Reviewerly/exHarmony huggingface dataset:
Analysis of Feature Extraction and Anti-Interference of Face Image under Deep Reconstruction Network Algorithm

In face recognition systems, highly robust facial feature representation and good classification algorithm performance can affect the effect of face recognition under unrestricted conditions. To explore the anti-interference performance of convolutional neural network (CNN) reconstructed by deep learning (DL) framework in face image feature extraction (FE) and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the random gradient descent (SGD) and triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild (LFW) face database. Then, the portrait pyramid segmentation and local feature point segmentation are applied to extract the features of face images, and the matching of face feature points is achieved using Euclidean distance and joint Bayesian method. Finally, Matlab software is used to simulate the algorithm proposed in this paper and compare it with other algorithms. The results show that the proposed algorithm has the best face recognition effect when the learning rate is 0.0004, the attenuation coefficient is 0.0001, the training method is SGD, and dropout is 0.1 (accuracy: 99.03%, loss: 0.0047, training time: 352 s, and overfitting rate: 1.006), and the algorithm proposed in this paper has the largest mean average precision compared to other CNN algorithms. The correct rate of face feature matching of the algorithm proposed in this paper is 84.72%, which is higher than LetNet-5, VGG-16, and VGG-19 algorithms, the correct rates of which are 6.94%, 2.5%, and 1.11%, respectively, but lower than GoogleNet, AlexNet, and ResNet algorithms. At the same time, the algorithm proposed in this paper has a faster matching time (206.44 s) and a higher correct matching rate (88.75%) than the joint Bayesian method, indicating that the deep reconstruction network algorithm proposed in this paper can be used in face image recognition, FE, and matching, and it has strong anti-interference.
Bartlett correction of an independence test in a multivariate Poisson model

We consider a system of dependent Poisson variables, where each variable is the sum of an independent variate and a common variate. It is the common variate that creates the dependence. Within this system, a test of independence may be constructed where the null hypothesis is that the common variate is identically zero. In the present paper, we consider the maximum log likelihood ratio test. For this test, it is well‐known that the asymptotic distribution of the test statistic is an equal mixture of zero and a chi‐square distribution with one degree of freedom. We examine a Bartlett correction of the test, in the hope that we will get better approximation of the nominal size for moderately large sample sizes. A correction of this type is explicitly derived, and its usefulness is explored in a simulation study. For practical purposes, the correction is found to be useful in dimension two, but not in higher dimensions.
Self-determination impact on motivation in the conditions of professional education.

Introduction: Self-determination theory plays a crucial role in self-awareness forming. It suggests that people are motivated to grow and change as well as to be able to become self-determined by three universal psychological needs: for fulfillment of competence, connection, and autonomy. The article examines the main criteria and indicators of self-determination. Socio-pedagogical conditions are pro-posed which provide the effectiveness of the process of forming social self-determination among students. Materials and methods: the methodological basis is the ideas of personal and pro-fessional development as the basis the foundation of achieving a professional “acme” in the works of such scientists as A.S. Anisimov, A.A. Bodalev, A.A. Derkach, V.G. Zazykin, A.K. Markova, N.V. Kuzmina and others. Scientists focused on interdiscipli-nary connections and integration approaches in the study of the problems of personality self-determination. Results: recommendations are given for the design of activating methods of self-determination. The research hypothesis is based on the assumption that the for-mation of self-determination in the educational process of the university will be effective if proposed conditions are realized: providing the interconnection of formation processes and person’s self-development; inclusion of students in a variety of activities in order to acquire social experience and a system of social roles for further self-development; trainings for motivational growth and classes aimed at developing skills of self-organization and self-regulation in stressful situations. Discussion and Conclusions: according to these conclusions, a set of recommenda-tions were developed: external and internal factors of social self-determination should be considered in the system, with its inherent connection with the environ-ment, since changes in the social environment entail changes in the system of factors; in order to study personal properties, develop mutual understanding and determine ways of development and correction, one should use high-quality psychodiagnostic techniques, trainings for motivational growth, aimed at developing skills of self-organization and self-regulation in stressful situations; it is necessary to ensure the activity of students in a variety of activities.
A High-Quality Authenticatable Visual Secret Sharing Scheme Using SGX

