Reviewerly/exHarmony huggingface dataset:
Few-Shot Class-Incremental Learning from an Open-Set Perspective

The continual appearance of new objects in the visual world poses considerable challenges for current deep learning methods in real-world deployments. The challenge of new task learning is often exacerbated by the scarcity of data for the new categories due to rarity or cost. Here we explore the important task of Few-Shot Class-Incremental Learning (FSCIL) and its extreme data scarcity condition of one-shot. An ideal FSCIL model needs to perform well on all classes, regardless of their presentation order or paucity of data. It also needs to be robust to open-set real-world conditions and be easily adapted to the new tasks that always arise in the field. In this paper, we first reevaluate the current task setting and propose a more comprehensive and practical setting for the FSCIL task. Then, inspired by the similarity of the goals for FSCIL and modern face recognition systems, we propose our method -- Augmented Angular Loss Incremental Classification or ALICE. In ALICE, instead of the commonly used cross-entropy loss, we propose to use the angular penalty loss to obtain well-clustered features. As the obtained features not only need to be compactly clustered but also diverse enough to maintain generalization for future incremental classes, we further discuss how class augmentation, data augmentation, and data balancing affect classification performance. Experiments on benchmark datasets, including CIFAR100, miniImageNet, and CUB200, demonstrate the improved performance of ALICE over the state-of-the-art FSCIL methods.
Learning Shadow Correspondence for Video Shadow Detection

Video shadow detection aims to generate consistent shadow predictions among video frames. However, the current approaches suffer from inconsistent shadow predictions across frames, especially when the illumination and background textures change in a video. We make an observation that the inconsistent predictions are caused by the shadow feature inconsistency, i.e., the features of the same shadow regions show dissimilar proprieties among the nearby frames.In this paper, we present a novel Shadow-Consistent Correspondence method (SC-Cor) to enhance pixel-wise similarity of the specific shadow regions across frames for video shadow detection. Our proposed SC-Cor has three main advantages. Firstly, without requiring the dense pixel-to-pixel correspondence labels, SC-Cor can learn the pixel-wise correspondence across frames in a weakly-supervised manner. Secondly, SC-Cor considers intra-shadow separability, which is robust to the variant textures and illuminations in videos. Finally, SC-Cor is a plug-and-play module that can be easily integrated into existing shadow detectors with no extra computational cost. We further design a new evaluation metric to evaluate the temporal stability of the video shadow detection results. Experimental results show that SC-Cor outperforms the prior state-of-the-art method, by 6.51% on IoU and 3.35% on the newly introduced temporal stability metric.
Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking

In this study, we have tailored a pixel tracking method for temporal extrapolation of the ventricular segmentation masks in cardiac magnetic resonance images. The pixel tracking process starts from the end-diastolic frame of the heart cycle using the available manually segmented images to predict the end-systolic segmentation mask. The superpixels approach is used to divide the raw images into smaller cells and in each time frame, new labels are assigned to the image cells which leads to tracking the movement of the heart wall elements through different frames. The tracked masks at the end of systole are compared with the already available manually segmented masks and dice scores are found to be between 0.81 to 0.84. Considering the fact that the proposed method does not necessarily require a training dataset, it could be an attractive alternative approach to deep learning segmentation methods in scenarios where training data are limited.
Tackling Neural Architecture Search With Quality Diversity Optimization

Neural architecture search (NAS) has been studied extensively and has grown to become a research field with substantial impact. While classical single-objective NAS searches for the architecture with the best performance, multi-objective NAS considers multiple objectives that should be optimized simultaneously, e.g., minimizing resource usage along the validation error. Although considerable progress has been made in the field of multi-objective NAS, we argue that there is some discrepancy between the actual optimization problem of practical interest and the optimization problem that multi-objective NAS tries to solve. We resolve this discrepancy by formulating the multi-objective NAS problem as a quality diversity optimization (QDO) problem and introduce three quality diversity NAS optimizers (two of them belonging to the group of multifidelity optimizers), which search for high-performing yet diverse architectures that are optimal for application-specific niches, e.g., hardware constraints. By comparing these optimizers to their multi-objective counterparts, we demonstrate that quality diversity NAS in general outperforms multi-objective NAS with respect to quality of solutions and efficiency. We further show how applications and future NAS research can thrive on QDO.
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond

