Reviewerly/exHarmony huggingface dataset:
On the Problems of Modern Education. I. On the Concept of Contemporary Science Education

The article formulates the fundamental principles and the concept of modern scientific education in general. It is emphasized that only after formulating the goals of education in full it is possible to proceed to the discussion of various technical proposals on the methods of teaching, which are the vast majority of discussions. The article proposes and analyzes a systematic approach that combines various disciplines of secondary and higher education with a unified goal and structure. It is emphasized that a mandatory facet of the educational process, in addition to the student and the teacher, is the state, since a significant part of education is funded by the civil society, and as an investor it has the right to take care of his interests and investment results. Properly structured education is an important factor in the success of public administration (regardless of who is currently in power), and moreover, a factor in the strategic security of the country, the prosperity of descendants and even the survival of the civil nation as a whole. The most important task of education, according to the authors, is to give students the practice of critical analysis in the process of solving real practical problems and making the right life decisions (to the best of a student's abilities). And such a structuring of educational processes is possible! The knowledge (and disciplines) required to achieve the stated goals is analyzed. The features of teaching are discussed: informatics and natural sciences; mathematics and scientific thinking in general; history and culture in general. The ultimate goals of education are defined and analyzed. The second article will be devoted to the technologies of contemporary science education.
Challenges and Pitfalls of Bayesian Unlearning

Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model. Approximate unlearning are one class of methods for this task which avoid the need to retrain the model from scratch on the retained data. Bayes' rule can be used to cast approximate unlearning as an inference problem where the objective is to obtain the updated posterior by dividing out the likelihood of deleted data. However this has its own set of challenges as one often doesn't have access to the exact posterior of the model parameters. In this work we examine the use of the Laplace approximation and Variational Inference to obtain the updated posterior. With a neural network trained for a regression task as the guiding example, we draw insights on the applicability of Bayesian unlearning in practical scenarios.
Model-based Reinforcement Learning with Multi-step Plan Value Estimation

A promising way to improve the sample efficiency of reinforcement learning is model-based methods, in which many explorations and evaluations can happen in the learned models to save real-world samples. However, when the learned model has a non-negligible model error, sequential steps in the model are hard to be accurately evaluated, limiting the model's utilization. This paper proposes to alleviate this issue by introducing multi-step plans to replace multi-step actions for model-based RL. We employ the multi-step plan value estimation, which evaluates the expected discounted return after executing a sequence of action plans at a given state, and updates the policy by directly computing the multi-step policy gradient via plan value estimation. The new model-based reinforcement learning algorithm MPPVE (Model-based Planning Policy Learning with Multi-step Plan Value Estimation) shows a better utilization of the learned model and achieves a better sample efficiency than state-of-the-art model-based RL approaches.
English Language Students’ Perception on the Use of Zoom Application toward Their Speaking Performance at University of Muhammadiyah Gresik

Zoom Application is widely known as one of the most popular video conference applications used in online learning process during COVID-19 outbreak. Zoom application is considered as an interactive learning platform that enables students to interact with each other online. It provides lots of features for online learning that help students improve their speaking skill better. Therefore, this study specifically examined the perception of English students toward zoom application in online learning process. The research design used was a combined quantitative and qualitative to get more detailed information. The data collection used was in the form of questionnaire consisted of aspect of perception and speaking performance aspect. Unstructured interview in focus group discussion was used to support the result from questionnaire. The result showed that students had positive responses on their perception toward the use of Zoom application. Most of the students responded that Zoom application helped them perform speaking activity. The result of interview in focus group discussion also showed reasons that most students chose using Zoom for their speaking performance because it was easy to use and provided useful features for interaction with their teacher and other students. However, the main problem faced by students while using zoom application was internet connection. This gave bad effect for their learning process because they could not catch some of the material explained by the lecturer.
DIFFICULTIES AND FACTORS OF LOW ACHIEVERS IN SPEAKING

