Reviewerly/exHarmony huggingface dataset:
PGNN: A Parallel Granular BP Neural Network for Classification

Abstract Deep learning models based on BP neural networks are widely used in classification and regression problems. However, they have a wide range of parameters and insufficient local and uncertain information representations. Granular computing has great uncertainty characterization capability and allows input patterns to be abstracted at higher levels. A parallel granular BP neural network is proposed by combining clustered granulation to reconfigure the BP neural network. First, the optimal samples are defined and used as the references for granulating. Features are granulated into granules. And samples are granulated into granular vectors. Secondly, the granule vector is input to the granule BP neural network, and the granules for classification are output after the granule BP forward calculation, and are reduced to the original classification results by the threshing process. The granular BP neural network defines granular activation functions and a granular loss function to fit the input pattern of granular vectors while retaining the original fully connected neural network structure. The problem to be processed is extended into granules by granulation, and the structured characteristics of the granules make the granular BP neural network computable in parallel. Finally, compared with DNN, SVM, DT and NB, PGNN has better classification accuracy and improved computational efficiency and enhanced generalization performance.
Word Relation-based Graph Neural Network for Short Text Similarity Measurement

Abstract Short text similarity measurement is an important task, which is broadly applied to many fields. Due to the lack of contextual information, it is not easy to accurately compare the similarity between short texts. The relations between words are used to enhance the information. Since Graph Neural Networks (GNN) perform well in handling data with complex relations, the potential of GNN in this task has been explored recently. Unfortunately, the relationship between words is not adequately acquired by existing GNN-based methods. Furthermore, they ignore the deep relationships between words and do not take them into account. Typically, these methods use a fixed graph structure for a sentence and do not update the graph structure in the network. Based on the situation, we explore utilizing the deeper relations between words to improve the applicability of GNN for this task. In this letter, we present a framework called Word Relation-based Graph Neural Network (WRGNN). WRGNN can automatically discover and construct relations between words and reconstruct those relations in the processing. Two benchmark datasets are evaluated extensively, with WRGNN obtaining an average of 71.2 \% on STS-B and 82.9 \% on SICK.
Cyber Security Awareness in Using Digital Platforms Among Students in A Higher Learning Institution

: As COVID-19 took hold, online learning became the norm as universities and classrooms worldwide were forced to close their doors. Education institute closures impacted more than 1.2 billion students across 186 countries as of April 2020. This pandemic, too, has shifted our Malaysian education sector as well. Academicians and students are adopting online learning via open and distance learning platforms. Education 5.0 is the revolution of human intelligence and cognitive computing. When the world is moving towards this, the major challenge one will face is the challenges in this digitalisation era, the challenges on cyber security and the risk associated with it. This study uses a digital learning platform to examine the cyber security awareness among UiTM Terengganu Faculty of Computer and Mathematical Sciences students. The study is significant to focus on the weakness and to educate the students about being cyber victims. The data was collected using a set of questionnaires among 110 students. Numbers and statistics from open-ended and closed-ended questions helped obtain data. Descriptive analysis shows that many students have awareness and knowledge of cyber security, cyber-attack, and cyberbullying.
Comparative analysis of compression algorithms for four-dimensional light fields

Objectives . The widespread use of systems for capturing light fields is due to the high quality of the reproduced image. This type of capture, although qualitatively superior to traditional methods to capturing volumetric images, generates a huge amount of data needed to reconstruct the original captured 4D light field. The purpose of the work is to consider traditional and extended to four-dimensional image compression algorithms, to perform a comparative analysis and determine the most suitable. Methods . Mathematical methods of signal processing and methods of statistical analysis are used. Results . Algorithms are compared and analyzed in relation to the compression of four-dimensional light fields using the PSNR metric. The selected evaluation criterion is affected not only by the dimension of the compression algorithm, but also by the distance of the baseline of the capture setting, since the difference between images increases with the distance between the optical centers of each camera matrix. Thus, for installations consisting of an array of machine vision cameras located on racks and placed in a room, the obvious choice would be to use conventional image compression methods. Furthermore, based on the assessment of the arbitrariness of video compression methods, it should be noted that the XVC algorithm remains undervalued, although its results are higher. Algorithm AV1 can be considered the next in order of importance. It has been established that the latest compression algorithms show higher performance if compared to their predecessors. It has also been shown that with a small distance between the optical centers of the captured images, the use of video compression algorithms is preferable to the use of image compression algorithms, since they show better results in both three-dimensional and four-dimensional versions. Conclusions . A comparison of the results obtained shows the need to use algorithms from the video compression family (XVC, AV1) on installations with a long baseline (mounted on camera stands). When working with integrated light field cameras (Lytro) and setting the capture with a short baseline, it is recommended to use image compression algorithms (JPEG). In general, video compression algorithms are recommended, in particular XVC, since on average it shows an acceptable level of PSNR in both the case of a short and long installation baseline.
Analysis of strategic methodologies in the initial education during the Covid-19

