Reviewerly/exHarmony huggingface dataset:
Implementation Of Content Management System (CMS) Using The Business Model Canvas (BMC) Method At Fajar Collection Store

Fajar Collection is an individual business store that sells various products such as school uniforms, Muslim clothing, and casual clothes. In the existing business processes at Fajar Collection, using social media in the process of online sales transactions using Facebook and WhatsApp. However, with the online transaction business process, there are several obstacles experienced by the Fajar Collection store, namely, it is difficult for customers to find out stock availability, because there is no stock availability information on Facebook and WhatsApp, so customers have to ask the sales staff about stock inventory. Another obstacle is that the product promotion process is less than optimal, causing old products to be sold and slow to get new potential customers. Another obstacle is difficult in processing sales report data, because it is still done in bookkeeping by looking at sales notes to be included in the sales report, thus allowing different recordings of sales numbers and less than optimal in knowing sales data. Based on these constraints, an e-commerce website was designed using the Business Model Canvas (BMC) approach. Analysis of the strengths and weaknesses of the e-commerce business using the BMC methodology. The system was developed using the Unified Modeling Language (UML) approach and a Content Management System (CMS) based website with WordPress using the woocommerce plugin. Then it is optimized by applying Search Engine Optimization (SEO) so that the site is on the first page of search engines, making it easier for customers to find the site. With the E-Commerce website, it will expand product sales promotions, and help customers who want to buy directly from the website. It is hoped that by utilizing this E-Commerce website, it will be easier for customers to process the purchase transactions in Fajar Collection.
FEATURES OF SOFT SKILLS FORMATION IN HIGHER EDUCATION STUDENTS IN THE CONDITIONS OF THE HIGHER EDUCATION PEDAGOGICAL INSTITUTION

The article analyzes and summarizes Ukrainian and international scientific experience about soft skills, which are non-technical, non-material, specific personality skills, which in combination with hard skills, ensure the successful self-realization of higher education applicants in their future professional activities. A survey of employers of the specialty Secondary education in subject specializations was conducted and the need for higher education applicants to acquire such soft skills as critical thinking skills, effective oral and written communication, ability to work in a team, self-motivation, creativity, moral tolerance, stress resistance, pedagogical leadership, etc., and the need to update and improve the quality of training for higher education applicants in pedagogical institutions of higher education. Within the framework of a scientific study, taking into account the professional standard “Teacher of an institution of general secondary education”, an attempt was made to identify ways to form soft skills for higher education applicants in the implementation of educational and professional programs of the specialty Secondary education in subject specializations at the Sumy State Pedagogical University, in particular, the study of the cycle of general and vocational training, practical training, final certification, non-formal education, building an individual educational trajectory, etc. The forms and methods of teaching are considered – seminars and laboratory classes, trainings, independent work; essays, project activities, abstracts, discussions, presentations, which is of particular importance in the era of online learning. Theoretical and practical developments of scientific research that are structured on the basis of analysis, generalization, and empirical methods will contribute to the modernization of educational and professional programs, the improvement of the internal system for ensuring the quality of higher education.&#x0D; Keywords: soft skills; educational program; teacher, students; employers; forms and methods of teaching; professional activities; higher education institution.
Multistage Framework for Automatic Face Mask Detection Using Deep Learning

The whole world is fighting as one against a deadly virus. COVID-19 cases are upon us in waves, with subsequent waves turning out to be worse than the previous one. Scores of human lives are lost while the post-COVID-19 complications are on a rise. Monitoring the behaviour of people in public places and offices is necessary to mitigate the transmission of COVID-19 among humans. In this work, a low-cost, lightweight two-stage face mask detection model is proposed. In the first stage, the model checks if a face mask is worn. In the second stage, it detects if the mask is worn appropriately, by classifying and labelling them. The proposed models are trained to detect faces with and without masks for varied inputs such as images, recorded videos, and live streaming videos where it can efficiently detect multiple faces at once. The efficacy of the proposed approach is tested against conventional datasets as well as our proposed dataset, which includes no masks, surgical masks, and nonsurgical masks. In this work, multiple CNN models like MobileNetV2, ResNet50V2, and InceptionV3 have been considered for training and are evaluated based on transfer learning. We further rely on MobileNetV2 as the backbone model since it has an accuracy of 98.44%.
Multi-Modal Sentiment Analysis Based on Interactive Attention Mechanism

