Reviewerly/exHarmony huggingface dataset:
Garbage Classification Algorithm Based on Deep Learning

This paper proposes a set of automated garbage sorting system and designs the garbage sorting algorithm based on the neural network and YOLO algorithm. To realize the detection of books, cans, plastic bottles, and glass bottles, we design a feature extraction, two detection layers and corresponding anchors and a loss function in turn, which are based on a neural network, the FPN idea and the detection output characteristics, respectively. The results show that the network model designed in this paper is light, with an accuracy rate of 86.9% and a speed of 15ms. When detecting multi-scale targets, the detection accuracy is above 86.5%, which can meet the real-time and accuracy requirements of the system.
Informatization of education as a trend of modern educational activity

The purpose of the article is to reveal the problem of using information technology in educational institutions. Based on the analysis of the regulatory framework, scientific and methodological literature on the research topic, the specificity of the implementation of information technologies in the study of individual subjects was determined. Particular attention in the article is paid to the consideration of the influence of computerization on the quality of education, examples of the use of information technologies in education are revealed. The article describes the three main components of information technology as a complex of hardware, software and a system of organizational and methodological support; the description of analog and digital information technologies is presented. The authors list the most common multifunctional office applications and IT tools; the advantages of using IT in the educational process are highlighted. The authors come to the conclusion that the use of information technologies in education makes it possible to diversify a wide range of forms and methods of teaching, which are one of the modern ways to improve the educational process.
POPCORN: Progressive Pseudo-Labeling with Consistency Regularization and Neighboring

Semi-supervised learning (SSL) uses unlabeled data to compensate for the scarcity of annotated images and the lack of method generalization to unseen domains, two usual problems in medical segmentation tasks. In this work, we propose POPCORN, a novel method combining consistency regularization and pseudo-labeling designed for image segmentation. The proposed framework uses high-level regularization to constrain our segmentation model to use similar latent features for images with similar segmentations. POPCORN estimates a proximity graph to select data from easiest ones to more difficult ones, in order to ensure accurate pseudo-labeling and to limit confirmation bias. Applied to multiple sclerosis lesion segmentation, our method demonstrates competitive results compared to other state-of-the-art SSL strategies.
Goal-Oriented Script Construction.

The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems. We propose the Goal-Oriented Script Construction task, where a model produces a sequence of steps to accomplish a given goal. We pilot our task on the first multilingual script learning dataset supporting 18 languages collected from wikiHow, a website containing half a million how-to articles. For baselines, we consider both a generation-based approach using a language model and a retrieval-based approach by first retrieving the relevant steps from a large candidate pool and then ordering them. We show that our task is practical, feasible but challenging for state-of-the-art Transformer models, and that our methods can be readily deployed for various other datasets and domains with decent zero-shot performance.
Cyclic DNA codes over the ring $\mathbb{Z}_4+u\mathbb{Z}_4+u^2\mathbb{Z}_4$

In this work, we have investigated the one generator cyclic DNA codes with reverse and reverse complement constraints over the ring $R=\mathbb{Z}_4+u\mathbb{Z}_4+u^2\mathbb{Z}_4$ with $u^3=0$. Skew cyclic codes with reverse complement constraint are constructed over $R$. We have also determined a one-to-one correspondence between the elements of the ring $R$ and DNA codons satisfying the Watson-Crick complement. Finally, we have established some examples which satisfy the given constraints.
Design of CycleGAN model for SAR image colorization

In deep learning based image processing, the number of dataset is important to train the designed model. However, it is hard to secure SAR images, because satellite-based SAR is limited and high-resolution images are very expensive. Generative adversarial network (GAN) supplements this problem by learning two models, generator and discriminator, in an adversarial process at the same time. In this paper, we take one dataset as input data, and compare its accuracy using GAN models. CycleGAN is used to generate images among GAN models. Optical images are used for dataset and Chinese cities are selected for SAR images. The lack of dataset, a drawback of SAR images, is supplemented using data augmentation. SSIM, MSE, and PSNR of fake and original images are calculated using dataset and show that CycleGAN has the most lower MSE with 639.4379 and highest PSNR with 20.0728. Pix2pix has the most highest SSIM with 0.7842.
A Time-Distributed Convolutional Long Short-Term Memory for Hand Gesture Recognition

