Reviewerly/exHarmony huggingface dataset:
Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training

Language grounding aims at linking the symbolic representation of language (e.g., words) into the rich perceptual knowledge of the outside world. The general approach is to embed both textual and visual information into a common space -the grounded space- confined by an explicit relationship. We argue that since concrete and abstract words are processed differently in the brain, such approaches sacrifice the abstract knowledge obtained from textual statistics in the process of acquiring perceptual information. The focus of this paper is to solve this issue by implicitly grounding the word embeddings. Rather than learning two mappings into a joint space, our approach integrates modalities by implicit alignment. This is achieved by learning a reversible mapping between the textual and the grounded space by means of multi-task training. Intrinsic and extrinsic evaluations show that our way of visual grounding is highly beneficial for both abstract and concrete words. Our embeddings are correlated with human judgments and outperform previous works using pretrained word embeddings on a wide range of benchmarks. Our grounded embeddings are publicly available here.
Dynamic Forecasting of Conversation Derailment

Online conversations can sometimes take a turn for the worse, either due to systematic cultural differences, accidental misunderstandings, or mere malice. Automatically forecasting derailment in public online conversations provides an opportunity to take early action to moderate it. Previous work in this space is limited, and we extend it in several ways. We apply a pretrained language encoder to the task, which outperforms earlier approaches. We further experiment with shifting the training paradigm for the task from a static to a dynamic one to increase the forecast horizon. This approach shows mixed results: in a high-quality data setting, a longer average forecast horizon can be achieved at the cost of a small drop in F1; in a low-quality data setting, however, dynamic training propagates the noise and is highly detrimental to performance.
Study on the Management Strategy of Online Education in off-campus Training Institutions From Learner Perspective

At present, training orgnization on-line education develops quickly outside school in our country, outside school training orgnization on-line education management problem has become the topic of concern.At present, there is a lack of research on the governance of online education in off-campus training institutions from the perspective of learners. This study analyzes the governance of online education outside school from the perspective of learners.Firstly, the online education survey model of off-campus training institutions from the perspective of learners was constructed, and questionnaires and interviews were conducted.The research results show that the online education of off-campus training institutions has certain application prospects and advantages, and has been more recognized; The online training environment needs further optimization and improvement; There are some differences in learners' participation in online education in off-campus training institutions; Through chi-square test, it was found that there was no significant difference in the subjects involved in gender dimension in the off-campus online education, but there was significant difference in English and Chinese.n the grade dimension, through the chi-square test, it was found that there was a significant difference in the number of students who chose, a significant difference in English, the most in junior middle school, the least in primary school, and the least in senior high school, but no significant difference in Chinese and mathematics.Based on the above research, the following countermeasures and Suggestions are proposed:More specific policies will be introduced to encourage the development of online education;Strengthen the supervision of online education of off-campus training institutions, including the establishment of teacher access standards testing system and platform standards; Guidance based on differences in online education participation in off-campus training institutions;the school strengthened the teaching of subjects, especially math and English;.
ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization

Abstractive text summarization is one of the areas influenced by the emergence of pre-trained language models. Current pre-training works in abstractive summarization give more points to the summaries with more words in common with the main text and pay less attention to the semantic similarity between generated sentences and the original document. We propose ARMAN, a Transformer-based encoder-decoder model pre-trained with three novel objectives to address this issue. In ARMAN, salient sentences from a document are selected according to a modified semantic score to be masked and form a pseudo summary. To summarize more accurately and similar to human writing patterns, we applied modified sentence reordering. We evaluated our proposed models on six downstream Persian summarization tasks. Experimental results show that our proposed model achieves state-of-the-art performance on all six summarization tasks measured by ROUGE and BERTScore. Our models also outperform prior works in textual entailment, question paraphrasing, and multiple choice question answering. Finally, we established a human evaluation and show that using the semantic score significantly improves summarization results.
MULTILITERASI ERA DIGITAL DALAM RITUS GREBEG PANCASILA