Visual cryptography scheme (VCS) is a secret-sharing scheme which encrypts images as shares and can decrypt shares without digital devices. Although a participant can reveal the secret image by merely stacking a sufficient number of shares, the visual quality of recovered images is reduced, and malicious adversaries can cheat participants by giving faked shares. The paper presents a novel VCS called T-VCS (trusted VCS) which consists of two main components: a high-quality VCS and an enhanced verification scheme of shares based on the emerging Intel Software Guard eXtensions (SGX). While providing high-quality recovery, T-VCS keeps the size of the shares the same as the original secret image. We use SGX to act as a trusted third party (TTP) to verify the validity of the shares in an attested enclave without degrading the image quality. The experimental results show that T-VCS can achieve a balance among contrast, share size, and verification efficiency.
Video lecture as an example of digital technologies implementation in the educational process of the university

The active development of digital technologies has had a significant impact on the educational process. Higher education institutions en masse switch to distance learning courses, vlogs, video hosting, popular science educational platforms. All of these platforms provide lectures in various branches of knowledge, regardless the curriculum, level of education, or even professional orientation. The development of modern media formats implies the possibility of active interaction with content. Can the format of video lecture meet these requirements? Or does the content consumer remain passive and cant influence anything? How long will such training format exist, and what are its prospects? In such rapidly changing conditions of life, the skills of flexibility and adaptation are applied to the educational process and teaching formats likewise. To improve the efficiency of work, everyone, including teachers, lecturers and students, needs to quickly adjust and adapt. The authors of the article attempted to explore the popularity and effectiveness of video lectures, identify the problems related to this form of teaching, and offer some possible solutions to create an educational model of online interaction that would promote the development of communication and learning skills.
Play the Shannon Game with Language Models: A Human-Free Approach to Summary Evaluation

The goal of a summary is to concisely state the most important information in a document. With this principle in mind, we introduce new reference-free summary evaluation metrics that use a pretrained language model to estimate the information content shared between a document and its summary. These metrics are a modern take on the Shannon Game, a method for summary quality scoring proposed decades ago, where we replace human annotators with language models. We also view these metrics as an extension of BLANC, a recently proposed approach to summary quality measurement based on the performance of a language model with and without the help of a summary. Using transformer based language models, we empirically verify that our metrics achieve state-of-the-art correlation with human judgement of the summary quality dimensions of both coherence and relevance, as well as competitive correlation with human judgement of consistency and fluency.
NEW STUDENT ADMISSIONS INFORMATION SYSTEM WITH CLIENT SERVER BASED SMS GATEWAY

Palembang State Junior High School 58 every year accepts new students, every year SMP N 58 always uses the form in registration and collects diplomas, SKHUN, and other files in hardcopy format so that they often experience file loss for that we need a new student admission information system based client-server and SMS gateway. This information system was designed using the Software Development Life Cycle (SDLC) method, and an analysis and design were carried out using a Data Flow Diagram. This information system has the function of saving, delete, update, report automatically, and can send information in the form of SMS. The new student admission information system is user friendly so that it can be easily used, so the admission process is more effective and there are no more missing files
Multi-Objective Regression Test Selection

Regression testing is challenging, yet essential, for maintaining evolving complex soft- ware. Efficient regression testing that minimizes the regression testing time and maximizes the detection of the regression faults is in great demand for fast-paced software develop- ment. Many research studies have been proposed for selecting regression tests under a time constraint. This paper presents a new approach that first evaluates the fault detectability of each regression test based on the extent to which the test is impacted by the changes. Then, two optimization algorithms are proposed to optimize a multi-objective function that takes fault detectability and execution time of the test as inputs to select an optimal subset of the regression tests that can detect maximal regression faults under a given time constraint. The validity and efficacy of the approach were evaluated using two empirical studies on industrial systems. The promising results suggest that the proposed approach has great potential to ensure the quality of the fast-paced evolving systems.
A*+BFHS: A Hybrid Heuristic Search Algorithm

We present a new algorithm called A*+BFHS for solving problems with unit-cost operators where A* and IDA* fail due to memory limitations and/or the existence of many distinct paths between the same pair of nodes. A*+BFHS is based on A* and breadth-first heuristic search (BFHS). A*+BFHS combines advantages from both algorithms, namely A*'s node ordering, BFHS's memory savings, and both algorithms' duplicate detection. On easy problems, A*+BFHS behaves the same as A*. On hard problems, it is slower than A* but saves a large amount of memory. Compared to BFIDA*, A*+BFHS reduces the search time and/or memory requirement by several times on a variety of planning domains.
The use of audio-visual media in improving Culinary students learning outcomes in Chicken Carcass material

