Reviewerly/exHarmony huggingface dataset:
Effective and Efficient PageRank-based Positioning for Graph Visualization

Graph visualization is a vital component in many real-world applications (e.g., social network analysis, web mining, and bioinformatics) that enables users to unearth crucial insights from complex data. Lying in the core of graph visualization is the node distance measure, which determines how the nodes are placed on the screen. A favorable node distance measure should be informative in reflecting the full structural information between nodes and effective in optimizing visual aesthetics. However, existing node distance measures yield sub-par visualization quality as they fall short of these requirements. Moreover, most existing measures are computationally inefficient, incurring a long response time when visualizing large graphs. To overcome such deficiencies, we propose a new node distance measure, PDist, geared towards graph visualization by exploiting a well-known node proximity measure,personalized PageRank. Moreover, we propose an efficient algorithm Tau-Push for estimating PDist under both single- and multi-level visualization settings. With several carefully-designed techniques, TauPush offers non-trivial theoretical guarantees for estimation accuracy and computation complexity. Extensive experiments show that our proposal significantly outperforms 13 state-of-the-art graph visualization solutions on 12 real-world graphs in terms of both efficiency and effectiveness (including aesthetic criteria and user feedback). In particular, our proposal can interactively produce satisfactory visualizations within one second for billion-edge graphs.
ANALISIS TINGKAT PENERIMAAN PENGGUNAAN NOTION DALAM MENERAPKAN AGILE SCRUM PADA PROYEK GAMELAN METAVERSE

The Gamelan Metaverse Project is a project for creating the Virtual Reality Metaverse Gamelan application with an Agile Scrum framework that aims to educate people about gamelan and how to play it. This project was carried out by apprentices from PT Arutala, which consisted of students from several universities in Yogyakarta. Project participants were asked to use a project management tool, Notion, in the hope of facilitating coordination and reducing miscommunication. The purpose of this study is to determine what factors influence apprentices' acceptance of Notion at PT Arutala, as well as how helpful the apprentices are when using Notion. This research will use the Technology Acceptance Model (TAM) model in analyzing Notion acceptance factors for PT Arutala apprentices. The author used the TAM model to investigate the variables that influenced the acceptance of Notion among the Arutala internship.The research used seven hypotheses in the TAM model to determine its level of acceptance, and found that all hypotheses had a positive relationship, with six of them are significant and one is not significant.These results was obtained by running path coefficient and t-stistic test on the TAM model that author used.This research concludes that while the interns have acknowledged the benefits form using Notion, it requires time for them to incorporate its usage into their project management routine.
Tecnologías de la comunicación utilizadas por el personal de enfermería para captar donantes de sangre en el servicio de hemoterapia

Objective: to identify the communication technologies used by nurses for recruiting blood donors at the hemotherapy unit. Methods: a theoretical-reflective study characterized by its reflective, logical and interpretative nature, through the assessment of as-pects associated with the qualitative changes occurring in the phenomena analysed. A paired search for articles published from 2011 to 2022 was conducted by researchers in the following databases / virtual libraries: Latin American and the Caribbean Literature on Health Sciences (LILACS - Literatura Latinoamericana y Caribeña en Ciencias de la Salud); Pubmed through Medline Medical Literature Analysis and Retrieval, SciElo, and Google Scholar as grey literature. Results: communication technology in health contributes to a better direction of the nurse regarding the care offered to patients. There is a need for organizational methods, machines, systems, and technical methodologies to ensure care and full access to health system users. Blood banks stand out in terms of adopting and developing new technological means, guaranteeing from the quality of the blood extracted and blood products to safety for professionals and patients, including the recruitment of new donors. Conclusion: nurses act efficiently and are able to use a wide variety of technologies in their favour; they are considered essential pro-fessionals for recruiting blood donors, because the use of technology and their specialized care increase the number of donors and guarantee success throughout this process.
Zero-relabelling mobile-app identification over drifted encrypted network traffic

