Reviewerly/exHarmony huggingface dataset:
Magnitude Comparison and Sign Detection based on the 4-Moduli Set {2n+1, 2n−1, 2n+3, 2n−3}

The 4-moduli set residue number system (RNS),{2n+1, 2n−1, 2n+3, 2n−3}, with a wide dynamic range, has recently been proposed as a balanced 4-moduli set for utilizing the cases that demand fast calculations such as deep learning and implementation of asymmetric cryptographic algorithms. Up to now, only an unsigned reverse converter has been designed for this moduli set. Thus, there is a need for two separate units, a sign detection circuit, and a comparator to use this set in cases requiring sign and comparison. Nevertheless, the existence of these components demands high hardware that makes the implementation of the RNS impractical. Therefore, this paper presents the design of a sign detection circuit and a signed reverse converter that can overcome this problem by reusing the hardware. To achieve an integrated hardware design, first, we optimized the previous unsigned reverse converter for this 4-moduli set and next, we derived an approach from the structure of the reverse convertor for detecting signs and recognizing comparators. Finally, using the sign signals extracted from the reverse converter, we change reverse convertor into a unit that perform sign detection and comparison. The simulation has been conducted using ISE Design Suite 14.7 tool and the Spartan6 family technology. Empirical results show that, the proposed multifunctional unit has an approximately identical performance with respect to delay and area compared to the previous reverse converter. Besides, the proposed signed reverse converter relies on a 46% and 28% reduction in area and delay compared to the previous unsigned reverse converter which uses a comparator and also a multiplexer to detect a sign in the output.
The “Internet of Things” Boosts Agricultural Livelihoods in India

New technology is bringing better connectivity to remote areas, helping farmers improve crop yields and allowing fisherfolk to venture more safely out into the sea.
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition

Recent work on speech self-supervised learning (speech SSL) demonstrated the benefits of scale in learning rich and transferable representations for Automatic Speech Recognition (ASR) with limited parallel data. It is then natural to investigate the existence of sparse and transferrable subnetworks in pre-trained speech SSL models that can achieve even better low-resource ASR performance. However, directly applying widely adopted pruning methods such as the Lottery Ticket Hypothesis (LTH) is suboptimal in the computational cost needed. Moreover, contrary to what LTH predicts, the discovered subnetworks yield minimal performance gain compared to the original dense network. In this work, we propose Prune-Adjust- Re-Prune (PARP), which discovers and finetunes subnetworks for much better ASR performance, while only requiring a single downstream finetuning run. PARP is inspired by our surprising observation that subnetworks pruned for pre-training tasks only needed to be slightly adjusted to achieve a sizeable performance boost in downstream ASR tasks. Extensive experiments on low-resource English and multi-lingual ASR show (1) sparse subnetworks exist in pre-trained speech SSL, and (2) the computational advantage and performance gain of PARP over baseline pruning methods. On the 10min Librispeech split without LM decoding, PARP discovers subnetworks from wav2vec 2.0 with an absolute 10.9%/12.6% WER decrease compared to the full model. We demonstrate PARP mitigates performance degradation in cross-lingual mask transfer, and investigate the possibility of discovering a single subnetwork for 10 spoken languages in one run.
Anti-occlusion Depth Reconstruction from Light Field with Gaussian Weighted Matching Norm

We proposed the Gaussian weighted matching norm (GWMN) to measure the region matching distance for light field, which can effectively suppress the mismatch from occlusion, and realize high-accuracy scene depth reconstruction.
Causal Augmentation for Causal Sentence Classification

Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification. In particular, we found that models misclassify on augmented sentences that have been negated or strengthened with respect to its causal meaning. This is worrying since minor linguistic differences in causal sentences can have disparate meanings. Therefore, we propose the generation of counterfactual causal sentences by creating contrast sets (Gardner et al., 2020) to be included during model training. We experimented on two model architectures and predicted on two out-of-domain corpora. While our strengthening schemes proved useful in improving model performance, for negation, regular edits were insufficient. Thus, we also introduce heuristics like shortening or multiplying root words of a sentence. By including a mixture of edits when training, we achieved performance improvements beyond the baseline across both models, and within and out of corpus’ domain, suggesting that our proposed augmentation can also help models generalize.
Analysis of Students’ Perception of Online Learning (A Study of three universities in Aceh)

