Reviewerly/exHarmony huggingface dataset:
MULTIMEDIA IN PICTURE SERIES AS TEACHING STRATEGY IN ENCOURAGING ENGLISH LEARNING MOTIVATION TO BENGKALA ELEMENTARY STUDENTS

This research focuses on using the strategy of multimedia in picture series to encourage elementary students’ motivation in learning English. The motivation and excitement of students in learning English at the elementary school specifically at SD Inklusi still lack excitement. Find out how does multimedia work to increase the students' learning excitement in English lesson. This study uses a qualitative method. The data source will be conducted for the grade 5th in SD No.2 Inklusi Desa Bengkala Buleleng. Methods of data collection using direct observation and interviews. Direct observation can be used in two months to learn more about the process of teaching and learning English by using multimedia in picture series. The data analysis techniques proposed by Miles and Huberman include data reduction, data presentation, and conclusions or verification. There are three points in this research method. The first point is how data is collected, the second point is how data is analyzed, and the third is how data analysis is presented. The results of this study indicate that using multimedia by picture series can increase students' learning desire and motivation with excitement which is reflected in their active response in a class by raising their hands trying to speak and expressing their ideas and thoughts automatically.&#x0D;
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization

Multi-task regression attempts to exploit the task similarity in order to achieve knowledge transfer across related tasks for performance improvement. The application of Gaussian process (GP) in this scenario yields the non-parametric yet informative Bayesian multi-task regression paradigm. Multi-task GP (MTGP) provides not only the prediction mean but also the associated prediction variance to quantify uncertainty, thus gaining popularity in various scenarios. The linear model of coregionalization (LMC) is a well-known MTGP paradigm which exploits the dependency of tasks through linear combination of several independent and diverse GPs. The LMC however suffers from high model complexity and limited model capability when handling complicated multi-task cases. To this end, we develop the neural embedding of coregionalization that transforms the latent GPs into a high-dimensional latent space to induce rich yet diverse behaviors. Furthermore, we use advanced variational inference as well as sparse approximation to devise a tight and compact evidence lower bound (ELBO) for higher quality of scalable model inference. Extensive numerical experiments have been conducted to verify the higher prediction quality and better generalization of our model, named NSVLMC, on various real-world multi-task datasets and the cross-fluid modeling of unsteady fluidized bed.
GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization

Since data is presented long-tailed in reality, it is challenging for Federated Learning (FL) to train across decentralized clients as practical applications. We present Global-Regularized Personalization (GRP-FED) to tackle the data imbalanced issue by considering a single global model and multiple local models for each client. With adaptive aggregation, the global model treats multiple clients fairly and mitigates the global long-tailed issue. Each local model is learned from the local data and aligns with its distribution for customization. To prevent the local model from just overfitting, GRP-FED applies an adversarial discriminator to regularize between the learned global-local features. Extensive results show that our GRP-FED improves under both global and local scenarios on real-world MIT-BIH and synthesis CIFAR-10 datasets, achieving comparable performance and addressing client imbalance.
Towards Inclusive HRI: Using Sim2Real to Address Underrepresentation in Emotion Expression Recognition

Robots and artificial agents that interact with humans should be able to do so without bias and inequity, but facial perception systems have notoriously been found to work more poorly for certain groups of people than others. In our work, we aim to build a system that can perceive humans in a more transparent and inclusive manner. Specifically, we focus on dynamic expressions on the human face, which are difficult to collect for a broad set of people due to privacy concerns and the fact that faces are inherently identifiable. Furthermore, datasets collected from the Internet are not necessarily representative of the general population. We address this problem by offering a Sim2Real approach in which we use a suite of 3D simulated human models that enables us to create an auditable synthetic dataset covering 1) underrepresented facial expressions, outside of the six basic emotions, such as confusion; 2) ethnic or gender minority groups; and 3) a wide range of viewing angles that a robot may encounter a human in the real world. By augmenting a small dynamic emotional expression dataset containing 123 samples with a synthetic dataset containing 4536 samples, we achieved an improvement in accuracy of 15% on our own dataset and 11% on an external benchmark dataset, compared to the performance of the same model architecture without synthetic training data. We also show that this additional step improves accuracy specifically for racial minorities when the architecture's feature extraction weights are trained from scratch.
LEVELS AND ASSESSMENT OF HUMANITIES TEACHERS’ PREPAREDNESS FOR THE CAREER GUIDANCE IN EDUCATIONAL INSTITUTION

