Reviewerly/exHarmony huggingface dataset:
Offline-Signature Verification System using Transfer Learning VGG-19

Nowadays, Signature verification is one of the most common and effective biometric systems that used to recognize people in many institutions. In modern era of technology, advanced neural networks have provided us an option to solve this issue. In this study, The Robinreni Signature Dataset was utilized to classify the signatures of 64 people, each of whom had 64 original signatures and 64 fake signatures. One of the most popular CNN architecture, namely, VGG19, were used. Firstly, the dataset was distributed accordingly 1649 and 500 for training and validation. Secondly, preprocess the data to train the model. After that the model training process is started using transfer learning approach. Obtained experimental results that VGG19 is best suited for datasets with a validation accuracy of 98.79%.. Everyone has their own unique signature that used to identify and verify important documents and legal transactions. Our study shows the effectiveness of VGG19 for Signature Verification task. The findings will aid in the development of more effective Deep Learning-based signature verification methods.
Application of Machine Intelligence in Smart Societies: A Critical Review of the Opportunities and Risks

The integration of machine intelligence (MI) which includes techniques such as machine learning (ML), deep learning (DL), and artificial intelligence (AI), is increasingly being used to enable smart societies. Smart societies, which include smart buildings, homes, cities, entire regions, and the people living in them, aim to improve the quality of life of inhabitants by providing more efficient, safer, and more comfortable environments. The use of MI in smart societies has the potential to bring significant benefits, such as improved energy efficiency and traffic management, but also raises a number of challenges and risks. This chapter provides an overview of the opportunities and risks of using MI in smart societies. It begins by discussing the ways in which MI can be used in smart societies, such as energy management, transportation, security, etc. Then, it describes the potential risks and challenges of using MI in smart societies, including ethical and legal issues, the potential for perpetuating biases and discrimination, and the need for robust regulation and governance. Finally, the chapter concludes by suggesting potential research directions and recommendations for future work in the field.
HIEROGLYPH AND THE FOUNDATION OF CHILDREN'S TEACHING - COSMIC LANGUAGE -- COSMIC LANGUAGE = WORLD GENERAL TYPE UNIVERSAL LANGUAGE, HIEROGLYPH ENCODING LANGUAGE FOR SPELL CHARACTERS

Cosmic Language is a new type of language and writing, suitable for children and people around the world to learn and master.It has the characteristics of hieroglyph, it also has the encoding characteristics of spell characters.It uses symbols, numbers, letters, Chinese characters, and other ethnic languages with certain characteristics as the units of character creation, similar to letters.Form hieroglyph structures similar to Chinese characters, such as "口,吕,品,田,朋" and string structures etc., and use the four corner codes method of Chinese characters for coding.This open universal language has specific text encoding rules that are suitable for computer classification and retrieval.It is also suitable for people of all races to quickly understand and master, as well as communicate with extraterrestrial creatures.
MANAJEMEN PROYEK PEMBUATAN DASHBOARD UNTUK VISUALISASI CURAH HUJAN DI JAKARTA

Project management is a way for organizing to managing and completing projects from start to finish. Using weather data collected from BMKG to carry out data processing by creating a dashboard to display rainfall visualizations using Power BI. This methode research implements use the BI roadmap method. The purpose of this research is to be one of the ways to make decisions. The purpose of the results in this research, it is hoped that making a dashboard visualizations can be useful for users, namely everyone who needs weather information for outdoor activities.
Retracted Article: Creative thinking and its influence on students’ scientific achievements

