Reviewerly/exHarmony huggingface dataset:
Multicalibrated Regression for Downstream Fairness

We show how to take a regression function $\hat{f}$ that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints. The post-processing requires no labeled data, and only a modest amount of unlabeled data and computation. The computational and sample complexity requirements of computing $\hat f$ are comparable to the requirements for solving a single fair learning task optimally, but it can in fact be used to solve many different downstream fairness-constrained learning problems efficiently. Our post-processing method easily handles intersecting groups, generalizing prior work on post-processing regression functions to satisfy fairness constraints that only applied to disjoint groups. Our work extends recent work showing that multicalibrated regression functions are ``omnipredictors'' (i.e. can be post-processed to optimally solve unconstrained ERM problems) to constrained optimization.
Rethinking Round-Trip Translation for Machine Translation Evaluation

Automatic evaluation on low-resource language translation suffers from a deficiency of parallel corpora. Round-trip translation could be served as a clever and straightforward technique to alleviate the requirement of the parallel evaluation corpus. However, there was an observation of obscure correlations between the evaluation scores by forward and round-trip translations in the era of statistical machine translation (SMT). In this paper, we report the surprising finding that round-trip translation can be used for automatic evaluation without the references. Firstly, our revisit on the round-trip translation in SMT evaluation unveils that its long-standing misunderstanding is essentially caused by copying mechanism. After removing copying mechanism in SMT, round-trip translation scores can appropriately reflect the forward translation performance. Then, we demonstrate the rectification is overdue as round-trip translation could benefit multiple machine translation evaluation tasks. To be more specific, round-trip translation could be used i) to predict corresponding forward translation scores; ii) to improve the performance of the recently advanced quality estimation model; and iii) to identify adversarial competitors in shared tasks via cross-system verification.
3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling

Facial 3D Morphable Models are a main computer vision subject with countless applications and have been highly optimized in the last two decades. The tremendous improvements of deep generative networks have created various possibilities for improving such models and have attracted wide interest. Moreover, the recent advances in neural radiance fields, are revolutionising novel-view synthesis of known scenes. In this work, we present a facial 3D Morphable Model, which exploits both of the above, and can accurately model a subject's identity, pose and expression and render it in arbitrary illumination. This is achieved by utilizing a powerful deep style-based generator to overcome two main weaknesses of neural radiance fields, their rigidity and rendering speed. We introduce a style-based generative network that synthesizes in one pass all and only the required rendering samples of a neural radiance field. We create a vast labelled synthetic dataset of facial renders, and train the network on these data, so that it can accurately model and generalize on facial identity, pose and appearance. Finally, we show that this model can accurately be fit to "in-the-wild" facial images of arbitrary pose and illumination, extract the facial characteristics, and be used to re-render the face in controllable conditions.
A Light Recipe to Train Robust Vision Transformers

In this paper, we ask whether Vision Transformers (ViTs) can serve as an underlying architecture for improving the adversarial robustness of machine learning models against evasion attacks. While earlier works have focused on improving Convolutional Neural Networks, we show that also ViTs are highly suitable for adversarial training to achieve competitive performance. We achieve this objective using a custom adversarial training recipe, discovered using rigorous ablation studies on a subset of the ImageNet dataset. The canonical training recipe for ViTs recommends strong data augmentation, in part to compensate for the lack of vision inductive bias of attention modules, when compared to convolutions. We show that this recipe achieves suboptimal performance when used for adversarial training. In contrast, we find that omitting all heavy data augmentation, and adding some additional bag-of-tricks ($\varepsilon$-warmup and larger weight decay), significantly boosts the performance of robust ViTs. We show that our recipe generalizes to different classes of ViT architectures and large-scale models on full ImageNet-1k. Additionally, investigating the reasons for the robustness of our models, we show that it is easier to generate strong attacks during training when using our recipe and that this leads to better robustness at test time. Finally, we further study one consequence of adversarial training by proposing a way to quantify the semantic nature of adversarial perturbations and highlight its correlation with the robustness of the model. Overall, we recommend that the community should avoid translating the canonical training recipes in ViTs to robust training and rethink common training choices in the context of adversarial training.
Machine Reading, Fast and Slow: When Do Models "Understand" Language?

