Reviewerly/exHarmony huggingface dataset:
Design of Distance Teaching System for Textile Pattern Design Course Based on Online and Offline Integration

In order to reduce the CPU utilization of the distance teaching system of textile pattern design course, this paper designs a distance teaching system of textile pattern design course integrating online and offline. The hardware design part includes power supply module, wireless communication module, logic control module and data reading module. The software design adopts B/S architecture to establish a three-tier structure model. Microsoft SQL server is adopted as the database management system; Use Hadoop framework to build cloud computing platform; Pick KX algorithm is used to realize dynamic load balancing and allocate server resources; Correct the difference of data name by entity alignment; Through ASP Net script file to design web interactive page. The test results show that when the number of concurrent users is 200 and 500, the maximum CPU utilization of database server is 53.7% and 65.5%, and the maximum CPU utilization of web server is 47.5% and 56.7%, which reduces the CPU utilization of the system as a whole.
THE PROBLEM OF UNTRANSLATABILITY IN TRANSLATION STUDIES

The article is dedicated to the issue of translatability/untranslatability, possible challenges in translation and the ways of overcoming them.It is proved that translatability and untranslatability are related to the psycholinguistic and ethnolinguistic translation adequacy comprising such factors as intellectual, mental, cultural and philosophical.The active participants include the author of the source text as the representative of the authentic foreign culture and the translator, as the mediator and interpreter.Among the possible challenges of translation there are language discrepancies, their asymmetry, the uniqueness of the source text atmosphere as the reflection of the national mentality, and the insufficient amount of preliminary background information necessary for adequate translation.In the focus of the article there are words in Ukrainian, English and German which don't have exact equivalents in the target language or vice versa may have multiple partial correspondences.Expressions or lexical units which cannot be rendered in translation are termed as lacunas.Lacunas don't prove the impossibility of translation, they indicate at the absence of the proper equivalent with reference to the social, cultural, ethnic and historic peculiarities.The processes of demotivation, loss of mental and cultural coloring in translation and the resulting idiomaticity are also investigated, possible semantic breeches as well as cases of interlinguistic homonymy and paronymy are taken into consideration.The most efficient translation strategies and operations are worked out.Descriptive translation and translator's commentary are suggested as the most appropriate translation transformations.The overview of the theoretical groundings comprises the most relevant research works on the issue of untranslatability in the paradigm of different approaches and trends.It is resumed that untranslatability mostly stands for the challenges in translation caused by the absence of the full equivalent either due to the lack of intelligence or competence in a certain field of knowledge or activity, sometimes this may also be predetermined by the insufficient psycholinguistic or sociocultural preparation of the translator.The perspective for further research is seen in the study of the linguistic, social, cultural and psycholinguistic levels in translation.
Design of Educational Resource Sharing System for Financial Management Specialty Based on Data Mining

Due to the long response time of the traditional financial management education resource sharing system, a financial management education resource sharing system based on data mining is designed. Firstly, the hardware configuration structure and function of the financial management education resource sharing system are optimized to improve the hardware performance of the system. Then, the massive financial management education resources are effectively stored and classified, and the targeted real-time extraction and sharing of teaching information are carried out. Finally, the experiment proves that the response time of the educational resource sharing system of financial management specialty based on data mining is short, and it has high practicability in the process of practical application.
Research on Quality Evaluation of Accounting Online Education Based on Artificial Intelligence Technology

In view of the low accuracy of the traditional methods in the evaluation of the quality of accounting online education, artificial intelligence technology is used to study the evaluation methods of the quality of accounting online education. The dgk means algorithm is used to mine the education quality evaluation index data. According to the mining results, the tndicator unit is constructed under the basic principles of the evaluation system, the evaluation index is obtained, the correlation coefficient of the evaluation index is calculated, and the online education quality evaluation is carried out through the established BP evaluation neural network. Simulation experiments show that the proposed method has high accuracy and good evaluation effect.
Early Warning System of Computerized Accounting Teaching Data Quality Based on Artificial Intelligence

In order to solve the problems of low warning accuracy and long warning time in the existing teaching quality early warning system, this paper presents a computerized accounting teaching data quality early warning system based on artificial intelligence. In the hardware part of the system, the data acquisition module, storage module, processor module and LCD display module are mainly designed. In the software part of the system, the data are collected and cleaned in advance, then the data association relationship is mined, and the early warning method is put forward, so as to realize the early warning of computerized accounting teaching data quality based on artificial intelligence. The experimental results show that the proposed early warning system effectively improves the accuracy and efficiency of early warning.
AONet: Attentional Occlusion-Aware Network for Occluded Person Re-identification