This study aimed to know the difficulties and the factors causing faced by the low achievers in speaking. The research used a qualitative method by doing observation and interview. The researchers used the purposive sampling technique. This research had 21 participants out of 97 students from the third semester who were taking Public Speaking subject in English Education Department of Universitas Muhammadiyah Purwokerto. The data collection was done by using observation and semi-structured interviews. The questions consisted of 11 items. In a conclusion, the problems in speaking refer to linguistic problems (pronunciation, vocabulary, grammar, performance variables, and fluency), and non-linguistic problems (inhibition, nothing to say, low or uneven participation, and mother tongue use). The factors causing problems in speaking refer to non-psychological factors (lack of vocabulary, inadequate strategic competence, and teaching strategies), and psychological factors (fear of mistakes, shyness, anxiety, lack of confidence, and lack of motivation).
An Embedding-Based Grocery Search Model at Instacart

The key to e-commerce search is how to best utilize the large yet noisy log data. In this paper, we present our embedding-based model for grocery search at Instacart. The system learns query and product representations with a two-tower transformer-based encoder architecture. To tackle the cold-start problem, we focus on content-based features. To train the model efficiently on noisy data, we propose a self-adversarial learning method and a cascade training method. AccOn an offline human evaluation dataset, we achieve 10% relative improvement in RECALL@20, and for online A/B testing, we achieve 4.1% cart-adds per search (CAPS) and 1.5% gross merchandise value (GMV) improvement. We describe how we train and deploy the embedding based search model and give a detailed analysis of the effectiveness of our method.
Articulated 3D Human-Object Interactions from RGB Videos: An Empirical Analysis of Approaches and Challenges

Human-object interactions with articulated objects are common in everyday life. Despite much progress in single-view 3D reconstruction, it is still challenging to infer an articulated 3D object model from an RGB video showing a person manipulating the object. We canonicalize the task of articulated 3D human-object interaction reconstruction from RGB video, and carry out a systematic benchmark of five families of methods for this task: 3D plane estimation, 3D cuboid estimation, CAD model fitting, implicit field fitting, and free-form mesh fitting. Our experiments show that all methods struggle to obtain high accuracy results even when provided ground truth information about the observed objects. We identify key factors which make the task challenging and suggest directions for future work on this challenging 3D computer vision task. Short video summary at https://www.youtube.com/watch?v=5tAlKBojZwc
DiffeoRaptor: Diffeomorphic Inter-modal Image Registration using RaPTOR

Purpose: Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task. Methods: We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio (RaPTOR) metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration. Results: The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image normalization (SyN). Conclusions: Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor.
Semantic2Graph: Graph-based Multi-modal Feature Fusion for Action Segmentation in Videos

Video action segmentation have been widely applied in many fields. Most previous studies employed video-based vision models for this purpose. However, they often rely on a large receptive field, LSTM or Transformer methods to capture long-term dependencies within videos, leading to significant computational resource requirements. To address this challenge, graph-based model was proposed. However, previous graph-based models are less accurate. Hence, this study introduces a graph-structured approach named Semantic2Graph, to model long-term dependencies in videos, thereby reducing computational costs and raise the accuracy. We construct a graph structure of video at the frame-level. Temporal edges are utilized to model the temporal relations and action order within videos. Additionally, we have designed positive and negative semantic edges, accompanied by corresponding edge weights, to capture both long-term and short-term semantic relationships in video actions. Node attributes encompass a rich set of multi-modal features extracted from video content, graph structures, and label text, encompassing visual, structural, and semantic cues. To synthesize this multi-modal information effectively, we employ a graph neural network (GNN) model to fuse multi-modal features for node action label classification. Experimental results demonstrate that Semantic2Graph outperforms state-of-the-art methods in terms of performance, particularly on benchmark datasets such as GTEA and 50Salads. Multiple ablation experiments further validate the effectiveness of semantic features in enhancing model performance. Notably, the inclusion of semantic edges in Semantic2Graph allows for the cost-effective capture of long-term dependencies, affirming its utility in addressing the challenges posed by computational resource constraints in video-based vision models.
A Gaussian variational inference approach to motion planning

