Reviewerly/exHarmony huggingface dataset:
Comparison of Lossless Image Formats

In recent years, a bag with image and video compression formats has been torn. However, most of them are focused on lossy compression and only marginally support the lossless mode. In this paper, I will focus on lossless formats and the critical question: "Which one is the most efficient?" It turned out that FLIF is currently the most efficient format for lossless image compression. This finding is in contrast to that FLIF developers stopped its development in favor of JPEG XL.
Retraction: Jin, R. et al. Blockchain-Enabled Charging Right Trading Among EV Charging Stations. Energies 2019, 12, 3922

It has come to our attention that the majority of the content in the title paper [...]
Analogy-based morphology: The Kasem number system

This paper presents a formalization of proportional analogy using typed feature structures, which retains all key elements of analogical models of morphology. With the Kasem number system as an example, I show that using this model it is possible to express partial analogies which are unified into complete analogies. The analysis presented is accompanied by a complete TRALE implementation.
The Role of Information in Classical and Modern Paradigms

Information is a key concept of our time. We know that a piece of news can change or transform our thoughts, our emotions, and our actions in both individual and social respects. The emerging concept of information is broader than its traditional usage. Unlike artificial systems, living systems are affected by the information they receive. This study outlines the concept of information as it appears in integrated conceptions of the natural and human world: in so-called “paradigms,” classical as well as modern.
Empowerment of Aisyiyah kindergarten teachers in early detection of tuberculosis

The increase in tuberculosis incidence in adults has implications for the increase in cases of tuberculosis in children. Tuberculosis in children has a long-term effect on the growth and development of children so that early prevention efforts are needed. Kindergarten school teachers are the closest people after parents who have an important role in early detection of tuberculosis in children. This activity aims to empower kindergarten school teachers in conducting early detection of tuberculosis in school children through health education activities. This activity was carried out on August 18-19, 2020 virtually, with lecture, discussion and simulation techniques, and was attended by 79 Aisyiyah Kindergarten teachers in DKI Jakarta. The evaluation results show that the average value of the pretest was 57.78 and the average value of the posttest was 72.02. Statistically, there is a significant difference in the average knowledge value of participants before and after health education. In addition, 74% of participants felt that the material provided was very useful, but there were also those who felt that the network was constrained, such as voices that were not clear and echoed.
Personal Digital Health Assistants

Personal Digital Health Assistants (PDHA) are applications that can run on virtually any computer or mobile device including a smart phone/ personal digital assistant (PDA). A PDHA acquires, stores, and analyzes health related information of an individual. It usually communicates with remote servers of a large organization which can connect it to various resources. PDHAs are an integral part of telehealth. Their utility has increased manyfold since the start of the pandemic. The systems are increasingly more complex and are involved in all aspects of care. The PDHA are usually utilized by patients or their caregivers with assistance from professionals. Many automated systems can be utilized free of charge. Some are available as part of a prepaid health plan.
Replication and Automatic Updates of File Fragments in Cloud

Generally, in cloud computing the data is outsourced to controls of third party, which may lead to issue of securitys. The data in cloud may lost because of attacks by unknown users and nodes inside the cloud .Therefore, the data within in the cloud must be kept in secured state by using different techniques .In this paper, we propose division and replication of data within in the cloud to improve optimal performance and security .In the present work we separate a file into multiple fragment sand replicate fragments on different nodes in the cloud. Each node, should store fragment only once. Therefore, no information can be get by the attacker. The node storing the fragments within the cloud are separated using a method called T-colouring, which avoids the attacker to find the location of nodes that contain related fragments. The traditional cryptographic techniques for data security are not used in this work, so that computationally expensive methodologies are reduced. The more advanced feature in this work is an automatic update of file fragments on nodes whenever any changes are done by the third person on that particular node.
Blockchain Technology and Electronic Wills

In this study we examine the impact of information technology on inheritance law, especially in the context of drafting electronic wills.We consider it a subject of great importance, noting that, although we are unable to approach in a diverse manner the natural fact of death, we can personalize, according to our own expectations and principles, our way out of the scene.In this context, the certainty and predictability of the fulfillment of the last will dispositions are essential.Given that the writing of a traditional will has multiple limitations, we will analyze the manner in which technology offers the necessary mechanisms to fulfill exactly the dispositions of the last will.To this end, we will evaluate the implications of the Blockchain technology on the inheritance issue, examining both the shortcomings and the arguments underlying the implementation of the technology on a large scale.
Management of digital records for knowledge preservation in academic institutions for a sustainable development in education sector