In recent years, multi-modal sentiment analysis has become more and more popular in the field of natural language processing. Multi-modal sentiment analysis mainly concentrates on text, image and audio information. Previous work based on BERT utilizes only text representation to fine-tune BERT, while ignoring the importance of nonverbal information. Most current research methods are fine-tuning models based on BERT that do not optimize BERT’s internal structure. Therefore, in this paper, we propose an optimized BERT model that is composed of three modules: the Hierarchical Multi-head Self Attention module realizes the hierarchical extraction process of the features; the Gate Channel module replaces BERT’s original Feed-Forward layer to realize information filtering; the tensor fusion model based on self-attention mechanism utilized to implement the fusion process of different modal features. In CMU-MOSI, a public mult-imodal sentiment analysis dataset, the accuracy and F1-Score were improved by 0.44% and 0.46% compared with the original BERT model using custom fusion. Compared with traditional models, such as LSTM and Transformer, they are improved to a certain extent.
Unsupervised multi-source domain adaptation with graph convolution network and multi-alignment in mixed latent space

This paper proposes an unsupervised multi-source domain adaptation algorithm with graph convolution network and multi-alignment in mixed latent space, which leverages domain labels, data structure, and category labels in a unified network but improves domain-invariant semantic representation by several innovations. Specifically, a novel data structure alignment is proposed to exploit the inherent properties of different domains while using current domain alignment and classification result alignment. Through this design, category consistency can be considered in both latent space, and domain and structure discrepancy between different source domains and the target domain can be eliminated. Moreover, we also use category alignment based on both CNN and GCN features to optimize category decision boundary. Experiment results show that the proposed method brings sufficient improvement especially for adaptation tasks with large shift in data distribution.
An Analysis of Abstractive Text Summarization Using Pre-trained Models

Rehman, Tohida Das, Suchandan Sanyal, Debarshi Kumar Chattopadhyay, SamiranPeople nowadays use search engines like Google, Yahoo, and Bing to find information on the Internet. Due to the explosion in data, it is helpful for users if they are provided relevant summaries of the search results rather than just links to webpages. Text summarization has become a vital approach to help consumers swiftly grasp vast amounts of information. In this paper, different pre-trained models for text summarization are evaluated on different datasets. Specifically, we have used three different pre-trained models, namely google/pegasus-cnn-dailymail, T5-base, facebook/bart-large-cnn. We have considered three different datasets, namely CNN-dailymail, SAMSum, and BillSum to get the output from the above three models. The pre-trained models are compared over these different datasets, each of 2000 examples, through ROUGH and BLEU metrics.
Analytic Function Approximation by Path-Norm-Regularized Deep Neural Networks

We show that neural networks with an absolute value activation function and with network path norm, network sizes and network weights having logarithmic dependence on 1/ε can ε-approximate functions that are analytic on certain regions of Cd.
Stabilizing Dynamic Output Feedback Control for Takagi-Sugeno Fuzzy Systems

This paper presents the stabilization using dynamic output feedback controller for Takagi-Sugeno fuzzy systems that can represent nonlinear systems accurately. Many different stabilization conditions by state feedback control have been obtained in the literature. However, few papers have considered stabilizing output feedback controllers, which is useful for practical systems, of Takagi-Sugeno fuzzy systems. Therefore, we propose a control design based on dynamic output feedback controllers in this paper. Based on the idea of Lyapunov function having the structure of the integral of the membership functions, new stabilization conditions are obtained. This approach is known to reduce the conservativeness of the stabilization conditions. Furthermore, relaxation lemmas are used to reduce the conservativeness of the conditions. Finally, numerical examples are given to show the effectiveness of the design methods presented in the paper.
Saliency-Based Multiple Region of Interest Detection From a Single 360° Image

360°images are informative -it contains omnidirectional visual information around the camera.However, the areas that cover a 360°image is much larger than the human's field of view, therefore important information in different view directions is easily overlooked.To tackle this issue, we propose a method for predicting the optimal set of Region of Interest (RoI) from a single 360°image using the visual saliency as a clue.To deal with the scarce, strongly biased training data of existing single 360°image saliency prediction dataset, we also propose a data augmentation method based on the spherical random data rotation.From the predicted saliency map and redundant candidate regions, we obtain the optimal set of RoIs considering both the saliency within a region and the Interaction-Over-Union (IoU) between regions.We conduct the subjective evaluation to show that the proposed method can select regions that properly summarize the input 360°image.
COTIIP: a new covert channel based on incomplete IP packets

A Covert Channel is a communication channel able to transfer information between two or more hosts, using the resources of another communication channel, so that who can read the information carried by the latter cannot realize an information exchange is ongoing in the former. Many types of covert channels have been described in literature, together with countermeasures to block them. After an overview of the literature on the subject, the present paper will describe COTIIP (COmmunicating Through Incomplete IP Packets) a new covert channel based on the TCP/IP protocol. COTIIP is based on the transmission of incomplete fragmented IP packets. The receiver will use the information about incomplete or missing packets to restore the original message. A first assessment of some possible countermeasures aimed at suppressing or exposing the use of such channel will also be proposed.
Phoneme Based Approach for Transliteration of Konkani Language

