Reviewerly/exHarmony huggingface dataset:
"Self-scoring tables" by Tikhon Sotnikov in the light of the development of the idea of self-education in Russian music pedagogy

The article tells about the first didactic development in the history of Russian music pedagogy on elementary music theory, created in the 1930s by Rostov composer and teacher Tikhon Ivanovich Sotnikov for the independent development of musical and theoretical knowledge. The author of the article points out that the need to create such an educational and methodological manual for self-study was dictated by the current situation in the cultural life of the USSR since the 1930s, when there was an active growth of amateur artistic activity in the country. The gradual complication of the repertoire of collectives required the growth of musical literacy of its participants. Analyzing the historical situation, the author states that despite the developing system of music education, there were not enough schools and qualified music teachers nationwide. The situation was aggravated by the war years, when many libraries and archives were destroyed, and even the search for educational literature presented a significant problem for those who wanted to study musical literacy. The table and thematic cards developed by Sotnikov for independent studies contained not just brief information on musical literacy, but also made it possible to train theoretical knowledge in practice without the participation of a teacher. In addition, the versatility of the Rostov composer's development was also in the fact that his methodology for independent studies could be used in tandem with any of the textbooks on music theory that existed at that time. The idea was highly appreciated by professors of the leading musical universities of the USSR and was recommended for publication nationwide. In conclusion, the author of the article comes to the conclusion that in the context of the historical situation of the mid-twentieth century in the USSR, Sotnikov's development was not just relevant, but with timely publication could have a revolutionary significance for the development of musical culture in the country, and its significance for the history of music pedagogy is undeniable.
Penyuntingan Teks Cerita Rakyat Nusantara Berbasis Pendidikan Karakter untuk Media Pembelajaran Tingkat Sekolah Dasar

The purpose of this study was to edit the book Collection of Indonesian Folklore (KCRN) with a focus on editing the simplification of language and condensing the content of the story by referring to character education so that the folklore can be used as a medium of learning for the SD/MI level. The method used is the descriptive analysis method. This research has four main steps namely; 1. Read KCRN carefully, 2. Identify elements that are not in accordance with character education (SD/MI), 3. Determine folklore that will be further edited, and 4. Conduct character education-based editing. Based on the results of the study, there were 24 folk tales that did not meet the criteria and there were 12 filk tales that met the criteria and the character education-based children’s stories had been edited so that they could be used as learning media for SD/MI education level (lower level/class).
Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language

This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The best models achieved 15$^{th}$ rank with an F1-score of 0.5924 for subtask-A and 12$^{th}$ in subtask-B with a macro-F1 score of 0.3763.
Positivity of permutation pattern character polynomials

Let $N_σ(π)$ denote the number of occurrences of a permutation pattern $σ\in S_k$ in a permutation $π\in S_n$. Gaetz and Ryba showed using partition algebras that the $d$-th moment $M_{σ,d,n}(π)$ of $N_σ$ on the conjugacy class of $π$ is given by a polynomial in $n,m_1,\dots,m_{dk}$, where $m_i$ denotes the number of $i$-cycles of $π$. They also show that the coefficient $\langle χ^{λ[n]}, M_{σ,d,n}\rangle$ agrees with a polynomial $a_{σ,d}^λ(n)$ in $n$. This work is motivated by our conjecture that when $σ=12...k$ is the identity permutation, all of these coefficients $a_{12...k}^λ(n)$ are nonnegative. We directly compute closed forms for these polynomials in the cases $λ=(1),(1,1),$ and $(2)$, and use this to verify our positivity conjecture for those cases by showing that the polynomials are real-rooted with all roots less than $k$. We also study the case $a_σ^{(1)}(n)$, for which we give a formula for the polynomials and their leading coefficients.
Log-Sobolev inequality and proof of Hypothesis of the Gaussian Maximizers for the capacity of quantum noisy homodyning

In the present paper we give proof that the information-transmission capacity of the approximate position measurement with the oscillator energy constraint, which underlies noisy Gaussian homodyning in quantum optics, is attained on Gaussian encoding. The proof is based on general principles of convex programming. Rather remarkably, for this particular model the method reduces the solution of the optimization problem to a generalization of the celebrated log-Sobolev inequality. We hope that this method should work also for other models lying out of the scope of the "threshold condition" ensuring that the upper bound for the capacity as a difference between the maximum and the minimum output entropies is attainable.
EVOTER: Evolution of Transparent Explainable Rule-sets