Occluded person Re-identification (Occluded ReID) aims to verify the identity of a pedestrian with occlusion across non-overlapping cameras. Previous works for this task often rely on external tasks, e.g., pose estimation, or semantic segmentation, to extract local features over fixed given regions. However, these external models may perform poorly on Occluded ReID, since they are still open problems with no reliable performance guarantee and are not oriented towards ReID tasks to provide discriminative local features. In this paper, we propose an Attentional Occlusion-aware Network (AONet) for Occluded ReID that does not rely on any external tasks. AONet adaptively learns discriminative local features over latent landmark regions by the trainable pattern vectors, and softly weights the summation of landmark-wise similarities based on the occlusion awareness. Also, as there are no ground truth occlusion annotations, we measure the occlusion of landmarks by the awareness scores, when referring to a memorized dictionary storing average landmark features. These awareness scores are then used as a soft weight for training and inferring. Meanwhile, the memorized dictionary is momenta updated according to the landmark features and the awareness scores of each input image. The AONet achieves $$53.1\%$$ mAP and $$66.5\%$$ Rank1 on the Occluded-DukeMTMC, significantly outperforming state-of-the-arts without any bells and whistles, and also shows obvious improvements on the holistic datasets Market-1501 and DukeMTMC-reID, as well as the partial datasets Partial-REID and Partial-iLIDS. The code and pre-trained models will be released online soon.
Effectiveness of Using Center and Circle Methods to Increase Interest in Learning Early in Children

The purpose of this study was to determine the effectiveness of the Center and Circle method as an effort to increase interest in early childhood learning. The research subjects were children in KB TK Almutaqin Yogyakarta. The results showed that there were differences in interest in learning before and after getting the Center and Circle method. This is indicated by the results of the t=-4,735 and p=0,000 test (p&lt;0,05). The average interest in learning for students before receiving treatment with the center and circle method was 1,667, while the average after receiving treatment with the center and circle method was 5,600. This confirms that the Sentra and Circle methods are effective in increasing children's interest in learning at KB Almutaqin Kindergarten, Sleman Yogyakarta.
Independent Curriculum Learning Plan of Reading and Writing in Inclusive Schools

This study aims to explore the planning of ATP and teaching modules for grade 1 literacy learning with independent learning curriculum at the inclusive school of SDN Sumbersari 2 Malang. This study uses a qualitative approach. Sources of data used by researchers in the form of ATP documentation and Indonesian Language Teaching Module in class I SDN Sumbersari 2 Malang. Data collection is done by means of documentation and interview techniques. The results showed that there was no difference in planning for literacy learning in the form of ATP (Learning Objectives Flow) and Teaching Modules designed by teachers for regular students, hyperactivity/ADHD and mild hearing loss. Teachers use ATP (Learning Objectives Flow) from the Ministry of Education and Culture, teachers do not modify ATP (Learning Objectives Flow) for hyperactive students and mild hearing loss. Teachers design Teaching Modules that are in accordance with teacher handbooks published by the Ministry of Education and Culture. Teachers also do not design/modify special teaching modules for hyperactive/ADHD and mild hearing-impaired students.
The Influence of the Role-Playing Method to Improve Speaking Skills in the Merauke Pertiwi Kindergarten Group A

The ability to speak is one aspect of language development that needs to be instilled from an early age. The purpose of this study was to (1) describe the application of role-playing in improving speaking skills in group A children of Pertiwi Merauke Kindergarten (2) describe the improvement of group A children's speaking skills through the application of role-playing in Pertiwi Merauke Kindergarten. This research uses Classroom Action Research. This study used two cycles where each cycle of data analysis used qualitative and quantitative analysis. The research instrument used an observation sheet. The results of this study indicate that the child's speaking ability from cycle I to II has increased.
Tindak Tutur Pemimpin dalam Rapat Manajemen: Kajian Pragmatik Konsep Gender dalam Perspektif Budaya dan Bahasa