The choice of profession as a complex and responsible vital task that needs to be solved by modern man is actualized. It is emphasized that the comprehensive implementation of career guidance by teachers of general secondary education requires high school students to take a sensible approach to choose a future profession and understand the social and professional role of this profession in society. Emphasis is placed on the phenomenon of career guidance work as a holistic system of professional selection, adaptation, education and counselling; a set of organizational and scientific activities in working with students; consolidation of professional interests of the subjects of the system at the level of the school, college, higher school, organization. The essence of the concept of “career guidance” is described, which is constantly evolving with the constant change of society’s perception of the goals, objectives, methods and forms of career guidance work. To design career guidance goals and their implementation, teachers need to be prepared for effective career guidance work. The levels of readiness of the teacher for the career guidance work with students in the educational process are presented and characterized: intuitive (low) with the teacher’s inherent lack of career guidance skills; reproductive (secondary), where career guidance activities are carried out by the teacher on the basis of examples and certain patterns; productive (high) with a successful performance by the teacher of the basic career guidance actions and creative (higher) with a successful performance by the teacher of the main career guidance actions. The assessment of the levels of readiness of teachers of humanities for career guidance work with students is given and the methods of research of level components of readiness of teachers in the conditions of profile training such as observation, interrogation, and analysis of documents are defined.
AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks

The aggressive performance optimizations in modern microprocessors can result in security vulnerabilities. For example, timing-based attacks in processor caches can steal secret keys or break randomization. So far, finding cache-timing vulnerabilities is mostly performed by human experts, which is inefficient and laborious. There is a need for automatic tools that can explore vulnerabilities given that unreported vulnerabilities leave the systems at risk. In this paper, we propose AutoCAT, an automated exploration framework that finds cache timing-channel attack sequences using reinforcement learning (RL). Specifically, AutoCAT formulates the cache timing-channel attack as a guessing game between an attack program and a victim program holding a secret. This guessing game can thus be solved via modern deep RL techniques. AutoCAT can explore attacks in various cache configurations without knowing design details and under different attack and victim program configurations. AutoCAT can also find attacks to bypass certain detection and defense mechanisms. In particular, AutoCAT discovered StealthyStreamline, a new attack that is able to bypass performance counter-based detection and has up to a 71% higher information leakage rate than the state-of-the-art LRU-based attacks on real processors. AutoCAT is the first of its kind in using RL for crafting microarchitectural timing-channel attack sequences and can accelerate cache timing-channel exploration for secure microprocessor designs.
Statistical Decoding 2.0: Reducing Decoding to LPN

The security of code-based cryptography relies primarily on the hardness of generic decoding with linear codes. The best generic decoding algorithms are all improvements of an old algorithm due to Prange: they are known under the name of information set decoders (ISD). A while ago, a generic decoding algorithm which does not belong to this family was proposed: statistical decoding. It is a randomized algorithm that requires the computation of a large set of parity-checks of moderate weight, and uses some kind of majority voting on these equations to recover the error. This algorithm was long forgotten because even the best variants of it performed poorly when compared to the simplest ISD algorithm. We revisit this old algorithm by using parity-check equations in a more general way. Here the parity-checks are used to get LPN samples with a secret which is part of the error and the LPN noise is related to the weight of the parity-checks we produce. The corresponding LPN problem is then solved by standard Fourier techniques. By properly choosing the method of producing these low weight equations and the size of the LPN problem, we are able to outperform in this way significantly information set decodings at code rates smaller than $0.3$. It gives for the first time after $60$ years, a better decoding algorithm for a significant range which does not belong to the ISD family.
Education Harmony in Society Cultural Perspective

The balance of community life is created through an educational process that is developed through basic values , in general, to live with the relationships between individuals that are implanted.Socio-cultural-based harmony education has not been designed optimally in the curriculum presented by the school.Students lose the grip of culture in society.Students do not get formally their own cultural values.Which is basically the motto of education in Mianangkabau, namely "adat basandi sayarak, syarak basandi Kitabbullah"..Besides that, Tanah Datar Regency, especially Batusangkar City, is known as the City of Culture.Not integrated into existing subjects.How and what about the existing slogans have not yet reached the stage of deeper meaning by students for the balance of their life in the community.This research was conducted through interviews with traditional community leaders and administrators of the education council and the education office.The results of the research community hope that the values of Minangkabau traditional culture, especially in Tanah Datar Regency, will still be carried out in the learning process at school.Minangkabau Natural Culture (BAM) subjects have been studied by students at school.Parents and the community do not have the time and opportunity to provide formal provisions to students.The education office will review the implementation of traditional education in schools.
An analysis of IQ LINK$^{TM}$

