Reviewerly/exHarmony huggingface dataset:
Corrigendum to Child Skills and Teacher Qualifications: Associations with Elementary Classroom Teachers’ Reading Instruction for Struggling Readers

Learning Disabilities Research & PracticeVolume 38, Issue 2 p. 173-174 CORRIGENDUM Corrigendum to Child Skills and Teacher Qualifications: Associations with Elementary Classroom Teachers’ Reading Instruction for Struggling Readers First published: 26 May 2023 https://doi.org/10.1111/ldrp.12309Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume38, Issue2May 2023Pages 173-174 RelatedInformation
Generating large dataset for software requirements prioritization and selection under fuzzy environment

Requirements elicitation is a key sub-process of requirements engineering in which various types of software requirements such as functional and non-functional requirements, testing requirements etc., are identified according to the needs of the stakeholders. After the completion of the requirements elicitation process, there may be a large set of requirements and practically it is not possible to implement all requirements due to budget, time and other constraints of an organization. Under this situation, software developers select the highest ranked software requirements from the list of the elicited software requirements for different releases of software so that a successful software product can be developed. The selection of software requirements for different releases is a difficult task because it requires the participation of various stakeholders. In the field of science and engineering, datasets play an important role during experimental work. Based on our review, we found that in the literature of software engineering less attention is given on the generation of the datasets particularly in the domain of software requirements prioritization and selection (SRPS). Therefore, to address this issue, in this paper we present a four-step method for generating the dataset for SRPS research, i.e. (a) identification of stakeholders, (b) elicitation of software requirements using the goal-oriented concept, (c) formation of decision maker committee and (d) evaluation of the functional requirements and non-functional requirements by decision makers under fuzzy environment. The proposed method has been applied to generate the dataset for the requirements of an institute examination system.
Secure Communication for 6TiSCH Wireless Networks Based on Hybrid ECC and AES Algorithms

The 6TiSCH protocol stack has been widely utilized in the industry to build highly reliable and energy efficiency wireless sensor networks (WSNs). The communication security between nodes mainly relies on the AES encryption algorithm, but the 6TiSCH protocol does not regulate the protection strategy for encryption keys in the network. Therefore, this paper proposes a hybrid ECC-AES data encryption scheme based on the 6TiSCH protocol stack. The Elliptic Curve Diffie Hellman (ECDH), Elliptic Curve Qu-Vanstone (ECQV) algorithms are used to negotiate the shared key for each two nodes in a WSN, and AES encryption with dynamic session keys are derived from the shared key during the nodes’ communication phase. Meanwhile, since the ECC algorithm is used in the resource-constrained nodes, this paper considers the influence of the underlying elliptic curve operations on the computation speed of the shared key, and proposes a regular window algorithm to accelerate the scalar multiplication operation based on previous researches. To prove the scheme’s viability, we conduct simulation experiments on the generation time of shared keys, and the experimental results prove that the encryption scheme under the regular window scalar multiplication has an impressive key generation speed.
A Performance Evaluation Method for a Class of Cross-Chain Systems

In recent years, the heterogeneity among the blockchains has become the driving force behind the development of cross-chain technologies. Due to the limited processing speed of cross-chain system, excessive cross-chain transactions in the short term may cause network congestion and negatively impact. For this reason, it is essential to evaluate and optimize the performance of the cross-blockchain transaction process. However, existing research ignores the limitations of cross-chain systems. Much research is carried out to model, simulate and analyze the performance of traditional blockchain systems rather than cross-blockchain processes. To bridge this gap, our study proposes a queuing theoretical model based on system finite space, using the case of Cosmos, a typical cross-blockchain implemented by the relay mode. The solution of the steady-state equations are established by two-dimensional continuous time Markov process, and the performance measures such as average queue length, transaction rejection probability, and transaction response time are given. Finally, we simulated the analytical solutions of the relevant performance measures through experiments to verify the model’s effectiveness. We believe this analytical approach can be generalized to other cross-blockchain systems.
A Survey on a conversion of handscript to text and speech using the neural network and gTTS tools

