Reviewerly/exHarmony huggingface dataset:
AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain

During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated. The vocabulary generated based on the pretrained data is suboptimal for downstream data when domain discrepancy exists. We propose to consider the vocabulary as an optimizable parameter, allowing us to update the vocabulary by expanding it with domain-specific vocabulary based on a tokenization statistic. Furthermore, we preserve the embeddings of the added words from overfitting to downstream data by utilizing knowledge learned from a pretrained language model with a regularization term. Our method achieved consistent performance improvements on diverse domains (i.e., biomedical, computer science, news, and reviews).
APLIKASI PENJADWALAN UJIAN SKRIPSI DAN KERJA PRAKTEK

This research is conducted due to the constraints that are often found in organizing the thesis defense and practical work schedules at the departement of informatics Engineering, Faculty of Engineering, State University of Gorontalo. The constraints are in the form of frequent delays in scheduling because the admin has to check the room beforehand, the lecturers’ absence, and the time to prevent conflicts in scheduling the research method is a prototype. The research result is an application in scheduling the thesis defense and practical work, which will help organize the thesis defense and practical work schedules in addition, the admin is also able to easilydetermine both schedules in the Departement of Informatics Engineering, Faculty of Engineering, State University of Gorontalo. Keywords: Scheduling Application, Thesis, practical work
Teacher as a media figure in modern higher education

This study discusses the issues of evaluating a teacher as a media figure in modern higher education within the framework of a competence-based approach to perform this evaluation. The relevance of the subject matter is determined by the importance of media education problems in the development of modern pedagogy and the need to find effective ways to resolve the issues of evaluating the teacher’s role as a media figure considering their place in the system of modern higher education. The prospects for further research on this subject are determined by the importance of the proper development of the media educational space in a modern university, considering the acute need of society for a media-competent teacher, as well as the importance of a proper evaluation of the teacher’s role as a media figure in this process. The practical value of this study lies in the possibility of practical application of its results to develop an objective outlook on the evaluation of the teacher’s role and functions as a media figure in modern higher education.
Frequency Estimation in the Shuffle Model with Almost a Single Message

We present a protocol in the shuffle model of differential privacy (DP) for the \textit{frequency estimation} problem that achieves error $\omega(1)\cdot O(\log n)$, almost matching the central-DP accuracy, with $1+o(1)$ messages per user. This exhibits a sharp transition phenomenon, as there is a lower bound of $\Omega(n^{1/4})$ if each user is allowed to send only one message. Previously, such a result is only known when the domain size $B$ is $o(n)$. For a large domain, we also need an efficient method to identify the \textit{heavy hitters} (i.e., elements that are frequent enough). For this purpose, we design a shuffle-DP protocol that uses $o(1)$ messages per user and can identify all heavy hitters in time polylogarithmic in $B$. Finally, by combining our frequency estimation and the heavy hitter detection protocols, we show how to solve the $B$-dimensional \textit{1-sparse vector summation} problem in the high-dimensional setting $B=\Omega(n)$, achieving the optimal central-DP MSE $\tilde O(n)$ with $1+o(1)$ messages per user. In addition to error and message number, our protocols improve in terms of message size and running time as well. They are also very easy to implement. The experimental results demonstrate order-of-magnitude improvement over prior work.
PENGARUH KUALITAS SUMBER DAYA MANUSIA TERHADAP TINGKAT KRIMINALITAS DI INDONESIA

This study aims to analyze the effect of factor (X1) on human development index, (X2) unemployment, (X3) population density on crime, in Indonesia. This type of research is descriptive and associative. Data type is secondary data. This study uses panel data with 31 provinces in Indonesia using the Fixed Effect Model (FEM) approach. The results of the study show that (1) the human development index has a significant effect on crime. (2) Unemployment does not have a significant effect on crime. (3) Population density has a significant effect on criminality (4) Taken together there is a significant influence between the human development index, unemployment and population density on crime in Indonesia. Therefore, the government and the authorities are expected to disseminate information to the community about the laws and regulations on criminal offenders and the need to improve education as an effort to improve the quality of Indonesian human resources.
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation

Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages. In this work, we propose a self-learning framework that further utilizes unlabeled data of target languages, combined with uncertainty estimation in the process to select high-quality silver labels. Three different uncertainties are adapted and analyzed specifically for the cross lingual transfer: Language Heteroscedastic/Homoscedastic Uncertainty (LEU/LOU), Evidential Uncertainty (EVI). We evaluate our framework with uncertainties on two cross-lingual tasks including Named Entity Recognition (NER) and Natural Language Inference (NLI) covering 40 languages in total, which outperforms the baselines significantly by 10 F1 on average for NER and 2.5 accuracy score for NLI.
Most-Wanted English Learning Mobile Apps

Covid-19 pandemic has contributed a great encouragement for autonomous learners in using mobile learning to learn foreign languages, including English. Geared under qualitative design, the present study aims to find out the availability of mobile apps that most people use in learning English. It concerns to review and make comparisons among the top three most popular apps gathered on the Google Playstore list to investigate what they teach, how they teach, and what technology they use in order to figure out on which aspects they are distinct to each other and on which they are similar, where are they lacking, and what they do well. The results point out that the apps were developed in such easy and fun ways of learning with various materials, ie daily life situations, and stories. Though enriched with vast exercises in receptive skills, they are commonly offered limited practice in productive skills.
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps

Deep neural networks are susceptible to adversarially crafted, small and imperceptible changes in the natural inputs. The most effective defense mechanism against these examples is adversarial training which constructs adversarial examples during training by iterative maximization of loss. The model is then trained to minimize the loss on these constructed examples. This min-max optimization requires more data, larger capacity models, and additional computing resources. It also degrades the standard generalization performance of a model. Can we achieve robustness more efficiently? In this work, we explore this question from the perspective of knowledge transfer. First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation. Second, we propose a novel robustness transfer method called Mixup-Based Activated Channel Maps (MixACM) Transfer. MixACM transfers robustness from a robust teacher to a student by matching activated channel maps generated without expensive adversarial perturbations. Finally, extensive experiments on multiple datasets and different learning scenarios show our method can transfer robustness while also improving generalization on natural images.
Perbedaan Persepsi dan Kemandirian Belajar Siswa SMA Pada Mata Pelajaran Matematika antara Kelas IPS dan IPA di Pembelajaran Daring

This study aims to find differences in the perception and self regulated learning of high school students in mathematics subjects between class X IPA and X IPS in online learning. This study uses a quantitative approach to the type of comparative research. The data collection technique used in this study was a questionnaire on student perceptions and self regulated learning. Data analysis in this study used the Mann Whitney test. The results showed that 1) There was no difference in the perception of high school students in mathematics between class X IPA and class X IPS studies in online learning, but perceptions related to interactions between friends during online learning were more intertwined in social studies class than in science class. 2) There is no difference in the self regulated learning of high school students in mathematics between class X IPA and class X IPS studies in online learning
Variational Adiabatic Gauge Transformation on Real Quantum Hardware for Effective Low-Energy Hamiltonians and Accurate Diagonalization

Effective low-energy theories represent powerful theoretical tools to reduce the complexity in modeling interacting quantum many-particle systems. However, common theoretical methods rely on perturbation theory, which limits their applicability to weak interactions. Here we introduce the Variational Adiabatic Gauge Transformation (VAGT), a nonperturbative hybrid quantum algorithm that can use nowadays quantum computers to learn the variational parameters of the unitary circuit that brings the Hamiltonian to either its block-diagonal or full-diagonal form. If a Hamiltonian can be diagonalized via a shallow quantum circuit, then VAGT can learn the optimal parameters using a polynomial number of runs. The accuracy of VAGT is tested through numerical simulations, as well as simulations on Rigetti and IonQ quantum computers.
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation

News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users' historical news click behavior data, which may lead to privacy concerns and hazards. Federated Learning is a privacy-preserving framework for multiple clients to collaboratively train models without sharing their private data. However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients. In this paper, we propose an efficient federated learning framework for privacy-preserving news recommendation. Instead of training and communicating the whole model, we decompose the news recommendation model into a large news model maintained in the server and a light-weight user model shared on both server and clients, where news representations and user model are communicated between server and clients. More specifically, the clients request the user model and news representations from the server, and send their locally computed gradients to the server for aggregation. The server updates its global user model with the aggregated gradients, and further updates its news model to infer updated news representations. Since the local gradients may contain private information, we propose a secure aggregation method to aggregate gradients in a privacy-preserving way. Experiments on two real-world datasets show that our method can reduce the computation and communication cost on clients while keep promising model performance.
WIKIBIAS: Detecting Multi-Span Subjective Biases in Language

