Reviewerly/exHarmony huggingface dataset:
A Robust Residual Shrinkage Balanced Network for Image Recognition from Japanese Historical Documents

Kuzushiji, as characters written in a cursive style, had been continuously used for both publishing and handwriting in Japan before the end of the 19th century. However, well into the twentieth century, most modern Japanese lost the ability to read Kuzushiji due to changes in writing systems. Therefore, how to develop advanced machine learning algorithms to identify Japanese historical characters has great significance in preserving cultural knowledge and promoting sustainable development in urban living. Unlike traditional digit image recognition, Kuzushiji image recognition is a more challenging task due to the complex script for cursive handwriting, multiple variations between characters, and imbalanced data. To address such issues, a novel deep learning-based character recognition method called residual shrinkage balanced network (RSBN) is developed for classifying Japanese historical characters. A novel residual shrinkage structure with a soft threshold function is constructed to reduce redundant features using specially designed skip-connected subnetworks. Then, multiple residual shrinkage modules are stacked together to learn hierarchical features from raw input. Furthermore, a novel class rebalancing strategy is further designed to improve learning performance from imbalanced data. The experiments are conducted on Japanese classical literature datasets. The results demonstrate that the proposed method obtains more than 89.01% testing accuracy on Kuzushiji-MNIST dataset, which outperforms the existing deep learning-based character image recognition methods. Moreover, without additional data augmentation techniques, the proposed approach also achieves 97.16% balanced accuracy on Kuzushiji-49 dataset, which is the best one compared with existing published results. When considering the computational performance, the number of trainable parameters of the proposed method is competitive with that of the typical ResNet-18.
Internet of things-blockchain lightweight cryptography to data security and integrity for intelligent application

The industrial internet of things (IoT) plays a major role in the growth of automation and increasing digital connectivity for machine-to-machine communication. The research community has extensively investigated the possibility of IoT and blockchain integration for the last couple of years. The major research is focused on the benefits of integrating blockchain with IoT. In this work, we first focus on the issue of integrating IoT nodes with blockchain networks, especially for non-real-time IoT nodes that do not have an in-built clock mechanism. As a result, they cannot establish communication with real-time blockchain networks. Another critical security issue is protecting data coming from IoT devices to blockchain networks. Blockchain is enough mature to protect the data in its ecosystem. However, information coming from outside of the world does not have any guarantee of data integrity and security. This paper first addresses the clock synchronization issue of IoT nodes with blockchain using a network time protocol and then proposes an IoT-blockchain light-weight cryptographic (IBLWC) approach to secure the entire IoT-blockchain ecosystem. This paper also presents the performance analysis of IBLWC as a suitable and cost-effective solution that incurs less processing overhead for IoT-blockchain-based applications.
An advance extended binomial GLMBoost ensemble method with synthetic minority over-sampling technique for handling imbalanced datasets

&lt;span lang="EN-US"&gt;Classification is an important activity in a variety of domains. Class imbalance problem have reduced the performance of the traditional classification approaches. An imbalance problem arises when mismatched class distributions are discovered among the instances of class of classification datasets. An advance extended binomial GLMBoost (EBGLMBoost) coupled with synthetic minority over-sampling technique (SMOTE) technique is the proposed model in the study to manage imbalance issues. The SMOTE is used to solve the proposed model, ensuring that the target variable's distribution is balanced, whereas the GLMBoost ensemble techniques are built to deal with imbalanced datasets. For the entire experiment, twenty different datasets are used, and support vector machine (SVM), Nu-SVM, bagging, and AdaBoost classification algorithms are used to compare with the suggested method. The model's sensitivity, specificity, geometric mean (G-mean), precision, recall, and F-measure resulted in percentages for training and testing datasets are 99.37, 66.95, 80.81, 99.21, 99.37, 99.29 and 98.61, 54.78, 69.88, 98.77, 96.61, 98.68, respectively. With the help of the Wilcoxon test, it is determined that the proposed technique performed well on unbalanced data. Finally, the proposed solutions are capable of efficiently dealing with the problem of class imbalance.&lt;/span&gt;
Deep learning-based switchable network for in-loop filtering in high efficiency video coding