ABSTRACTThe central goal of this research was to analyse the practice and potential of using the 5P teaching model compared to the traditional model and identify its influence on students’ creative and research-oriented thinking. The study was conducted among 80 students from Russia, Ecuador and China divided into experimental (EG) and control (CG) groups. They undertook specially developed pre- and post-tests. The results of EG and CG’s tests revealed that students who learned using the 5P model (EG) performed better on all dimensions. The influence of the chosen learning model is very significant for increasing creative thinking and research abilities, but it strongly depends on the time allocated for learning. Using the 5P teaching model proved to be the most effective. The practical significance of the findings lies in improving the implementation of the 5P teaching model and revealing the critical factors motivating the transition to this approach in teaching.KEYWORDS: 5P teaching modelformation of creative thinking in scientific activitymeasurement of academic, research, and creative abilitiesresearch creativityresearch-oriented approach to teaching Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData will be available on request.Additional informationFundingThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Notes on contributorsTeresa Mirian Santamaria LopezTeresa Mirian Santamaria Lopez is an Associate professor at the Universidad De Guayaquil, computer science career. Her knowledge areas include streaming, e-learning, education, home automation, and networks. Email: tsantamaria710@gmail.com.Demeng XuDemeng Xu has a Master's Degree in Music. He is a Doctoral student and Associate professor at the Academy of Music, Ningbo University, China. He studies modern approaches to education and creative thinking of students.Dmitry BokovDmitry Bokov is a PhD in Pharmaceutical Sciences, Research Assistant at the Institute of Pharmacy, Sechenov First Moscow State Medical University, Moscow, Russia; Laboratory of Food Chemistry, Federal Research Center of Nutrition, Biotechnology and Food Safety, Moscow, Russia. His research areas are the psychological and pedagogical foundations of higher medical and pharmaceutical education. Email: bokov_d_o@staff.sechenov.ru.
Moroccan EFL Teachers' Perceptions towards the Use of Authentic Materials to Improve Students' Reading Skill

It has been well-documented that the use of authentic materials while teaching reading comprehension can be one of the keys to motivate students interacts with the reading texts. Moroccan ELT textbook sometimes go beyond learners' level and interest. For these reasons, it becomes very necessary for teachers to vary materials and strategies while teaching reading comprehension. This paper aimed to investigate the attitudes of Moroccan EFL teachers towards the use of authentic materials in teaching reading comprehension to senior high schools. Six English teachers took part in completing a survey questionnaire for the purpose of the study. The researcher, therefore, aims to find out whether the use authentic materials can motivate learners interact with the reading texts and voice out their opinions. The results obtained indicated most teachers agree that texts mentioned in the textbook go beyond the level of students' understanding because of the difficulty students face while dealing with texts in the textbook. Most teachers perceive the use of authentic materials as useful for motivating students to be engaged in the learning process. That is, English teachers have positive attitude towards the use of authentic materials.
Differentiable Quantum Programming with Unbounded Loops

The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Existing work has formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q# and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.
ARTS EDUCATION IN COMBINED PRIMARY CLASSES IN THE REPUBLIC OF CROATIA

The work in combined classrooms is very specific and demanding for primary education teachers because one teacher simultaneously performs lessons for more than one class.According to the official data from the Ministry of Education in Croatia, there are 148,233 students in the first four classes, wherein 9,879 (6,7%) students in composite classrooms classes.In combined, i.e. split-year classes, students are heterogeneous with regard to their age and abilities.Due to the nature of instruction that is complex, teachers need to be creative in the teaching methodology as well as in organizing overall lessons.Artistic subjects (music and visual arts) represent specific educational fields, and they are taught by class teachers in the first four grades of primary school.The goal of this work was to examine how primary education teachers conduct combined instruction in the artistic fields of music and visual arts with regard to age-diverse class composition.Besides, the advantages and drawbacks of such work and challenges teachers are faced with were also investigated.The research was conducted in 2023.Teachers working in two-, three-and four-year combinations participated in the study.The qualitative research method was applied, i.e. participants were interviewed in order to gain specific information, that is, a more in-depth insight into the work in combined classrooms within artistic fields.In their interview, female teachers underlined the beauty and challenge of such work with regard to peer mentoring, tolerance and mutual appreciation.The drawbacks of working in combined classes are connected to the organisation of teaching (planned and effective time use).
PRACTICING SOCIAL-EMOTIONAL AND COGNITIVE TEACHING STRATEGIES AND STEAM ACTIVITIES IN EARLY CHILDHOOD EDUCATION

STEAM education is recognized as an efficient practice of holistic education, which is already relevant at the stage of early childhood education.STEAM research focuses on the search for models of interdisciplinary integration and the justification of innovative educational approaches.The aim of this paper is to reveal the relationship between practicing social-emotional and cognitive teaching strategies and STEAM activities in early childhood education.The conducted quantitative study reveals that the population of early childhood teachers is heterogeneous in terms of practicing teaching strategies and STEAM activities.Early childhood teachers, who use social-emotional and cognitive teaching strategies with equal frequency, also often use integrated STEAM activities in young children education.Other teachers prioritise social-emotional teaching strategies and pay less attention to cognitive teaching strategies, although the latter have a slightly stronger link to the development of STEAM practices.Teachers who are less likely to use cognitive strategies to teach young children tend to use STEAM activities less frequently.
ADOPTING ONLINE LEARNING APPROACHES IN PROFESSIONAL MUSIC TRAINING - THE TUT EXPERIENCE