Handwriting reputation refers to the ability of a tool to acquire and interpret handwritten entries from numerous homes which incorporates paper files, pics, and phone show display screen gadgets.it's far a discipline of research focused on spotting each handwritten and device-generated individual, with programs in banks, places of work, and industries.The number one goal of this mission is to boom a professional system that makes use of neural networks and the g TTS (Google text-to-Speech) tool to transform handwritten scripts into digital text and speech.The technique employed in this project involves converting handwritten or scanned paper documents and character images into digital text using neural networks.This process eliminates the need for manual transcription or data entry.The resulting digital text can then be transformed into speech using the gTTS tools, allowing it to be saved as an MP3 file.This approach enhances accessibility and usability by enabling users to listen to the content instead of solely relying on visual information.
BlockAuth: A Blockchain-based Solution for Authenticating Products and Combating Counterfeit

As of now, many fake products have been available in the market which has a massive impact on the original product.The phony product originally destroys the name and fame of international brands.So we have developed an idea that will eliminate the fake products which are circulating in the market.Our Fake Product Identification system utilizes blockchain technology's power to maintain the authenticity of original products.We guarantee the genuineness and verify the product whether faux or real.Our System generates a unique encrypted hash QR Code that can be physically applied to a variety of products which then can be scanned by consumers to verify the product's originality.
Audio and Image Caption Generator from Image using VGG-16

This project presents a system that generates text and audio captions for images using the VGG16 convolutional neural network and Long Short-Term Memory (LSTM) recurrent neural network on the Flickr8k dataset.The proposed system comprises several stages, including image augmentation, image feature extraction, text cleaning, tokenization, and LSTM-based caption generation, to ensure accurate and diverse captions.To enhance the system's generalization ability and increase the dataset's size, image augmentation is performed, randomly applying transformations such as rotations, flips, and translations to the original images.Next, visual features are extracted from the augmented images using the pre-trained VGG16 model, which is a powerful image recognition model that extracts high-level features from images.After feature extraction, the text data undergoes text cleaning and tokenization to eliminate any unwanted characters such as punctuation marks and special symbols.The text is tokenized into a sequence of words, with each word mapped to a unique integer index.Finally, the LSTM model is trained on the pre-processed text and visual features to generate descriptive captions.The system's performance is evaluated using the BLEU score, which measures the similarity between the generated captions and the ground-truth captions.The experimental results demonstrate that the proposed system generates accurate and diverse captions for both text and audio modalities.Moreover, the system outperforms several state-of-theart caption generation systems on the Flickr8k dataset.This system can be useful in several applications, including assisting visually impaired individuals in understanding visual content or enriching multimedia content with more descriptive captions.
Construction and effect of information management platform of life science experimental teaching center

To give full play to lab potential and fulfill the unique feature of training talents for valuing practice and seeking innovation, an intelligent, efficient, and open information management platform has been built in the experimental teaching center of life sciences in Northwestern Polytechnical University. This study constructs an intelligent, efficient and open source information management platform. The platform is composed of five modules: comprehensive lab management system, instrument and equipment management system, reagents and consumables management system, laboratory opening platform, and laboratory teaching management system. The use of the management platform has solved the problems of miscellaneous teaching data, insufficient sharing of teaching resources, conflict of course arrangement and low lab open utilization under the traditional management mode. It also stimulates the enthusiasm and initiative of students in innovation and practice, perfects the management mechanism of making teachers play a leading role in experimental teaching, and improves the work efficiency of laboratory managers.
ANALISIS FAKTOR PENYEBAB KESULITAN BELAJAR SISTEM BLENDED LEARNING PADA MATERI PROGRAM LINIER

In the midst of the Covid-19 pandemic, the government established a blended learning model as a learning solution. May encounter difficulties in its application, requiring identification of these difficulties to study effectively. The purpose of this study was to find out the internal and external variables that affect students which are the most significant causes of learning difficulties in blended learning material in linear program material in the mathematics study program at Pamulang University. Factor analysis the confirmatory factor analysis model was used in this study. The research sample consisted of 180 students of the Mathematics Study Program at Pamulang University who took linear program courses. Questionnaires and documents are used to collect data. The results of the study are: 1) there are five internal factors that cause learning problems in a blended learning system in linear program material, with learning ability being one of the five biggest contributing factors which has an Eigenvalue of 5.402 and a variance value of 33 ,7%. 2) associates, having an Eigenvalue of 5.336 and a variance value of 38.3%, is one of the four external variables that create learning problems in a blended learning system in linear programming material. Based on the findings of this study, it can be concluded that the internal factor, namely the learning ability factor, is the most significant source of learning difficulties faced by students in the blended learning system and the external factor is the social friend factor
Life and Creations of Komi Republic Writers in the Best Biobibliographical Edition 2022

