Reviewerly/exHarmony huggingface dataset:
Current Issues and Improvement Plans in Korean Language Education’s Cultural Experience Class - Focusing on the regular curriculum of university institutions before and after COVID19

세계적인 유행병 시대에 디지털 콘텐츠에 대한 소비 패턴은 전 세계의 여가 문화를 새로운 삶의 형태로 재편했고, 한국 콘텐츠의 확산은 한국 문화를 직접 체험하고 싶은 욕구를 더욱 강화하게 되었다. 한국 문화체험에 대한 수요가 한국어 학습 인구의 증가로 이어져 한국에서 공부하는 사람이 더 많아지기 위해서는 비학위 과정의 문화 수업을 글로벌 평생교육의 맥락에서 재구성해야 하고 그러려면 본 연구와 같이 문화체험 수업 설계를 위한 구조화 원칙이 필요하다. 코로나19 직후 대학부설 한국어교육기관에서는 각 기관의 사정에 따라 대면 문화수업을 이어간 곳도 있지만 대다수는 1년 넘게 언어 교육만 시행하였다. 이에 본 연구에서는 코로나19 이전과 이후의 12개 대학 부설 한국어교육기관을 대상으로 다양한 문화체험 유형을 조사하여 현황을 파악하고 문화체험 교실의 운영 원칙이 있는지를 알아보았다. 이는 향후 어떠한 여건에서도 지속 가능한 문화체험 수업 방안을 모색하기 위함이다. 전국 12개 한국어교육기관의 기관장이나 소속 강사에게 2021년 10월 8일부터 12일까지 5일간 구글 설문지 링크를 보내 조사를 실시하였다. 조사 결과 학습자가 선호하는 현장 체험 방식은 코로나19가 창궐하는 동안 질병관리본부에서 정한 소규모로 진행되었고 온라인 강의를 통한, 중․고급 학습자 대상의 문화지식 수업이 주를 이루었다. 문화체험 교실 특유의 운영 원칙은 합의된 것이 없어 한국어 숙달 단계와 유사하게 모방, 변형, 창의의 단계별 문화체험 과제가 보완될 필요를 확인하였다.In the era of the global pandemic, the consumption pattern of digital content has reorganized leisure culture around the world into a new form of life, and the spread of Korean content has further strengthened the desire to directly experience Korean culture. To increase the number of people studying in Korea, following the increase in the population of Korean language learners, cultural classes in non-degree programs need to be restructured in the context of global lifelong education. For this purpose, structured principles for designing cultural experience classes such as those in this study are necessary. After the COVID-19 outbreak, some Korean language institutions under certain universities continued to offer face-to-face cultural classes depending on their respective situations, but most of them conducted only language education for over a year. In this study, various experience types of 12 Korean Language Institutions before and after COVID-19 were surveyed to understand their situational exigency and to find out the principle of operating cultural experience classes. This aims to explore sustainable approaches for cultural experiential lessons that can be implemented under any challenging circumstances in the future. From October 8th to 12th, 2021, a survey was conducted by sending Google Forms links to the directors or instructors of 12 Korean language education institutions nationwide for a period of 5 days. The survey results showed that the preferred on-site experience for learners was small-scale activities that were conducted during the COVID-19 as designated by the Korea Disease Control and Prevention Agency, and universities provided only online cultural knowledge classes for intermediate to advanced learners. As there are no agreed-upon operating principles for cultural experience classrooms, we confirmed the need to supplement cultural experience tasks at the imitation, transformation, and creative stages, similar to the Korean language proficiency stages.
Histobot: Question Generation System Using Deep Learning Techniques

From the analysis of recent researches of automatic question generation using deep learning techniques, we examined papers between 2022 and early 2023 from the examination of recent research on automatic question production using deep learning techniques. Our study comes after the survey report that broadens the analysis of earlier evaluations of AQG content that surfaced between late 2014 and early 2019. We examined the researched works that were included, looking at things like the (1) framework for question generation and (2) generating method. We discovered that contemporary methods fre- quently rely on generative frameworks that deploy Transformer-based models and GPT-n series, which are more efficient in terms of analysis and perfor- mances. We discovered that question creation has gained popularity recently and has significantly improved the educational field. Yet, it can be challeng- ing to produce automatic questions and create the necessary question patterns, structures, and forms. Our additional research advises testing out more prac- tical, efficient models and strategies for autonomous question generating.
Gambaran pengetahuan dokter gigi di rsgm ladokgi Tni al r.e martadinata dalam ketepatan penulisan odontogram