Masked autoencoders are scalable vision learners, as the title of MAE \cite{he2022masked}, which suggests that self-supervised learning (SSL) in vision might undertake a similar trajectory as in NLP. Specifically, generative pretext tasks with the masked prediction (e.g., BERT) have become a de facto standard SSL practice in NLP. By contrast, early attempts at generative methods in vision have been buried by their discriminative counterparts (like contrastive learning); however, the success of mask image modeling has revived the masking autoencoder (often termed denoising autoencoder in the past). As a milestone to bridge the gap with BERT in NLP, masked autoencoder has attracted unprecedented attention for SSL in vision and beyond. This work conducts a comprehensive survey of masked autoencoders to shed insight on a promising direction of SSL. As the first to review SSL with masked autoencoders, this work focuses on its application in vision by discussing its historical developments, recent progress, and implications for diverse applications.
An Actor Critic Method for Free Terminal Time Optimal Control

Optimal control problems with free terminal time present many challenges including nonsmooth and discontinuous control laws, irregular value functions, many local optima, and the curse of dimensionality. To overcome these issues, we propose an adaptation of the model-based actor-critic paradigm from the field of Reinforcement Learning via an exponential transformation to learn an approximate feedback control and value function pair. We demonstrate the algorithm's effectiveness on prototypical examples featuring each of the main pathological issues present in problems of this type.
Batching Circuits to Reduce Compilation in Quantum Control Hardware

At Sandia National Laboratories, QSCOUT (the Quantum Scientific Computing Open User Testbed) is an ion-trap based quantum computer built for the purpose of allowing users low-level access to quantum hardware. Commands are executed on the hardware using Jaqal (Just Another Quantum Assembly Language), a programming language designed in-house to support the unique capabilities of QSCOUT. In this work, we describe a batching implementation of our custom software that speeds the experimental run-time through the reduction of communication and upload times. Reducing the code upload time during experimental runs improves system performance by mitigating the effects of drift. We demonstrate this implementation through a set of quantum chemistry experiments using a variational quantum eigensolver (VQE). While developed specifically for this testbed, this idea finds application across many similar experimental platforms that seek greater hardware control or reduced overhead.
The mapping of TOEFL scores among EFL students in Aceh

The purpose of this study was to describe the proficiency level of English for Foreign Language (EFL) students based on their TOEFL scores. The samples for this descriptive quantitative study consisted of 30 English Education Department students from Syiah Kuala University and Ar-Raniry State Islamic University. The scores gathered from the organised TOEFL test were analysed according to the CEFR reference levels to categorise the English proficiency level of the students. The total TOEFL scores gathered showed that, on average, the students placed in the B1 (intermediate) level of English proficiency. Individually, there were four students in the A2 (elementary) level, 15 students in the B1 (intermediate) level, ten students in the B2 (upper intermediate) level, and one student in the C1 (advanced) level of proficiency. Regarding the skills proficiency, on average, the students achieved the B2 (upper intermediate) level in the first section, which was higher than the remaining two sections that averaged on the B1 (intermediate) level. The students’ proficiency levels found in this study showed that, on average, they were able to pass the universities’ TOEFL graduation requirement but still fall short for abroad scholarship admission.
Research on the second-grade students' reading comprehension ability through fix-up options strategy implementation

Problems in reading comprehension are usually identified when the information obtained is incongruent with the reading text. Consequently, a reading strategy is made to support the comprehension ability. This study was aimed to determine the impact of reading comprehension ability on students of SMP Pos Keadilan Peduli Ummah (Henceforth called PKPU) and their responses to the implementation of the fix-up options strategy. It focused on improving reading comprehension by identifying the main idea, summarizing, making inferences, and interpreting vocabulary. A mixed-explanatory research design was used as the method of the study. Forty-four students of PKPU junior high school's second grade were involved as samples. They were split into two groups: experimental and control. The reading test was used, with data analyzed using SPSS 20 series for quantitative analysis and questionnaires analyzed using interactive analysis for qualitative analysis. The findings showed a significant improvement in students' reading comprehension. The dominant aspects of improvement were making inferences and interpreting vocabulary. The students' responses were positive to applying fix-up strategies to improve their reading comprehension. Thus, using the fix-up options strategy was found to help students improve their reading comprehension ability.
A pragmatic analysis of politeness strategies in Mulan movie