Two of the most fundamental challenges in Natural Language Understanding (NLU) at present are: (a) how to establish whether deep learning-based models score highly on NLU benchmarks for the 'right' reasons; and (b) to understand what those reasons would even be. We investigate the behavior of reading comprehension models with respect to two linguistic 'skills': coreference resolution and comparison. We propose a definition for the reasoning steps expected from a system that would be 'reading slowly', and compare that with the behavior of five models of the BERT family of various sizes, observed through saliency scores and counterfactual explanations. We find that for comparison (but not coreference) the systems based on larger encoders are more likely to rely on the 'right' information, but even they struggle with generalization, suggesting that they still learn specific lexical patterns rather than the general principles of comparison.
Enabling Touch in E-commerce

Touch is the first sense that develops at the infancy stage. Hence, it is said to be the primary sense among human beings. E-commerce space is expanding at an exponential rate. However, the product listings are done on e-commerce websites and digital platforms satisfy only two senses, i.e. vision and audition. The reliance is only on these two stimuli. The lack of physical inspection has remained a huge barrier in this space. Here, a simple solution has been proposed for the somatosensory receptors for enabling touch in e-commerce.
Pengembangan Media Pembelajaran Berbasis Android Dengan Pemanfaatan Ispring Suite Pada Mata Pelajaran Akidah Akhlak

The purpose of this research is to create an Android-based learning medium using the iSpring Suite, as well as to assess the positive and significant association between the usage of an Android-based Microsoft PowerPoint iSpring Suite as a learning medium. The Research and Development (R&amp;D)approach is used in this investigation. Students from MTsN 3 Jombang participated in the development study trial. The results of the questionnaire on the needs of class VIII students indicate that they have difficulty learning materials that are only sourced from books/LKS and that they require interesting learning media that can be accessed at any time to assist students in learning and as motivation for enthusiasm for learning. The validation results from material experts are 94.20% in the Very Eligible category. The validation results from linguists were 93.75% in the Very Eligible category. The validation results from media experts are 75% in the Eligible category. The average result obtained from the student response questionnaire is 81.02% in the Very Eligible category. The average result of pre-test data is 74.20 and the average result of post-test data is 87.62. The results of the analysis through the t-test with two samples, namely: tcount is greater than ttable, meaning Ha is accepted and Ho is rejected.So, there is a positive and significant effect before using android-based learning media and after using android-based learning media.
Roles of the teacher for increasing learning quality of students

Background: Understanding the role of teachers in improving the quality of student learning and knowing that students even though using e-learning can learn effectively or not and can improve the quality of student learning, even though they do not learn face-to-face or directly but the teacher must be able to know the ability of students to master and understand this learning and students get maximum and effective learning from the teacher because the teacher is an educator. In this education, especially in e-learning, teachers or educators do not provide maximum learning or are less effective in E-learning.&#x0D; Purpose: In order to know the teacher's perspective on improving the quality of student learning in E-learning.&#x0D; Design and methods: This type of research uses the case study method, data sources: documents and interviews using a qualitative approach. The research subjects were 6 elementary school teacher respondents. Data validation used inter-researcher triangulation.&#x0D; Results: the results of this study show that there are various kinds of teacher roles that are very important in improving the quality of student learning, even though e-learning is less effective in improving the quality of student learning.
Penguatan Literasi Digital Dalam Pembelajaran Bahasa Inggris Bagi Guru-Guru TK

Improving teacher competence is very important to support the quality of education. There is still a lack of digital literacy mastery among kindergarten teachers in learning, especially learning English. There are many obstacles faced by teachers in mastering digital literacy such as a lack of knowledge about the features of laptops, and lack of understanding how to operate laptops or smartphones. The other obstacles in learning English in kindergarten is the teachers educational background who are not from the English Education study program. The purpose of this Community Partnership Service Activities is to improve the teacher competence in mastering ICT. The method used is through training, question and answer and direct mentoring. Participants who participated in this activity were kindergarten teachers from cluster 3 of Jayapura city consisting of 12 kindergarten teachers. This activity conducted for two days. At the beginning of the activity, a pre-test was distributed to measure participants' understanding of digital literacy in learning English. Monitoring and evaluation are done by giving post-test directly to participants. The results of this this Community Partnership Service Activities are 82% or 10 teachers from 12 teachers have increased their competence in mastery of digital literacy in learning English in kindergarten.
Pelatihan Penulisan Artikel Bagi Mahasiswa IAI Sumbar