This word consists of various countries and each country has rites or rituals of traditional ceremonies that are full of meaning and multiliteration that are characteristic of the culture in that country. Indonesia with a variety of tribes and cultural diversity also has a variety of traditional ceremonial rites. The city of Blitar as part of this country also has a various rites, one of which is Grebeg Pancasila.The purpose of this research is to discribe multiliteration in the Grebeg Pancasila rite in order to preserve the history of the birth of the Pancasila. This rite is held on June 1 every year. The Rite Grebeg Pancasila has five processions, namely: (1) Bedholan Grebeg, (2) Tirakatan Night, (3) Cultural Ceremony, (4) Kirab Gunungan Limo, (5) Kenduri Pancasila. The five processions are full of meaning to be studied in depth to uncover the multiliterasion contained in it to support the learning of elementary school students. In this digital era literacy studies are increasingly widespreead not only reading and writing. The diversity of these studies is called multiliteration. The approach and type of research used in this study is descriptive qualitative with the library research method (library research). Multiliteration which is examined in the Grebeg Pancasila rire includes religion literacy, cultural literacy, human literacy, beauty literacy, media and technology literacy, number literacy, economic literacy, reading literacy, mathematical literacy, scientific literacy, communication literacy, and others. This is an action taken to commemorate and preserve the spirit of strunggle in upholding Pancasila as the philosophy of life of the Indonesian people.
An iterative refinement method of point cloud for binocular vision 3D reconstruction

Abstract This paper introduces a new binocular stereo deep learning network based on point cloud, which can realize higher precision point cloud reconstruction through continuous iteration of the network. Our method directly carries out point cloud processing on the target, calculates the difference between the current depth map and the real depth, estimates the loss according to the predicted point cloud and the information of the dual view input image, and then uses the appropriate loss function to iteratively process the point cloud. In addition, we can customize the number of iterations to achieve higher precision point cloud effect. The proposed network basically achieves good results on KITTI data set.
Generating Realistic Natural Language Counterfactuals

Counterfactuals are a valuable means for understanding decisions made by ML systems. However, the counterfactuals generated by the methods currently available for natural language text are either unrealistic or introduce imperceptible changes. We propose CounterfactualGAN: a method that combines a conditional GAN and the embeddings of a pretrained BERT encoder to model-agnostically generate realistic natural language text counterfactuals for explaining regression and classification tasks. Experimental results show that our method produces perceptibly distinguishable counterfactuals, while outperforming four baseline methods on fidelity and human judgments of naturalness, across multiple datasets and multiple predictive models.
THE USE OF CODE SWITCHING IN ENGLISH TEACHING LEARNING PROCESS IN JUNIOR HIGH SCHOOL

This study concerns investigates the value of the use of code switching in English teaching learning process in junior highschool. Student and a teacher in SMP INTI NUSANTARA Tebing Tinggi involve as the subjects of this study. The purpose ofthis research is to investigate the value of lesson study in used code switching by the teacher. This case study obtaine thedata by ways of observations among students and teacher in class. This research aim to analyze the types of code switchingused by the teacher and the students. The reserach methodology uses qualitative. Qualitative research involves collectingdata from observation. The function of this research is how far is the useful of code switching in teaching learning in class.Generally this study has a treatment which came up from the teaching learning process in class.
Sophisticated and modernized library running system with OCR algorithm using IoT

An Internet of things (IOT) is an exclusive method, were its impact on the enactments of human life is very terendy.This research on library control system operates on the basis of IOT and Optical character recognition algorithm (OCR) algorithm rules and its training. A Closed-circuit television (CCTV) watched mechanism is created to control the book issuing and returning phenomenon via Tag studying system in the library. In this proposed work text file is converted into an audio file. This audio file is being played and the contents of the book can be heard via the headset. This unique function of the OCR helps blind people. Now a days OCR widely focused in machine processes such as Machine Transformation, Text to Speech extraction and Text data mining. It utilized in various area of research in Artificial Intelligence, Computer Vision and Pattern recognition. Using OCR to scan the damaged book in the library converted into pdf format the book gets new life and sharing the contents to multiple readers. In this paper aims to implement IOT based library management system to maintaining books in digital format.
COMBO: State-of-the-Art Morphosyntactic Analysis

We introduce COMBO – a fully neural NLP system for accurate part-of-speech tagging, morphological analysis, lemmatisation, and (enhanced) dependency parsing. It predicts categorical morphosyntactic features whilst also exposes their vector representations, extracted from hidden layers. COMBO is an easy to install Python package with automatically downloadable pre-trained models for over 40 languages. It maintains a balance between efficiency and quality. As it is an end-to-end system and its modules are jointly trained, its training is competitively fast. As its models are optimised for accuracy, they achieve often better prediction quality than SOTA. The COMBO library is available at: https://gitlab.clarin-pl.eu/syntactic-tools/combo.
Symbol-Pair Distances of Repeated-Root Negacyclic Codes of Length 2s over Galois Rings