This review tells about the biobibliographic dictionary in 2 volumes “The writers of Komi Republic” from the 19th century to nowadays . This bulky book has been published jointly by the Institute of the Language, Literature and History of Komi Science Centre of the Russian Academy of Sciences, Literary Museum of I.A. Kuratov and the National Library of Komi Republic. It received the diploma of the best bibliographic edition prepared by Russian libraries in 2022.
HOPPING ROBOT

While exploring complex environments, conventional mobile robots can overcome obstacles of limited height and jumping robots can overcome obstacles several times their own size.When jumping motion and wheeled or tracked motion are combined to form a composite motion, robots have even better ability to traverse rugged terrain.This project aims to design and build a small-scale robot inspired by the froghopper, a jumping insect that can leap up to half a meter.The robot will be capable of vertical jumping using a spring actuation system, and will be controlled using a microcontroller.Our studies will show that the robot will have a significant jumping advantage in terms of energy efficiency, and we will present our findings in this review.However, the project will not be without challenges, and we will discuss some of the design and fabrication challenges we anticipate encountering, as well as ideas for future work to improve the robot or expand upon our findings.
Realistic and broad-scope learning simulations: first results and challenges

Abstract There is a current ‘theory crisis’ in language acquisition research, resulting from fragmentation both at the level of the approaches and the linguistic level studied. We identify a need for integrative approaches that go beyond these limitations, and propose to analyse the strengths and weaknesses of current theoretical approaches of language acquisition. In particular, we advocate that language learning simulations, if they integrate realistic input and multiple levels of language, have the potential to contribute significantly to our understanding of language acquisition. We then review recent results obtained through such language learning simulations. Finally, we propose some guidelines for the community to build better simulations.
Research on Online Foreign Language Teaching Based on CBIR Algorithm of Internet Deep Information Retrieval

This paper proposes a novel content-based image retrieval CBIR algorithm model, and also proposes a novel clustering retrieval method suitable for image data, which improves the clustering accuracy by automatically updating the weights of cluster features, thereby improving the retrieval effect.It aims to build a reasonable online teaching model, effectively guide foreign language online teaching in colleges and universities, provide reference for mixed teaching research, and promote students to improve their comprehensive foreign language skills and optimize their learning outcomes.The changes in the grades of the experimental class were also analyzed separately, and the results of the experiment were supplemented by post-study questionnaires and interviews.
Creating Bengali Freebase Using Wikidata

Freebase is a large collaborative knowledge base and database of general, structured information for public use.Its structured data had been harvested from many sources, including individual, user-submitted wiki contributions.Its aim is to create a global resource so that people (and machines) can access common information more effectively which is mostly available in English.In this research work, we have tried to build the technique of creating the Freebase for Bengali language.Today the number of Bengali articles on the internet is growing day by day.So it has become a necessary to have a structured data store in Bengali.It consists of different types of concepts (topics) and relationships between those topics.These include different types of areas like popular culture (e.g.films, music, books, sports, television), location information (restaurants, geolocations, businesses), scholarly information (linguistics, biology, astronomy), birth place of (poets, politicians, actor, actress) and general knowledge (Wikipedia).It will be much more helpful for relation extraction or any kind of Natural Language Processing (NLP) works on Bengali language.In this work, we identified the technique of creating the Bengali Freebase and made a collection of Bengali data.We applied SPARQL query language to extract information from natural language (Bengali) documents such as Wikidata which is typically in RDF (Resource Description Format) triple format.
New blockchain-based supply chain management system model

Leveraging technological advances to provide a permanent audit log for a tracked asset is the main goal of blockchain-based supply chain management systems. The challenge, however, is to monitor the asset as it moves through various blockchain-based distribution networks. The paper suggests a model that was developed specifically to solve these problems. The proposed model allows you to do the following: track an asset in various blockchain - based supply chain systems; cryptographically verify the asset's supply chain; define asset data in a standard format; and describe it using several standard formats. Thus, the model offers a significant advantage in terms of interaction between different blockchain-driven supply chains compared to other models presented in the literature.
Fundamental results in probability theory