Mobile-app identification over encrypted network traffic plays a vital role in network management, cyberspace security, and advertising analysis. Combining machine learning algorithms and traffic features is the mainstream approach, which assumes the training and test traffic is independent and identically distributed (i.i.d). However, the distribution of test flows could drift due to the updating app, diverse mobile platforms, and regions, resulting in low accuracy. Existing methods relabel the non-i.i.d traffic manually and retrain the model from scratch, which takes lots of human effort and thus limits deployment. In this paper, we propose a Flow Domain Adaptation Neural Network (FDAN) to improve the accuracy in identifying mobile apps over non-i.i.d traffic under zero-relabelling. FDAN transforms the drifted test flows into approximate i.i.d samples by reducing the differences between traffic distributions in a trainable feature space. Specifically, we adopt two domain discriminators and a feature generator to strengthen the feature’s invariance under an adversarial loss. An app predictor is used to enhance the discrimination with supervised data from the source domain. We conduct extensive experiments on public and private datasets, including changing app versions, platforms, and regions. With theoretical guarantees, our FDAN achieves a remarkable improvement (7.8% ∼47%↑ in F1) with zero-relabelling and outperforms other comparisons.
Photogrammetry and Face Carvings: Exploring the ‘Face’ of the Egyptian Anthropoid Coffins by 3D Modeling

This paper aims to illustrate the initial results of a new methodological approach to the study of anthropoid coffins. We analyze the coffins using a new methodological approach that will support and complement traditional analytical methods used for these finds. The project is based on the analysis of “facial features” carved on the lid and the so-called “minor attributes.” 3D modeling provides us with an excellent record of surface morphology.
LIMEADE: From AI Explanations to Advice Taking

Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow AI to take advice from humans in response to explanations are similarly useful. While both capabilities are well developed for transparent learning models (e.g., linear models and GA 2 Ms) and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, little attention has been given to advice methods for opaque models. This article introduces LIMEADE, the first general framework that translates both positive and negative advice (expressed using high-level vocabulary such as that employed by post hoc explanations) into an update to an arbitrary, underlying opaque model. We demonstrate the generality of our approach with case studies on 70 real-world models across two broad domains: image classification and text recommendation. We show that our method improves accuracy compared to a rigorous baseline on the image classification domains. For the text modality, we apply our framework to a neural recommender system for scientific papers on a public website; our user study shows that our framework leads to significantly higher perceived user control, trust, and satisfaction.
Fast Dual Deep Convolutional Autoencoder Network for Denoising Monte Carlo Rendering

Monte Carlo (MC) rendering has attracted unprecedented attention for denoising and creating high-quality images, while deep learning-based MC denoisers also aim to improve rendering quality. Recently, there has been rapid development in using MC rendering synthesis along with deep learning, and significant progress has been reported on denoising using models trained on convolutional neural networks. Such usage and recent novel ideas have contributed to generating denoising rendering results, and sufficient details for image reconstruction from sparse MC samples or basic geometric auxiliary features. In this paper, we present a fast method for denoising Monte Carlo Rendering with a few samples per pixel. The basic idea is to use Dual Deep AutoEncoder Networks (DDAE) with a skip connection, for end-to-end inference from noisy rendered images to reconstructed realistic images. The first autoencoder accepts the noisy image together with the auxiliary buffers and outputs a sampling map. The second autoencoder accepts the noisy image together with the sampling map and outputs the final image. Consequently, our approach is less time-consuming and keeps the high-frequency details features lossless. In addition, we compare our DDAE results against state-of-the-art results by using the mean squared error (MSE) values with the peak signal-to-noise ratio (PSNR) values, and our approach reported better performance effects.
The Pedagogical use of Social Networks to improve Writing Skills with EFL Students from Nabón Third Grade High School

Social Networks have become essential resources for modern citizens in all aspects of life. It is a need in the whole features of society, and education is not an exception. The use of social media somehow could improve the students’ achievement in English language learning. The research proposal aims to investigate which Social Networks are the most useful for students from “Nabón High School” and incorporate social media in a lesson plan sample to motivate and help learners to develop their English Writing Skills in a dynamic context. Quantitative and qualitative research methods were used to carry out this study. The instrument used in this study includes a survey questionnaire that was conducted for students in Third Grade high school in a major computing system. During this process, students demonstrated their freedom of expression in the survey due to their practice through social media connections in their daily life. On the other hand, the gathered data revealed positive viewpoints on using Social Networks such as Facebook, WhatsApp, and TIKTOK. The research proposal suggests that teachers should change their teaching methods in order to make their classes more interesting by applying innovative strategies
Mediating Effects of Career Adaptability in Achievement Goal Orientations and Career Decision-Making Level among Specialized High School Students