Pragmatics deals with language use in the context that relates to the speech acts' meaning. Politeness strategies and politeness principles, as the manifestation of pragmatics, are two determining communication factors of both speaker and hearer. This qualitative study was intended to analyze the politeness strategies used by the main character Mulan and her family. The data were collected using the observation checklist adapted from Brown and Levinson (1987) framework for politeness strategies. The findings showed that all of Brown and Levinson’s strategies were found in Mulan’s and the other characters’ utterances in the movie. The most frequently used strategy was positive politeness, which was found to occur four times in the movie. Bald-on record and negative politeness were the second most used strategies in the movie with three occurrences each. While off-record and don’t do FTA occurred twice and once, respectively. The result of the pragmatic analysis showed that the use of politeness strategies in Mulan movie was influenced and determined by the characters’ cultural background.
The Use of Microsoft Sway as A Learning Medium for Teaching Reading at 8th Grade Junior High School

Nowadays, education and the learning process are changing rapidly along with the development of technology used as a learning media. There are many digital-based media platforms that can be easily accessed and used as learning media to support the smooth learning process. One of the digital-based media platforms used is Microsoft Sway. This digital-based media platform is part of the Microsoft Office Family of Products managed by Microsoft Corporation. Microsoft Sway is a Microsoft Office application that you can access through Microsoft Office 365. It makes interactive essays, reflections, projects, and presentations simple to create and share. The purpose of this research is to examine how the Microsoft Sway application is used, especially in teaching reading at the 8 grade of junior high school level. The method used in this study was qualitative research. Collection technique the data used in this study namely interviews, observations and documentation with research subjects, there are teacher and student of SMPN 7 Penukal. The results show that Microsoft Sway is suitable for use as a learning medium for teaching reading in 8th grade of junior high school.
Code-mixing Analysis on Narasi News Room with Cinta Laura Kiehl

This research aimed to analyze the details of types, factors, and gestures that emerge through the utilization of code-mixing by Cinta Laura Kiehl in the Narasi Newsroom which talks about animal harassment in Indonesia. This is quite an attractive study because the subject of the research has mastered multi-language skills with an intercultural background. In addition, code-mixing is becoming a present-day lifestyle of language utilization in formal and informal cases. This is descriptive qualitative research that analyzed by code-mixing approach in sociolinguistics theory through the Cinta Laura’s answers utterance. The primary data were gathered by conducting video observation on YouTube and doing note-taking of the manuscript which consists of code-mixing. Moreover, the secondary data was collected from several books and journals that have been cited. This research found two code-mixing types commonly utilized; insertion type and congruent-lexicalization. Then, family, educational background, environments, and multi-language skills are measured as the main sociolinguistics factors that affect code-mixing. Moreover, during the show, there is co-gesture analyzed consciously emphasis while the subject said the different language to strengthen addressee comprehension.
Analyzing Students' Perception about the Use of Duolingo Application Towards Vocabulary Mastery at UIN Prof K.H Saifuddin Zuhri Purwokerto

In this 4.0 industrial revolution, technology has many important roles in many aspects of life, such as in language education. If the students want mastering the four skills of English, the students have to master the English vocabulary. Unluckily, there are so many students who lack of vocabulary. It could be happened because the teacher used uninteresting media in teaching vocabulary. Now, the technology can solve that problem in order to help the students in mastering English vocabulary. One of the technologies that can help in the students to enrich the vocabulary is Duolingo Application. This research aim is to investigating the students’ of UIN Prof. K.H Saifuddin Zuhri Purwokerto perception toward the use of Duolingo application to enrich the students’ vocabulary. This research is using quantitative method. The data were collected using questionnaire that distributed to 30 students at UIN Prof. K.H Saifuddin Zuhri Purwokerto. In collecting the data, the researchers used 10 question in questionnaire, which were used to collect the data. Based on the data, the results shows that most of the students have good perception towards Duolingo application and they believe that this application can enrich their English vocabulary. So, it can be one of the effective media to enrich the students’ vocabulary mastery.
Systematic Literature Review: The Effectiveness of Quizizz Application as Tool for Reading Assessment in Senior High School

In this generation reading book becoming a bored activity that they have to do to get more information. A reading assessment with long text make them bored and not enjoy the reading proses. So that, reading assessment is not interesting for students in this generation. Information Communication Technology (ICT) is the answer of this teaching problem. ICT is about the technology that use to help the communication problem in teaching. One of the application in ICT is Quizizz. Quizizz is application to make quiz. That are so many quiz we could find and give it to out students. You may make your own quiz or modify the quiz. So, in this research it will investigate how Quizizz would help the reading assessment becoming more interesting and enjoyable for the students. The object of this research is the students of Senior High School. This research used Systematically Literature Review (SLR) as a method to investigate the lates articles that have the similar problem and to answer the problem of this research. Along with the research have found 24 articles with similar problem that is the effectiveness of Quizizz for reading assessment. Most of that article shows that Quizizz is effective for reading assessment especially for formative reading assessment. This research also using the criteria or CALL to analysis the effectiveness of Quizizz as a tool of reading assessment in Senior High School Students.
The Role of Google Translate as a Learning Medium for EFL Students: Systematic Review

