menunjukkan bahwa dari 5 aspek membaca, tertinggi adalah aspek menentukan ide pokok dan terendah aspek memahami kosakata. Akhirnya, dapat disimpulkan bahwa pembelajaran strategi membaca secara eksplisit dapat mempengaruhi kemampuan membaca siswa. This research was conducted to find out which aspect of reading comprehension was influenced most by explicit reading strategy instruction and to know the students’ perception about it . Th e design of this research was pre-test post-test control group design with two classes ; experimental and control class. The instruments were reading test and questionnaire. This research used content and construct validity. To measure the reliability of the instrument, iteman was used. The subjects were two classes of eleventh year students of senior high school. The result s of the data analysis showed that from five aspects of reading comprehension , determining main idea became the most improved aspect; in contrary understanding vocabulary became the least improved aspect . Then, concerning students’ perception towards explicit reading strategy instruction, the result showed that most of students perceived positively that explicit reading strategy instruction could improve their reading comprehension. Finally, it could be concluded that explicit reading strategies could affect the students’ reading comprehension. Keywords: Explicit reading strategies, implicit reading strategies, reading comprehension.
UofL Libraries: ORCID iD and Your UofL Research Profile: Integrate ORCID iD with DMPTool

Instructions on how to integrate ORCID iD with DMPTool, a data management system that UofL is affiliated.
International Algorithmic Law: Emergence and the Indications of Jus Cogens Framework and Politics

The nature of cyberspace is currently narrowing from its hard power definitions to more delicate and rapid soft power notions in international law and relations. From the realpolitik assertions of cybersecurity and big data in a neoliberal economic world, the concept of international algorithmic law is proposed to emerge, considering two important trends in the field of international law and the politics of cyberspace and digital economics, i.e., the evolution of the politics and economics involving artificial intelligence ethics at a global level and the transition of the rules-based international order from neoliberalism and unipolarity/bipolarity to neorealism and multipolarity. The Discussion Paper as a part of the Indian Strategy for AI & Law, 2020, there-fore analyses the proposed field of International Algorithmic Law and its political basis with some key cases. Further propositions include the supposed sources of international law based on the existential and structural purpose of the field, the diplomatic considerations and strategic constraints that constitute the extensive the political utility of the field, analyses of certain factors in proposition in connection with IAL, i.e., (1) Market Economics and Neoliberalism; (2) the ideology of Multilateralism; and (3) the Synergy of Algorithmic Diplomacy with Cybersecurity and its Politics in line with International Law related to Cyber Warfare. Conclusions are reflective.
Hierarchy Analysis Of Artificial Intelligence In Oral Cancer

AIMThe study aims to appraise the role of artificial intelligence in the diagnosis and management of oral cancer by assessing several articles from various electronic databases.METHODOLOGYThe study has assimilated data on articles published around the globe from different databases like Google Scholar, Cochrane, Web of Science, Grey Literature and PubMed on the aspect of Artificial Intelligence in oral cancer. The articles matching the keywords and with an abstract was taken into consideration. Keywords such as oral cancer, artificial intelligence, machine learning, oral squamous cell carcinoma, premalignant lesions were used. Tabulations were made based on the criteria such as the number of authors, ranking based on citations, outcomes of the research.RESULTThe citations of the articles range from 46-0.  Most of these articles were based on the field of treatment and were conducted mostly between the years 2010-2020 with a maximum number of articles being joint authorship articles.       CONCLUSIONThe establishment of a better life for an oral cancer patients is well within reach if we are ready to device a resourceful approach in implementing A. I and its subsets in the field of oral cancer. A. I can contribute immensely to the diagnostic and prognostic results of oral cancer patients. The development in the adaptation of Artificial Intelligence in oral cancer is of utmost value as it can improve the living standards.
Three-layer Feedforward Evolutionary Neural Network Method for Classification Based on Beetle Antennae Search Algorithm

Artificial neural network depends largely on its parameters and structures, such as weights and biases in parameters. Most of the existing evolutionary neural network methods rely on different heuristic algorithms to optimize the neural network’s structure and parameters, such as particle swarm optimization, genetic algorithm, etc. However, due to the large population size of this method, a lot of computing resources are consumed for optimization, resulting in the low efficiency of the algorithm. In order to construct more efficient and accurate neural network, a beetle antennae search (BAS) based evolutionary neural network is proposed, which is called BASNN. In this algorithm, we firstly design a three-layer feedforward neural network with link switches for classification. Then, the structure of the neural network and its parameters are evolved simultaneously by beetle antennae search algorithm. We evaluate our algorithm on two practical classification problems and compare it with some related works. The result of this experiment shows that BASNN algorithm can construct a high precious neural network at very fast speed.
Multimodal Few-Shot Learning with Frozen Language Models

When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). Using aligned image and caption data, we train a vision encoder to represent each image as a sequence of continuous embeddings, such that a pre-trained, frozen language model prompted with this prefix generates the appropriate caption. The resulting system is a multimodal few-shot learner, with the surprising ability to learn a variety of new tasks when conditioned on examples, represented as a sequence of multiple interleaved image and text embeddings. We demonstrate that it can rapidly learn words for new objects and novel visual categories, do visual question-answering with only a handful of examples, and make use of outside knowledge, by measuring a single model on a variety of established and new benchmarks.
Analyzing and Visualizing Projects and their Relations in Software Ecosystems

