Reviewerly/exHarmony huggingface dataset:
Land Cover Mapping in Lake Rawa Pening Using Landsat 9 Imagery and Google Earth Engine

Lake Rawa Pening, in Semarang Regency, is one of the super lakes of revitalization priority. Lake revitalization is an activity to restore the natural function of the lake as a water reservoir through lake dredging, cleaning of invasive alien plants, and land use planning. This makes land use and land cover information in Lake Rawa Pening useful for formulating policy strategies related to revitalization. This study will discuss land cover mapping in Lake Rawa Pening. Mapping using Landsat 9 Imagery and machine learning on Google Earth Engine (GEE). Machine Learning used in this study is CART and RF. The research result shows that the land cover map with the best accuracy is obtained from machine learning RF with an overall accuracy of around 0.78, while CART machine learning is approximately 0.76. The overall accuracy values for CART and RF are not much different because they are both decision tree-based machine learning. This research needs to be developed using cloud masking, comparing image transformations, and comparing its predecessor data, namely Landsat 8. This is useful for providing representative land cover data as the basis for the policy of revitalizing Lake Rawa Pening.
Digital Fatigue among Students in Current Covid-19 Pandemic: A Study of Higher Education

Purpose: TPresent study attempted to measure the level of digital fatigue among the students from University of Jammu (J&amp;K). Study further examines the impact of multiple online platforms used by faculty and technology overload on digital fatigue among students in selected university. Design/Methodology/Approach: A survey approach was employed to collect data from a total of 400 students from selected university. Data was examined statistically by using structural educational modelling (SEM). Findings: Results indicated that multiple online platforms and technology overload have a significant positive impact on student's digital fatigue. Practical Implications: Study highlights that special focus and effort are required to cope up with the digital fatigue among the students. The students should be provided with the proper support to handle the technology, single online platform should be encouraged for online learnig to enhance the student well being. Originality/Value: This research to the small but increasing body of knowledge on a topic that is important to both academics and practitioners. The current study looks at the many reasons of digital fatigue caused by an overreliance on technology in the online education system.
MisGAN: Learning from Incomplete Data with Generative Adversarial Networks

Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks. However, typical GANs require fully-observed data during training. In this paper, we present a GAN-based framework for learning from complex, high-dimensional incomplete data. The proposed framework learns a complete data generator along with a mask generator that models the missing data distribution. We further demonstrate how to impute missing data by equipping our framework with an adversarially trained imputer. We evaluate the proposed framework using a series of experiments with several types of missing data processes under the missing completely at random assumption.
Graver Bases via Quantum Annealing with Application to Non-Linear Integer Programs

We propose a novel hybrid quantum-classical approach to calculate Graver bases, which have the potential to solve a variety of hard linear and non-linear integer programs, as they form a test set (optimality certificate) with very appealing properties. The calculation of Graver bases is exponentially hard (in general) on classical computers, so they not used for solving practical problems on commercial solvers. With a quantum annealer, however, it may be a viable approach to use them. We test two hybrid quantum-classical algorithms (on D-Wave)--one for computing Graver basis and a second for optimizing non-linear integer programs that utilize Graver bases--to understand the strengths and limitations of the practical quantum annealers available today. Our experiments suggest that with a modest increase in coupler precision--along with near-term improvements in the number of qubits and connectivity (density of hardware graph) that are expected--the ability to outperform classical best-in-class algorithms is within reach, with respect to non-linear integer optimization.
$k$-partial permutations and the center of the wreath product $\mathcal{S}_k\wr \mathcal{S}_n$ algebra

We generalize the concept of partial permutations of Ivanov and Kerov and introduce $k$-partial permutations. This allows us to show that the structure coefficients of the center of the wreath product $\mathcal{S}_k\wr \mathcal{S}_n$ algebra are polynomials in $n$ with non-negative integer coefficients. We use a universal algebra $\mathcal{I}_\infty^k$ which projects on the center $Z(\mathbb{C}[\mathcal{S}_k\wr \mathcal{S}_n])$ for each $n.$ We show that $\mathcal{I}_\infty^k$ is isomorphic to the algebra of shifted symmetric functions on many alphabets.
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization

We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human performance in WikiSQL dataset. We revisit and discuss diverse popular methods in NL2SQL literature, take a full advantage of BERT {Devlin et al., 2018) through an effective table contextualization method, and coherently combine them, outperforming the previous state of the art by 8.2% and 2.5% in logical form and execution accuracy, respectively. We particularly note that BERT with a seq2seq decoder leads to a poor performance in the task, indicating the importance of a careful design when using such large pretrained models. We also provide a comprehensive analysis on the dataset and our model, which can be helpful for designing future NL2SQL datsets and models. We especially show that our model's performance is near the upper bound in WikiSQL, where we observe that a large portion of the evaluation errors are due to wrong annotations, and our model is already exceeding human performance by 1.3% in execution accuracy.
The Second Conversational Intelligence Challenge (ConvAI2)

We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots. Some key takeaways from the competition are: (i) pretrained Transformer variants are currently the best performing models on this task, (ii) but to improve performance on multi-turn conversations with humans, future systems must go beyond single word metrics like perplexity to measure the performance across sequences of utterances (conversations) -- in terms of repetition, consistency and balance of dialogue acts (e.g. how many questions asked vs. answered).
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional changes, e.g. due to interventions, actions of agents and other sources of non-stationarities. We show that under this assumption, the correct causal structural choices lead to faster adaptation to modified distributions because the changes are concentrated in one or just a few mechanisms when the learned knowledge is modularized appropriately. This leads to sparse expected gradients and a lower effective number of degrees of freedom needing to be relearned while adapting to the change. It motivates using the speed of adaptation to a modified distribution as a meta-learning objective. We demonstrate how this can be used to determine the cause-effect relationship between two observed variables. The distributional changes do not need to correspond to standard interventions (clamping a variable), and the learner has no direct knowledge of these interventions. We show that causal structures can be parameterized via continuous variables and learned end-to-end. We then explore how these ideas could be used to also learn an encoder that would map low-level observed variables to unobserved causal variables leading to faster adaptation out-of-distribution, learning a representation space where one can satisfy the assumptions of independent mechanisms and of small and sparse changes in these mechanisms due to actions and non-stationarities.
A Local Discrete Text Data Mining Method in High-Dimensional Data Space

Abstract Aiming at the problems of low accuracy, the long time required, and the large memory consumption of traditional data mining methods, a local discrete text data mining method in high-dimensional data space is proposed. First of all, through the data preparation and preprocessing step, we obtain the minimum data divergence and maximize the data dimension to meet the demand for data in high-dimensional space; second, we use the information gain method to mine the pre-processed discrete text data to establish an objective function to obtain the highest information gain; finally, the objective functions established in data preparation, preprocessing, and mining are combined to form a multi-objective optimization problem to realize local discrete text data mining. The simulation experiment results show that our method effectively reduces the time and improves the accuracy of data mining, where it also consumes less memory, indicating that the multi-objective optimization method can effectively solve multiple problems and effectively improve the data mining effect.
AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing

Today's Mixed Reality head-mounted displays track the user's head pose in world space as well as the user's hands for interaction in both Augmented Reality and Virtual Reality scenarios. While this is adequate to support user input, it unfortunately limits users' virtual representations to just their upper bodies. Current systems thus resort to floating avatars, whose limitation is particularly evident in collaborative settings. To estimate full-body poses from the sparse input sources, prior work has incorporated additional trackers and sensors at the pelvis or lower body, which increases setup complexity and limits practical application in mobile settings. In this paper, we present AvatarPoser, the first learning-based method that predicts full-body poses in world coordinates using only motion input from the user's head and hands. Our method builds on a Transformer encoder to extract deep features from the input signals and decouples global motion from the learned local joint orientations to guide pose estimation. To obtain accurate full-body motions that resemble motion capture animations, we refine the arm joints' positions using an optimization routine with inverse kinematics to match the original tracking input. In our evaluation, AvatarPoser achieved new state-of-the-art results in evaluations on large motion capture datasets (AMASS). At the same time, our method's inference speed supports real-time operation, providing a practical interface to support holistic avatar control and representation for Metaverse applications.
Contrastive Masked Autoencoders are Stronger Vision Learners