The use of Google Translate (GT) in this present time is already well known as an application that helps the learning and teaching activity especially for EFL students. GT provides so many features that help the learners to find new vocabulary, pronunciation checker, spelling checker, and provides translation. GT also gives a free access for anyone to use it. Therefore, the study is aimed to analyze the role of Google Translate as a medium in EFL learning activity. This research was conducted by using Systematic Literature Review (SLR) method. Then, the data are collected from the database that used three keywords, such as Google Translate, EFL, and learning activity in total of 549 journal article. Based on the obtained data, there are 14 article journals that related to the topic specifically. The articles that mention the positive effect of using GT is 9 positive, 3 for neutral, and 2 negative. The positive effect of using Google Translate in EFL learning are improving writing ability, enriching vocabulary, translating word and effective to help in teaching and learning activities. In the other hand, there are some negative effects for student, those are being lazy of trying to translate by themselves and sometimes use GT for cheating in doing the assignment or examination because they rely too much in Google translate.
Introducing English vocabulary to young learners with flashcards

English has become the most commonly spoken language in the modern era. English should be taught in every primary school because it is widely used. The most important skill to master when studying the English language is vocabulary. Hence, before learning any language skill, such as reading, writing, or listening, students must first learn vocabulary. Flashcards are a visually appealing medium that can help students understand vocabulary. This study aimed to improve the vocabulary of sixth-grade pupils at an elementary school in Lam Awee, Aceh Besar, using flashcards. The sample was taken from the sixth grade which consisted of 6 students. The data was collected using a vocabulary matching test consisting of questions covering parts of body, family, color, and fruit. The data were analyzed by testing the hypothesis at the significance level of 0.05. The results showed that the student vocabulary score improved significantly after learning the language using flashcards. Therefore, it is recommended for teachers to use flashcards to provide a basic vocabulary to beginners because it helps boost students’ motivation to learn new English vocabulary.
Stera—A College Enquiry Chatbot

A chatbot is an application that simulates human conversation. It is an artificial intelligence (AI) feature which could be deployed across verticals such as business, education, and medical. Chatbots are virtual assistant which can assist the users on how to do something. Students in schools and colleges expect instant answers to questions or problems. Nowadays, classes, exams, and graduation are conducted online, and it is essential to have chatbots in college/university that could solve issues during the admission process. Students need not come to college nor searching for expertise to answer their queries. They can just have a conversation with chatbot and get necessary information. Chatbots can give immediate response for queries asked by students and it is accessible 24/7. Moreover, students may find difficulties in choosing the right course with their previous education. Therefore, in this paper, we present the chatbot—Stera—that is designed to provide information about hostels, college campus, career guidance training, courses, GPA for particular semester, and much more. Students might have lot of queries regarding courses and college activities, and without a chatbot, college/university cannot give instant reply to each and every student. The main drive of creating an AI-powered Stera chatbot for college/university is to provide the needed information for students without delay in response.
The Using of Bilingual to Write A Visual Identity of Product Labels EFL Economic Students

This study aims to examine the use of bilinguals (local language and English) in writing visual identities on SME product labels for students of the Faculty of Economics. The research problems were; 1) how does the perception of students relates to bilingual teaching materials; and 2) how does the effect of bilingual teaching materials on a visual identity of product labels to enhance students’ English writing? The study was a quantitative descriptive study with pre-test and post-test designs. The data were analyzed using the Statistical Package for Social Sciences (SPSS) 26.0, which was followed by qualitative interpretation. Data from the questionnaire deployed numerical calculation of the percentage, while data from the written test were presented quantitatively. Findings of the study revealed that students' perceptions of bilingual have very good criteria to improve students' writing skills (Indicator-A: SA = 40% + A = 50% = 90%; Indicator-B: SA = 40% + A = 50% = 90%, Indicator-C: SA = 60% + A = 40% = 100%, and Indicator-D: SA = 50% + A = 50% = 100%), and have a significant effect on students’ English writing ability (sig. (2-tailed) 0.000 &lt;0.05, and the t-count value of 7.726&gt; t-table 2.09302).