Having been asked to respond to a question from the Ministry of Education, Taiwan, about whether educational technologies used during the COVID-19 pandemic can serve as a knowledge base and be incorporated as learning tools based on their impacts on music education curricula, what follows are some observations and reflections from the Tainan University of Technology (TUT), Taiwan, about its educators' experiences of teaching live lessons one-to-one and in groups online.The idea put forward is that technology applications reflect an emergent sensibility in music lessons, and this represents an opportunity for music educators to reconfigure and strengthen their pedagogical approaches.By recognizing the accessibility of new and varied forms of musicianship and acknowledging the ways in which course curricula continue to grow in their range of practices and necessary literacies, pedagogies can be developed in concert with digital technologies to support broader and more cohesive, inclusive, diverse, meaningful, and useful experiences for music students.
An Earlier Experiences Towards Optimizing Apache Spark Over Frontera Supercomputer

Apache Spark has become a very popular computing engine that allows distributing computing tasks on a compute cluster. However, the current approaches lack necessary optimizations and often lead to non-optimal resource usage. This paper briefly describes our earlier experiences on the Frontera supercomputer towards deployment and optimizing the Apache Spark components and presents our approach to running Apache Spark jobs efficiently. We ran the simulation at different scales to understand the performance trend of the proposed implementation.
EFFECTS OF POVERTY ON LEARNER’S ACADEMIC PERFORMANCE: A CASE OF SELECTED SECONDARY SCHOOLS IN LUFWANYAMA DISTRICT OF COPPERBELT PROVINCE, ZAMBIA

Poverty directly affects academic achievement due to the lack of resources available for student success.Low achievement is closely correlated with lack of resources, and numerous studies have documented the correlation between low socioeconomic status and low achievement.Children living in poverty often struggle academically, with lower test scores and graduation rates than their peers.They may have poor attendance, low motivation and limited access to educational resources, all of which affect their academic performance.Poverty has become one of the most prevalent indicators of academic achievement in our Zambian schools today.Hence, the study aimed at analyzing the effects of poverty on learner's academic performance in Lufwanyama district of Copperbelt Province in Zambia.The study employed both the qualitative and quantitative methods and a descriptive research design that sampled head teachers, teachers, pupils and parents from the selected schools.Data was obtained from the respondents by means of interviews and questionnaires.Frequency tables, graphs, figures and pie-charts were used to analyze the qualitative data.Quantitative data were analyzed by the use of the Statistical Package for Social Sciences (version 26) and Microsoft Excel (version 16).The findings indicated that poverty is caused by cultural factors, social factors and environmental factors.Children were found to have stopped school on order to make ends meet.These resulted into pregnancies and early marriages as among the reasons for poverty.The other revelation to the findings was that most of the effects of poverty on learners took place in secondary section due to payments of extra lessons and other school requirements despite having free education.In this regard, several recommendations to various stake holders were made such as; Ministry of Education, DEBS office and school administrators.
Kajian Kemampuan Bangunan Hikmat Menjaga Keamanan Insfrastruktur Kereta Api dari Bahaya Banjir

In an effort to improve accessibility between regions and economic equality, the government has built many transportation supporting infrastructure such as toll roads, ports and railways. The construction of road and rail-based transportation lines will have an impact on the environment, especially transportation routes that cut through watersheds. The cutting of the watershed by the infrastructure of transportation facilities causes changes in the flow pattern of rainwater falling in the catchment area resulting in flooding. On the railway-based transportation line, to drain flood discharge, the Hikmat Building (BH) was built. The Wisdom Building is a building that cuts a railway track similar to a bridge or culvert, so that water can flow under the railway track. As a flood control building, wisdom building must be able to drain flood discharge that occurs both from upstream and from the halir of a watershed. The flood discharge that must be served by the wisdom building consists of three sources, first from the water discharge of the railway line drainage channel and second from the Catchment Area (DTA) / Catchment Area, and third the flow of water due to overflowing water discharge from the nearest river downstream of the Catchment Area. The calculation of the flood discharge of the Lematang Hilir Sub watershed with a 25-year anniversary of 131.94 m3 / s, while the installed wisdom building is only able to drain 78.74 m3 / s. For this reason, it is necessary to build a new wisdom building in the form of a box culvert with dimensions of 1.5 x 1.5 m as many as 10 units with a total drainage capacity of 77.10 m3 / s. The analysis only considered the ability of the Lematang River to receive flood discharge from upstream under normal conditions.
SAFE: Simultaneous Alignment of Features and Predictions for Dense Object Detectors