Building and managing digital collections or information resources for knowledge perseveration in academic institutions, especially tertiary institutions, has increasingly become more needful than ever before as many believe it remains the way out for sustainable academic teaching and learning in the 21st century. This belief stems from the fact that the 21st-century teaching and learning activities, as well as research, has already moved to a digital base, especially as the new normal, having witnessed the sudden emergence of coronavirus 19 with physical restrictions to buildings where physical collections are housed such as the library and other information centres. From the data collection stage to the research data analysis to the publication stage, knowledge preservation has fully embraced digital processes where Google forms, Survey Hearts and Monkeys are now used for building data. Questionnaires, interviews, and so many other instruments are now done through digital processes and mediums, thereby building the collection of knowledge in the digital format. The issue may not really be the processes of building the collections in the digital format, however, but the management of these digital collections in the digital format to preserve the knowledge for posterity via different databases and digital preservation processes. Therefore, this opinion paper is designed to explore different kinds of literature on this subject area of digital collections management to close the gap in this area of digital collections for a 21st-century academic institution and to proffer some recommendations with a special interest in institutional repositories moving fully into automated version. Therefore, the opinion of this paper is to review relevant literature to argue whether digital collections in academic institutions is the way out for sustainable development in the knowledge management aspect.
Automatic Scoring Method of Short-Answer Questions in the Context of Low-Resource Corpora

Short-answer questions offer a method to evaluate whether students have acquired a domain of knowledge. Because it is very time-consuming to evaluate the answer of the questions, many automatic scoring methods have been proposed. However, most of these models require large quantities of training data. This study proposes a method for automatic creation of a concept map based on small quantities of corpora. The concept map verified by experts is available and reliable. In addition, this study proposes a method to score the correctness of students' answers to short-answer questions using a machine-generated concept map. The preliminary experiment shows that the correctness of scoring results of the proposed method is close to that of the BERT-based model. Since the proposed method does not require large quantities of training data, it is suitable for the instruction system only using low-resource corpora.
A Practical Alzheimer Disease Classifier via Brain Imaging-Based Deep Learning on 85,721 Samples: A Multicentre, Retrospective Cohort Study

Background: Beyond detecting brain lesions or tumors, comparatively little success has been attained in identifying brain disorders such as Alzheimer’s disease (AD), based on magnetic resonance imaging (MRI). Many machine learning algorithms to detect AD have been trained using limited training data, meaning they often generalize poorly when applied to scans from previously unseen populations. Therefore, we built a practical brain MRI-based AD diagnostic classifier using deep learning/transfer learning on dataset of unprecedented size and diversity. Methods: A retrospective MRI dataset pooled from more than 217 sites/scanners constituted the largest brain MRI sample to date (85,721 scans from 50,876 participants) between January 2017 and August 2021. Next, a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, was built as a sex classifier with high generalization capability. The sex classifier achieved 94·9% accuracy and served as a base model in transfer learning for the objective diagnosis of AD. Findings: After transfer learning, the model fine-tuned for AD classification achieved 91·3% accuracy in leave-sites-out cross-validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI, 6,857 samples) dataset and 94·2%/93·6%/90·5% accuracy for direct tests on three unseen independent datasets (AIBL, 669 samples / MIRIAD, 644 samples / OASIS, 1,123 samples). When this AD classifier was tested on brain images from unseen mild cognitive impairment (MCI) patients, MCI patients who finally converted to AD were 3 times more likely to be predicted as AD than MCI patients who did not convert (65·2% vs 20·6%). Predicted scores from the AD classifier showed significant correlations with illness severity. Interpretation: In sum, the proposed AD classifier could offer a medical-grade marker that have potential to be integrated into AD diagnostic practice.Funding Information: National Key R&D Program of China, National Natural Science Foundation of China, the 13th Five-year Informatization Plan of Chinese Academy of Sciences, the Key Research Program of the Chinese Academy of Sciences, and Beijing Nova Program of Science and Technology.Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: Deidentified data were contributed from datasets approved by local Institutional Review Boards. The reanalysis of these data was approved by the Institutional Review Board of Institute of Psychology, Chinese Academy of Sciences. All participants had provided written informed consent at their local institution.
Corrigendum: Tether: A Study on Bubble-Networks

CORRECTION article Front. Blockchain, 19 January 2022Sec. Blockchain Technologies Volume 4 - 2021 | https://doi.org/10.3389/fbloc.2021.842255
Human Face Recognition from Part of a Facial Image based on Image Stitching