Most AI systems are black boxes generating reasonable outputs for given inputs. Some domains, however, have explainability and trustworthiness requirements that cannot be directly met by these approaches. Various methods have therefore been developed to interpret black-box models after training. This paper advocates an alternative approach where the models are transparent and explainable to begin with. This approach, EVOTER, evolves rule-sets based on simple logical expressions. The approach is evaluated in several prediction/classification and prescription/policy search domains with and without a surrogate. It is shown to discover meaningful rule sets that perform similarly to black-box models. The rules can provide insight into the domain, and make biases hidden in the data explicit. It may also be possible to edit them directly to remove biases and add constraints. EVOTER thus forms a promising foundation for building trustworthy AI systems for real-world applications in the future.
Survey on Vision Based Hand Gesture Interface for Controlling Multimedia Player

As computers become additional pervasive in society, facilitating natural human–computer interaction (HCI) can have a positive impact on their use. Hence, there has been growing interest within the development of recent approaches and technologies for bridging the human-computer barrier. the last word aim is to bring HCI to a regime wherever interactions with computers are going to be as natural as associate degree interaction between humans, and to the current finish, incorporating gestures in HCI is a crucial analysis space. Gestures have long been thought-about as associate degree interaction technique that may doubtless deliver additional natural, creative, and intuitive strategies for human activity with our computers. Hand gesture recognition is one amongll|one amongst|one in every of} the systems that may notice the gesture of the hand in a period of time video. The gesture of hand is classed inside a definite space of interest. during this study, planning hand gesture recognition is one among the difficult jobs that involves 2 major issues. foremost is that the detection of the hand. Another drawback is to form an indication that's appropriate to be used one hand at a time. This project concentrates on however a system might notice, acknowledge and interpret hand gesture recognition through computer vision with the difficult factors that variability within the create, orientation, location, and scale. To perform well for developing this project, differing kinds of gestures like numbers and sign languages got to be created during this system. The image taken from the period of time video is analyzed via Haar-cascade Classifier to notice the gesture of hand before the image process is finished or in different words to notice the looks of hand in a very frame. during this project, the detection of hand are going to be done mistreatment the theories of Region of Interest (ROI) via Python programming. the reason of the results are going to be targeted on the simulation half since the distinction for the hardware implementation is that the ASCII text file to scan the period of time input video. the event of hand gesture recognition mistreatment Python, OpenCV, and YOLO V3 will be enforced by applying the theories of hand segmentation and also the hand detection system that uses the Haar-cascade classifier.
Deficit hawks: robust new physics searches with unknown backgrounds

Searches for new physics often face unknown backgrounds, causing false detections or weakened upper limits. This paper introduces the deficit hawk technique, which mitigates unknown backgrounds by testing multiple options for data cuts, such as fiducial volumes or energy thresholds. Combining the power of likelihood ratios with the robustness of the interval-searching techniques, deficit hawks could improve mean upper limits on new physics by a factor two for experiments with partial or speculative background knowledge. Deficit hawks are well-suited to analyses that use machine learning or other multidimensional discrimination techniques, and can be extended to permit discoveries in regions without unknown background.
Microcalcification Detection in Mammograms Using Deep Learning

Background: Mammography is the most reliable and popular method in the clinical diagnosis of breast cancer. Calcifications are subtle lesions in mammograms that can be cancerous and difficult to detect for radiologists. Computer-aided detection (CAD) can help radiologists identify malignant lesions. Objectives: This study aimed to propose a deep learning based CAD system for detecting calcifications in mammograms. Patients and Methods: A total of 815 in-house mammograms were collected from 204 women undergoing screening mammography. Calcifications in the mammograms were annotated by specialists. Each mammogram was divided into patches of fixed size, and then, patches containing calcifications were extracted, along with the same number of normal patches. A ResNet-50 Convolutional Neural Network (CNN) was trained for classification of patches into normal and calcification groups using training data and then the performance of the trained CNN was tested with new test data. Results: The proposed patch learning approach (PLA) showed a classification accuracy of 96.7% in the binary classification of patches. Therefore, it could detect calcification regions in a given mammogram. The PLA achieved sensitivity and specificity of 96.7% and 96.7%, respectively, with an area under the curve of 98.8%. Conclusion: The present results highlighted the efficacy of the proposed PLA, especially for limited training data. Direct comparison with previous studies is not possible due to differences in datasets. Nevertheless, the PLA accuracy in detecting calcifications was higher than that of deep learning based CAD systems in previous studies. The effective performance of PLA may be attributed to the manual removal of uninformative patches, as they were not used in the training set.
Towards An End-to-End Framework for Flow-Guided Video Inpainting

Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories. However, the hand-crafted flow-based processes in these methods are applied separately to form the whole inpainting pipeline. Thus, these methods are less efficient and rely heavily on the intermediate results from earlier stages. In this paper, we propose an End-to-End framework for Flow-Guided Video Inpainting (E$^2$FGVI) through elaborately designed three trainable modules, namely, flow completion, feature propagation, and content hallucination modules. The three modules correspond with the three stages of previous flow-based methods but can be jointly optimized, leading to a more efficient and effective inpainting process. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively and shows promising efficiency. The code is available at https://github.com/MCG-NKU/E2FGVI.
Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging

We propose a novel self-supervised Video Object Segmentation (VOS) approach that strives to achieve better object-background discriminability for accurate object segmentation. Distinct from previous self-supervised VOS methods, our approach is based on a discriminative learning loss formulation that takes into account both object and background information to ensure object-background discriminability, rather than using only object appearance. The discriminative learning loss comprises cutout-based reconstruction (cutout region represents part of a frame, whose pixels are replaced with some constant values) and tag prediction loss terms. The cutout-based reconstruction term utilizes a simple cutout scheme to learn the pixel-wise correspondence between the current and previous frames in order to reconstruct the original current frame with added cutout region in it. The introduced cutout patch guides the model to focus as much on the significant features of the object of interest as the less significant ones, thereby implicitly equipping the model to address occlusion-based scenarios. Next, the tag prediction term encourages object-background separability by grouping tags of all pixels in the cutout region that are similar, while separating them from the tags of the rest of the reconstructed frame pixels. Additionally, we introduce a zoom-in scheme that addresses the problem of small object segmentation by capturing fine structural information at multiple scales. Our proposed approach, termed CT-VOS, achieves state-of-the-art results on two challenging benchmarks: DAVIS-2017 and Youtube-VOS. A detailed ablation showcases the importance of the proposed loss formulation to effectively capture object-background discriminability and the impact of our zoom-in scheme to accurately segment small-sized objects.
Iterative Deep Homography Estimation

We propose Iterative Homography Network, namely IHN, a new deep homography estimation architecture. Different from previous works that achieve iterative refinement by network cascading or untrainable IC-LK iterator, the iterator of IHN has tied weights and is completely trainable. IHN achieves state-of-the-art accuracy on several datasets including challenging scenes. We propose 2 versions of IHN: (1) IHN for static scenes, (2) IHN-mov for dynamic scenes with moving objects. Both versions can be arranged in 1-scale for efficiency or 2-scale for accuracy. We show that the basic 1-scale IHN already outperforms most of the existing methods. On a variety of datasets, the 2-scale IHN outperforms all competitors by a large gap. We introduce IHN-mov by producing an inlier mask to further improve the estimation accuracy of moving-objects scenes. We experimentally show that the iterative framework of IHN can achieve 95% error reduction while considerably saving network parameters. When processing sequential image pairs, IHN can achieve 32.7 fps, which is about 8x the speed of IC-LK iterator. Source code is available at https://github.com/imdumpl78/IHN.
Contrastive Test-Time Adaptation

Test-time adaptation is a special setting of unsupervised domain adaptation where a trained model on the source domain has to adapt to the target domain without accessing source data. We propose a novel way to leverage self-supervised contrastive learning to facilitate target feature learning, along with an online pseudo labeling scheme with refinement that significantly denoises pseudo labels. The contrastive learning task is applied jointly with pseudo labeling, contrasting positive and negative pairs constructed similarly as MoCo but with source-initialized encoder, and excluding same-class negative pairs indicated by pseudo labels. Meanwhile, we produce pseudo labels online and refine them via soft voting among their nearest neighbors in the target feature space, enabled by maintaining a memory queue. Our method, AdaContrast, achieves state-of-the-art performance on major benchmarks while having several desirable properties compared to existing works, including memory efficiency, insensitivity to hyper-parameters, and better model calibration. Project page: sites.google.com/view/adacontrast.
E-Learning platform for music instruments

We’re living in the wоrk frоm hоme оr remоte wоrk erа аnd аll аre hоme bоund where everyоne wаnts tо leаrn new things whether it’s their lоst раssiоn оr their сuriоsity tо leаrn sо this study is based on the evolving advantages and challenges of e-learning рlаtfоrm thаt will be helрful in leаrning different musiс instruments аt their оwn соmfоrtаble рlасe. This study focuses on the depth knowledge of ease of e-learning in music sector. This study will enсоurаge the user tо leаrn аnd exсel in the music sector by the use of distance learning applications by sitting comfortably at their home or any other place. In аdditiоn, it focuses on the аdорtiоn оf e-Leаrning оutсоmes tо reduсe envirоnmentаl imрасt, with minimаl use оf mоbility аnd рарer use tо fасilitаte learning. It is imроrtаnt tо nоte the fасt thаt the attention is more given on the process more thаn оn the соntent оf eduсаtiоn. This change in the mode of learning has result in convenient, аs well аs the rарid develорment оf music education for students. In this regаrd, e- Leаrning is imроrtаnt in the field оf eduсаtiоn.
Media Pembelajaran Pobiqobra Berbasis Blended Learning untuk Meningkatan Motivasi dan Hasil Belajar Siswa MAN 3 Jombang