The applications of human-robot interaction have recently raised many research interests, and hand gesture recognition to recognize human gestures in video-based problems is one of them. In the recent decade, deep learning techniques have proven their promising performance in the fields of pattern recognition and computer vision. This study presents an improved version of the Convolutional Neural Network in combination with Long Short-Term Memory for hand gesture recognition. The proposed structure is fully considered in a time-distributed framework to effectively train the network of the frame-level classification. Hence, employing a time-distributed framework, a TD-CNN-LSTM method is developed. Finally, the efficacy of our proposed architecture is evaluated on the recent publicly available GRIT corpus dataset, and we also show that our method outperforms the recent state of the art CNN-LSTM method.
Towards Active Learning Based Smart Assistant for Manufacturing

Smart assistants in manufacturing can guide and aid on decision-making while also provide means to collect additional insights and information available to the users. A general approach for building a smart assistant that provides users with machine learning forecasts and a sequence of decision-making options is presented in this work. The system provides means for knowledge acquisition by gathering data from users. To minimize interactions and friction with users, we envision active learning can be used to get data labels for most data instances expected to be most informative. The system is demonstrated on a demand forecasting use case in manufacturing. The methodology can be extended to several use cases in manufacturing.
Impact of HR Practices on Employee Retention in Oil and Gas Industries of Oman

Effective human resource management fosters workplace loyalty and boosts an organization's productivity to new heights.Gulf nations, especially Oman, have similar problems as other countries.A WTO treaty has been signed and developed, requiring Omani businesses to demonstrate their presence in global markets and compete internationally.Omani companies are left with few options.Either they adapt, or they die in today's fiercely competitive marketplace.Omani oil firms are being compelled to abandon their traditional work culture in favor of modern work environments.The primary aim of this study is to investigate the current practices of human resource management employed by oil and gas companies of Oman and assess how to retain human resources in Oman's oil and gas companies.It has been found that the majority of respondents in Oman's oil and gas sector endorse the company's human resource policies and the company's staff retention strategy.At the same time, according to Pearson Correlations Coefficient and Multiple Regression Analysis, an apparent positive connection can be seen within human resource strategies and employee retention in Oman's oil and gas sector.
The Efficiency of Aggregation Methods in Ensemble Filter Feature Selection Models

Ensemble feature selection is recommended as it proves to produce a more stable subset of features and a better classiﬁcation accuracy when compared to the individual feature selection methods. In this approach, the output of feature selection methods, called base selectors, are combined using some aggregation methods. For filter feature selection methods, a list aggregation method is needed to aggregate output ranked lists into a single list, and since many list aggregation methods have been proposed the decision on which method to use to build the optimum ensemble model is a de facto question.&#x0D; In this paper, we investigate the efficiency of four aggregation methods, namely; Min, Median, Arithmetic Mean, and Geometric Mean. The performance of aggregation methods is evaluated using five datasets from different scientific fields with a variant number of instances and features. Besides, the classifies used in the evaluation are selected from three different classes, Trees, Rules, and Bayes.&#x0D; The experimental results show that 11 out of the 15 best performance results are corresponding to ensemble models. And out of the 11 best performance ensemble models, the most efficient aggregation methods are Median (5/11), followed by Arithmetic Mean (3/11) and Min (3/11). Also, results show that as the number of features increased, the efficient aggregation method changes from Min to Median to Arithmetic Mean. This may suggest that for a very high number of features the efficient aggregation method is the Arithmetic Mean. And generally, there is no aggregation method that is the best for all cases.
Best VM Selection for Big Data Applications across Multiple Frameworks by Transfer Learning

Cloud providers are presented with a bewildering choice of VM types for a range of contemporary data processing frameworks today. However, existing performance modeling and machine learning efforts cannot pick optimal VM types for multiple frameworks simultaneously, since they are difficult to balance model accuracy and model training cost.
Why Plain Language? Linguistic Accessibility in Inclusive Higher Education

Plain language writing is clear, succinct, and jargon-free, and is organized in a way that facilitates understanding. It is a reader-centered way of writing, with the end goal of readers being able to access, comprehend, and utilize information. Like Universal Design, plain language writing benefits all readers while ensuring crucial access for some.
Multiple Self-attention Network for Intracranial Vessel Segmentation