Background: All medical practitioners and dentists are required to create medical records. Medical record writing must be in accordance with established regulations, because it has a very important role as written evidence in court. Odontogram is a part of the dental medical record which contains information about a person's teeth and is a reliable means of identifying teeth. At present not all dentists and dental nurses in Indonesia have properly recorded odontogram medical records, there is still no uniformity in writing or terminology used in odontograms. Purpose: This study aims to obtain an overview of the level of knowledge of dentists at RSGM LADOKGI TNI AL R.E MARTADINATA in odontograms writing accuracy. Method: Descriptive observational study with cross sectional design conducted through online. The data collected from 47 respondents, all are dentists who working in RSGM LADOKGI TNI AL R.E MARTADINATA. A questionnaire used to measure the dentists’ knowledge, it consists of 15 closed-ended questions. Result: Based on the result of research data calculation, 30 respondents (63,8%) have good knowledge (76%-100%), 12 respondents (25,5%) have sufficient knowledge (56%-75%), and 5 respondents (10,7%) have deficient knowledge (less than 55%). Conclusion: Most of the dentists at RSGM LADOKGI TNI AL R.E MARTADINATA have a good knowledge in odontograms writing accuracy.
Pemindai intraoral 3d pada Ortodonti: tinjauan naratif

Background: Nowadays, 3D intraoral scanners have become an ongoing trend in the development of digital orthodontics. Its utility is to minimize patient discomfort during scanning while maximizing the accuracy of the images and shortening the diagnosis time. Objective: To determine the accuracy of 3D intraoral scanners in orthodontics in cases of crowded teeth. Methods: The study was conducted by narrative review by PRISMA guidelines and using electronic databases, PubMed, Google Scholar, and ScienceDirect which were then processed and reviewed. Results: Four journals were found, which were then presented in the extraction table. Each journal discusses the accuracy of 3D intraoral scanners in orthodontics in cases of crowded teeth using their respective research methods. Conclusion: The 3D intraoral scanner showed no significant difference in the accuracy of the study models in the case of crowded teeth. Clinically, the 3D intraoral scanner is acceptable for diagnosis and treatment planning in dentistry.
RUI: A Web-based Road Updates Information System using Google Maps API

Knowing the current situation on every road in an area is still difficult to anticipate. Commuters, riders, and drivers are still dependent on road situations from a local news agency to be well informed and be updated on possible road updates such as vehicular accidents, government road and bridge projects/construction, and other related road obstructions. To give solutions regarding road updates, a web-based roads update information system has been developed that uses Google Maps API allowing people to view and be notified of the real-time updates of the road situation of a specific area. This paper discusses the main system functionalities, including sub-systems and modules of the system, the research approach and methodology, which is the Agile Model, and its impact on disseminating road information and its status. The project has been evaluated using ISO 25010. Based on the evaluation result, the project has been rated 4.21, signifying an excellent performance based on qualitative description through a Likert scale descriptive interpretation. The project has been running and hosted on the world wide web and is expected to expand its coverage area from its origin country to the rest of the world. Based on the initial findings of the study, the respondents agreed that the developed web system was functional and a massive help to commuters, riders, and people who travel a lot. The system's overall effectiveness and performance were excellent based on the criteria set by ISO/IEC 25010. It is recommended for future development to expand the coverage of the road updates, if possible, including the entire Philippine archipelago for long-drive commuters and drivers to be more updated in terms of road updates. Also, include the use of mobile applications for more user-friendly design and interactions.
Minimum Levels of Interpretability for Artificial Moral Agents

As artificial intelligence (AI) models continue to scale up, they are becoming more capable and integrated into various forms of decision-making systems. For models involved in moral decision-making, also known as artificial moral agents (AMA), interpretability provides a way to trust and understand the agent's internal reasoning mechanisms for effective use and error correction. In this paper, we provide an overview of this rapidly-evolving sub-field of AI interpretability, introduce the concept of the Minimum Level of Interpretability (MLI) and recommend an MLI for various types of agents, to aid their safe deployment in real-world settings.
On the Complexity of Robust Eventual Inequality Testing for C-Finite Functions

We study the computational complexity of a robust version of the problem of testing two univariate C-finite functions for eventual inequality at large times. Specifically, working in the bit-model of real computation, we consider the eventual inequality testing problem for real functions that are specified by homogeneous linear Cauchy problems with arbitrary real coefficients and initial values. In order to assign to this problem a well-defined computational complexity, we develop a natural notion of polynomial-time decidability of subsets of computable metric spaces which extends our recently introduced notion of maximal partial decidability. We show that eventual inequality of C-finite functions is polynomial-time decidable in this sense.
Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training