The present work is an exploratory and descriptive study of the methodological strategies implemented by teachers working online in five educational institutions during the COVID-19 pandemic. The study is based on the review of an extensive bibliography and the use of quantitative and qualitative methods.Parents and students answered a survey with in-depth structured questions. Results showed that although parents were unhappy with the teaching methodologies used, teachers were highly motivated and committed to their teaching practice.
The Implementation of Online Learning from Teachers’ Perspective

Formal education that used to be done face-to-face in schools is now being conducted online. Changes in the implementation of this learning certainly have an impact on various parties. The purpose of this study was to determine the perception of the implementation of online learning from the teacher's point of view and what obstacles were encountered. The research method used in this research was descriptive-qualitative method with questionnaires and interviews as instruments. As a result, there were three obstacles in the implementation of online learning, namely quotas and signals, dependence on parents, and boredom of students in the learning process. The media used by the teachers were WhatsApp, Zoom Meeting, Edmodo, Google Classroom, Google Meet, Google Form, and Youtube. All of them hope that face-to-face class activities as usual can be carried out soon because the effectiveness of learning is better if it is done face-to-face.
Bayesian estimation for the Tukey GH distribution with an application

Tukey GH is a transformed normal distribution, having two parameters (g,h).The g parameter represents skew measuring, and the h represents kurtosis measuring.Our motivation is to affect these parameters on the behavior for a simulation data and real data, such as the Iraqi stock market Index (ISX60) and the Standard and Poor's (SP500) index using the Bayesian framework.Then, our aim is to find the estimation for the parameters in this distribution using empirical and parametric Bayesian methods, and study the effective on the simulation and real data.The simulation study will be shown the behavior of the parameters with a different number of sampling and prior distributions.We will use the real data from ISX60 and SP500 index.
Trust factors and their impact on patients satisfaction of private clinics in city of Ramadi - Iraq

The target of the research to identify the trust factors represented by (communication, competence, Service Quality) and its impact on the satisfaction of patients private medical clinics in the city of Ramadi, and for the purpose of achieving research objectives and reaching answers to questions (study problem), the Researchersadopted the analytical descriptive approach, a questionnaire was designed as a key tool for collecting preliminary data, and using the method (method) random sample, distributed (384) questionnaire forms to patients reviewing private medical clinics in the city, Of these, 368 were recovered, of which 11 were not eligible (invalid), bringing the number of questionnaires valid for statistical analysis (357) to 357, i.e. a response rate (93%) of recovered questionnaires, and the data analysis and statistically processed by the program (SPSS.V.26) as well as the statistical program (AMOS.V.26) , the most prominent findings of the research were the existence of a correlation and a positive effect with statistical significance between the factors Trust and patient satisfaction In addition, the results showed that the level of Trust of patients through their factors in the private medical clinics researched was at a (medium) level, as the contact came first and then solved the Competence second and the Service Quality iii, as well as the level of patient satisfaction with the medical services provided by the clinics in general was (average), and in the light of those results the Researchersmade a set of recommendations that could help medical clinic staff to enhance and increase the level of Trust of patients with them and strengthen them which reflects positively on the satisfaction of the patients and the permanence and prosperity of their relationship.
ANALISIS LAPORAN PERSEDIAAN BARANG DAGANG DALAM UPAYA PENINGKATAN LABA PENJUALAN PADA UD. KURNIAWAN KECAMATAN BANDAR HULUAN