This research aimed to find out the effectiveness of audio-visual media (picture, video, audio) on culinary students’ learning outcomes in Chicken Carcass material. The researcher implemented a quasi-experimental method with a nonequivalent control group design. The subjects of this study were the 10th-grade students of SMK Negeri 8 Medan in the Academic Year of 2016/2017. Cluster random sampling was applied in this study, which consisted of one experimental class and one control class. Meanwhile, some hypothetical tests used in this study were n-gain, independent t-test, and dependent t-test. The n-gain score of learning outcomes in the experimental class is higher than the control class, which is 0.5 and categorized as moderate. Students’ learning outcomes between those who were taught using audio-visual media and without audio-visual media in Chicken Carcass material indicated a significant difference with a significance score of 0.000 ≤ 0.05. The audio-visual media effectively improve the learning outcomes of the 10th-grade students of the culinary department in Chicken Carcass material.
An effective segmentation approach for liver computed tomography scans using fuzzy exponential entropy

Accurate segmentation of liver plays important in contouring during diagnosis and the planning of treatment. Imaging technology analysis and processing are wide usage in medical diagnostics, and therapeutic applications. Liver segmentation referring to the process of automatic or semi-automatic detection of liver image boundaries. A major difficulty in segmentation of liver image is the high variability as; the human anatomy itself shows major variation modes. In this paper, a proposed approach for computed tomography (CT) liver segmentation is presented by combining exponential entropy and fuzzy c-partition. Entropy concept has been utilized in various applications in imaging computing domain. Threshold techniques based on entropy have attracted a considerable attention over the last years in image analysis and processing literatures and it is among the most powerful techniques in image segmentation. In the proposed approach, the computed tomography (CT) of liver is transformed into fuzzy domain and fuzzy entropies are defined for liver image object and background. In threshold selection procedure, the proposed approach considers not only the information of liver image background and object, but also interactions between them as the selection of threshold is done by find a proper parameter combination of membership function such that the total fuzzy exponential entropy is maximized. Differential Evolution (DE) algorithm is utilizing to optimize the exponential entropy measure to obtain image thresholds. Experimental results in different CT livers scan are done and the results demonstrate the efficient of the proposed approach. Based on the visual clarity of segmented images with varied threshold values using the proposed approach, it was observed that liver segmented image visual quality is better with the results higher level of threshold.
TEACHERS’ STRATEGIES IN TEACHING READING COMPREHENSION

Abstract This study is aimed at portraying teacher’s strategies in teaching reading comprehension to students by involving an English teacher. In collecting the data, interview was conducted to identify the strategies of teaching reading comprehension. The result revealed that the strategies used by the teacher were understanding text, individual and group learning strategy, and QAR strategy. The result indicated that teachers need to be equipped by strategies in teaching reading comprehension in order to help the students to cope with the difficulties in learning English. The choice of strategies was adapted by the teachers with the materials, the syllabus and curriculum. Those strategies were effective in teaching reading comprehension because it can help student to comprehend the text and they could exchange their opinion with their friends. Keywords: teachers’ strategy, reading comprehension
Application of the Diamond Gate in Quantum Fourier Transformations and Quantum Machine Learning

As we are approaching actual application of quantum technology, it is essential to exploit the current quantum resources in the best possible way. With this in mind, it might not be beneficial to use the usual standard gate sets, inspired by classical logic gates, while compiling quantum algorithms when other less standardized gates currently perform better. We, therefore, consider a promising native gate, which occurs naturally in superconducting circuits, known as the diamond gate. We show how the diamond gate can be decomposed into standard gates and, using single-qubit gates, can work as a controlled-not swap (cns) gate. We then show how this cns gate can create a controlled-phase gate. Controlled-phase gates are the backbone of the quantum Fourier-transform algorithm and we, therefore, show how to use the diamond gate to perform this algorithm. We also show how to use the diamond gate in quantum machine learning; namely, we use it to approximate nonlinear functions and classify two-dimensional data.
A Corpus Based Transformation-Based Learning for Hausa Text Parts of Speech Tagging