This is a theoretical and computational strategy exploration of the visually attractive game IQ-Link. Not all games which are visually appealing are worthy of your time as a puzzlist. This analysis gives a would-be addict some idea of what type of game they are falling for.
Duals of linearized Reed-Solomon codes

We give a description of the duals of linearized Reed-Solomon codes in terms of codes obtained by taking residues of Ore rational functions. Our construction shows in particular that, under some assumptions on the base field, the class of linearized Reed-Solomon codes is stable under duality. As a byproduct of our work, we develop a theory of residues in the Ore setting.
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation

Temporal action segmentation is a task to classify each frame in the video with an action label. However, it is quite expensive to annotate every frame in a large corpus of videos to construct a comprehensive supervised training dataset. Thus in this work we propose an unsupervised method, namely SSCAP, that operates on a corpus of unlabeled videos and predicts a likely set of temporal segments across the videos. SSCAP leverages Self-Supervised learning to extract distinguishable features and then applies a novel Co-occurrence Action Parsing algorithm to not only capture the correlation among sub-actions underlying the structure of activities, but also estimate the temporal path of the sub-actions in an accurate and general way. We evaluate on both classic datasets (Breakfast, 50Salads) and the emerging fine-grained action dataset (FineGym) with more complex activity structures and similar sub-actions. Results show that SSCAP achieves state-of-the-art performance on all datasets and can even outperform some weakly-supervised approaches, demonstrating its effectiveness and generalizability.
AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation

In video segmentation, generating temporally consistent results across frames is as important as achieving frame-wise accuracy. Existing methods rely either on optical flow regularization or fine-tuning with test data to attain temporal consistency. However, optical flow is not always avail-able and reliable. Besides, it is expensive to compute. Fine-tuning the original model in test time is cost sensitive. This paper presents an efficient, intuitive, and unsupervised online adaptation method, AuxAdapt, for improving the temporal consistency of most neural network models. It does not require optical flow and only takes one pass of the video. Since inconsistency mainly arises from the model's uncertainty in its output, we propose an adaptation scheme where the model learns from its own segmentation decisions as it streams a video, which allows producing more confident and temporally consistent labeling for similarly-looking pixels across frames. For stability and efficiency, we leverage a small auxiliary segmentation network (AuxNet) to assist with this adaptation. More specifically, AuxNet readjusts the decision of the original segmentation network (Main-Net) by adding its own estimations to that of MainNet. At every frame, only AuxNet is updated via back-propagation while keeping MainNet fixed. We extensively evaluate our test-time adaptation approach on standard video benchmarks, including Cityscapes, CamVid, and KITTI. The results demonstrate that our approach provides label-wise accurate, temporally consistent, and computationally efficient adaptation (5+ folds overhead reduction comparing to state-of-the-art test-time adaptation methods).
IMPROVING STUDENTS’ ACHIEVEMENT IN WRITING DESCRIPTIVE BY USING ENVIRONMENTAL MEDIA

This study attempted to improve students’ descriptive text writing through environtmental media. It was conducted by using classroom action research. The subject of the research was the eighth grade students of SMP Al Washliyah 4 Medan consisting of 18 students. The research was conducted in two cycles and each cycle consisted of three meetings. The instruments for collecting data were essay test, observation sheet, diary notes, and questionnaire sheet. Based on the descriptive text test results, students’ score kept improving in every test. In the orientation test, there were 8 students getting ≥75 and the mean was 66. In the first cycle, there were 9 students obtaining ≥75, and the mean was 71. In the second cycle, there were 15 students gaining ≥75 and the mean was 83. Based on the diary notes and questionnaire sheet, it was shown that the expression and excitement of the students in studying descriptive text writing also improved. Based on the diary notes and observation, it was found that the teaching learning process ran well, the students were active and interested in studying descriptive text writing. Thus, the results of research showed that the environtmental media improved students’ descriptive text writing.
BERTifying Sinhala -- A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification

This research provides the first comprehensive analysis of the performance of pre-trained language models for Sinhala text classification. We test on a set of different Sinhala text classification tasks and our analysis shows that out of the pre-trained multilingual models that include Sinhala (XLM-R, LaBSE, and LASER), XLM-R is the best model by far for Sinhala text classification. We also pre-train two RoBERTa-based monolingual Sinhala models, which are far superior to the existing pre-trained language models for Sinhala. We show that when fine-tuned, these pre-trained language models set a very strong baseline for Sinhala text classification and are robust in situations where labeled data is insufficient for fine-tuning. We further provide a set of recommendations for using pre-trained models for Sinhala text classification. We also introduce new annotated datasets useful for future research in Sinhala text classification and publicly release our pre-trained models.
Formative Assessment as the Path to a Higher Quality of Knowledge

Formative assessment of students' knowledge is seen as the bridge between the learning and teaching process.It mainly involves providing feedback and developing the student's ability to self-evaluate.In essence, the teacher shifts from the role of knowledge provider to the role of a guide through the learning process.Formative assessment increases the quality of knowledge, impacts the students' involvement in the learning process, and encourages them to be responsible for their own knowledge and learning.It is indirectly already a part of the teaching process of many Slovenian elementary schools that provide education in line with the national education programme, whereas at schools that offer The International Baccalaureate (IB) international education programme, students can only formally be graded after prior formative assessment.The article discusses the discrepancies in understanding the importance of formative assessment in two established education programmes.In the international education programme, it serves as a valid basis for grading and is an integral part of the final grade, while in the national education programme, formative assessment is considered an activity parallel to knowledge assessment, which, according to the rules, should not be considered when grading.The final grade under the national education programme is only a snapshot of the student's knowledge at that moment.It does not allow taking into account the student's performance in partial knowledge assessments, which deprives children and parents of applicative and useful feedback about the student's knowledge or their progress.Sophisticated teaching methods, such as individualization, differentiation, and personalization, are considered quality tools of formative assessment as they encourage comprehensive learning and critical thinking, motivate curiosity and imagination, as well as strengthen the student's ability to connect knowledge from various fields.
Feature Learning and Network Structure from Noisy Node Activity Data

In the studies of network structures, much attention has been devoted to developing approaches to reconstruct networks and predict missing links when edge-related information is given. However, such approaches are not applicable when we are only given noisy node activity data with missing values. This work presents an unsupervised learning framework to learn node vectors and construct networks from such node activity data. First, we design a scheme to generate random node sequences from node context sets, which are generated from node activity data. Then, a three-layer neural network is adopted training the node sequences to obtain node vectors, which allow us to construct networks and capture nodes with synergistic roles. Furthermore, we present an entropy-based approach to select the most meaningful neighbors for each node in the resulting network. Finally, the effectiveness of the method is validated through both synthetic and real data.
Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective

Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware features directly from the source signal, and the discrete-time analog transmission (DTAT) of these features. Compared with traditional digital communications, semantic communications with DeepJSCC provide superior reconstruction performance at the receiver and graceful degradation with diminishing channel quality, but also exhibit a large peak-to-average power ratio (PAPR) in the transmitted signal. An open question has been whether the gains of DeepJSCC come from the additional freedom brought by the high-PAPR continuous-amplitude signal. In this paper, we address this question by exploring three PAPR reduction techniques in the application of image transmission. We confirm that the superior image reconstruction performance of DeepJSCC-based semantic communications can be retained while the transmitted PAPR is suppressed to an acceptable level. This observation is an important step towards the implementation of DeepJSCC in practical semantic communication systems.
Estimating Image Depth in the Comics Domain

Estimating the depth of comics images is challenging as such images a) are monocular; b) lack ground-truth depth annotations; c) differ across different artistic styles; d) are sparse and noisy. We thus, use an off-the-shelf unsupervised image to image translation method to translate the comics images to natural ones and then use an attention-guided monocular depth estimator to predict their depth. This lets us leverage the depth annotations of existing natural images to train the depth estimator. Furthermore, our model learns to distinguish between text and images in the comics panels to reduce text-based artefacts in the depth estimates. Our method consistently outperforms the existing state-ofthe-art approaches across all metrics on both the DCM and eBDtheque images. Finally, we introduce a dataset to evaluate depth prediction on comics. Our project website can be accessed at https://github.com/IVRL/ComicsDepth.