This study analyzed students’ perception of online learning in three universities in Aceh, IAIN Takengon, Malikussaleh University, and Samudera University. It studied how they perceived the application of online learning during the Covid-19 pandemic. In analyzing their perception, researchers considered three aspects of perception; learning process, learning environment, and learning motivation. To gather the data and information, researchers designed a self-administered questionnaire consisting of 16 items to the respondents online using Google Form. In addition, a discussion in the form of FGD with respondents was conducted in each site to gain a more comprehensive insight on the subject. A total of 449 respondents from three universities participated in the questionnaires. Researchers utilized purposive random sampling under the criteria of active students who have experienced online learning on campus. Some students who have responded to the questionnaires were then invited for FGD in each of the universities. Researchers utilized SPSS software to analyze the data and present the phenomenon and general findings to be elaborated in the study. The results of the study uncovered that students have a negative attitude towards online learning in the learning process, learning environment, and learning motivation. However, the learning process has a positive correlation with both the learning environment and learning motivation.
Learning to match via inverse optimal transport

We propose a unified data-driven framework based on inverse optimal transport that can learn adaptive, nonlinear interaction cost function from noisy and incomplete empirical matching matrix and pr...
Remember What You Want to Forget: Algorithms for Machine Unlearning

We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset $S$ drawn i.i.d. from an unknown distribution, and outputs a model $\widehat{w}$ that performs well on unseen samples from the same distribution. However, at some point in the future, any training datapoint $z \in S$ can request to be unlearned, thus prompting the learner to modify its output model while still ensuring the same accuracy guarantees. We initiate a rigorous study of generalization in machine unlearning, where the goal is to perform well on previously unseen datapoints. Our focus is on both computational and storage complexity. For the setting of convex losses, we provide an unlearning algorithm that can unlearn up to $O(n/d^{1/4})$ samples, where $d$ is the problem dimension. In comparison, in general, differentially private learning (which implies unlearning) only guarantees deletion of $O(n/d^{1/2})$ samples. This demonstrates a novel separation between differential privacy and machine unlearning.
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment

Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification. While knowledge bases contain a large number of entities in high-resource languages such as English and French, corresponding entities for lower-resource languages are often missing. To address this, we propose Lexical-Semantic-Phonetic Align (LSP-Align), a technique to automatically mine cross-lingual entity lexica from mined web data. We demonstrate LSP-Align outperforms baselines at extracting cross-lingual entity pairs and mine 164 million entity pairs from 120 different languages aligned with English. We release these cross-lingual entity pairs along with the massively multilingual tagged named entity corpus as a resource to the NLP community.
Teachers’ language politeness, students’ academic motivation and self-efficacy during school from home

This study aims to investigate the influence of teachers’ language politeness on students’ academic motivation and self-efficacy during online learning (school from home) and to examine the relationship between students’ academic motivation and self-efficacy during online learning. This study used a quantitative approach with the correlational method. The participants of the study were 150 elementary students (male: 47%; female: 53%). Three types of scales were used in this study, namely the teachers’ language politeness scale, the academic motivation scale and the self-efficacy scale. The hypothesis of the study was tested using analysis of variance assisted with SPSS version 26. The study found that teachers’ language politeness significantly influences students’ academic motivation and self-efficacy during online learning. Teachers’ language politeness was more likely to have greater influence on students’ self-efficacy than on students’ academic motivation. As shown in the model, the value of the relationship between students’ academic motivation and self-efficacy was 0.497.&#x0D; &#x0D; Keywords: Language politeness, academic motivation, self-efficacy, school from home.
Program pengembangan minat baca di Perpustakaan Kota Banjar Patroman

Introduction. This study focuses on the management of the Reading and Literacy Tourism program in the aspect of POAC (Planning, Organizing, Actuating, Controlling). The development of reading interest is an effort made to increase people's reading interest and habits, with the existence of a reading interest development program it can encourage an increase in people's reading interest. One of the roles to develop people's reading interest is the library. The City Library of Banjar Patroman has one program in an effort to develop interest in reading, especially for early childhood in PAUD, TK and RA in Banjar Patroman City. This activity is called Reading and Literacy Tourism which is carried out by traveling while reading books.Data Collection Methods. The research method used is descriptive qualitative method. Research data obtained through interviews, observation and document analysis.Data Analysis. The analysis uses several objects in the form of behavior, perception, motivation, action, and others holistically, and by means of description in the form of words and language, in a special natural context and by utilizing various natural methods.Results dan Discussion. The City Library of Banjar Patroman uses the planning concept that has been regulated in the Terms of Reference (KAK), then organizing there are 15 committee members who divided into 6 divisions, the implementation is carried out 2 times a week or 24 times a year and the implementation of this activity is carried out during the Covid-19 pandemic with a limitation of 20 participants consisting of accompanying teachers and participants, supervision of this activity uses direct supervision, indirect supervision, internal control and external supervision.Conclusion. The results of this study indicate that the Banjar Patroman City Library in developing early childhood reading interest through Reading and Literacy Tourism activities in managing its activities using the POAC aspect.
Pelatihan Pembuatan Video Ajar Dengan Open Broadcaster Software Bagi Guru Di SMP Negeri 1 Karang Bahagia Kabupaten Bekasi