Negacyclic codes of length [Formula: see text] over the Galois ring [Formula: see text] are linearly ordered under set-theoretic inclusion, i.e., they are the ideals [Formula: see text], [Formula: see text], of the chain ring [Formula: see text]. This structure is used to obtain the symbol-pair distances of all such negacyclic codes. Among others, for the special case when the alphabet is the finite field [Formula: see text] (i.e., [Formula: see text]), the symbol-pair distance distribution of constacyclic codes over [Formula: see text] verifies the Singleton bound for such symbol-pair codes, and provides all maximum distance separable symbol-pair constacyclic codes of length [Formula: see text] over [Formula: see text].
Tracking People with 3D Representations

We present a novel approach for tracking multiple people in video. Unlikepast approaches which employ 2D representations, we focus on using 3Drepresentations of people, located in three-dimensional space. To this end, wedevelop a method, Human Mesh and Appearance Recovery (HMAR) which in additionto extracting the 3D geometry of the person as a SMPL mesh, also extractsappearance as a texture map on the triangles of the mesh. This serves as a 3Drepresentation for appearance that is robust to viewpoint and pose changes.Given a video clip, we first detect bounding boxes corresponding to people, andfor each one, we extract 3D appearance, pose, and location information usingHMAR. These embedding vectors are then sent to a transformer, which performsspatio-temporal aggregation of the representations over the duration of thesequence. The similarity of the resulting representations is used to solve forassociations that assigns each person to a tracklet. We evaluate our approachon the Posetrack, MuPoTs and AVA datasets. We find that 3D representations aremore effective than 2D representations for tracking in these settings, and weobtain state-of-the-art performance. Code and results are available at:https://brjathu.github.io/T3DP.
UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis.

Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations. In this paper, instead of investigating these control signals separately, we propose a new two-stage architecture, UFC-BERT, to unify any number of multi-modal controls. In UFC-BERT, both the diverse control signals and the synthesized image are uniformly represented as a sequence of discrete tokens to be processed by Transformer. Different from existing two-stage autoregressive approaches such as DALL-E and VQGAN, UFC-BERT adopts non-autoregressive generation (NAR) at the second stage to enhance the holistic consistency of the synthesized image, to support preserving specified image blocks, and to improve the synthesis speed. Further, we design a progressive algorithm that iteratively improves the non-autoregressively generated image, with the help of two estimators developed for evaluating the compliance with the controls and evaluating the fidelity of the synthesized image, respectively. Extensive experiments on a newly collected large-scale clothing dataset M2C-Fashion and a facial dataset Multi-Modal CelebA-HQ verify that UFC-BERT can synthesize high-fidelity images that comply with flexible multi-modal controls.
IndoNLI: A Natural Language Inference Dataset for Indonesian

We present IndoNLI, the first human-elicited NLI dataset for Indonesian. We adapt the data collection protocol for MNLI and collect ~18K sentence pairs annotated by crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. Experiment results show that XLM-R outperforms other pre-trained models in our data. The best performance on the expert-annotated data is still far below human performance (13.4% accuracy gap), suggesting that this test set is especially challenging. Furthermore, our analysis shows that our expert-annotated data is more diverse and contains fewer annotation artifacts than the crowd-annotated data. We hope this dataset can help accelerate progress in Indonesian NLP research.
Implementasi Algoritma Backpropagation Untuk Memprediksi Harga Jual Rumah Berdasarkan Harga Bahan Baku

Housing developers often find it difficult to determine the selling price of the house. One of the main factors to determine the selling price of the house. One of the main factors to determine the selling price of the homes is information regarding the use of raw material prices. Companies need information in prices of row material price fluctuations, air, causing the company is difficult to predict the selling price of the house next period. Forecasting methods used in this system is backpropagation neural network method that refers to a component of time series data forecasting random or random variance with the initial process for determining autocorrelation input variables. This propagation method is proven to predict the selling price of the house.
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability

Abuse on the Internet is an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse across various platforms. The psychological effects of abuse on individuals can be profound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abusive language detection in the field of NLP. In this position paper, we discuss the role that modeling of users and online communities plays in abuse detection. Specifically, we review and analyze the state of the art methods that leverage user or community information to enhance the understanding and detection of abusive language. We then explore the ethical challenges of incorporating user and community information, laying out considerations to guide future research. Finally, we address the topic of explainability in abusive language detection, proposing properties that an explainable method should aim to exhibit. We describe how user and community information can facilitate the realization of these properties and discuss the effective operationalization of explainability in view of the properties.
Calibration and Consistency of Adversarial Surrogate Losses