We propose a Gaussian variational inference framework for the motion planning problem. In this framework, motion planning is formulated as an optimization over the distribution of the trajectories to approximate the desired trajectory distribution by a tractable Gaussian distribution. Equivalently, the proposed framework can be viewed as a standard motion planning with an entropy regularization. Thus, the solution obtained is a transition from an optimal deterministic solution to a stochastic one, and the proposed framework can recover the deterministic solution by controlling the level of stochasticity. To solve this optimization, we adopt the natural gradient descent scheme. The sparsity structure of the proposed formulation induced by factorized objective functions is further leveraged to improve the scalability of the algorithm. We evaluate our method on several robot systems in simulated environments, and show that it achieves collision avoidance with smooth trajectories, and meanwhile brings robustness to the deterministic baseline results, especially in challenging environments and tasks.
Sample Complexity of an Adversarial Attack on UCB-based Best-arm Identification Policy

In this work I study the problem of adversarial perturbations to rewards, in a Multi-armed bandit (MAB) setting. Specifically, I focus on an adversarial attack to a UCB type best-arm identification policy applied to a stochastic MAB. The UCB attack presented in [1] results in pulling a target arm K very often. I used the attack model of [1] to derive the sample complexity required for selecting target arm K as the best arm. I have proved that the stopping condition of UCB based best-arm identification algorithm given in [2], can be achieved by the target arm K in T rounds, where T depends only on the total number of arms and $\sigma$ parameter of $\sigma^2-$ sub-Gaussian random rewards of the arms.
Concept-Based Explanations for Tabular Data

The interpretability of machine learning models has been an essential area of research for the safe deployment of machine learning systems. One particular approach is to attribute model decisions to high-level concepts that humans can understand. However, such concept-based explainability for Deep Neural Networks (DNNs) has been studied mostly on image domain. In this paper, we extend TCAV, the concept attribution approach, to tabular learning, by providing an idea on how to define concepts over tabular data. On a synthetic dataset with ground-truth concept explanations and a real-world dataset, we show the validity of our method in generating interpretability results that match the human-level intuitions. On top of this, we propose a notion of fairness based on TCAV that quantifies what layer of DNN has learned representations that lead to biased predictions of the model. Also, we empirically demonstrate the relation of TCAV-based fairness to a group fairness notion, Demographic Parity.
Recovery from Non-Decomposable Distance Oracles

A line of work has looked at the problem of recovering an input from distance queries. In this setting, there is an unknown sequence $s \in \{0,1\}^{\leq n}$, and one chooses a set of queries $y \in \{0,1\}^{\mathcal{O}(n)}$ and receives $d(s,y)$ for a distance function $d$. The goal is to make as few queries as possible to recover $s$. Although this problem is well-studied for decomposable distances, i.e., distances of the form $d(s,y) = \sum_{i=1}^n f(s_i, y_i)$ for some function $f$, which includes the important cases of Hamming distance, $\ell_p$-norms, and $M$-estimators, to the best of our knowledge this problem has not been studied for non-decomposable distances, for which there are important special cases such as edit distance, dynamic time warping (DTW), Frechet distance, earth mover's distance, and so on. We initiate the study and develop a general framework for such distances. Interestingly, for some distances such as DTW or Frechet, exact recovery of the sequence $s$ is provably impossible, and so we show by allowing the characters in $y$ to be drawn from a slightly larger alphabet this then becomes possible. In a number of cases we obtain optimal or near-optimal query complexity. We also study the role of adaptivity for a number of different distance functions. One motivation for understanding non-adaptivity is that the query sequence can be fixed and the distances of the input to the queries provide a non-linear embedding of the input, which can be used in downstream applications involving, e.g., neural networks for natural language processing.
EFEKTIVITAS PEMBELAJARAN DARING MENGGUNAKAN MOODLE PADA MATA PELAJARAN MATEMATIKA SELAMA MASA PANDEMI COVID-19 DI SMP

The Covid-19 pandemic has affected almost all aspects of human life. To break the chain of transmission of the virus, many restrictions have been put in place by the government, including the Indonesian government. The use of multimedia and internet technology can change the style in transferring knowledge which is an alternative to learning in conventional classes. The purpose of this study is to provide an overview of the implementation of Learning from Home at the Junior High School Education Level during the Corona Virus Disease 19 (Covid-19) Pandemic. Based on data processing and analysis of student responses, online learning carried out by teachers in online mathematics learning using Moodle was carried out well with positive student responses in the implementation of online learning, material understanding and effectiveness in the application of online learning using Moodle.
Psychological and pedagogical comprehension of "thinking of military higher educational institution cadets"