&lt;span lang="EN-US"&gt;The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper’s goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering.&lt;/span&gt;
Development of android-based learning media applications on mole concept topics for high school students

This study aims to develop a product in the form of an Android-based learning media application based on the mole concept material for class X SMA/MA that is valid and practical. The study uses the Plomp development research model, which consists of three steps: preliminary research, prototyping phase, and assessment phase. This research is limited to the prototyping phase for the practicality stage of the small group evaluation. Three chemistry lecturers carried out the product validity test at the Faculty of Mathematics and Natural Sciences, Padang State University, two Al-istiqamah High School chemistry teachers as material experts, and two lecturers of informatics engineering at Padang State University as media experts. Fifteen students and two chemistry teachers carried out the practicality test. The validity data analysis technique uses Aiken's V index. The validity test results showed that the Android-based learning media application developed belongs to the valid category with an average V value of 0.86 for material experts and an average V value of 0.93 for media experts. The practicality data analysis technique uses a percentage formula. The results of the practicality test in small groups are classified as "very practical," with a practicality percentage value of 90% for students' practicality and a practicality percentage value of 92% for teachers' practicality. Based on the results of the validation and practicality tests, it was found that applying android-based learning media to the mole concept material was valid and practical.
Prediction on RAC Tickets in Indian Railways

Abstract: The “PREDICTION ON RAC TICKETS IN INDIAN RAILWAYS” is based on the Rail transport. It is one of the prominent modes of transportation with reference to goods as well as passenger’s movement in any Economy. Indian Railways also played important role in Economic development of our country and will be at key position in future too. Despite of lots of changes and development in Indian Railways with reference to passenger transportation i.e., increase in number of Railways services, trains, types of trains, facilities inside and outside the trains especially on platforms, ticket reservation system and its procedure via technological advancement and digitalization, information to the passengers regarding their ticket status, and so on, still we find certain basic missing link in the services of Indian Railways and due to which it suffers not only revenue losses but many more. An attempt is being made to address that basic deficiency of Indian Railways reservation system especially with reference to wait listed reservation ticket with the passengers enroute and no information about actual position of the waitlisted ticket enroute in case of vacant seat after departure from the origin point and II charting done. Thus, in this project solution for better convenience of enroute passengers especially with waitlisted or RAC tickets and prevent TTE’s for making illegal money and save revenue loss to the railway through efficiently implementation of suggested ICT within existing systems.
Improved Hungarian algorithm–based task scheduling optimization strategy for remote sensing big data processing

With the development of remote sensing technology and computing science, remote sensing data present typical big data characteristics. The rapid development of remote sensing big data has brought a large number of data processing tasks, which bring huge challenges to computing. Distributed computing is the primary means to process remote sensing big data, and task scheduling plays a key role in this process. This study analyzes the characteristics of batch processing of remote sensing big data. This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow, called optimal sequence dynamic assignment algorithm, which is applicable to heterogeneously distributed computing environments. This strategy has two core contents: the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism. Moreover, the strategy solves the dependency, mismatch, and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks. The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm. We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy. Compared with the processing before optimization, the makespan of the proposed method was shortened by at least 20%. Compared with popular scheduling algorithm, the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.
TA Discussion: Pros and Cons of the Theory of Personality Adaptations

A part of the discussion of Shustov D.I. and Mark Widdowson about pros and cons of adaptations theory in TA.
A STRATEGIC DEVELOPMENT FINANCE APPROACH IN ALLEVIATING POVERTY IN CAMEROON