Deep learning models undergo a significant increase in the number of parameters they possess, leading to the execution of a larger number of operations during inference. This expansion significantly contributes to higher energy consumption and prediction latency. In this work, we propose EAT, a gradient-based algorithm that aims to reduce energy consumption during model training. To this end, we leverage a differentiable approximation of the $\ell_0$ norm, and use it as a sparse penalty over the training loss. Through our experimental analysis conducted on three datasets and two deep neural networks, we demonstrate that our energy-aware training algorithm EAT is able to train networks with a better trade-off between classification performance and energy efficiency.
Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin

Developing effective spoken language processing systems for low-resource languages poses several challenges due to the lack of parallel data and limited resources for fine-tuning models. In this work, we target on improving upon both text classification and translation of Nigerian Pidgin (Naija) by collecting a large-scale parallel English-Pidgin corpus and further propose a framework of cross-lingual adaptive training that includes both continual and task adaptive training so as to adapt a base pre-trained model to low-resource languages. Our studies show that English pre-trained language models serve as a stronger prior than multilingual language models on English-Pidgin tasks with up to 2.38 BLEU improvements; and demonstrate that augmenting orthographic data and using task adaptive training with back-translation can have a significant impact on model performance.
WavePaint: Resource-efficient Token-mixer for Self-supervised Inpainting

Image inpainting, which refers to the synthesis of missing regions in an image, can help restore occluded or degraded areas and also serve as a precursor task for self-supervision. The current state-of-the-art models for image inpainting are computationally heavy as they are based on transformer or CNN backbones that are trained in adversarial or diffusion settings. This paper diverges from vision transformers by using a computationally-efficient WaveMix-based fully convolutional architecture -- WavePaint. It uses a 2D-discrete wavelet transform (DWT) for spatial and multi-resolution token-mixing along with convolutional layers. The proposed model outperforms the current state-of-the-art models for image inpainting on reconstruction quality while also using less than half the parameter count and considerably lower training and evaluation times. Our model even outperforms current GAN-based architectures in CelebA-HQ dataset without using an adversarially trainable discriminator. Our work suggests that neural architectures that are modeled after natural image priors require fewer parameters and computations to achieve generalization comparable to transformers.
Saibot: A Differentially Private Data Search Platform

Recent data search platforms use ML task-based utility measures rather than metadata-based keywords, to search large dataset corpora. Requesters submit a training dataset and these platforms search for augmentations (join or union compatible datasets) that, when used to augment the requester's dataset, most improve model (e.g., linear regression) performance. Although effective, providers that manage personally identifiable data demand differential privacy (DP) guarantees before granting these platforms data access. Unfortunately, making data search differentially private is nontrivial, as a single search can involve training and evaluating datasets hundreds or thousands of times, quickly depleting privacy budgets. We present Saibot, a differentially private data search platform that employs Factorized Privacy Mechanism (FPM), a novel DP mechanism, to calculate sufficient semi-ring statistics for ML over different combinations of datasets. These statistics are privatized once, and can be freely reused for the search. This allows Saibot to scale to arbitrary numbers of datasets and requests, while minimizing the amount that DP noise affects search results. We optimize the sensitivity of FPM for common augmentation operations, and analyze its properties with respect to linear regression. Specifically, we develop an unbiased estimator for many-to-many joins, prove its bounds, and develop an optimization to redistribute DP noise to minimize the impact on the model. Our evaluation on a real-world dataset corpus of 329 datasets demonstrates that Saibot can return augmentations that achieve model accuracy within 50 to 90% of non-private search, while the leading alternative DP mechanisms (TPM, APM, shuffling) are several orders of magnitude worse.
Weighted Anisotropic-Isotropic Total Variation for Poisson Denoising

Poisson noise commonly occurs in images captured by photon-limited imaging systems such as in astronomy and medicine. As the distribution of Poisson noise depends on the pixel intensity value, noise levels vary from pixels to pixels. Hence, denoising a Poisson-corrupted image while preserving important details can be challenging. In this paper, we propose a Poisson denoising model by incorporating the weighted anisotropic-isotropic total variation (AITV) as a regularization. We then develop an alternating direction method of multipliers with a combination of a proximal operator for an efficient implementation. Lastly, numerical experiments demonstrate that our algorithm outperforms other Poisson denoising methods in terms of image quality and computational efficiency.
GenRec: Large Language Model for Generative Recommendation