Scientific article authors, having analyzed separate aspects of philosophical, psychological and pedagogical literature, operationalize and find out comprehension of "thinking of military higher educational institution cadets". The article notes that social and psychological nature of thinking should be appropriately researched with activity based approach, because cognitive activity of military higher educational institution cadet is founded on thinking. Such an approach allows the authors to discover personal (the necessity of knowledge, significance of the acquired knowledge, the goals and plan of cognitive activity) and processional (analytical and synthetical activity) aspects of military higher educational institution cadets. In brief authors describe the following mental phenomena: mental activity (system of mental actions and operations aimed to definite goal achievement), mental operations (analysis, synthesis, generalization, classification and abstraction, etc.), types of thinking (analytical, critical, practical, economical, ecological, scientific, etc.), forms of thinking (concept, judgment, inference). The authors emphasize that understanding of social and psychological nature of thinking of military higher educational institution cadets will allow the military teacher to appropriately implement such modern pedagogical techniques as situational teaching (case-method), critical (emergency) situation technique, complete understanding technique, heuristic teaching technique, simulation technologies, etc. The final step is to determine the prospects of scientific research, which the authors see in the formation of certain types of thinking (for example, analytical, critical, professional, creative, etc.) of cadets by means of the modern pedagogical technologies.
A Capsule Network for Hierarchical Multi-Label Image Classification

Image classification is one of the most important areas in computer vision. Hierarchical multi-label classification applies when a multi-class image classification problem is arranged into smaller ones based upon a hierarchy or taxonomy. Thus, hierarchical classification modes generally provide multiple class predictions on each instance, whereby these are expected to reflect the structure of image classes as related to one another. In this paper, we propose a multi-label capsule network (ML-CapsNet) for hierarchical classification. Our ML-CapsNet predicts multiple image classes based on a hierarchical class-label tree structure. To this end, we present a loss function that takes into account the multi-label predictions of the network. As a result, the training approach for our ML-CapsNet uses a coarse to fine paradigm while maintaining consistency with the structure in the classification levels in the label-hierarchy. We also perform experiments using widely available datasets and compare the model with alternatives elsewhere in the literature. In our experiments, our ML-CapsNet yields a margin of improvement with respect to these alternative methods.
Concealing Sensitive Samples against Gradient Leakage in Federated Learning

Federated Learning (FL) is a distributed learning paradigm that enhances users privacy by eliminating the need for clients to share raw, private data with the server. Despite the success, recent studies expose the vulnerability of FL to model inversion attacks, where adversaries reconstruct users private data via eavesdropping on the shared gradient information. We hypothesize that a key factor in the success of such attacks is the low entanglement among gradients per data within the batch during stochastic optimization. This creates a vulnerability that an adversary can exploit to reconstruct the sensitive data. Building upon this insight, we present a simple, yet effective defense strategy that obfuscates the gradients of the sensitive data with concealed samples. To achieve this, we propose synthesizing concealed samples to mimic the sensitive data at the gradient level while ensuring their visual dissimilarity from the actual sensitive data. Compared to the previous art, our empirical evaluations suggest that the proposed technique provides the strongest protection while simultaneously maintaining the FL performance.
Neural3Points: Learning to Generate Physically Realistic Full-body Motion for Virtual Reality Users

Animating an avatar that reflects a user's action in the VR world enables natural interactions with the virtual environment. It has the potential to allow remote users to communicate and collaborate in a way as if they met in person. However, a typical VR system provides only a very sparse set of up to three positional sensors, including a head-mounted display (HMD) and optionally two hand-held controllers, making the estimation of the user's full-body movement a difficult problem. In this work, we present a data-driven physics-based method for predicting the realistic full-body movement of the user according to the transformations of these VR trackers and simulating an avatar character to mimic such user actions in the virtual world in real-time. We train our system using reinforcement learning with carefully designed pretraining processes to ensure the success of the training and the quality of the simulation. We demonstrate the effectiveness of the method with an extensive set of examples.