A large funding gap looms on the horizon as the 2030 deadline for bringing an end to poverty worldwide gradually approaches. The number of poor people in the world reduced to 10% in 2015 but this number is expected to rise particularly in sub-Saharan Africa due to the Covid-19 pandemic. In Cameroon, despite experiencing sustained economic growth in the last decade, the country still suffers from chronic underdevelopment and poverty rate is high at a 37.5% estimate since 2014 particularly in rural areas due to insufficient mobilisation of development funds. The country’s weak capacity to effectively mobilise development funds have severely hindered developments efforts and the fight against poverty in Cameroon. Government programmes aimed at ameliorating and improving the living and social conditions of Cameroonians as originally intended have largely failed. This study examined Development Finance and Poverty as the core concepts and tried to establish a hypothetical relationship between development finance and poverty alleviation in Cameroon. The study was guided by the epistemology of positivism and followed the conclusive case study design. A sample of 407 participants using multi-stage sampling was drawn from the centre region of Cameroon. Questionnaire was used to collect data that was analysed using statistical packages such as SPSS 23 and AMOS 24. Hypotheses were tested using structural equation modelling. The results revealed that private domestic funds and public domestic funds both have a significant positive effect on poverty alleviation in Cameroon. The study further revealed that Public Domestic Funds has a significant positive mediating effect on the relationship between private domestic funds and poverty alleviation in Cameroon. Based on these results, the study concluded that domestic resource mobilisation is a more important source of development funds to alleviate poverty in Cameroon than external resource mobilisation. The study thus recommended a strategic development finance model for poverty alleviation in Cameroon.
Daily Solar Radiation Forecasting for Northwest Nigeria Using Long Short-Term Memory

In order to ensure energy security and environmental sustainability, transition to renewable energy sources is required. One of the most viable and sustainable renewable energy sources is solar. However, developing solar energy systems requires solar radiation data which is scarce for most locations including Northwest Nigeria. In order to address this challenge, solar radiation is usually estimated from the available meteorological parameters. Several previous studies have used various methods including geospatial techniques and machine learning to predict monthly and yearly solar radiation, while few studies have focused on the estimation of daily solar radiation. Meanwhile, providing daily solar radiation data is necessary for the development of solar energy systems. Deep learning has been shown to be effective in solar radiation forecasting. To evaluate the performance of the deep learning method for daily solar radiation prediction, a Long Short-Term Memory (LSTM) based deep learning model was developed in this study. The forecasting model was created using daily solar radiation data collected over a 21-year period by the Nigerian Meteorological Agency in three major towns in North West Nigeria: Kano, Kaduna, and Katsina. The model was evaluated using two statistical indicators: coefficient of determination (R2) and Root Mean Square Error (RMSE). Results showed that R2 of 0.79 and 0.78 were obtained for the training and testing datasets respectively, while RMSE of 0.46 and 0.47 were obtained for the training and testing datasets respectively. Overall, the LSTM deep learning model has been proven to be effective in forecasting daily solar radiation.
Applications of RFID Technology in Library A Study

In a present day, RFID has shift from dimness into popular applications, helps to manage materials in speedy way.RFID allow recognizing the material from a distance, and similarly like bar-code system, it does so without requiring a line of sight.In this article, we introduce the concept of RFID, its technological role and applications, and to reviews the challenges will face while deploying this technology.In a library dissemination and shelving of the reading materials is quite a hefty work which kills most of the time of the library staff.RFID gives an answer to such type of situations or problem, by reduce the amount of time required to perform circulation activities.This paper explains the RFID components, technical features, its advantages and disadvantages related to use of RFID in libraries.
Digital Literacy Analysis of Middle School Students in Bogor Regency, West Java, Indonesia

Digital literacy played an important role in human life now and in the future. This study aimed to describe and analyze the digital literacy of junior high school students in Bogor district. The number of respondents was 76 students from 2 junior high schools in Bogor district, West Java. The method used is a quantitative descriptive method. The results of the study prove that the intensity of implementing the use of digital literacy in learning activities and variations of digital-based reading materials and teaching aids is 73.2%, the remaining 22.8% do not yet have sufficient digital literacy. Based on the scores above, it can be said that digital literacy in the 2 schools is quite good. Schools are expected to increase digital use gradually, balanced with tiered training for students and teachers as well as staff employees in the school environment
Underwater Image Enhancement for Instance Segmentation using Deep Learning Models