Karang Bahagia Subdistrict, Bekasi Regency, West Java Province which enforces PSBB. Based on Bekasi Regent Regulation No. 37 of 2020 concerning the implementation of Large-Scale Social Restrictions (PSBB) in handling the corona virus disease in Bekasi Regency, applies to all elements of activity. In Article 6 Bekasi Regent Regulation No. 37 of 2020, it is also explained that the process of implementing learning in schools is carried out at home or where students live through distance learning. Many teachers at SMP Negeri 1 Karang Bahagia, Bekasi Regency, do not understand how to make learning media that can be used for distance learning. The face-to-face learning method that has been done so far must be changed into a digital format that displays text, audio and visual so that the subject matter can be delivered optimally. Therefore, assistance and training are needed for teachers in making teaching videos with software commonly used in presenting online video broadcasts, namely Open Broadcaster Software (OBS). based on the results of the pre-test and post-test given to 144 training participants, 59 % of participants' knowledge increased related to video-making techniques. participants' knowledge increases regarding video-making techniques.
Cross-Modal Retrieval Augmentation for Multi-Modal Classification

Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. Here, we explore the use of unstructured external knowledge sources of images and their corresponding captions for improving visual question answering (VQA). First, we train a novel alignment model for embedding images and captions in the same space, which achieves state-of-the-art image-caption retrieval performance w.r.t. similar methods. Second, we show that retrieval-augmented multi-modal transformers using the trained alignment model significantly improve results on VQA over strong baselines. We further conduct extensive experiments to establish the promise of this approach, and examine novel applications for inference time such as hot-swapping indices.
Sampling the Variational Posterior with Local Refinement

Variational inference is an optimization-based method for approximating the posterior distribution of the parameters in Bayesian probabilistic models. A key challenge of variational inference is to approximate the posterior with a distribution that is computationally tractable yet sufficiently expressive. We propose a novel method for generating samples from a highly flexible variational approximation. The method starts with a coarse initial approximation and generates samples by refining it in selected, local regions. This allows the samples to capture dependencies and multi-modality in the posterior, even when these are absent from the initial approximation. We demonstrate theoretically that our method always improves the quality of the approximation (as measured by the evidence lower bound). In experiments, our method consistently outperforms recent variational inference methods in terms of log-likelihood and ELBO across three example tasks: the Eight-Schools example (an inference task in a hierarchical model), training a ResNet-20 (Bayesian inference in a large neural network), and the Mushroom task (posterior sampling in a contextual bandit problem).
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models

Despite their success in a variety of NLP tasks, pre-trained language models, due to their heavy reliance on compositionality, fail in effectively capturing the meanings of multiword expressions (MWEs), especially idioms. Therefore, datasets and methods to improve the representation of MWEs are urgently needed. Existing datasets are limited to providing the degree of idiomaticity of expressions along with the literal and, where applicable, (a single) non-literal interpretation of MWEs. This work presents a novel dataset of naturally occurring sentences containing MWEs manually classified into a fine-grained set of meanings, spanning both English and Portuguese. We use this dataset in two tasks designed to test i) a language model’s ability to detect idiom usage, and ii) the effectiveness of a language model in generating representations of sentences containing idioms. Our experiments demonstrate that, on the task of detecting idiomatic usage, these models perform reasonably well in the one-shot and few-shot scenarios, but that there is significant scope for improvement in the zero-shot scenario. On the task of representing idiomaticity, we find that pre-training is not always effective, while fine-tuning could provide a sample efficient method of learning representations of sentences containing MWEs.
Neural Production Systems