Simultaneously with the rise of machine translation, there has been a surge in the research field of machine transliteration. Despite the fact that the two processes are distinct and serve separate purposes, transliteration aids in the optimization of machine translation models. For languages such as Arabic, Korean, Japanese, Persian, Urdu, and Hindi etc, several methodologies have been developed. In this paper we present the implementation of the phoneme-based transliteration of Konkani scripture to Roman scripture using Direct Character Mapping technique and discuss the performance with respect to the ratings from a survey conducted from a small sample of Konkani speaking individuals. From the survey conducted we obtained an average score of 3.5 with respect to word accuracy.
Replication or Reinvention: Educators’ Narratives on Teaching in Higher Education During the COVID-19 Pandemic

Objectives: The purpose of the study was to examine narratives about the effect of the sudden transition from face-to-face teaching to emergency remote teaching necessitated by the COVID-19 pandemic on post-secondary educators. Method: We conducted interviews with 11 post-secondary educators from five post-secondary institutes in one province in Canada. Educators were asked to reflect on their experiences during the transition from in-person to remote teaching and learning. Results: Our thematic analysis revealed that educators’ experiences were influenced by three main factors: (a) student engagement, interactions, and persistence in learning; (b) competence in the application of teacher technological pedagogical content knowledge (TPACK); and (c) overall well being of faculty and students. Conclusions: Participants had unique experiences, and institutions varied in the ways they supported students and staff. Those educators who had expertise, experience, or professional support in technology and teaching seemed to have an easier transition. Implication for Theory and Practice: Higher education institutes should support educators in enhancing their technological pedagogical knowledge and in facilitating learning in various delivery modalities.
New insights into the source of gold in the Youjiang basin, SW China

Abstract Mantle plume rich in gold is considered to be important for the formation of giant epigenetic gold deposits. The Youjiang basin, SW China, is the world’s second largest Carlin-type gold province, but the ultimate source of its gold remains enigmatic. In this study, we report that the Middle–Late Permian basaltic rocks in the basin are rich in native gold grains. These gold grains are scattered in the interstices of pyrite and marcasite and in the amorphous silica cavities. Mineralogy and S-Pb isotope geochemistry of the auriferous sulfides suggest that the gold was largely derived from the gold-rich Late Permian (ca. 260 Ma) Emeishan plume and was released to a near-surface volcanogenic massive sulfide (VMS) metallogenic system, where it accumulated. The native gold grains from the basalts may have been inherited by the younger (ca. 140 Ma) Carlin-type ores in the Youjiang basin, which are indicative of gold pre-enrichment in the basin. Our study highlights that golden plume upwelling could carry abundant gold into the upper crust, even into shallow-level metallogenic systems, and thus provides an alternative view on the source of gold in the Youjiang basin.
Do Schools Promote Executive Functions? Differential Working Memory Growth Across the School Year and Summer Months

Children's working memory (WM) skills, which support academic and social success, continue to improve significantly through the school years.This study leverages the first nationallyrepresentative dataset with direct assessments of elementary school students' WM to examine whether WM grows more during the school-year or summer months, and whether WM growth rates differ by household income.Results demonstrate that WM grows more during the school-year months compared to the summer months, suggesting that school environments provide children with unique opportunities to improve their WM.Further, lower-income children have faster WM growth rates in the first two years of school and the intervening summer, compared to their higher-income peers, leading to a narrowing in income-based WM disparities during the early school years. ObjectiveChildren's working memory (WM) skills, which allow them to hold, update, and manipulate information in the mind over short periods of time, are key predictors of their academic and social success (de Wilde et al., 2016;Vandenbroucke et al., 2017).Leveraging the Early Childhood Longitudinal Study, Kindergarten Class of 2010-2011 (ECLS-K:2010), the only nationallyrepresentative dataset including direct assessments of children's WM skills, this study examines how growth rates in children's WM skills differ between the school-year and summer months.These analyses extend previous work demonstrating seasonal differences in children's academic skill growth rates (Downey et al., 2004), to understand if schooling is similarly associated with benefits in WM development.While WM is not directly targeted by schooling instruction, children's experiences in the classroom and on the playground afford them with many opportunities to further develop and practice their WM skills.Testing whether growth rates differ between school-year and summer months provides insight about which contexts may be most promotive of WM development.Second, this study explores whether WM growth rates differ by children's household income.Given the importance of WM for long-term outcomes, it is critical to understand whether schools are reducing or exacerbating income-based disparities in children's WM.These analyses provide information about when school-or home-based interventions might be best targeted to reduce WM gaps between children from lower-and higher-income families.
Pen testing vs red teaming