I Abstract In an organization the leader has an important role as an agent of change. This is also reflected in speech acts that have an intersection with culture and the use of language in conveying messages. This study aims to explain the differences in the use of language forms of speech between male and female leaders in official meetings, BKD management meetings in Banjarnegara Regency. The paradigm used is constructivist, with a qualitative approach and case study method. The informants who become the data sources are the echelon IV-II leaders in the BKD Banjarnegara Regency who occupy positions as structural officials, selected based on purposive sampling and criterion based selection. The research data is in the form of lingual units of speech acts used by female and male leaders in management meeting discourse. The technique of collecting data was by recording, listening, taking notes, observing (observation) and interviewing techniques. The analysis refers to pragmatic analysis which emphasizes means-end analysis and heuristics. The results show that culture and language cannot be separated and culture is always attached, integrated in the context of language. All cultures have a set of perceptions, behaviors, and values that are related to many things, one of which is gender roles and authority. The speeches conveyed by female leaders in management meetings tend to be expressive, sympathetic and negative, while the speeches of male leaders tend to be directive. Expressive and sympathetic speech is intended to please others because it does not address the needs of the speaker but rather the needs of the listener. Meanwhile, directive utterances tend to be confrontational and competitive and lead to the needs of speakers rather than listeners. The negative utterances spoken by female leaders tend to be in the form of questions, doubtful whether the choice of words they use is wrong or less acceptable to the listeners by using non-literal, indirect techniques. However, negative utterances at male leaders tend to get to the heart of the problem, in the form of asking something literal, rational and direct.
Food Deserts and k-Means Clustering

Food deserts are regions where people lack access to healthy foods.In this article we use k-means clustering to cluster the food deserts in two Bay Area counties.The centroids (means) of these clusters are optimal locations for intervention sites (such as food pantries) since they minimize the distance that a person within a food desert cluster would need to travel to reach the resources they require.We present the results of both a standard and a weighted k-means clustering algorithm.The weighted algorithm takes into account the poverty levels in each food desert when determining the placement of a centroid.We find that this weighting can make significant changes to the proposed locations of intervention sites.
Image Retrieval and Clustering Using Image Mining

There is an interdisciplinary field which is known as the image mining, it has special features like machine vision, picture handling, picture recovery, information mining. Al, data sets, and man-made reasoning. Notwithstanding the way that many examinations have been led in every one of these areas, picture mining and arising issues research is as vet in its outset. Information mining strategies, for instance, can't naturally remove valuable data from a lot of information, like pictures. In this theory, we examined the overall method of the examination and the fundamental procedures of picture recovery by introducing the exceptional highlights of picture recovery and bunching utilizing picture mining. Finally, in order to make progress and development in this area, we investigated various image retrieval and elustering systems, as well as knowledge extraction from images. In the current scenarin, image retrieval is the primary requirement task. The popular image retrieval system is content-based image retrieval, which retrieves the target image based on the useful features of the given image. If images are clustered correctly, they can be retrieved relatively quickly. The concepts of (Content-Based Image Retrieval) CBIR, image clustering, and image mining have been combined in this thesis, and a new clustering technique has been introduced to improve the speed of the image retrieval system. To improve computational efficiency, the CBIR system employs clustering and deep learning. To obtain detailed and valuable information, the Fuzzy C-based algorithm and technique for CBIR will be used for color-based image retrieval.
Efficient Large-scale Scene Representation with a Hybrid of High-resolution Grid and Plane Features

Existing neural radiance fields (NeRF) methods for large-scale scene modeling require days of training using multiple GPUs, hindering their applications in scenarios with limited computing resources. Despite fast optimization NeRF variants have been proposed based on the explicit dense or hash grid features, their effectivenesses are mainly demonstrated in object-scale scene representation. In this paper, we point out that the low feature resolution in explicit representation is the bottleneck for large-scale unbounded scene representation. To address this problem, we introduce a new and efficient hybrid feature representation for NeRF that fuses the 3D hash-grids and high-resolution 2D dense plane features. Compared with the dense-grid representation, the resolution of a dense 2D plane can be scaled up more efficiently. Based on this hybrid representation, we propose a fast optimization NeRF variant, called GP-NeRF, that achieves better rendering results while maintaining a compact model size. Extensive experiments on multiple large-scale unbounded scene datasets show that our model can converge in 1.5 hours using a single GPU while achieving results comparable to or even better than the existing method that requires about one day's training with 8 GPUs.
Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimization

Hyper-parameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles to the utilization of general-purpose black-box optimization techniques. This paper proposes an agent-based collaborative technique for finding near-optimal values for any arbitrary set of hyper-parameters (or decision variables) in a machine learning model (or general function optimization problem). The developed method forms a hierarchical agent-based architecture for the distribution of the searching operations at different dimensions and employs a cooperative searching procedure based on an adaptive width-based random sampling technique to locate the optima. The behavior of the presented model, specifically against the changes in its design parameters, is investigated in both machine learning and global function optimization applications, and its performance is compared with that of two randomized tuning strategies that are commonly used in practice. According to the empirical results, the proposed model outperformed the compared methods in the experimented classification, regression, and multi-dimensional function optimization tasks, notably in a higher number of dimensions and in the presence of limited on-device computational resources.
Testing the Channels of Convolutional Neural Networks

Neural networks have complex structures, and thus it is hard to understand their inner workings and ensure correctness. To understand and debug convolutional neural networks (CNNs) we propose techniques for testing the channels of CNNs. We design FtGAN, an extension to GAN, that can generate test data with varying the intensity (i.e., sum of the neurons) of a channel of a target CNN. We also proposed a channel selection algorithm to find representative channels for testing. To efficiently inspect the target CNN's inference computations, we define unexpectedness score, which estimates how similar the inference computation of the test data is to that of the training data. We evaluated FtGAN with five public datasets and showed that our techniques successfully identify defective channels in five different CNN models.
A polar prediction model for learning to represent visual transformations

All organisms make temporal predictions, and their evolutionary fitness level depends on the accuracy of these predictions. In the context of visual perception, the motions of both the observer and objects in the scene structure the dynamics of sensory signals, allowing for partial prediction of future signals based on past ones. Here, we propose a self-supervised representation-learning framework that extracts and exploits the regularities of natural videos to compute accurate predictions. We motivate the polar architecture by appealing to the Fourier shift theorem and its group-theoretic generalization, and we optimize its parameters on next-frame prediction. Through controlled experiments, we demonstrate that this approach can discover the representation of simple transformation groups acting in data. When trained on natural video datasets, our framework achieves better prediction performance than traditional motion compensation and rivals conventional deep networks, while maintaining interpretability and speed. Furthermore, the polar computations can be restructured into components resembling normalized simple and direction-selective complex cell models of primate V1 neurons. Thus, polar prediction offers a principled framework for understanding how the visual system represents sensory inputs in a form that simplifies temporal prediction.
Photonic quantum computing with probabilistic single photon sources but without coherent switches

We present photonic quantum computing architectures that can deal with both probabilistic (heralded) generation of single photons and probabilistic gates without making use of coherent switching. The only required dynamical element is the controllable absorption of all photons in a given mode. While the architectures in theory scale polynomially in the resources required for universal quantum computation, as presented their overhead is large and they are illustrative extreme points in the configuration space of photonic approaches, rather than a recipe that anybody should seriously pursue. They do, however, prove that many things presumed necessary for photonic quantum computing, in fact are not. Of potentially independent interest may be that the architectures make use of qubits which have many possible microstates corresponding to a single effective qubit state, and the technique for dealing with probabilistic operations is to, when necessary, just enlarge the set of such microstates to incorporate all possibilities, while making heavy use of the subsequent ability to `coherently erase' which particular microstate a given qubit is in.
Towards Composable Distributions of Latent Space Augmentations

We propose a composable framework for latent space image augmentation that allows for easy combination of multiple augmentations. Image augmentation has been shown to be an effective technique for improving the performance of a wide variety of image classification and generation tasks. Our framework is based on the Variational Autoencoder architecture and uses a novel approach for augmentation via linear transformation within the latent space itself. We explore losses and augmentation latent geometry to enforce the transformations to be composable and involuntary, thus allowing the transformations to be readily combined or inverted. Finally, we show these properties are better performing with certain pairs of augmentations, but we can transfer the latent space to other sets of augmentations to modify performance, effectively constraining the VAE's bottleneck to preserve the variance of specific augmentations and features of the image which we care about. We demonstrate the effectiveness of our approach with initial results on the MNIST dataset against both a standard VAE and a Conditional VAE. This latent augmentation method allows for much greater control and geometric interpretability of the latent space, making it a valuable tool for researchers and practitioners in the field.