Dense detectors have gained increasing attention due to their simplicity and efficiency. However, there are two inherent misalignments lying in the one-stage architecture that greatly inhibit the performance. One is the misalignment between features and predicted boxes, the other is the spatial prediction misalignment between classification and regression branches. In this work, we propose a Simultaneous Alignment of Features and prEdictions (SAFE) for dense object detectors to mitigate both issues. Regarding of the misalignment between features and corresponding boxes, we design an Adaptive Feature Alignment module that leverages the location information to align features in the classification branch. As for the inconsistency of spatial prediction, we introduce a Weighted Average of Predictions module to refine the predicted sub-optimal coordinates by autonomously selecting higher-quality predicted ones in the surrounding area. Extensive experiments validate the persistent improvement of our approach. Our best model SAFE-S achieves state-of-the-art performance (56.5 mAP) on MS-COCO.
Deep Metric Multi-View Hashing for Multimedia Retrieval

Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view features and utilize the metric information provided by the dissimilar samples, leading to limited retrieval precision. Current methods utilize weighted sum or concatenation to fuse the multi-view features. We argue that these fusion methods cannot capture the interaction among different views. Furthermore, these methods ignored the information provided by the dissimilar samples. We propose a novel deep metric multi-view hashing (DMMVH) method to address the mentioned problems. Extensive empirical evidence is presented to show that gate-based fusion is better than typical methods. We introduce deep metric learning to the multi-view hashing problems, which can utilize metric information of dissimilar samples. On the MIR-Flickr25K, MS COCO, and NUS-WIDE, our method outperforms the current state-of-the-art methods by a large margin (up to 15.28 mean Average Precision (mAP) improvement).
A Comparative Study of Neural Network Compilers on ARMv8 Architecture

The deployment of Deep Neural Network (DNN) models in far edge devices is a challenging task, because these devices are characterized by scarce resources. To address these challenges various deep learning toolkits and model compression techniques have been developed both from industry and academia. The available DNN toolchains can perform optimizations at different levels e.g., graph level, Intermediate Representation (IR) or machine-dependent optimizations, while they operate in an Ahead-of-Time (AOT) or Just-in-Time (JIT) manner. Although the area of DNN toolchains is an active research area, there is no available study that analyses the performance benefits achieved by the different optimization levels e.g., the performance boost reported by the graph-level vs. the machine-dependent optimizations. This work performs a comprehensive study of three popular neural network (NN) compiler frameworks that target (mainly) far edge devices: TensorFlow Lite for MCUs, GLOW, and IREE. For a fair comparison, our performance analysis targets to reveal the performance benefits offered by the different optimization levels for the three studied frameworks as well as the strength of specific graph-level optimizations e.g., in quantizing the input NN models. Our evaluation is based on various NN models with different computational/memory resources and the experiments are performed in a state-of-the-art high-performance embedded platform by Nvidia.
Decoupled learning for brain image registration

Image registration is one of the important parts in medical image processing and intelligent analysis. The accuracy of image registration will greatly affect the subsequent image processing and analysis. This paper focuses on the problem of brain image registration based on deep learning, and proposes the unsupervised deep learning methods based on model decoupling and regularization learning. Specifically, we first decompose the highly ill-conditioned inverse problem of brain image registration into two simpler sub-problems, to reduce the model complexity. Further, two light neural networks are constructed to approximate the solution of the two sub-problems and the training strategy of alternating iteration is used to solve the problem. The performance of algorithms utilizing model decoupling is evaluated through experiments conducted on brain MRI images from the LPBA40 dataset. The obtained experimental results demonstrate the superiority of the proposed algorithm over conventional learning methods in the context of brain image registration tasks.
Study on the Method of Tilt Error Compensation for a Laser On-machine Measurement System

Abstract This paper proposes a spindle vector compensation method based on laser triangulation for the inclination measurement error of a laser on-machine measurement system. By using the fixed relationship between the calibration vector and the spindle vector, the laser’s axis vector and the surface’s normal vector coincide in the actual measurement by adjusting the spindle vector. The tilt measurement and calculation errors are compensated simultaneously using this spindle vector compensation method.