The purpose of this service is to determine the scientific writing ability of students at IAI West Sumatra and the factors that influence it. The lack of student knowledge about article writing and how to publish inspiring articles is done by community service to convey socialization and provide training on writing this article. If students understand the importance of writing articles in the academic world, it is hoped that they will be motivated to write scientific articles and publish scientific articles as contributors to the development of science, and can practice it directly by utilizing journals managed by the campus. Based on the partner's main problem, namely the lack of knowledge of students about writing scientific articles, the solution offered to partners is to provide materials related to writing scientific articles, reference managers, submit and publish articles to increase student understanding in writing scientific articles. The methods used are presentation, questions and answers, and simulations. The target to be achieved in this community service activity is the introduction of article writing for West Sumatra IAI students. The output target of this program is to increase the ability to write articles for IAI West Sumatra students and the ability to publish articles in national journals. The data sources studied were IAI West Sumatra students.
Optimal False Discovery Rate Control for Large Scale Multiple Testing with Auxiliary Information

Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the hypotheses, are available. Exploiting such auxiliary information can boost statistical power. To this end, we propose a framework based on a two-group mixture model with varying probabilities of being null for different hypotheses a priori, where a shape-constrained relationship is imposed between the auxiliary information and the prior probabilities of being null. An optimal rejection rule is designed to maximize the expected number of true positives when average false discovery rate is controlled. Focusing on the ordered structure, we develop a robust EM algorithm to estimate the prior probabilities of being null and the distribution of $p$-values under the alternative hypothesis simultaneously. We show that the proposed method has better power than state-of-the-art competitors while controlling the false discovery rate, both empirically and theoretically. Extensive simulations demonstrate the advantage of the proposed method. Datasets from genome-wide association studies are used to illustrate the new methodology.
Resolution Limits of Non-Adaptive 20 Questions Search for Multiple Targets

We study the problem of simultaneous search for multiple targets over a multidimensional unit cube and derive fundamental resolution limits of non-adaptive querying procedures using the 20 questions estimation framework. The performance criterion that we consider is the achievable resolution, which is defined as the maximal $L_\infty$ norm between the location vector and its estimated version where the maximization is over all target location vectors. The fundamental resolution limit is defined as the minimal achievable resolution of any non-adaptive query procedure, where each query has binary yes/no answers. We drive non-asymptotic and second-order asymptotic bounds on the minimal achievable resolution, using tools from finite blocklength information theory. Specifically, in the achievability part, we relate the 20 questions problem to data transmission over a multiple access channel, use the information spectrum method by Han and borrow results from finite blocklength analysis for random access channel coding. In the converse part, we relate the 20 questions problem to data transmission over a point-to-point channel and adapt finite blocklength converse results for channel coding. Our results extend the purely first-order asymptotic analyses of Kaspi \emph{et al.} (ISIT 2015) for the one-dimensional case: we consider channels beyond the binary symmetric channel and derive non-asymptotic and second-order asymptotic bounds on the performance of optimal non-adaptive query procedures.
Dispersion of digital $(0,m,2)$-nets

We study the dispersion of digital $(0,m,2)$-nets; i.e. the size of the largest axes-parallel box within such point sets. Digital nets are an important class of low-discrepancy point sets. We prove tight lower and upper bounds for certain subclasses of digital nets where the generating matrices are of triangular form and compute the dispersion of special nets such as the Hammersley point set exactly.
Financial Fraud Identification Based on Stacking Ensemble Learning Algorithm: Introducing MD&amp;A Text Information

In recent years, there have been frequent incidents of financial fraud committed through various means. How to more efficiently identify financial fraud and maintain capital market order is a problem that scholars from all walks of life are discussing and urgently seeking to resolve. In this study, a financial fraud identification model is constructed based on the stacking ensemble learning algorithm, and the text of the management discussion and analysis (MD&A) chapter in annual reports is introduced based on financial and nonfinancial variables, using sentiment polarity, emotional tone, and text readability as text variables. The results show that when considering financial and nonfinancial variables and introducing text variables, the recognition effect of the stacking ensemble learning model constructed in this study is significantly better than the classification results of each single classifier model. In addition, the model recognition effect is better after adding text variables. Therefore, the model is expected to provide a new and more effective method of identifying financial fraud.
Blackbox Testing on the ReVAMP Results of The DutaTani Agricultural Information System

