Reviewerly/exHarmony huggingface dataset:
Pengembangan Buku Pengayaan Pada Materi Tembang Dolanan Bagi Siswa Kelas III SD

In learning at school, sometimes teachers find it difficult to explain and teach the tembang dolanan due to the lack of teaching materials that explain details about it. The purpose of this study was to determine the teacher's needs for the development of enrichment books, compile a prototype for the development of enrichment books, and describe the results of the expert validation test on the development of enrichment books. This research was conducted using an R&amp;D approach from Borg &amp; Gall. The target sources data are students, teachers, media experts, and material experts. Data collection techniques are interview techniques and filling out questionnaires. The results of this study include (1) the analysis of teacher needs for the development of enrichment books on the material (tembang dolanan), that teachers want interesting complementary teaching materials for learning tembang dolanan for third-grade elementary school students. (2) the development of the prototype includes a book cover designed with children's pictures, while the contents of the book consist of ten lists of dolanan songs and their meanings. (3) The results of the expert validation test got several improvements from the validator, namely, adding page numbers, changing the title font of the dolanan song, correcting wrong diction, correcting wrong lyrics, and replacing some dolanan songs that are not in accordance with learning.
Pagelaran Wayang Sebagai Upaya Peningkatan Menulis Cerita Melalui Metode Sugesti Imajinasi

This study aims to determine the improvement of the results of writing stories through the method of imagination suggestion in class IXA students of SMP Negeri 27 Semarang in semester 1 and to increase students' love for writing stories through the method of imagination suggestion in class IXA students of SMP Negeri 27 Semarang. This type of research is classroom action research (CAR) which consists of pre-cycle, cycle I, and cycle II. The average pre-cycle score reached 62.93 so it was categorized as incomplete and students' completeness was only 37%. The average value of my cycle reached 76.40 so it was completed completely and students' completeness was 97%. In the second cycle, the average value of the class was 82.06% with the category of attention and students who achieved 100% completeness. In the pre-cycle students are given the freedom of the theme of the wayang story. In cycle I the story is titled Bedhah Alengka. In cycle II, the story of Anoman Duta is from the famous dhalang Ki Bayu Aji Prasetya. From the results of the non-test, it was found that most of the students were happy and enthusiastic watching the wayang kulit performance as a medium with the method of imagination suggestion. This method can also help students' difficulties in writing wayang stories.
LEKSIKOGRAFI BERGAMBAR INDONESIA-GAYO-INGGRIS-ARAB: KONSEP DASAR, FUNGSI, JENIS, DAN ISI KAMUS

The existence of local and vernacular languages in Indonesian of more than six hundreds needs to be defend. It can be done by fostering high loyalty to keep using the language, especially the mother tongue (vernacular) amid the strike of more popular languages. The phenomena of language extinction in the future, such as the decrease in the number of active speakers, the reduction of certain language use, and the ignorance to the mother tongue have become a big problem nowadays. One solution in language preservation is through lexicography or ilm al-shina'ah al-mu'jamiyah or dirasah mu'jamiyah. Lexicography is the art and technique of compiling a dictionary by selecting words to uptake (dakhil), defining word meanings, discussing the completeness of the dictionary components, and giving other information to provide a correct and easy-to-understand meaning of vocabulary. Compiling a dictionary is one of efforts in maintaining the existence of both Indonesian and vernacular language, like Gayo language. Aside from that, arranging Indonesian-Gayo-English-Arabic lexicography, will also help position Indonesian language among global languages. In addition, more Indonesian people may also be able to master second or foreign languages as a vehicle for learning science and modern technology.
Development of Educational Games as Learning Media for English Learning for Primary Students

Industrial era 4.0 brought many changes and innovations in the educational field. Technology is one of the things that is highly developed in the 4.0 era, which has a significant effect on the teaching and learning process. Teachers are required to be able to take advantage of this technological development to be involved in the learning process. As well as in teaching English to the young learner in this 21st century learning, the teacher must be able to create a fun learning environment through the help of learning media, which games. This study aims to develop an educational game as a learning media for English learning for primary students as well as the validity of the product. The Design and Development (DD) and ADDIE model, which consist of five stages, are chosen in this study. The validation from the expert uses three experts, and for the user evaluation, this study uses five students as the respondent. The result showed that in expert judgment validation, the media got 89%, and for the user evaluation, the media got 90%. Based on the result, the educational game for 6th-grade primary school students is feasible to be used in English learning as learning media.
Usability Evaluation for Mobile-Based Nutritional Food Recommender System