Underwater instance segmentation greatly depends on color-blended underwater images. In this work, a combination of Generalized Color Fourier Descriptor (GCFD), Convolutional Neural Network (CNN) and Mask Region-based Convolutional Neural Network (Mask R-CNN) models were employed to generate a mask for each bounding-boxed Region of Interest (ROI) to obtain enhanced individual underwater segmented images from their complex background accurately. By this image enhancement approach, individual underwater instances are segmented from their complex background accurately. The Patch-based Contrast Quality Index (PCQI) evaluation of our proposed image enhancement method (GCFD) after conducting experiment on the employed datasets shows performance accuracy of 1.1336, which is higher than the 1.1126 performance accuracy achieved by the Contrast-enhancement Algorithm (CA).
Introduction of the Media Industry and Its Optimization to Residents of Komplek Kebersihan RT13 / RW10 Cengkareng West Jakarta

There are so many news portals available but not many people know the methods of using journalistic techniques. The purpose of this community service is to make it easier for the community to gain the ability to learn about journalism and demonstrate the use of online news to the community, especially residents of Komplek Kebersihan RT13/RW10 Cengkareng, West Jakarta. The result of community service is getting good knowledge about journalism after studying the Nachannel.my.id News Portal which has various features both in reporting information, videos and polls, which also includes features to promote products. In conclusion, with this community service, the community can gain knowledge about basic techniques of news journalism and also get training in using news portal application features on Android smartphones on mobile devices (smartphones) and laptops used by residents, especially residents of the Komplek kebersihan RT13/RW10 Cengkareng West Jakarta.
SNBT Debriefing for Students of SMA Kanzul Mubarok Bekasi

The implementation of the SNBT (National Selection Based on Test) will be carried out in 2023, so it is necessary to provide debriefing so that students have a thorough picture and preparation for facing the SNBT later. The purpose of this activity is to introduce and provide SNBT material which will be tested later. This activity was attended by 34 grade 12 students of SMA Kanzul Mubarok, Bekasi. The activity was carried out with 1 face-to-face counseling and 5 face-to-face material deepening and 2 try outs. The material provided includes introduction to SNBT procedures, general reasoning, reading comprehension, general knowledge and understanding, Indonesian language literacy, English literacy and quantitative knowledge. Based on the results, students scored 64 in the first tryout and got 69.9 in the second tryout. Counseling to parents also determines the results of trials that have been carried out by students. The activity needs to be continued by carrying out more challenging practice questions.
Outline Technique in Teaching Writing Skill at MTS Negeri 1, Morotai Island

Writing is one of the skills in English. It should be learned for the students because they have to produce the text in English. Some students face any problems in learning and practice of writing skill. For this case, the researchers found the solution in students writing. the teacher should be creative and innovative in teaching writing because in this part, the teacher has the big role to make the students know how to write in a good way. The researchers applying the outline technique in teaching writing. Outline technique is the technique that used for the students to organize the idea and arrangement of the idea and pour into the text. The aim of this research is to know whether the students improve their writing skill in using outlining technique or not, and what is the problem that face by the students when applying this technique. This research is happened in MTs State 1 of Morotai Island. This research is quantitative research using T-test which is the result show that the score was 0.755 and it was higher that r table 0.367. it can be said that the data is accepted. The result of this result is the application of outline technique improve the students’ writing skill.
PERANCANGAN SISTEM INFORMASI CATATAN DAN PENGAWASAN HEWAN TERNAK MENGGUNAKAN QR CODE BERBASIS WEB DENGAN METODE AGILE

Livestock as one of the mainstay sectors for development progress. The first role of animal husbandry is to produce high quality food ingredients such as: meat, milk and eggs which contain complete animal protein with essential amino acids. Second, livestock is also a source of income and employment opportunities for the community, in caring for it the community has a tradition of releasing buffalo, goats or sheep simultaneously in plantation areas, rice fields and even to the streets, this release makes it difficult for breeders to know which livestock belong to other breeders because livestock do not have markings, in recording mating data, administering vaccines, as well as the health condition of goats manually using a book. However, this will be very troublesome if the number of livestock being raised is very large, moreover the data that must be recorded is also complicated. With the existence of an Information System for livestock records and supervision using a web-based qr code with an agile method that utilizes the use of barcode technology. The data collection method used is interview, observation, literature study documentation. System development method created by Extreme Programming (XP). The Extreme Programming method is one of the most frequently used methodologies in Agile Development. With UML design. The purpose of designing this information system is that it is hoped that breeders can be assisted in recording and surveillance