Network SecurityVol. 2022, No. 7 The FirewallPen testing vs red teamingGemma MooreGemma MooreCyberisSearch for more papers by this authorGemma MoorePublished Online:9 Aug 2022https://doi.org/10.12968/S1353-4858(22)70044-XAboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 July 2022Volume 2022Issue 7ISSN (print): 1353-4858ISSN (online): 1872-9371 Metrics History Published online 9 August 2022 Published in print 1 July 2022 Information© MA Business Ltd.
Myocardial Perfusion Classification Using A Markov Random Field Constrained Gaussian Mixture Model

Dynamic Contract Enhanced Magnetic Resonance (MR) Imaging (DCE-MRI) has been widely used as a non-invasive assessment approach to estimate the myocardial blood flow (MBF).The delineation of a hypo-perfused region (low MBF region) is important for understanding a patient's heart condition in clinical diagnosis.In this paper, a Markov random field constrained Gaussian mixture model (GMM-MRF) classification method is introduced to classify MBF maps using myocardial perfusion DCE-MRI data.The GMM-MRF method, trained with an ICM algorithm, makes use of spatial neighbourhood information to improve classification accuracy.The proposed method is applied to and assessed on both synthetic and clinical data, and compared with established classification methods.
Comparison of U -NET backbones for morphometric measurements of white blood cell

Measurements of Morphometric Parameters of Blood Cells (MPBC) playa key role in haematological examination, and it is considered as one of the principal needs for clinicians in the diagnosis of various diseases in human and animals. Obliviously, the correctness of the diagnosis, and, as a consequence, the effectiveness of clinician actions is highly dependent on the accuracy of MPBC measurements. In this context, deep learning based MPBC measurement systems are a promising solution. In recent studies, researchers have applied semantic segmentation with various backbone networks for white blood cell measurements. Vice versa, few investigations were done about the achieved accuracy. Indeed, accurate segmentation of white blood cell remains a challenging task because of the complex nature of cell images, staining techniques, and imaging conditions which strongly affects the accuracy of the MPBC measurements. This paper presents a comparison among the segmentation performance carried out by U-Net deep learning algorithm with different backbones typically used for MPBC. The goal is to make a first step towards a whole MPBC measurement system capable of evaluating the effects of the influencing magnitudes, attenuate them (if possible), and evaluate the accuracy of the measurements. The aims are to increase measurement reliability and to give clinicians further information to take their decisions.
HUBUNGAN POLA ASUH ORANG TUA TERHADAP PRESTASI BELAJAR SISWA KELAS IV DAN V DI SD N 3 CANDIKUNING

The Relationship Between Parenting Style And Learning Achievement Of Fourth And Fifth Grade Students At SD N 3 Candikuning&#x0D; Desak Gede Yenny Apriani1, Desak Made Firsia Sastra Putri2, Wira Sukanada3&#x0D; 1The Study Program Of Nursing Science Nurses, 2The Health College Of Advaita Medika Tabanan&#x0D; email : yennyapriani2004@gmail.com&#x0D; ABSTRACT&#x0D; &#x0D; Background: Children's learning achievement cannot be separated from the role of parents, because parenting provided by parents can affect children's learning achievement. Children who get good parenting can increase their learning achievement, while poor parenting will result in a decrease in learning achievement in children. &#x0D; &#x0D; Objective: This research aims to determine the relationship between parenting styles and student achievement in grades IV and V at SD N 3 Candikuning. &#x0D; Methods: This research is a descriptive analytic research with a cross sectional approach. The sampling technique is Total Sampling. The research subjects were 40 students in grades 4 and 5 of Candikuning State Elementary School 3. The independent variables include parenting style and the dependent variable is learning achievement. Data were analyzed using Spearman's rho test. &#x0D; &#x0D; Results: The results of the measurement of parenting patterns of fourth and fifth graders at SD N 3 Candikuning obtained data on the majority of democratic parenting as many as 23 people with a percentage of 57.5%. The category of student achievement in grades IV and V at SD N 3 Candikuning mostly good learning achievements as many as 18 people with a percentage of 45%.. and there is a relationship between parenting styles and learning achievement of fourth and fifth grade students at SD N 3 Candikuning which was tested by Sperman Rho in getting a significant value of p = 0.033 Conclusion: Based on the results of the study, it can be concluded that there is a relationship between parenting styles and learning outcomes for fourth and fifth grade students at SD N 3 Candikuning, therefore parents are expected to provide extra care and education at home in order to help improve learning achievement from child.&#x0D; Keywords: Parenting Pattern, Learning Achievement.