COVID-19 was designated as a global pandemic by WHO and Government Regulation of the Republic of Indonesia Number 21 of 2020 concerning Large-Scale Social Restrictions for the handling of Corona Virus Disease 2019 (COVID-19). Eating healthy foods with balanced nutrition can increase the body's immunity during a COVID-19 pandemic. Since many foods that sold freely are not guaranteed in nutritional ingredients, cooking becomes a better alternative. system is a mobile application that provides a recommendation of healthy food recipe by using the Simple Additive Weighting (SAW) algorithm, which has been studied in the previous research of recipe recommendation and gives significant result. Previous research has not examined the appropriate variables and the data are still general food. We conducted a user study to measure the usability of the proposed system. The result of usability testing shows that there is a significant increase in SUS score from 63.5 to 87.
Guiding Students to Investigate What Google Speech Recognition Knows about Language

Today, children of all ages interact with speech recognition systems but are largely unaware of how they work. Teaching K-12 students to investigate how these systems employ phonological, syntactic, semantic, and cultural knowledge to resolve ambiguities in the audio signal can provide them a window on complex AI decision-making and also help them appreciate the richness and complexity of human language. We describe a browser-based tool for exploring the Google Web Speech API and a series of experiments students can engage in to measure what the service knows about language and the types of biases it exhibits. Middle school students taking an introductory AI elective were able to use the tool to explore Google’s knowledge of homophones and its ability to exploit context to disambiguate them. Older students could potentially conduct more comprehensive investigations, which we lay out here. This approach to investigating the power and limitations of speech technology through carefully designed experiments can also be applied to other AI application areas, such as face detection, object recognition, machine translation, or question answering.
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract)

Intentionally crafted adversarial samples have effectively exploited weaknesses in deep neural networks. A standard method in adversarial robustness assumes a framework to defend against samples crafted by minimally perturbing a sample such that its corresponding model output changes. These sensitivity attacks exploit the model's sensitivity toward task-irrelevant features. Another form of adversarial sample can be crafted via invariance attacks, which exploit the model underestimating the importance of relevant features. Previous literature has indicated a tradeoff in defending against both attack types within a strictly L-p bounded defense. To promote robustness toward both types of attacks beyond Euclidean distance metrics, we use metric learning to frame adversarial regularization as an optimal transport problem. Our preliminary results indicate that regularizing over invariant perturbations in our framework improves both invariant and sensitivity defense.
Shared Tasks as Tutorials: A Methodical Approach

In this paper, we discuss the benefits and challenges of shared tasks as a teaching method. A shared task is a scientific event and a friendly competition to solve a research problem, the task. In terms of linking research and teaching, shared-task-based tutorials fulfill several faculty desires: they leverage students' interdisciplinary and heterogeneous skills, foster teamwork, and engage them in creative work that has the potential to produce original research contributions. Based on ten information retrieval (IR) courses at two universities since 2019 with shared tasks as tutorials, we derive a domain-neutral process model to capture the respective tutorial structure. Meanwhile, our teaching method has been adopted by other universities in IR courses, but also in other areas of AI such as natural language processing and robotics.
Stability-Based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning

Recently, some mixture algorithms of pointwise and pairwise learning (PPL) have been formulated by employing the hybrid error metric of “pointwise loss + pairwise loss” and have shown empirical effectiveness on feature selection, ranking and recommendation tasks. However, to the best of our knowledge, the learning theory foundation of PPL has not been touched in the existing works. In this paper, we try to fill this theoretical gap by investigating the generalization properties of PPL. After extending the definitions of algorithmic stability to the PPL setting, we establish the high-probability generalization bounds for uniformly stable PPL algorithms. Moreover, explicit convergence rates of stochastic gradient descent (SGD) and regularized risk minimization (RRM) for PPL are stated by developing the stability analysis technique of pairwise learning. In addition, the refined generalization bounds of PPL are obtained by replacing uniform stability with on-average stability.
Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks

A lot of theoretical and empirical evidence shows that the flatter local minima tend to improve generalization. Adversarial Weight Perturbation (AWP) is an emerging technique to efficiently and effectively find such minima. In AMP we minimize the loss w.r.t. a bounded worst-case perturbation of the model parameters thereby favoring local minima with a small loss in a neighborhood around them. The benefits of AWP, and more generally the connections between flatness and generalization, have been extensively studied for i.i.d. data such as images. In this paper, we extensively study this phenomenon for graph data. Along the way, we first derive a generalization bound for non-i.i.d. node classification tasks. Then we identify a vanishing-gradient issue with all existing formulations of AWP and we propose a new Weighted Truncated AWP (WT-AWP) to alleviate this issue. We show that regularizing graph neural networks with WT-AWP consistently improves both natural and robust generalization across many different graph learning tasks and models.
Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images (Student Abstract)

The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inversion of real images and determination of controllable latent directions are computationally expensive operations. Moreover, the determination of controllable latent directions requires additional human supervision. This work aims to explore the efficacy of mask-guided feature modulation in the latent space of a Deep Generative Model as a solution to these bottlenecks. To this end, we present the SemanticStyle Autoencoder (SSAE), a deep Generative Autoencoder model that leverages semantic mask-guided latent space manipulation for highly localized photorealistic style editing of real images. We present qualitative and quantitative results for the same and their analysis. This work shall serve as a guiding primer for future work.
Simultaneously Updating All Persistence Values in Reinforcement Learning

In Reinforcement Learning, the performance of learning agents is highly sensitive to the choice of time discretization. Agents acting at high frequencies have the best control opportunities, along with some drawbacks, such as possible inefficient exploration and vanishing of the action advantages. The repetition of the actions, i.e., action persistence, comes into help, as it allows the agent to visit wider regions of the state space and improve the estimation of the action effects. In this work, we derive a novel operator, the All-Persistence Bellman Operator, which allows an effective use of both the low-persistence experience, by decomposition into sub-transition, and the high-persistence experience, thanks to the introduction of a suitable bootstrap procedure. In this way, we employ transitions collected at any time scale to update simultaneously the action values of the considered persistence set. We prove the contraction property of the All-Persistence Bellman Operator and, based on it, we extend classic Q-learning and DQN. After providing a study on the effects of persistence, we experimentally evaluate our approach in both tabular contexts and more challenging frameworks, including some Atari games.
Efficient and Accurate Learning of Mixtures of Plackett-Luce Models

Mixture models of Plackett-Luce (PL), one of the most fundamental ranking models, are an active research area of both theoretical and practical significance. Most previously proposed parameter estimation algorithms instantiate the EM algorithm, often with random initialization. However, such an initialization scheme may not yield a good initial estimate and the algorithms require multiple restarts, incurring a large time complexity. As for the EM procedure, while the E-step can be performed efficiently, maximizing the log-likelihood in the M-step is difficult due to the combinatorial nature of the PL likelihood function. Therefore, previous authors favor algorithms that maximize surrogate likelihood functions. However, the final estimate may deviate from the true maximum likelihood estimate as a consequence. In this paper, we address these known limitations. We propose an initialization algorithm that can provide a provably accurate initial estimate and an EM algorithm that maximizes the true log-likelihood function efficiently. Experiments on both synthetic and real datasets show that our algorithm is competitive in terms of accuracy and speed to baseline algorithms, especially on datasets with a large number of items.
GLUECons: A Generic Benchmark for Learning under Constraints

Recent research has shown that integrating domain knowledge into deep learning architectures is effective; It helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models. However, the research community lacks a convened benchmark for systematically evaluating knowledge integration methods. In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints. We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying related research challenges. It will facilitate further research for alleviating some problems of state-of-the-art neural models.
Diffusing Gaussian Mixtures for Generating Categorical Data

Learning a categorical distribution comes with its own set of challenges. A successful approach taken by state-of-the-art works is to cast the problem in a continuous domain to take advantage of the impressive performance of the generative models for continuous data. Amongst them are the recently emerging diffusion probabilistic models, which have the observed advantage of generating high-quality samples. Recent advances for categorical generative models have focused on log likelihood improvements. In this work, we propose a generative model for categorical data based on diffusion models with a focus on high-quality sample generation, and propose sampled-based evaluation methods. The efficacy of our method stems from performing diffusion in the continuous domain while having its parameterization informed by the structure of the categorical nature of the target distribution. Our method of evaluation highlights the capabilities and limitations of different generative models for generating categorical data, and includes experiments on synthetic and real-world protein datasets.
Efficient Dynamic Batch Adaptation (Student Abstract)