The purpose of this study was to investigate the mediating effects of career adaptability on the relationship between achievement goal orientation and career decision-making level among vocational high school students.Data on achievement goal orientation, career adaptability, and career decision-making level were collected from Auguest, 22, 2022 to September, 30, 2022 as a purpose sampling method among vocational high school students.The 236 participants responses of the survey was used as final analysis data, with the exception of questionnaires that are not suitable for use in research.Data analysis using SPSS version 25.0 was conducted by frequency analysis, descriptive statistics, correlations, and regreesion analysis.Results of this study were as follows: First, there was a significant correlation between achievement goal orientation (performance approach, performance avoidance, the mastery approach and mastery avoidance), career adaptability, and career decisionmaking level in vocational high school students.Second, achievement goal orientation had a positive effect of the career decision-making level.Third, there was a partial mediating effect of career adaptavility in the relationship between achievement goal orientation and career decision-making level.The further implications of this study and suggestions for future research were discussed.
Location Privacy Protection and Location Verification Mechanism of Vehicle in VANET

Due to the social networks of vehicle are opportunistic and social, this topic attempts to reveal and portray the general laws of vehicle behavior of VSN networks. Based on real data, using big data analysis methods to validate the model; based on space, time and semantics and their interrelationships, research and explore the discrete vehicle location privacy protection mechanism under limited road space. This research build a efficient and accurate location verification mechanism.
Deep Learning Based Fusion Models for Sensitive Information Identification

The rapid expansion of information on the Internet and mobile Internet has led to the proliferation of sensitive information, which affects the harmony of the Internet. However, the traditional text sensitive information identification method based on sensitive words matching have the problems of ignoring context semantics, resulting in high false positive rate and low accuracy. This paper proposes a sensitive information identification fusion model combining sensitive words matching and deep learning. This novel fusion model can effectively solve the problem of high false positive rate caused by the lack of semantic understanding in traditional identification method, thus improving the accuracy and efficiency of sensitive information identification. This paper also proposes a sensitive information identification system using the fusion model and carries out experimental verification.
The social representation of slangs in the experience of today’s affective relationships of young people

Currently, it is observed that the slangs are used to name affective relationships. The aim of the present study was to analyze how young people name, using slang or not, and describe their affective experiences. This is a qualitative research, carried out with students from a health college in Recife-PE, in 2022. A sociodemographic questionnaire and interview were used. Students aged 18 to 25 years of both sexes in an affective relationship participated. The thematic analysis resulted in four categories: definition and importance of the affective relationship; commenting on slang and affective relationships; importance of slang related to affective relationships; social contribution of slang. The results conclude that young people use the slangs crush, ficante and peguete to name their relationships, however, they present some divergences regarding their connotation, concept and behavioral description. Further studies on the subject are suggested to minimize the likelihood that the use of such slang will result in violence in relationships.
An Experience Report of Remote Teaching about the IT Leadership Course - A Practical Approach

Universities and extension projects had to redesign their teaching approaches and methods as a consequence to the replacement of face-to-face teaching by distance learning. The project LearningLab presents a proposal for a practical laboratory that offers courses to students in order to promote and expand their knowledge. This paper reports the findings of the application of the mentioned methodology on the course ‘Leadership in IT: A Practical Approach’. For this, examples of real situations from daily life in the labor market were used. The data obtained were analyzed qualitatively using the grounded theory method and quantitatively to identify positive and negative points related to the implementation of the course. The results show that the main positive points mentioned by the students were (a) the content covered in the course, (b) the teaching methodology, (c) examples of real situations, and (d) the didactics of the lecturers. 100% of the students pointed out that: (i) the teaching methodology was easy to learn, (ii) the content was easy to remember, (iii) the teaching was objective and clear, and (iv) questions were answered promptly. All the participants also did not find it tedious to submit the final project of the course. Therefore, the main contribution of this study can be noted on the student’s professional development of project LearningLab.
Pixel Value Graphical Password Scheme: Analysis on Time Complexity performance of Clustering Algorithm for Passpix Segmentation