Merchandise inventory report on UD.Kurniawan has not complied with PSAK NO. 14.This study aims to view and find out the report on the inventory of merchandise in accordance with the Financial Accounting Standards as stated in the Statement of Financial Accounting Standards (PSAK) NO. 14 in an effort to increase sales profit.The object of this research was conducted at UD. Kurniawan whose address is at Jl. Bandar Tongah, Bandar Huluan District, Simalungun Regency.In this study, the data used is a report on the physical inventory of merchandise at UD. Kurniawan and reports on the purchase of merchandise at UD. Kurniawan.The data collection carried out in this study used a direct interview technique with the owner of the UD.Kurniawan, Documentation by collecting the required data, as well as library studies by collecting theories from various sources and literature related to research.The results showed that UD.Kurniawan in compiling the merchandise inventory report is not in accordance with PSAK.NO. 14 because it still uses a physical recording system (periodic).Meanwhile, according to PSAK NO.The appropriate inventory recording system is to use the perpetual method because it makes it easier for UD.Kurniawan in recording his inventory.
Detection of hand gestures with human computer recognition by using support vector machine

Many applications, such as interactive data analysis and sign detection, can benefit from hand gesture recognition.We offer a low-cost approach based on human-computer interaction for predicting hand movements in real time.Our technique involves using a color glove to train a random forest classifier and then predicting a naked hand at the pixel level.Our algorithm anticipates all pixels at a rate of around 3 frames per second and is unaffected by differences in the surroundings.It's also been proven that HCI-based data augmentation is more effective than any other way for enhancing interactive data.In addition, the augmentation experiment was carried out on multiple subsets of the original hand skeleton sequence dataset, each with a different number of classes, as well as on the entire dataset.On practically all subsets, the proposed base architecture improved classification accuracy.When the entire dataset was used, there was even a modest improvement.Correct identification could be regarded as a quality indicator.The best accuracy score was 94.02 percent for the HCI-model with support vector machine (SVM) classifier.
Observações sobre as disciplinas em temática sobre Gestão de Documentos dos cursos de Arquivologia no Brasil

University programs are the way to develop knowledge and improvescientific research, as well as the locusof knowledge to be applied in professional practice. Records Management in the theoretical field of Archival Science has become one of the main supports for document organization, facilitating the preservation of produced documents, rationalizing their storage, and making access to archival information more efficient. In this perspective, the objective was to observe how the theme of Records Management is presented in university programs in Archival Science in Brazil. Research was carried out with a basic nature, a quali-quantitative approach, with emphasis on quantitative data collected inthe Political Projects of Programs, having the support of the documental and bibliographic research method to understand how Records Management and its theoretical and practical approaches are being taught in university programs. Based on the data collected from the 16 Archival Science programs in Brazil,located in public universities, 51 compulsory subjects that cover the theme were identified in the PoliticalProjectsof Programs, drawinga total of 987 bibliographic references amongthose considered basic and complementary.
Basic Operations on Images

In the previous chapter, we started with using Pillow for image processing. We also used Tkinter for displaying images. In this chapter, we will learn various arithmetic and logical operations to use on images. We will explore using Image, ImageChops, and ImageOps modules in Pillow to implement these operations. We will also learn how to use the slide bar in Tkinter to dynamically change the program input.
Os documentos fotográficos na perspectiva Teórico-metodológica da Diplomática um estudo da Fundação Fernando Henrique Cardoso

Observing the statusused for photographs in archives today, since the recognition of their character as an archival document, their differencesin organization, packaging and availability for access, it is evident the need to deepen the discussions about these documents in the archival scope. For this reason, it is proposed to discuss photography and its nuances, envisioning its documentary and, consequently, archival nature, from its production context and the theoretical-methodological perspective that diplomatic analysis offers as a valid tool to understand it while result of an action registered in the contemporary world, that is, a document. For that, the methodology applied was the case study, being the Fernando Henrique Cardoso Foundation the research universe. It is concluded that, more than applying diplomatic criticism to photographic documents, it is necessary to reflect on the production of images as documents, as well as their characteristics and their condition of production, that is, their context.
Zeroth-Order Randomized Subspace Newton Methods

Zeroth-order methods have become important tools for solving problems where we have access only to function evaluations. However, the zeroth-order methods only using gradient approximations are $n$ times slower than classical first-order methods for solving n-dimensional problems. To accelerate the convergence rate, this paper proposes the zeroth order randomized subspace Newton (ZO-RSN) method, which estimates projections of the gradient and Hessian by random sketching and finite differences. This allows us to compute the Newton step in a lower dimensional subspace, with small computational costs. We prove that ZO-RSN can attain lower iteration complexity than existing zeroth order methods for strongly convex problems. Our numerical experiments show that ZO-RSN can perform black-box attacks under a more restrictive limit on the number of function queries than the state-of-the-art Hessian-aware zeroth-order method.
Interpretable Aesthetic Analysis Model for Intelligent Photography Guidance Systems