Most of the current techniques for face recognition require the presence of a full face of the person to be recognized, and this situation is difficult to achieve in practice, the required person may appear with a part of his face, which requires prediction of the part that did not appear.Most of the current forecasting processes are done by what is known as image interpolation, which does not give reliable results, especially if the missing part is large.In this work, we adopted the process of stitching the face by completing the missing part with the flipping of the part shown in the picture, depending on the fact that the human face is characterized by symmetry in most cases.To create a complete model, two facial recognition methods were used to prove the efficiency of the algorithm.The selected face recognition algorithms that are applied here are Eigenfaces and geometrical methods.Image stitching is the process during which distinctive photographic images are combined to make a complete scene or a high-resolution image.Several images are integrated to form a wide-angle panoramic image.The quality of the image stitching is determined by calculating the similarity among the stitched image and original images and by the presence of the seam lines through the stitched images.The Eigenfaces approach utilizes PCA calculation to reduce the feature vector dimensions.It provides an effective approach for discovering the lower-dimensional space.In addition, to enable the proposed algorithm to recognize the face, it also ensures a fast and effective way of classifying faces.The phase of feature extraction is followed by the classifier phase.Displacement classifiers using square Euclidean and City-Block distances are used.The test results demonstrate that the proposed algorithm gave a recognition rate of around 95%, to validate the proposed algorithm; it compared to the existing CNN and Multibatch estimator method.
Repositories

Laravel is a fairly unique framework because it abstracts away many of the details that a new application would normally require, such as routing, event management, and database access, so that we can focus on the more important tasks—the ones that actually set our application apart from others of its kind. This same principle exists in DDD, only it presents itself differently. In DDD, the patterns and building blocks are the foundational components of almost any system and are all concentrated on the core domain itself, instead of being bogged down in the (sometimes) overwhelming sea of details. This similarity between Laravel and DDD is, in part, what makes the combination of the two so powerful: they both set out to abstract away the monotonous, repetitive tasks that have already been built, rebuilt, and reinvented a bazillion times to allow you to focus your efforts on developing the domain.
A Lightweight Learning Method for Stochastic Configuration Networks Using Non-Inverse Solution

Stochastic configuration networks (SCNs) face time-consuming issues when dealing with complex modeling tasks that usually require a mass of hidden nodes to build an enormous network. An important reason behind this issue is that SCNs always employ the Moore–Penrose generalized inverse method with high complexity to update the output weights in each increment. To tackle this problem, this paper proposes a lightweight SCNs, called L-SCNs. First, to avoid using the Moore–Penrose generalized inverse method, a positive definite equation is proposed to replace the over-determined equation, and the consistency of their solution is proved. Then, to reduce the complexity of calculating the output weight, a low complexity method based on Cholesky decomposition is proposed. The experimental results based on both the benchmark function approximation and real-world problems including regression and classification applications show that L-SCNs are sufficiently lightweight.
"Abstract model of the Ternary Machine Signal Encoding"

Aims/ Objectives: Current Chapter is the next in the Ternary Series event dedicated to analysis of the incoming/outcoming signal and it’s conversion from analogue to digital The importance of the above mentioned problem can not be undermined as in physical realm we are dealing with natural phenomena and conversion into digital is our main goal Quoting one of the previous publications by the same author:” Any number just as any signal is presented by F(x) = anx^n + an-1x^n-1 + an-2x^n-2 + …….. + a1x +a , and is further converted into either binary or ternary” And another quote:” The outcome is a number which we can either be presented in the form of or or From where = X= ”This second quote will become perhaps more clear after necessary explanations have been givenOff we go We were going to take a look at the ternary principle from the mathematical stand point A signal can be presented in the form of a polynomial. F(x) = anx^n + an-1x^n-1 + an-2x^n-2 + ........ + a1x +a ; and by doing so one can write an equation of time frequency amplitude etc.In other words we can describe a signal as a function In our case we are interested in the trigonometric function namely a sin(x) function First of all its easy to deal with and its physical parameters amplitude period and phase ,or more physically speaking amplitude frequency and phase are easy to convert into numbers or digits to be more specific
A Study on Identity Design Class using PBL in Specialized High School