The capture of long-distance dependencies presents an efficient approach for feature learning and extraction. Especially the Transformers models that explore the dependencies within a long sequence, have swept the fields of natural language processing with their powerful performance. However, Transformers require extremely high computing power due to the huge amount of parameters, and it cannot achieve parallelism since it outputs tokens one by one. In this work, inspired by Transformers, we propose a self-attention encoder module (SAEM) that focuses on learning the connections between each position and all other positions in the image, which preserves the efficiency of Transformers but with less calculation and faster inference speed. In our SAEM, different group of internal feature maps within images captured by multiple scaled self-attentions are cascaded to generate global context information. Based on our SAEM, a lightweight and parallel network is designed for segmentation of intracranial blood vessels. Moreover, a data augmentation method is proposed, called sliced mosaic permutation, which makes the original image features richer and alleviates the problem of category imbalance, via cutting the original images with different scales and recombining randomly. We apply SAEM and sliced mosaic permutation to the task of intracranial blood vessel segmentation, the result shows that our method outperforms competitive methods in both visualization results and quantitative evaluation.
Weakly Supervised Contrastive Learning

Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent achievement of contrastive learning. Most of the existing contrastive learning frameworks adopt the instance discrimination as the pretext task, which treating every single instance as a different class. However, such method will inevitably cause class collision problems, which hurts the quality of the learned representation. Motivated by this observation, we introduced a weakly supervised contrastive learning framework (WCL) to tackle this issue. Specifically, our proposed framework is based on two projection heads, one of which will perform the regular instance discrimination task. The other head will use a graph-based method to explore similar samples and generate a weak label, then perform a supervised contrastive learning task based on the weak label to pull the similar images closer. We further introduced a K-Nearest Neighbor based multi-crop strategy to expand the number of positive samples. Extensive experimental results demonstrate WCL improves the quality of self-supervised representations across different datasets. Notably, we get a new state-of-the-art result for semi-supervised learning. With only 1\% and 10\% labeled examples, WCL achieves 65\% and 72\% ImageNet Top-1 Accuracy using ResNet50, which is even higher than SimCLRv2 with ResNet101.
A Pruning Optimized Fast Learn++NSE Algorithm

Due to the large number of typical applications, it is very important and urgent to study the fast classification learning of accumulated big data in nonstationary environments. The newly proposed algorithm, named Learn++.NSE, is one of the important research results in this research field. And a pruning version, named Learn++.NSE-Error-based, was given for accumulated big data to improve the learning efficiency. However, the studies have found that the Learn++.NSE-Error-based algorithm often encounters a situation that the newly generated base classifier is pruned in the next integration, which reduces the accuracy of the ensemble classifier. The newly generated base classifier is very important in the next ensemble learning and should be retained. Therefore, the two latest base classifiers are retained without being pruned, and a new pruning algorithm named NewLearn++.NSE-Error-based was proposed. The experimental results on the generated dataset and the real-world dataset show that NewLearn++.NSE-Error-based can further improve the accuracy of the ensemble classifier under the premise of obtaining the same time complexity as Learn++.NSE algorithm. It is suitable for fast classification learning of long-term accumulated big data.
The information and communication technologies in the learning process of students in the context of cognitive development

The main purpose of the research is to analyze information and communication technologies in the process of teaching students in the context of cognitive development. Every year the issue of ensuring an appropriate level of information services for the educational process becomes more and more urgent. Expanding the influence of information and communication technologies on the learning system is an ongoing and ongoing process. Information and communication technologies are a set of methods, tools and techniques used for the selection, processing, storage, presentation, transmission of various data and materials necessary to improve the efficiency of various activities. The result of our research is to identify the main aspects of information and communication technologies in the process of teaching students in the context of cognitive development.
Quantum Algorithms for the Solution of Matrix Equations in Electromagnetics

We present the Harrow/Hassidim/Lloyd (HHL) algorithm-a fundamental technique in quantum computing for matrix equations-and describe its application to matrix equations in electromagnetics. A method of moments (MoM) matrix equation is analyzed, and we compare the results of a simulated HHL solution with a classical solution.
On the Security of Subspace Subcodes of Reed–Solomon Codes for Public Key Encryption

This article discusses the security of McEliece-like encryption schemes using subspace subcodes of Reed–Solomon codes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> subcodes of Reed–Solomon codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}_{q^{m}}}$ </tex-math></inline-formula> whose entries lie in a fixed collection of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}_{q}}$ </tex-math></inline-formula> –subspaces of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}_{q^{m}}}$ </tex-math></inline-formula> . These codes appear to be a natural generalisation of Goppa and alternant codes and provide a broader flexibility in designing code based encryption schemes. For the security analysis, we introduce a new operation on codes called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">twisted product</i> which yields a polynomial time distinguisher on such subspace subcodes as soon as the chosen <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}_{q}}$ </tex-math></inline-formula> –subspaces have dimension larger than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m/2$ </tex-math></inline-formula> . From this distinguisher, we build an efficient attack which in particular breaks some parameters of a recent proposal due to Khathuria, Rosenthal and Weger.
Sharing Research Data: An Analysis of the Interest of Social Scientists in the Context of a Mexican University