More and more software projects are being consolidated into ecosystems to increase their discovery, usability, and usefulness. Some of the most popular ecosystems exist in npmjs, Python Package Indexing, and Apache Maven Repository. It is difficult for developers to relate these projects and use them to their full potential because of their number, the spread and depth of their features, and their intrinsic and accidental complexities. We present a technique—SECO Storms Maker—to capture and present the essential information from projects in an ecosystem to help developers join, use, and contribute to the ecosystem. We generate word-clouds based on the projects’ documentation via tokenization and distribution frequency. We identify relations among projects using grammar patterns scanning after part-of-speech tagging. We put these word-clouds into a graph to ease navigation and exploration. We evaluate our technique by manually building a ground truth and comparing a randomly-selected project with SECO to show its benefits.
Education and Teaching Development in the Era of Artificial Intelligence

Collecting and sorting out education and teaching data, setting education and teaching procedures through algorithms, and applying them to education and teaching preparation, education and teaching development, and education and teaching evaluation are the basic manifestations of education and teaching in the era of artificial intelligence. The positive effects of artificial intelligence on education and teaching are mainly reflected in: high-quality teaching environment, individualized learning strategies, process evaluation centered on data links, and open and flexible educational concepts. Realizing the symbiosis and co-prosperity of technology and education is the basic idea of education and teaching in the artificial intelligence era.
Machine Learning Methods in Medical Image Compression

A large amount of image data such as CT that needs storage and transmission is generated in medical research.It is hard for the hospital to handle all data of the numerous patients.Therefore, it is of vital importance to compress these image data.Recently, learning-based medical image compression has become a new research trend with the development of artificial intelligence.Traditional methods in medical data compression are firstly reviewed.Further study in learning-based approaches is made, and the compression performance of these approaches in different medical image data such as brain CT and liver CT are shown.In the meantime, the advantages and disadvantages of these approaches in various aspects such as compression ratio, algorithm complexity and reconstruction quality are systematically summarized.It is pointed out that the combination of learning-based method and ROI-based method achieves high compression ratio brought by lossy compression, while keeping the feature information of the critical regions.Consequently, this approach is much more suitable for medical image compression than others.Finally, the paper concluded with a discussion of future development in this field.
PENYULUHAN PENGENALAN ANTI FRAUD BAGI SISWA/I SMA DAN SMK SEBAGAI UPAYA PENCEGAHAN TINDAKAN KECURANGAN

This paper is a paper written based on the results of the implementation of Community Service for 190 students and several teachers at SMA Muhammadiyah 23 Jakarta and SMK Bina Harapan Ciseeng. The aim of this socialization is to provide an understanding of the various acts of fraud, dangers, and losses due to fraud. Moreover, students are able to instill anti-fraud character and attitude since school. In the implementation of this Community Service, there is a pre-test to find out studentsâ€™ level of knowledge about fraud and the materials were delivered by andragogy method, which is a combination of lecture, discussion, and question and answer methods. Thus, the activity ended with a documentation session and providing souvenirs for the best questioners and answerers. The results obtained are that the term â€œfraudâ€ and the level of understanding about fraud are still not familiar enough among these students so that through this activity it is hoped that they will be able to think and act ethically and help reduce, prevent, and eradicate fraud in the future.
Development of LKPD using a Project-Based Learning Model on Data Collection and Presentation Materials, Class V at UPT SDN 07 Silaut

Law Number 20 of 2003 Article 1 states that education is a conscious and plannedeffort to create an atmosphere of learning process. Learning mathematics is an interactiveactivity between teachers and students, where the teacher teaches mathematics and numbersrelated to their students. The goal is that students can understand well the material taught bythe teacher and achieve predetermined competencies.From the results of observations and interviews conducted at UPT SDN 07 Silaut, it isknown that in the learning process in schools the teaching materials used only come fromtextbooks using blackboard media. During the online learning process, the teacher gives moreassignments via WhatsApp and assignments of tasks that must be done by students in studenthandbooks, which results in students not understanding the material provided because of alack of explanation and interaction between teachers and students. So researchers conduct astudy with the title Development of LKPD Using a Project-Based Learning Model on DataCollection and Presentation Materials, Class V at UPT SDN 07 Silaut.The type of research used is development research with a 4-D model, namely define,design, development, and disseminate. This research is limited to 3-D research, which is at thedevelopment stage due to the Covid-19 pandemic. The research was conducted in the secondsemester of the 2020/2021 school year with the material for collecting and presenting data.The subjects of this study were all students of class Va at UPT SDN 07 Silaut. The researchinstrument includes a validation sheet and a practicality sheet. In the validation process,LKPD is validated by one didactic expert, content and appearance, and one linguist. In thepractical process, LKPD was tested on 1 teacher and 20 students.Based on the results of the study, the LKPD validation stage with the project-basedlearning model met the valid criteria with an average percentage of 80.26% and at thepracticality stage, LKPD with the project-based learning model included very practicalcriteria with an average percentage of 89.78%.From the results of this study, it can be concluded that the LKPD on the material forcollecting and presenting data using the project-based learning model fulfills the very validcriteria and is very practical to be used as one of the teaching materials in mathematicslearning, especially for fifth-grade students in elementary schools.