In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively unexplored. This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. In this paper, we present a novel LLM for generative recommendation (GenRec) that utilized the expressive power of LLM to directly generate the target item to recommend, rather than calculating ranking score for each candidate item one by one as in traditional discriminative recommendation. GenRec uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our proposed approach leverages the vast knowledge encoded in large language models to accomplish recommendation tasks. We first we formulate specialized prompts to enhance the ability of LLM to comprehend recommendation tasks. Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics. Our research underscores the potential of LLM-based generative recommendation in revolutionizing the domain of recommendation systems and offers a foundational framework for future explorations in this field. We conduct extensive experiments on benchmark datasets, and the experiments shows that our GenRec has significant better results on large dataset.
Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification

Prostate Cancer (PCa) is often diagnosed using High-resolution 3.0 Tesla(T) MRI, which has been widely established in clinics. However, there are still many medical centers that use 1.5T MRI units in the actual diagnostic process of PCa. In the past few years, deep learning-based models have been proven to be efficient on the PCa classification task and can be successfully used to support radiologists during the diagnostic process. However, training such models often requires a vast amount of data, and sometimes it is unobtainable in practice. Additionally, multi-source MRIs can pose challenges due to cross-domain distribution differences. In this paper, we have presented a novel approach for unpaired image-to-image translation of prostate mp-MRI for classifying clinically significant PCa, to be applied in data-constrained settings. First, we introduce domain transfer, a novel pipeline to translate unpaired 3.0T multi-parametric prostate MRIs to 1.5T, to increase the number of training data. Second, we estimate the uncertainty of our models through an evidential deep learning approach; and leverage the dataset filtering technique during the training process. Furthermore, we introduce a simple, yet efficient Evidential Focal Loss that incorporates the focal loss with evidential uncertainty to train our model. Our experiments demonstrate that the proposed method significantly improves the Area Under ROC Curve (AUC) by over 20% compared to the previous work (98.4% vs. 76.2%). We envision that providing prediction uncertainty to radiologists may help them focus more on uncertain cases and thus expedite the diagnostic process effectively. Our code is available at https://github.com/med-i-lab/DT_UE_PCa
Towards Unbiased Exploration in Partial Label Learning

We consider learning a probabilistic classifier from partially-labelled supervision (inputs denoted with multiple possibilities) using standard neural architectures with a softmax as the final layer. We identify a bias phenomenon that can arise from the softmax layer in even simple architectures that prevents proper exploration of alternative options, making the dynamics of gradient descent overly sensitive to initialisation. We introduce a novel loss function that allows for unbiased exploration within the space of alternative outputs. We give a theoretical justification for our loss function, and provide an extensive evaluation of its impact on synthetic data, on standard partially labelled benchmarks and on a contributed novel benchmark related to an existing rule learning challenge.
Generalized NOMP for Line Spectrum Estimation and Detection from Coarsely Quantized Samples

As radar systems will be equipped with thousands of antenna elements and wide bandwidth, the associated costs and power consumption become exceedingly high, primarily attributed to the high-precision (e.g., 10-12 bits) analog-to-digital converters (ADCs). To address this challenge, a potential solution is to adopt low-resolution quantization technology, which not only reduces data storage needs but also lowers power and hardware costs. In this context, the focus is on studying line spectral estimation and detection (LSE\&D) with few-bit ADCs, typically using 1-4 bits. This paper investigates the signal-to-noise ratio (SNR) loss, establishing a framework to understand the impact of intersinusoidal interference, the bit-depth of the quantizer, and the noise variance on weak signal detection in scenarios involving multiple sinusoids under low-resolution quantization. Additionally, a low-complexity, super-resolution, and constant false alarm rate (CFAR) algorithm, named generalized Newtonized orthogonal matching pursuit (GNOMP), is proposed. Extensive numerical simulations are conducted to validate the theoretical findings, particularly in terms of the detection probability bound. The effectiveness of GNOMP is demonstrated through comparisons with state-of-the-art algorithms, the Cram\'{e}r Rao bound, and the detection probability bound. Real data acquired by mmWave radar further substantiates the effectiveness of GNOMP in practical applications.
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory

Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learning (FL), where data is decentralized and subject to independent changes for each user. Continual Learning (CL) studies this so-called \textit{catastrophic forgetting} phenomenon primarily in centralized settings, where the learner has direct access to the complete training dataset. However, applying CL techniques to FL is not straightforward due to privacy concerns and resource limitations. This paper presents a framework for federated class incremental learning that utilizes a generative model to synthesize samples from past distributions instead of storing part of past data. Then, clients can leverage the generative model to mitigate catastrophic forgetting locally. The generative model is trained on the server using data-free methods at the end of each task without requesting data from clients. Therefore, it reduces the risk of data leakage as opposed to training it on the client's private data. We demonstrate significant improvements for the CIFAR-100 dataset compared to existing baselines.