In this paper we introduce Efficient Dynamic Batch Adaptation (EDBA), which improves on a previous method that works by adjusting the composition and the size of the current batch. Our improvements allow for Dynamic Batch Adaptation to feasibly scale up for bigger models and datasets, drastically improving model convergence and generalization. We show how the method is still able to perform especially well in data-scarce scenarios, managing to obtain a test accuracy on 100 samples of CIFAR-10 of 90.68%, while the baseline only reaches 23.79%. On the full CIFAR-10 dataset, EDBA reaches convergence in ∼120 epochs while the baseline requires ∼300 epochs.
Generative Decision Making Under Uncertainty

In the fields of natural language processing (NLP) and computer vision (CV), recent advances in generative modeling have led to powerful machine learning systems that can effectively learn from large labeled and unlabeled datasets. These systems, by and large, apply a uniform pretrain-finetune pipeline on sequential data streams and have achieved state-of-the-art-performance across many tasks and benchmarks. In this talk, we will present recent algorithms that extend this paradigm to sequential decision making, by casting it as an inverse problem that can be solved via deep generative models. These generative approaches are stable to train, provide a flexible interface for single- and multi-task inference, and generalize exceedingly well outside their training datasets. We instantiate these algorithms in the context of reinforcement learning and black-box optimization. Empirically, we demonstrate that these approaches perform exceedingly well on high-dimensional benchmarks outperforming the current state-of-the-art approaches based on forward models.
Information Transfer in Multitask Learning, Data Augmentation, and Beyond

A hallmark of human intelligence is that we continue to learn new information and then extrapolate the learned information onto new tasks and domains (see, e.g., Thrun and Pratt (1998)). While this is a fairly intuitive observation, formulating such ideas has proved to be a challenging research problem and continues to inspire new studies. Recently, there has been increasing interest in AI/ML about building models that generalize across tasks, even when they have some form of distribution shifts. How can we ground this research in a solid framework to develop principled methods for better practice? This talk will present my recent works addressing this research question. My talk will involve three parts: revisiting multitask learning from the lens of deep learning theory, designing principled methods for robust transfer, and algorithmic implications for data augmentation.
Learning Generalizable Batch Active Learning Strategies via Deep Q-networks (Student Abstract)

To handle a large amount of unlabeled data, batch active learning (BAL) queries humans for the labels of a batch of the most valuable data points at every round. Most current BAL strategies are based on human-designed heuristics, such as uncertainty sampling or mutual information maximization. However, there exists a disagreement between these heuristics and the ultimate goal of BAL, i.e., optimizing the model's final performance within the query budgets. This disagreement leads to a limited generality of these heuristics. To this end, we formulate BAL as an MDP and propose a data-driven approach based on deep reinforcement learning. Our method learns the BAL strategy by maximizing the model's final performance. Experiments on the UCI benchmark show that our method can achieve competitive performance compared to existing heuristics-based approaches.
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users' attitudes need to be satisfied locally, while a strict privacy guarantee for the global model is also required centrally. Personalized Local Differential Privacy (PLDP) is suitable for preserving users' varying local privacy, yet only provides a central privacy guarantee equivalent to the worst-case local privacy level. Thus, achieving strong central privacy as well as personalized local privacy with a utility-promising model is a challenging problem. In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model. To tighten the privacy bound, we quantify the heterogeneous contributions to the central privacy user by user. The contributions are characterized by the ability of generating “echos” from the perturbation of each user, which is carefully measured by proposed methods Neighbor Divergence and Clip-Laplace Mechanism. Furthermore, we propose a refined framework (S-APES) with the post-sparsification technique to reduce privacy loss in high-dimension scenarios. To the best of our knowledge, the impact of shuffling on personalized local privacy is considered for the first time. We provide a strong privacy amplification effect, and the bound is tighter than the baseline result based on existing methods for uniform local privacy. Experiments demonstrate that our frameworks ensure comparable or higher accuracy for the global model.