Biases continue to be prevalent in modern text and media, especially subjective bias -a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth.To tackle the problem of detecting and further mitigating subjective bias, we introduce a manually annotated parallel corpus WIKIBIAS with more than 4,000 sentence pairs from Wikipedia edits.This corpus contains annotations towards both sentencelevel bias types and token-level biased segments.We present systematic analyses of our dataset and results achieved by a set of state-ofthe-art baselines in terms of three tasks: bias classification, tagging biased segments, and neutralizing biased text.We find that current models still struggle with detecting multi-span biases despite their reasonable performances, suggesting that our dataset can serve as a useful research benchmark.We also demonstrate that models trained on our dataset can generalize well to multiple domains such as news and political speeches. 1
2nd Place Solution to Facebook AI Image Similarity Challenge Matching Track

This paper presents the 2nd place solution to the Facebook AI Image Similarity Challenge : Matching Track on DrivenData. The solution is based on self-supervised learning, and Vision Transformer(ViT). The main breaktrough comes from concatenating query and reference image to form as one image and asking ViT to directly predict from the image if query image used reference image. The solution scored 0.8291 Micro-average Precision on the private leaderboard.
Information System Of Student Rangking Management Of Faculty Of Engineering In Univercity Of Gorontalo

The present study was heavily motivated by the problems that often occurred in the students’ affairs of the Faculty of Engineering, Universitas Negeri Gorontalo. The process of student ranking in the students’ affair unit of the Faculty of Engineering employs a manual method of data collection of students’ achievements. All files of students’ achievements are photocopied, scanned, and archived into a computer. This method is lacking in a crucial aspect; students with achievements or recognitions cannot input and archive their achievements into the university database by themselves. Therefore, the data of achievements might get scattered and hinder the evaluation process. The study employed a prototype method. The results showed that an information system of student ranking management was developed to assist the compilation process of students’ achievements. By the developed information system, the students are enabled to input the achievements they have into the system by themselves. Keywords :  Management, Ranking, Student Ranking Management Information System, Prototype
EXPOSURE OF RURAL STUDENTS THROUGH ONLINE MEDIA IN INDIA: A REVIEW STUDY

Online media is an enormously popular medium that can result in positive as well as negative effects. The medium which is counted as a gift to mankind is not so common everywhere. The tech-savvy world has reached the stage of 5G connection at some places but on the other side, its proper connectivity is still a concern. Considering the aspect of India where the major chunk of the population lives in villages, the online media has not penetrated deep into every zone. Whereat one point it is counted as a benefit to increase business spread awareness and helps as a medium of study, on the other side it is counted as a means of distraction, a source of addiction and a medium which leads to mental issues. Thus, the present paper is a critical study that provides reviews about the exposure of rural students through online media in India.
Teachers’ professional readiness to implement digital technologies

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
Effectiveness of Pre-training for Few-shot Intent Classification

This paper investigates the effectiveness of pre-training for few-shot intent classification. While existing paradigms commonly further pre-train language models such as BERT on a vast amount of unlabeled corpus, we find it highly effective and efficient to simply fine-tune BERT with a small set of labeled utterances from public datasets. Specifically, fine-tuning BERT with roughly 1,000 labeled data yields a pre-trained model -- IntentBERT, which can easily surpass the performance of existing pre-trained models for few-shot intent classification on novel domains with very different semantics. The high effectiveness of IntentBERT confirms the feasibility and practicality of few-shot intent detection, and its high generalization ability across different domains suggests that intent classification tasks may share a similar underlying structure, which can be efficiently learned from a small set of labeled data. The source code can be found at https://github.com/hdzhang-code/IntentBERT.
노인과 가족원의 실버케어로봇기술에 대한 태도 및 이용의도

Purpose: This study aimed to investigate attitude toward and intention to use the so-called silver-care robot technology in older adults and family members. Methods: This descriptive correlational study recruited 80 older adults and 80 family members who were users of a general hospital in Seoul city. Data collection, conducted from February to March 2019, used the Almere model and self-reported questionnaires that included items on socio-demographic and robot-related characteristics. Results: The mean age of the older adults was 73.48±6.88 years and that of family members was 53.48±10.08 years. No differences were identified in attitude toward and intention to use silver-care robot technology among the older adults and family members. The older adults were more concerned with the trustworthiness (t=2.46, p=.015) of the care robot technology, whereas the family members were more concerned with ease of use (t=-2.97, p=.003). Intention to use was significantly affected by perceived enjoyment in the older adults (25.4%, p=.001) and perceived usefulness (p=.009) and ease of use (p=.002) in the family members (57.1%). Conclusion: The participants showed a positive attitude toward and intention to use silver-care robot technology. Perceived enjoyment, perceived usefulness and ease of use should be considered in the development and application of silver-care robot technology.
Learning to Disentangle Scenes for Person Re-identification