Adversarial robustness is an increasingly critical property of classifiers in applications. The design of robust algorithms relies on surrogate losses since the optimization of the adversarial loss with most hypothesis sets is NP-hard. But which surrogate losses should be used and when do they benefit from theoretical guarantees? We present an extensive study of this question, including a detailed analysis of the H-calibration and H-consistency of adversarial surrogate losses. We show that, under some general assumptions, convex loss functions, or the supremum-based convex losses often used in applications, are not H-calibrated for important hypothesis sets such as generalized linear models or one-layer neural networks. We then give a characterization of H-calibration and prove that some surrogate losses are indeed H-calibrated for the adversarial loss, with these hypothesis sets. Next, we show that H-calibration is not sufficient to guarantee consistency and prove that, in the absence of any distributional assumption, no continuous surrogate loss is consistent in the adversarial setting. This, in particular, proves that a claim presented in a COLT 2020 publication is inaccurate. (Calibration results there are correct modulo subtle definition differences, but the consistency claim does not hold.) Next, we identify natural conditions under which some surrogate losses that we describe in detail are H-consistent for hypothesis sets such as generalized linear models and one-layer neural networks. We also report a series of empirical results with simulated data, which show that many H-calibrated surrogate losses are indeed not H-consistent, and validate our theoretical assumptions.
Heavy Elements in Soil of West Qurna-1 Oil Field inBasrah Governorate, Southern Iraq

Soil samples were collected seasonally from ten locations in west Qurna-1 oil field from January-December, 2018 to determine theconcentrations of some heavy elements (lead (Pb), nickel (Ni), Copper (Cu), Cadmium (Cd), Manganese (Mn) and Iron (Fe) in exchangeableand residual phases of soil. The results of seasonal mean concentrations in exchangeable phase of (pb, Ni, Cu, Cd, Mn and, Fe) showedthat, the highest concentration were (16.85, 39.47, 21.21, 5.60, 358.42, and 641.44) μg/g dry weight respectively, while the lowestconcentration were (14.50, 31.37, 18.39, 3.90, 340.41 and 619.48) μg/g dry weight respectively . The result of seasonal meanconcentrations in residual phase of (pb, Ni, Cu, Cd, Mn and, Fe) showed that, the highest concentration were (27.04, 84.82, 28.75,7.73, 259.83, and 649.30) μg/g dry weight respectively, while the lowest concentration were (24.95 , 81.38, 26.74, 6.71, 228.46, 641.44) μg/gdry weight respectively. Heavy elements indices (Geo-accumulation index (I-geo), Contamination Factor (CF) and Enrichment Factor (EF) werecalculated for soil, according to I-geo Index, the soil in the study area was practically unpolluted with Ni, Cu, Mn, Fe, while the soil wasunpolluted to moderately polluted with Pb, and for Cd the soil was strongly polluted to extremely polluted.
Upaya Meningkatkan Hasil Belajar Materi Menceritakan Sejarah Indonesia Melalui Penerapan Metode Pembelajaran Word Square dan Scramble

This study aims to determine whether there is an increase in Learning Outcomes of Indonesian History Telling Materials through the application of Word Square and Scramble learning methods to 6th Grade students of SDN Medokan Ayu II/615 Surabaya, and to find out how much increase in Learning Outcomes of Indonesian History Telling Materials through the application of learning methods. Word Square and Scramble for Class 6 students at SDN Medokan Ayu II/615 Surabaya. Action research was carried out in two learning cycles by applying the Word Square and Scramble learning methods in Indonesian Language Subjects as the object of research. In the study, it was found that the authentic value and learning outcomes of students' storytelling history of Indonesia increased. This can be seen from the increase in students' learning outcomes of Telling Indonesian History from before this learning method was applied which only had an average of 53.8 to 71.3 in the first cycle and 77.7 in the second cycle. In addition, it was also found an increase in the percentage of classical completeness from cycle to cycle. Classical completeness in the first cycle was 59% with a moderately increasing category in the second cycle of 82% with a very good category. Based on the explanation of the results of the research and discussion, it can be concluded that there is an increase in Learning Outcomes of Indonesian History Telling Materials through the application of the Word Square and Scramble learning methods to the 6th grade students of SDN Medokan Ayu II/615 Surabaya.