Visual environments are structured, consisting of distinct objects or entities. These entities have properties -- both visible and latent -- that determine the manner in which they interact with one another. To partition images into entities, deep-learning researchers have proposed structural inductive biases such as slot-based architectures. To model interactions among entities, equivariant graph neural nets (GNNs) are used, but these are not particularly well suited to the task for two reasons. First, GNNs do not predispose interactions to be sparse, as relationships among independent entities are likely to be. Second, GNNs do not factorize knowledge about interactions in an entity-conditional manner. As an alternative, we take inspiration from cognitive science and resurrect a classic approach, production systems, which consist of a set of rule templates that are applied by binding placeholder variables in the rules to specific entities. Rules are scored on their match to entities, and the best fitting rules are applied to update entity properties. In a series of experiments, we demonstrate that this architecture achieves a flexible, dynamic flow of control and serves to factorize entity-specific and rule-based information. This disentangling of knowledge achieves robust future-state prediction in rich visual environments, outperforming state-of-the-art methods using GNNs, and allows for the extrapolation from simple (few object) environments to more complex environments.
Lei Aldir Blanc e o estímulo à animação cearense na pandemia / Aldir Blanc law and the stimulus to cearian animation in the pandemic

Due to the necessary sanitary measures as a result of the COVID-19 pandemic, a large number of workers in the cultural environment found themselves unable to exercise their profession.In order to mitigate the impacts of this crisis, especially in the cultural sector,
THREE-STEP INTERVIEW IN TEACHING SPEAKING

Three-Step Interview is a cooperative learning strategy that offers an alternative to the competitive approach of whole class question-answer, in which the teacher will provide a certain topic and students may ask questions in pair. The purpose of this research is to investigate students’ difficulties on implementing Three-Step Interview in speaking. This research used descriptive qualitative method. The tenth grade of IPA 3 in one Senior High School in Cimahi which consisted of 32 students were selected as the respondents of this research. Questionnaire and interview were used in order to obtain the data. The result of this research shows that most of the students feel difficulties in linguistic aspects, included vocabulary (94%), pronunciation (56%), and grammar (88%) out of 32 students. Meanwhile, there is no significance difficulties in implementing the technique, only 44% students who find difficulties in the second step. Keywords :  Three-Step Interview, Teaching Speaking, Difficulty
Uso de recursos didácticos tecnológicos interactivos para mejorar el aprendizaje del idioma inglés en los estudiantes universitarios

Learning the English languages ​​has always been difficult for the student from South America, especially from Ecuador, a country that is in the last place of English language proficiency according to a study carried out by EF Education First in 2019.If added to this we have that the COVID-19 pandemic has caused education to change in many aspects, changing face-to-face classes, for a virtuality that requires teachers to face new challenges, we have to start teaching from this new methodology, with different resources than those we have been using, resources where technology is not only necessary, but is the basis for them. Students today use technology not only to interact with society, but also to study, read, listen to music, play videos, and communicate in all possible ways.This leads us to the predominant use of interactive technological didactic resources that adjust to the new demands of society, and allow students to learn in a way that is more in line with the needs of each one of them, resources that develop their intelligences. And make knowledge not just theoretical or boring, but motivate and teach them to put it into practice.
약용 생물자원 빅데이터 구축 : 2019~2021

This study aims to describe the big data for medicinal biological resources and to discuss how to use the data. The medicinal biological resources data was constructed by producing and processing the information related to medicinal materials in medicinal biological resources big data center of forest big data platform. The big data center has produced 54 million 168 GB data for 27 datasets of 9 types from 2019 to 2021 and has opened it to the public for free. This study also presents valuable use cases in two fields. The information on medicinal biological resources can be used in personal health management in daily life based on Korean medicine. In addition, it can be used to build a big data platform that analyzes biomedical data by combining the compounds of medicinal materials and existing bio big data in the biomedical fields.
Language Interaction and Gender Discrimination in Conversational AI

The application of conversational AI increasingly shows up in people's intelligent devices, and brings great convenience to people's daily life.However, the majority of voice assistants are female voices.That causes a bad impact on female's original characteristic appeared in modern society and leads the perpetuating public gender bias to degenerate into unconscious gender discrimination.This paper briefly states the chatting AI's gender discrimination in AI algorithm designing issues caused by unconsciousness social gender stereotyping.Besides, this paper also explores the causes of conversational AI's gender discrimination which is related to gender characteristics in the context, and further discusses the continuous influences of AI's training statements in modern society.In the end, it will attempt to address former issues with the cutting point in the algorithms designing, objective bias avoidance and default settings of conversation AI to propose solutions.