There are many challenging problems in the person re-identification (ReID) task, such as the occlusion and scale variation. Existing works usually tried to solve them by employing a one-branch network. This one-branch network needs to be robust to various challenging problems, which makes this network overburdened. This paper proposes to divide-and-conquer the ReID task. For this purpose, we employ several self-supervision operations to simulate different challenging problems and handle each challenging problem using different networks. Concretely, we use the random erasing operation and propose a novel random scaling operation to generate new images with controllable characteristics. A general multi-branch network, including one master branch and two servant branches, is introduced to handle different scenes. These branches learn collaboratively and achieve different perceptive abilities. In this way, the complex scenes in the ReID task are effectively disentangled, and the burden of each branch is relieved. The results from extensive experiments demonstrate that the proposed method achieves state-of-the-art performances on three ReID benchmarks and two occluded ReID benchmarks. Ablation study also shows that the proposed scheme and operations significantly improve the performance in various scenes.
Revisiting Robust Neural Machine Translation: A Transformer Case Study

Transformers (Vaswani et al., 2017) have brought a remarkable improvement in the performance of neural machine translation (NMT) systems but they could be surprisingly vulnerable to noise. In this work, we try to investigate how noise breaks Transformers and if there exist solutions to deal with such issues. There is a large body of work in the NMT literature on analyzing the behavior of conventional models for the problem of noise but Transformers are relatively understudied in this context. Motivated by this, we introduce a novel data-driven technique called Target Augmented Fine-tuning (TAFT) to incorporate noise during training. This idea is comparable to the well-known fine-tuning strategy. Moreover, we propose two other novel extensions to the original Transformer: Controlled Denoising (CD) and Dual-Channel Decoding (DCD), that modify the neural architecture as well as the training process to handle noise. One important characteristic of our techniques is that they only impact the training phase and do not impose any overhead at inference time. We evaluated our techniques to translate the English--German pair in both directions and observed that our models have a higher tolerance to noise. More specifically, they perform with no deterioration where up to 10% of entire test words are infected by noise.
IMPACTO DA PANDEMIA NAS INCUBADORAS NO ESTADO DO PARANÁ

Incubators play a fundamental role in economic and social generation in the region where they are located.The development of incubators promotes the survival and maturation of small and medium enterprises.These types of companies are essential for generating employment and local income and contribute to our country's GDP.Being an entrepreneur is something difficult and requires a lot of work and study, will is just not enough, incubators help in this development process.2020 was a challenging year for all small and medium businesses that were most affected by the pandemic.The purpose of this article is to present the implications of the sanitary/economic crisis in incubators in the state of Paraná during the year 2020.Identifying which impacts were felt and which alternatives were taken to provide assistance to member companies.This study fits into the following research methods: qualitative, of an applied nature, as to the objectives being exploratory and descriptive, and as to the procedures, a survey was carried out.A questionnaire consisting of 19 questions was used, divided into two parts: the first a description of the profile and characteristics of the incubators, and the second part with the impacts of the pandemic.It concludes the importance of evaluating the impact on activities, services offered and in the financial area, suffered by incubators during this period of pandemic, where significant changes were felt by all segments of the world economy.
What to Pre-Train on? Efficient Intermediate Task Selection

Intermediate task fine-tuning has been shown to culminate in large transfer gains across many NLP tasks. With an abundance of candidate datasets as well as pre-trained language models, it has become infeasible to experiment with all combinations to find the best transfer setting. In this work, we provide a comprehensive comparison of different methods for efficiently identifying beneficial tasks for intermediate transfer learning. We focus on parameter and computationally efficient adapter settings, highlight different data-availability scenarios, and provide expense estimates for each method. We experiment with a diverse set of 42 intermediate and 11 target English classification, multiple choice, question answering, and sequence tagging tasks. Our results demonstrate that efficient embedding based methods, which rely solely on the respective datasets, outperform computational expensive few-shot fine-tuning approaches. Our best methods achieve an average Regret@3 of 1% across all target tasks, demonstrating that we are able to efficiently identify the best datasets for intermediate training.