This study aims to describe the impact of the application of blended learning-based "Pobiqobra" media on students' motivation and mathematics learning outcomes at MAN 3 Jombang.. The learning media "Pobiqobra" is applied by combining direct face-to-face learning combined with online learning, namely by using powerpoint media, learning blogs, quipper and short geogebra with "Pobiqobra". This study used descriptive qualitative method. The research subjects were students of class XI MIPA 4 at MA Negeri 3 Jombang, totaling 35 people. This research instrument uses a questionnaire and the data collected is analyzed qualitatively. The results of this study indicate that there is a positive impact on the high motivation and learning outcomes of students by applying the Pobiqobra learning media. The results of student questionnaire analysis on learning Mathematics are 86% of students feel happy and 98% say the learning media used is very effective and satisfying.
Modelling graph dynamics in fraud detection with "Attention"

At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions. Despite the variety of different models for deep learning on graphs, few approaches have been proposed for dealing with graphs that are both heterogeneous and dynamic. In this paper, we propose DyHGN (Dynamic Heterogeneous Graph Neural Network) and its variants to capture both temporal and heterogeneous information. We first construct dynamic heterogeneous graphs from registration and transaction data from eBay. Then, we build models with diachronic entity embedding and heterogeneous graph transformer. We also use model explainability techniques to understand the behaviors of DyHGN-* models. Our findings reveal that modelling graph dynamics with heterogeneous inputs need to be conducted with "attention" depending on the data structure, distribution, and computation cost.
Illumination-Invariant Active Camera Relocalization for Fine-Grained Change Detection in the Wild

Active camera relocalization (ACR) is a new problem in computer vision that significantly reduces the false alarm caused by image distortions due to camera pose misalignment in fine-grained change detection (FGCD). Despite the fruitful achievements that ACR can support, it still remains a challenging problem caused by the unstable results of relative pose estimation, especially for outdoor scenes, where the lighting condition is out of control, i.e., the twice observations may have highly varied illuminations. This paper studies an illumination-invariant active camera relocalization method, it improves both in relative pose estimation and scale estimation. We use plane segments as an intermediate representation to facilitate feature matching, thus further boosting pose estimation robustness and reliability under lighting variances. Moreover, we construct a linear system to obtain the absolute scale in each ACR iteration by minimizing the image warping error, thus, significantly reduce the time consume of ACR process, it is nearly $1.6$ times faster than the state-of-the-art ACR strategy. Our work greatly expands the feasibility of real-world fine-grained change monitoring tasks for cultural heritages. Extensive experiments tests and real-world applications verify the effectiveness and robustness of the proposed pose estimation method using for ACR tasks.
"FIJO": a French Insurance Soft Skill Detection Dataset

Understanding the evolution of job requirements is becoming more important for workers, companies and public organizations to follow the fast transformation of the employment market. Fortunately, recent natural language processing (NLP) approaches allow for the development of methods to automatically extract information from job ads and recognize skills more precisely. However, these efficient approaches need a large amount of annotated data from the studied domain which is difficult to access, mainly due to intellectual property. This article proposes a new public dataset, FIJO, containing insurance job offers, including many soft skill annotations. To understand the potential of this dataset, we detail some characteristics and some limitations. Then, we present the results of skill detection algorithms using a named entity recognition approach and show that transformers-based models have good token-wise performances on this dataset. Lastly, we analyze some errors made by our best model to emphasize the difficulties that may arise when applying NLP approaches.
Unsupervised Cross-Task Generalization via Retrieval Augmentation

Humans can perform unseen tasks by recalling relevant skills acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this paper, we aim to improve this kind of cross-task generalization ability of massive multi-task language models, such as T0 and FLAN, in an unsupervised setting. We propose a retrieval-augmentation method named ReCross that takes a few unlabelled examples as queries to retrieve a small subset of upstream data and uses them to update the multi-task model for better generalization. ReCross is a straightforward yet effective retrieval method that combines both efficient dense retrieval and effective pair-wise reranking. Our results and analysis show that it significantly outperforms both non-retrieval methods and other baseline methods.