Masked image modeling (MIM) has achieved promising results on various vision tasks. However, the limited discriminability of learned representation manifests there is still plenty to go for making a stronger vision learner. Towards this goal, we propose Contrastive Masked Autoencoders (CMAE), a new self-supervised pre-training method for learning more comprehensive and capable vision representations. By elaboratively unifying contrastive learning (CL) and masked image model (MIM) through novel designs, CMAE leverages their respective advantages and learns representations with both strong instance discriminability and local perceptibility. Specifically, CMAE consists of two branches where the online branch is an asymmetric encoder-decoder and the momentum branch is a momentum updated encoder. During training, the online encoder reconstructs original images from latent representations of masked images to learn holistic features. The momentum encoder, fed with the full images, enhances the feature discriminability via contrastive learning with its online counterpart. To make CL compatible with MIM, CMAE introduces two new components, i.e. pixel shifting for generating plausible positive views and feature decoder for complementing features of contrastive pairs. Thanks to these novel designs, CMAE effectively improves the representation quality and transfer performance over its MIM counterpart. CMAE achieves the state-of-the-art performance on highly competitive benchmarks of image classification, semantic segmentation and object detection. Notably, CMAE-Base achieves $85.3\%$ top-1 accuracy on ImageNet and $52.5\%$ mIoU on ADE20k, surpassing previous best results by $0.7\%$ and $1.8\%$ respectively. The source code is publicly accessible at \url{https://github.com/ZhichengHuang/CMAE}.
A Hazard Analysis Framework for Code Synthesis Large Language Models

Codex, a large language model (LLM) trained on a variety of codebases, exceeds the previous state of the art in its capacity to synthesize and generate code. Although Codex provides a plethora of benefits, models that may generate code on such scale have significant limitations, alignment problems, the potential to be misused, and the possibility to increase the rate of progress in technical fields that may themselves have destabilizing impacts or have misuse potential. Yet such safety impacts are not yet known or remain to be explored. In this paper, we outline a hazard analysis framework constructed at OpenAI to uncover hazards or safety risks that the deployment of models like Codex may impose technically, socially, politically, and economically. The analysis is informed by a novel evaluation framework that determines the capacity of advanced code generation techniques against the complexity and expressivity of specification prompts, and their capability to understand and execute them relative to human ability.
A Comparative Analysis of English Language Learning Trends in Korea and China Using the Metaverse

The purpose of this study was to examine the existing level of English learning in Korea and China using the Metaverse, as well as the possibilities for future educational developments.This comparative study between Korea and China collected and examined two types of materials: literature and Internet news related to the research topic in order to achieve the research objectives.First, different articles were searched using the keywords 'Metaverse' and 'English', and removed duplicate and irrelevant articles.Second, the researchers examined 12 Korean and 15 Chinese journal articles.Third, from February 1, 2021, to February 28, 2022, the researchers did a media analysis on 37 selected Korean articles and 35 selected Chinese articles using the keywords 'Metaverse' and 'English'.Many learner awareness surveys on learning English using Metaverse have been found in the literature in both China and South Korea.However, the media research shows that many educational companies are developing and attempting to provide a condensed development and technical support, that was required in the dissertation.The results of this study will lead to a long-term collaboration between educational companies and public education, as well as recommendations for educational responses to the Fourth Industrial Revolution in the midst of rapid change.
Block Diagonal Least Squares Regression for Subspace Clustering

Least squares regression (LSR) is an effective method that has been widely used for subspace clustering. Under the conditions of independent subspaces and noise-free data, coefficient matrices can satisfy enforced block diagonal (EBD) structures and achieve good clustering results. More importantly, LSR produces closed solutions that are easier to solve. However, solutions with block diagonal properties that have been solved using LSR are sensitive to noise or corruption as they are fragile and easily destroyed. Moreover, when using actual datasets, these structures cannot always guarantee satisfactory clustering results. Considering that block diagonal representation has excellent clustering performance, the idea of block diagonal constraints has been introduced into LSR and a new subspace clustering method, which is named block diagonal least squares regression (BDLSR), has been proposed. By using a block diagonal regularizer, BDLSR can effectively reinforce the fragile block diagonal structures of the obtained matrices and improve the clustering performance. Our experiments using several real datasets illustrated that BDLSR produced a higher clustering performance compared to other algorithms.
Application of Artificial Intelligence Algorithm and VR Technology in Vocal Music Teaching