Information technology continues to evolve unceasingly. In line with the evolvement, agricultural sciences also transform the sense of technology utilization in its information systems to improve its quality and service. The Government of Indonesia strongly supports the use of information system technology in agriculture. DutaTani research team has consistently developed Agricultural Information System (AIS) technology since 2016 to achieve precision agriculture. These developments must be followed by continuous improvement of information systems carried out sustainably following changes and developments in the technology used. Testing is sorely needed in the system repair phase so that changes or improvements do not cause conflicts or problems in any pre-existing functions. The number of technologies that are tried to be applied in the repair phase tends to cause high system failures when they are tested on users. Based on these problems, this study aims to implement Blackbox testing to increase the system's success rate before general users utilize it. Blackbox testing is considered capable of bridging the development team and random respondents representing general users later. This research also added iterations to increase the success rate of the system. Respondents are invited to use the system through several main scenarios, but they have to fill in the input with variables that they have never filled in before. Through several iterations and following a test scenario created by an independent test team with ten random respondents, this study increased the system's success rate by 11.79%.
A Systematic Literature Review: Critical Success Factor of Collaboration Working Tools

This research discusses the collaboration tool system that can be used for work from home activities. The form of this collaboration tool can be in the form of software or applications. This study aims to collect and analyze data about collaboration tools, as well as find out the usefulness of collaboration tools. This research is using the Systematic Literature Review method because this method is very useful for explaining the topic in detail. This collaboration tool is also flexible because it can be used effectively and efficiently anywhere. Therefore, this research was applied to find out the answers to the effect of the collaboration tools. Based on the analysis that has been done, the result is that this collaboration tool can support work from home activities. This research is also conducted to help workers and individuals to determine suitable collaboration tools to assist them in their task or organization.
An AI Virtual Teaching Assistant with Smart Search and Feedback

An AI Virtual Teaching Assistant with Smart Search and Feedback has been developed and is being evaluated. It supplements the tuition from the teachers with a Q&amp;amp;A system with higher accuracy of question mapping based on elimination and involvement of stop words in a two-step searching algorithm. An effective and efficient method is applied to estimate the level of students, providing feedback on teaching performance.
Case Studies of DRR (Disaster Risk Reduction) Delivery Lessons Using ICT in Elementary School Classroom in Japan – Lessons from COVID-19 to Future Practices

The COVID-19 pandemic made it difficult to conduct the face-to-face contact-based DRR (disaster risk reduction) delivery lesson we had been giving in an elementary school. To overcome this challenge, we developed a video tool and practiced delivery lessons by using ICT in schools. This paper reports on three cases. In study 1, we conducted a delivery lesson by connecting the school broadcasting room to the classroom in a one- way manner. In study 2, we practiced with connecting the laboratory in a university to individual student’s computers. In study 3, we visited the school and carried out the practice in connecting the school computer room with the classroom teacher’s computer in classroom. We summarized the differences among these three practices, as well as their various characteristics, and discussed the future directions of ICT-based DRR delivery lessons. The ICT-based DRR delivery lesson could bring new educational effects that have never been generated by the traditional delivery lessons.
The Zoom Meeting Application for Virtual Classroom under Security Perfective

The pandemic that is happening in this world greatly affects many outdoor activities. Especially in the sector of education, where all activities are carried out online. Almost all educational institutions ranging from elementary schools to lectures use video conferencing applications as a support for learning. Zoom is a web application and an application that is being used. Zoom really helps users to meet fellow users virtually. Even in a relatively young way and there is no time limit when we use a Pro account. however, this Zoom application has several security-related issues as well as audio and video displays. The security problem is a serious problem, therefore this matter is quickly handled by the Zoom. And quickly, even this application is developing very well. Then the problem Display audio and video. Sometimes, while Zooming is running, the sound volume produced is not stable. And also, there are often delays that can disrupt communication during meetings. This is certainly very important to handle because it involves the smooth running of the meeting, which certainly affects the quality of the application itself. We use the qualitative research methods such as Google Form for asking people online.