An aesthetics evaluation model is at the heart of predicting users' aesthetic experience and developing user interfaces with higher quality. However, previous methods on aesthetic evaluation largely ignore the interpretability of the model and are consequently not suitable for many human-computer interaction tasks. We solve this problem by using a hyper-network to learn the overall aesthetic rating as a combination of individual aesthetic attribute scores. We further introduce a specially designed attentional mechanism in attribute score estimators to enable the users to know exactly which parts/elements of visual inputs lead to the estimated score. We demonstrate our idea by designing an intelligent photography guidance system. Computational results and user studies demonstrate the interpretability and effectiveness of our method.
DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences

This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework. Due to the redundant nature of the backgrounds in surveillance videos, visual information of the background can be compressed into a low-dimensional subspace in the encoder part of the variational autoencoder, while the highly variant information of its moving foreground gets filtered throughout its encoding-decoding process. Our deep probabilistic background model (DeepPBM) estimation approach is enabled by the power of deep neural networks in learning compressed representations of video frames and reconstructing them back to the original domain. We evaluated the performance of our DeepPBM in background subtraction on 9 surveillance videos from the background model challenge (BMC2012) dataset, and compared that with a standard subspace learning technique, robust principle component analysis (RPCA), which similarly estimates a deterministic low dimensional representation of the background in videos and is widely used for this application. Our method outperforms RPCA on BMC2012 dataset with 23% in average in F-measure score, emphasizing that background subtraction using the trained model can be done in more than 10 times faster.
The effect of the information technology implementation on organizational performance in manufacturing companies

Information technology is essential and critical for companies when collaborating with external partners.This study examines the effect of information technology (IT) implementation on organizational performance through information sharing and inventory management.The study involved a distribution of questionnaires to 500 manufacturing companies that was followed by a data screening, resulting in 93 valid questionnaires.Partial Least Square (PLS) technique was employed for data processing.The results show that IT implementation affects information sharing, inventory management, and organizational performance.Meanwhile, information sharing has an influence on inventory management and organizational performance.In addition, inventory management affects organizational performance.This study also reveals that information sharing and inventory management mediate the relationship between IT implementation and organizational performance.IT implementation can improve organizational performance, particularly in punctual product delivery.Information sharing with partners will improve inventory, especially in maintaining safety stock while improving the company's performance.This result paves the way for practitioners to improve organizational performance in the context of supply chain management.
Quantum criticality driven by the cavity coupling in the Rabi-dimer model

The superradiant phase transition (SPT) controlled by the interacting strength between the two-level atom and the photons has been a hot topic in the Rabi model and the Rabi-dimer model. The latter describes two Rabi cavities coupled with an inter-cavity hopping parameter. Moreover, the SPT in the Rabi-dimer model is found to be the same universal class that in the Rabi model by investigating the correlation-length critical exponent. In this paper, we are concerned about whether the inter-cavity hopping parameter between two Rabi cavities (i.e., the Rabi-dimer model) will induce the SPT and to which the universal class of the phase transition belongs. We analytically derive the phase boundary of the SPT and investigate the ground-state properties of the system. We uncover that the inter-cavity induced SPT can be apparently understood from the ground-state energy and the ground-state photon population, as well as the ground-state expectation value of the squared anti-symmetric mode. From the scaling analysis of the fidelity susceptibility, we numerically verify that the SPT driven by the cavity coupling belongs to the same universal class as the one driven by the atom-cavity interaction. Our work enriches the studies on the SPT and its critical behaviors in the Rabi-dimer model.
Efficient NLP Model Finetuning via Multistage Data Filtering

As model finetuning is central to the modern NLP, we set to maximize its efficiency. Motivated by redundancy in training examples and the sheer sizes of pretrained models, we exploit a key opportunity: training only on important data. To this end, we set to filter training examples in a streaming fashion, in tandem with training the target model. Our key techniques are two: (1) automatically determine a training loss threshold for skipping backward training passes; (2) run a meta predictor for further skipping forward training passes. We integrate the above techniques in a holistic, three-stage training process. On a diverse set of benchmarks, our method reduces the required training examples by up to 5.3$\times$ and training time by up to 6.8$\times$, while only seeing minor accuracy degradation. Our method is effective even when training one epoch, where each training example is encountered only once. It is simple to implement and is compatible with the existing finetuning techniques. Code is available at: https://github.com/xo28/efficient- NLP-multistage-training