2015 개정 교육과정에서 디자인교육의 목표에서는 직무 수행에 필요한 기술적 사고력과 문제해결력을 강조하고 있으며, PBL은 직업인으로서 접하게 되는 실제적인 문제와 환경을 통해 문제해결 능력을 기를 수 있다. 이에 본 연구는 PBL을 활용한 아이덴티티 디자인 수업을 통하여 학생들의 문제해결 능력과 협업 능력, 자기 주도적 학습 능력 등을 키우고자 하였다. 이를 위해 PBL과 아이덴티티 디자인에 대하여 이론적으로 고찰하고, 설문조사를 통해 특성화고등학교 아이덴티티 디자인교육의 현황을 파악하였다. 이를 기반으로 PBL을 활용한 아이덴티티 디자인 수업 과정을 도입, 문제 만나기, 해결방안 탐색하기, 해결안 개발하기, 발표 및 평가하기의 다섯 단계로 구성하였으며, 효과 검증을 위해 경기도 소재의 특성화고등학교에서 수업을 진행하였다. 그 결과, 수업 과정에 대한 전반적인 만족도가 4.42로 나타나 높은 효과성을 입증하였다.In the 2015 revised curriculum, the goal of design education emphasizes technical thinking and problem-solving skills necessary for job performance, and PBL can develop problem-solving skills through practical problems and environments encountered by professionals. Therefore, this study attempted to develop students problem-solving ability, collaboration ability, and self-directed learning ability through identity design classes using PBL. To this end, PBL and identity design were theoretically considered, and the current status of identity design education in specialized high schools was grasped through a survey. Based on this, the identity design class course using PBL was composed of five stages: introduction, problem meeting, solution exploration, solution development, presentation, and evaluation, and classes were conducted at specialized high schools in Gyeonggi-do to verify the effectiveness. As a result, the overall satisfaction with the class was 4.42, proving high effectiveness.
Implementation Combination Cryptographic Algorithm Triangle Chain Cipher and Vigenere Cipher In Securing Data in Database

Data security is most important today, because the rampant data theft resulting in a lot of misuse of data by irresponsible parties so that it makes us anxious as data owners, for data storage it is usually stored in the database. From these problems the idea emerged to create a cryptographic system where the system can secure data by encrypting and decrypting also make data fully save and then the data owned by the user. This study aims to secure the data in the database by encrypting the original data without destroying the original data when later after decrypted. To perform this security, a cryptographic methodology is used with both of method that is Vigenere Cipher and Triangle Chain Cipher algorithms which are implemented in the application because both of methodology have same root that is classical cryptographic. This application will later be used as a medium for users to secure their data in the database so that later data theft will not to be easy. After doing fully research that produces applications that can implement combination of Vigenere Cipher and Triangle Chain Cipher algorithms,data in the encrypted database field is safe because encryption has been done to the data.
RANCANG BANGUN PENYIMPANAN BARANG BERBASIS PROGRAMMABLE LOGIC CONTROLLER PADA MESJID BAITUL HIKMAH

Storage for goods is an important part of a mosque. When we are going to pray in congregation, the items or bags we carry make us anxious and afraid of losing. The development of technology has replaced the storage of goods carried out by mosque officials into an automated storage system. But it also needs to be considered in terms of the security system for storing these items. Researchers formulate how automation and security systems use RRFID. The research method used is research and development. The storage of this item is programmed with the Arduino and CX Programmer applications by creating a security system using Radio Frequency Identification (RFID) as radio waves carrying data that will be received by the RFID reader. The result of this research is an automatic door prototype using RFID which will operate properly. Reader ability to detect id card very good starting when the card is affixed to the reader and the selenoid system will work to open the door when the id card is attached.
Game Development of “Prokes” to Socialize the Prevention of Covid-19

The level of awareness of the Indonesian people to orderly and comply with the implementation of health protocols is still very low. Special touch to young people is needed in socializing health protocols and new normal life. Game of “Prokes” is expected as a means for socialization of covid-19 prevention. The proposed method is agile method that is combined using shuffle random algorithm to randomize question in quiz of this game. The blackbox result was 100% functionality of the game has run well. There was a significant difference between user knowledge before and after playing the game of “Prokes” as indicated by t value of 2.297, t table value of 2.004, and the critical value of ? = 0.05. The proposed method had an GEQ result of 3.04. Therefore, the game feasible as a means of socialization in prevention covid-19 using health protocols and new normal for the young generations.
A Language Tutoring Tool based on AI and Paraphrase Detection

A language tutoring tool (LTT) helps learning a language through casual human-like conversations.Natural language understanding (NLU) and natural language generation (NLG) are two key components of an LTT.In this paper, we propose a paraphrase detection algorithm that is used as the building block of the NLU.Our proposed tree-LSTM with a selfattention method for paraphrase detection shows accuracy of 87% with a lower parameter of 6.5m, which is much robust and lighter than the other existing paraphrase detection algorithms.Furthermore, we discuss an LTT prototype using the proposed algorithm with having some featured components like-message analysis, grammar detection, dialogue management, and response generation component.Each component is discussed in detail in the methodology section of this paper.