Probability theory is an area of mathematics that deals with the concept of likelihood. Probability theory is the mathematical foundation of statistical reasoning, and understanding how unpredictability impacts data is crucial for data scientists. Gaussian (normal) distribution is the most widely used distribution. It has two parameters which are mean and variance and easy to interpret. Also, the central limit theorem tells us that sums of independent random variables make the least number of assumptions. In addition, Poisson, Laplace, Beta, Pareto, Dirichelt, Binomial and Gamma Distributions are useful in different areas. The multivariate Gaussian is the most widely used joint probability density function. Covariance and correlation are used to measure the degree between two random variable’s X and Y. Chebyshev Inequality defines a topological space, which includes a sequence of elements, and let the sequence be called . Strong Law of Large Numbers Theorem use in large number of random variable in pairwise independent identically distributed and Renewal Theory is and example in Strong Law of Large Numbers Theorem.
Improved YOLOv5 UAV Target Detection Algorithm by Fused Attention Mechanism

This paper proposes a modified YOLOv5 UAV target detection algorithm for the low detection accuracy caused by the dense target distribution and too small size in the UAV image. Firstly, the coordinate attention mechanism (Coordinate Attention, CA) is introduced in the backbone network CSPDarknet53 to enhance the feature extraction capability of the network; secondly, the multi-size feature pyramid network is designed to introduce a larger resolution feature map for feature fusion and prediction, and to improve the accuracy of small target detection. Experiments on the VisDrone2021 dataset, the results show that the average detection accuracy (Mean Average Precision, mAP) of the improved YOLOv5 algorithm reached 43.0%, 5.8 percentage points higher than the original algorithm, which fully proves the high efficiency of the proposed improved algorithm on the ground target detection of the UAV.
Cibernéticos Low Tech

[RESENHA DE EXPOSIÇÃO] Cibernéticos Low Tech, Espaço de Arte Daniel Bellora, junho a julho de 2019, Pelotas/RS, Brasil.Palavras-chave: Cibernética; low tech; colagem; simulacro.Low Tech CiberneticsAbstract: [EXIBITION REVIEW] Low Tech Cybernetics, Daniel Belora Art Space, June to July, 2019, Pelotas, RS, Brazil.Keywords: Cybernetics; low tech; collage; simulacrum.
Public Opinion Analysis of Presidential Candidate Using Naïve Bayes Method

Elections for president and vice president will take place in 2024. Heading into the election, promoted candidates were vying for public sympathy. People often discussed as presidential candidates are Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto. Therefore, we need a way to predict potential candidates and voter demographics from public opinion on Twitter using sentiment analysis. One of his methods commonly used to classify sentiment analysis is Naive Bayes. This study used the naive Bayes classifier and the TF-IDF extraction function to add weights to the text. Use the scikit-learn Python library to help determine the polarity of negative and positive sentiment classes in your dataset. The datasets used were Twitter datasets acquired from October to December 2022, for a total of 15,000 datasets. The best test scenario obtained by splitting the test and training data is 70% test data and 30% training data, with the highest accuracy generated from the 95% Ganjar dataset. Using the Anies, Ganjar, and Prabowo test data, the positive mood scores for each candidate were 833, 77, and 524, respectively, while the negative mood scores were 637, 1423, and 976, respectively. The test was performed using a confusion matrix and k-fold cross-validation, and the best results were obtained on the Ganjar data set. That is a confusion matrix of 94.93% and a k-fold cross-validation of 94.46%. The lowest f1-score for the positive class is 67% for the Anies dataset and 27% for the negative class for the Ganjar dataset.
Integration of Fuzzy C-Means and SAW Methods on Education Fee Assistance Recipients

&#x0D; &#x0D; &#x0D; &#x0D; Every year, UMTAS gets a quota for KIP tuition fee assistance provided by KEMDIKBUD. This program is intended for high school / vocational/equivalent graduates from poor and vulnerable families. The evaluation results of its implementation have problems, including the number of applicants exceeding the quota given by KEMDIKBUD and some applicants coming from well-off families. This research uses the fuzzy c-means method for data clustering and the SAW method for ranking. The results of data grouping using the fuzzy c-means method obtained the first cluster (C1) of 72 data and the second cluster (C2) of 119 data. Group C1 is closer to the provisions of aid recipients (eligible) compared to data group C2 (ineligible) because Data C1 consists of 100% DTKS recipients, 50% KIP and KKS card owners, 100% parental income &lt;750,000, 40.28% parental dependents &gt;=2 people and 29.17% applicants with achievements. 72 registrant data included in Data C1 are then ranked using the SAW technique to get weights, and 30 data with the highest weight will be used as a decision on recipients of KIP-Kuliah Education fee assistance according to the quota provided. The optimization of Fuzzy C-Means with SAW methods in selecting recipients of education fee assistance is objective and right on target.&#x0D; &#x0D; &#x0D; &#x0D;