Parts of Speech tagging also known as POS tagging is a division under semantic analysis in Natural Language Processing.It has been an active research area for a very long time especially for languages such as; English, Arabic, Mandarin, Czech, Bahasa Melayu, Wolof, and Igbo.Hausa language belongs to the West Chadic languages, it is spoken in parts of several countries such as; Nigeria, Niger, Benin, Cameroun, Chad, Burkina Faso, Sudan, Congo, Ghana, and Togo.However, despite all these wide number of speakers, the Hausa language lacks Natural Language Processing (NLP) resources such as POS taggers.This limits NLP research such as Information Retrieval, Machine Translation, and Word Sense Disambiguation on Hausa language.Different Machine Learning (ML) approaches have yielded varying performance in POS tagging, thus indicating the critical role ML approach plays on performance of POS taggers.In this study, we create a Hausa language POS tagset, called Hausa tagset (HTS), apply Transformation-based Learning as a hybrid tagger, Hidden Markov Model (HMM) and N-Gram as probabilistic taggers to perform POS tagging of the Hausa language.Results on taggers testing based on precision, recall, and f1-measure from this study shows that TBL tagger scored 64%, 52%, and 53% outperforming the HMM tagger which scored 55%, 7%, and 5%.Comparing TBL with the N-gram taggers, the TBL and Unigram taggers achieved 53% f1-measure while the bigram and the Trigram taggers achieved 52%.On recall, the TBL achieved 6% more than the Unigram and 7% more than the Bigram and Trigram.In terms of precision, the TBL scored lowest compared to the N-gram taggers by scoring 64%, while the Unigram tagger achieved 70%, followed by the Bigram and Trigram both scoring 69%.Although the TBL tagger majorly outperformed other (i.e.HMM, Unigram, Bigram, Trigram) taggers on all evaluation metrics except for Unigram precision, both TBL and Unigram tagger achieved same level of f1-measure, and differ on precision, and recall with a balanced difference as TBL exceeded the Unigram tagger by 6% on recall while the Unigram tagger exceeded the TBL tagger by 6% also on precision.
An empirical investigation of organic software product lines

Abstract Software product line engineering is a best practice for managing reuse in families of software systems that is increasingly being applied to novel and emerging domains. In this work we investigate the use of software product line engineering in one of these new domains, synthetic biology. In synthetic biology living organisms are programmed to perform new functions or improve existing functions. These programs are designed and constructed using small building blocks made out of DNA. We conjecture that there are families of products that consist of common and variable DNA parts, and we can leverage product line engineering to help synthetic biologists build, evolve, and reuse DNA parts. In this paper we perform an investigation of domain engineering that leverages an open-source repository of more than 45,000 reusable DNA parts. We show the feasibility of these new types of product line models by identifying features and related artifacts in up to 93.5% of products, and that there is indeed both commonality and variability. We then construct feature models for four commonly engineered functions leading to product lines ranging from 10 to 7.5 × 10 20 products. In a case study we demonstrate how we can use the feature models to help guide new experimentation in aspects of application engineering. Finally, in an empirical study we demonstrate the effectiveness and efficiency of automated reverse engineering on both complete and incomplete sets of products. In the process of these studies, we highlight key challenges and uncovered limitations of existing SPL techniques and tools which provide a roadmap for making SPL engineering applicable to new and emerging domains.
Unsupervised Hybrid approaches for Cyberbullying Detection in Instagram

In today's digital society, cyberbullying is serious and widespread issues affecting high number of Internet users, mostly teenager.However, increases in social media usage there is increase in the rise of cyberbullying.Cyberbullying is aggressive act carried out by some person using electronic forms of contact, repeatedly against people who cannot defend themselves.In the existing work distinguished the bullies from normal Instagram users by considering text, network and user related attributes using classifiers.Most existing cyberbullying detection methods are supervised and, thus, have mainly two key drawbacks such as labeling the data often take more time and labor and Current guidelines for labeling may not useful for future instances because of evolving social networks and different language usage.To address these limitations, this proposed work introduces for unsupervised cyberbullying detection method.The proposed detection method will be extract linguistic attributes such as idioms, sarcasm, irony and active or passive voice.In addition, a representation learning network that learns the multi-modal session representations and a multitask learning network will simultaneously estimate bullying energy and then models the comments arriving times from Instagram data set.
The Impact of CPD in Teaching, and the Role of Principal in Promoting CPD

Continuing Professional Development (CPD) has increasingly been seen as a fundamental element of learning, teaching and education worldwide.However, the concept of CPD lacks clarity in terms of how it is defined, as well as in its goals.This paper focuses specifically on the role of school principals in developing school and learning amongst teachers; discussing and outlining the principals' duties with regards to teachers' development; the way in which the principals' work is influenced, teachers' expansion and development, and the outcomes within the school setting.Carried out as a desk-based research that reviews extensive literature and empirical research from the last three decades, this paper concludes that CPD is not simply a form of teacher training, but a more holistic type of lifelong learning.CPD is an ongoing duty, embedded in the roles of both principals and teachers as educators, rather than a standalone programme or activity.Teachers are to be held accountable for their own development and must look to principals for guidance without depending on them completely.