Open Science and open research data have the potential to speed up the processes of science and to generate benefits to society. However, the openness of research data and science cannot be taken for granted since there is a trend toward the capitalization of knowledge. In addition, each area of knowledge differs in terms of the data used and the rules that govern each scientific community. The aim of this article is to analyze social researchers’ interest in sharing research data within the context of a Mexican university. Based on the constructivist grounded theory approach, 12 interviews were conducted with social scientists from a higher education institution in Mexico. From the analysis, four categories associated with the researchers’ attitudes of sharing their data emerged. The findings exhibit that researchers’ interest in sharing their scientific data is prone to (1) selectively sharing, (2) perpetuating the system, (3) protecting privacy and (4) considering resources. These results show that the scientists interviewed show an opposite inclination to Open Science, since they are not willing to share their data openly, including the fact that within the Mexican context, the practice of sharing data openly is not encouraged.
Sim-to-Real Visual Grasping via State Representation Learning Based on Combining Pixel-Level and Feature-Level Domain Adaptation

In this study, we present a method to grasp diverse unseen real-world objects using an off-policy actor-critic deep reinforcement learning (RL) with the help of a simulation and the use of as little real-world data as possible. Actor-critic deep RL is unstable and difficult to tune when a raw image is given as an input. Therefore, we use state representation learning (SRL) to make actor-critic RL feasible for visual grasping tasks. Meanwhile, to reduce visual reality gap between simulation and reality, we also employ a typical pixel-level domain adaptation that can map simulated images to realistic ones. In our method, as the SRL model is a common preprocessing module for simulated and real-world data, we perform SRL using real and adapted images. This pixel-level domain adaptation enables the robot to learn grasping skills in a real environment using small amounts of real-world data. However, the controller trained in the simulation should adapt to the real world efficiently. Hence, we propose a method combining a typical pixel-level domain adaptation and the proposed SRL model, where we perform SRL based on a feature-level domain adaptation. In evaluations of vision-based robotics grasping tasks, we show that the proposed method achieves a substantial improvement over a method that only employs a pixel-level or domain adaptation.
Face Mask Detection using Multi-Stage CNN Architecture

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Web-Based Sumedang Regency Organizational Administration Information System Design

Computer-based technology at this time has provided many very significant benefits for all human activities, in the field of business computer-based technology helps to handle existing inventories in a company, both large and small companies as well as in organizations. The use of information technology appropriately and correctly, will be able to provide accurate and precise information in making a final report. The Sumedang ORGANIZATION has problems entering cadre data so that it cannot know the number of existing cadres, as well as many of its cadres who do not understand the existing organizational and administrative regulations due to the lack of effectiveness in delivering information contained in the organization's manual. Based on the existing problems, the author intends to propose a solution by designing a web-based administrative information system
Newer is Not Always Better: Rethinking Transferability Metrics, Their Peculiarities, Stability and Performance

Fine-tuning of large pre-trained image and language models on small customized datasets has become increasingly popular for improved prediction and efficient use of limited resources. Fine-tuning requires identification of best models to transfer-learn from and quantifying transferability prevents expensive re-training on all of the candidate models/tasks pairs. In this paper, we show that the statistical problems with covariance estimation drive the poor performance of H-score—a common baseline for newer metrics—and propose shrinkage-based estimator. This results in up to $$80\%$$ absolute gain in H-score correlation performance, making it competitive with the state-of-the-art LogME measure. Our shrinkage-based H-score is 3–10 times faster to compute compared to LogME. Additionally, we look into a less common setting of target (as opposed to source) task selection. We demonstrate previously overlooked problems in such settings with different number of labels, class-imbalance ratios etc. for some recent metrics e.g., NCE, LEEP that resulted in them being misrepresented as leading measures. We propose a correction and recommend measuring correlation performance against relative accuracy in such settings. We support our findings with $$\sim $$ 164,000 (fine-tuning trials) experiments on both vision models and graph neural networks.