Traditional intelligent algorithms mainly artificially preselect the geometric features of the image and then classify and recognize the image based on these features. The geometric features of the manually selected working images are often interfered by human factors, resulting in inaccurate feature extraction and reduced classification accuracy. Aiming at the above problems, an intelligent recognition method based on artificial intelligence algorithm is proposed. We will deeply analyze the application of the core technology of the system and introduce the virtual education function of the application through relevant content. The application of this technology can effectively improve the efficiency of vocal music teaching and enable students to obtain a richer educational experience. Then, this paper finishes the other parts of the system again, realizes the functional modules of the system one by one, and elaborates the corresponding interface diagrams. Finally, a suitable testing method can be used to test the performance modules and performance conditions of the system to ensure that the system has good reliability and availability and put it into production. The voice teaching system based on virtual reality technology is more advanced and superior than other teaching systems on the market today and can provide users with a more realistic experience. In addition, users can have more choices in the process of virtual education, thereby improving the efficiency and quality of education.
Sequential Covariance Intersection Fusion Robust Time-Varying Kalman Filters with Uncertainties of Noise Variances for Advanced Manufacturing

This paper addresses the robust Kalman filtering problem for multisensor time-varying systems with uncertainties of noise variances. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, the robust local time-varying Kalman filters are presented. Further, the batch covariance intersection (BCI) fusion and a fast sequential covariance intersection (SCI) fusion robust time-varying Kalman filters are presented. They have the robustness that the actual filtering error variances or their traces are guaranteed to have a minimal upper bound for all admissible uncertainties of noise variances. Their robustness is proved based on the proposed Lyapunov equations approach. The concepts of the robust and actual accuracies are presented, and the robust accuracy relations are proved. It is also proved that the robust accuracies of the BCI and SCI fusers are higher than that of each local Kalman filter, the robust accuracy of the BCI fuser is higher than that of the SCI fuser, and the actual accuracies of each robust Kalman filter are higher than its robust accuracy for all admissible uncertainties of noise variances. The corresponding steady-state robust local and fused Kalman filters are also presented for multisensor time-invariant systems, and the convergence in a realization between the local and fused time-varying and steady-state Kalman filters is proved by the dynamic error system analysis (DESA) method and dynamic variance error system analysis (DVESA) method. A simulation example is given to verify the robustness and the correctness of the robust accuracy relations.
English Digital Literacy Practices Inside and Outside Class to Develop Students’ Speaking Skills

The development of technology has initiated new teaching systems. In teaching speaking, there are issues about whether the students should learn in the class or outside of the class using technology. This article describes the practice of students' digital English literacy practice at SMAN Kapuas to study this digital practice's pedagogical potential when students interact directly or through social networks such as live streaming on YouTube or Instagram, video calls, or chatting with friends. A mixed-method study using questionnaires and interviews analyzed deeper and richer data. The results show that many sentences were still wrong when spoken in class. However, they can understand speaking lessons based on digital literacy, which they play or watch outside of the class. This implied that students should be instructed in alternative speaking methods. They may enhance their speaking abilities more purposefully, carefully, and often by using suitable language acquisition tools outside of the class.&#x0D;
Using Wordwall to Improve Students’ Engagement in ESL Classroom

This research was conducted to investigate the use of Wordwall to improve students’ engagement in an ESL classroom. The objectives are to identify the impact of using Wordwall on students’ participation in the ESL classroom and to examine the students’ perception of using Wordwall in the ESL classroom. This study was designed based on a mixed-method research by using quantitative and qualitative data. Twenty-four students from a secondary school in Kuala Nerus, Terengganu participated in this study. The instruments used in data collection include video recording transcription, and survey. The findings revealed that incorporating Wordwall into the classroom has a beneficial impact on the students' participation in class. According to the findings of the study, the implementation of a Wordwall can improve students' understanding of the English language learning process as well as students’ engagement in ESL classrooms. Additionally, the study recommends for bigger sampling size could be used.
Sentiment analysis on Bangla conversation using machine learning approach

&lt;span&gt;Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.&lt;/span&gt;