Passpix is a key element in pixel value access control, containing a pixel value extracted from a digital image that users input to authenticate their username. However, it is unclear whether cloud storage settings apply compression to prevent deficiencies that would alter the file's 8-bit attribution and pixel value, causing user authentication failure. This study aims to determine the fastest clustering algorithm for faulty Passpix similarity classification, using a dataset of 1,000 objects. The source code for the K-Means, ISODATA, and K-Harmonic Mean scripts was loaded into a clustering experiment prototype compiled as Clustering.exe. The results demonstrate that the number of clusters affects the time taken to complete the clustering process, with the 20-cluster setting taking longer than the 10-cluster setting. The K-Harmonic Mean algorithm was the fastest, while K-Means performed moderately and ISODATA was the slowest of the three clustering algorithms. The results also indicate that the number of iterations did not affect the time taken to complete the clustering process. These findings provide a basis for future studies to increase the number of clusters for better accuracy.
Organizational fairness and task performance: a study between different activities and positions in the justice sector

Purpose: This study analyzes the effects of organizational justice on the performance of tasks in the justice sector, divided by activity and position. Design/methodology/approach: We applied structural equations to a sample of 474 employees of the Public Prosecutor. Findings: A direct and positive effect of distributive justice and interactional justice on task performance was discovered. However, aspects of procedural justice do not have a significant relationship with task performance. The performance of non-core activities is positively influenced by interactional justice and the final activity by distributive justice. As for the positions, the interactional dimension has a significant magnitude in the position of analyst, while the distributive and procedural justice affect the performance of the technician's positions. Practical implications: These findings provide an understanding of how organizations can build their organizational structure between middle and end activities, to increase the perception of justice of their servers, and consequently improving the performance of activities. Originality: The article explores in an unprecedented way the perception of organizational justice in different work functions - the core activity and the non-core activity.
Semantic Interaction Matching Network for Few-shot Knowledge Graph Completion

The prosperity of knowledge graphs (KG), as well as related downstream applications, have raised the urgent request of knowledge graph completion techniques for fully supporting the KG reasoning tasks, especially under the circumstance of training data scarcity. Though large efforts have been made on solving this challenge via few-shot learning tools, they mainly focus on simply aggregating entity neighbors to represent few-shot references, while the enhancement from latent semantic correlation within neighbors has been largely ignored. To that end, in this paper, we propose a novel few-shot learning solution, named as Semantic Interaction Matching network (SIM), which applies Transformer framework to enhance the entity representation with capturing semantic interaction between entity neighbors. Specifically, we first design entity-relation fusion module to adaptively encode neighbors with incorporating relation representation. Along this line, Transformer layers are integrated to capture latent correlation within neighbors, as well as the semantic diversification of the support set. Finally, a similarity score is attentively estimated with attention mechanism. Extensive experiments on two public benchmark datasets demonstrate that our model outperforms a variety of state-of-the-art methods with a significant margin.
On Quantified Analysis and Evaluation for Development of Early Infants Reading Brain Process (Artificial Neural Networks Approach)

interdisciplinary discipline incorporating Computational Neural Networks, Cognitive Neuronal Mechanism, Neuroscience, and Psycholinguistics.which originated from the challenging performance evaluation of reading native languages' vocabulary at the early infancy started by the first few / months /years of the infant's life.In other words, such learning of vocabulary language is motivated and originated directly by the effect of mother's speech interactive dialogue with her toddler.The first few months of life are so important for brain development because neural connections in the infant brain are emerging in the context of warm, predictable, and responsive social interactions between the parent and infant.Briefly, it concentrates on a very interesting and challenging issue tightly associated to quantitative learning performance of vocabulary evaluation at early infancy of human brain, while that affected with mother's speech.Therefore, due to IN addition to the environmental teaching / learning motivated by developing preliminary phase of reading brain in a significant way.The Presented ANN models have been closely corresponded to performance of these neurons for various learning rate values simulating the effect of interactive vocabulary learning observed via the dialogue of young children with their mothers.More specifically; presented models concerned with their important neurons' role played in carrying out cognitive brain function's learning outcomes.In this context, herein introduced work illustrates via ANN s simulation results: How ensembles of highly specialized neurons could be dynamically involved in performing the cognitive function of recognizing words' vocabulary during early infancy development of human reading brain.