Reviewerly/exHarmony huggingface dataset:
Four infinite families of ternary cyclic codes with a square-root-like lower bound

Cyclic codes are an interesting type of linear codes and have wide applications in communication and storage systems due to their efficient encoding and decoding algorithms. Inspired by the recent work on binary cyclic codes published in Tang and Ding (2022) [26] and the works in Liu et al. (2023) [15] and Liu et al. (2023) [16], the objectives of this paper are the construction and analysis of four infinite families of ternary cyclic codes with length n=3m−1 for odd m and dimension k∈{n/2,(n+2)/2} whose minimum distances have a square-root-like lower bound. Their duals have parameters [n,k⊥,d⊥], where k⊥∈{n/2,(n−2)/2} and d⊥ also has a square-root-like lower bound. These families of codes and their duals contain distance-optimal cyclic codes.
Object distance estimation using a monovision camera

In computer vision, most monovision cameras used for estimating the position of an object only estimate the 2D information of the object without the depth information. Estimating the depth information, which is the distance between the target object and the camera is quite challenging but, in this paper, a less computationally intensive method was used to estimate the object’s distance to complete the 3D information needed to determine the object’s location in cartesian space. In this method, the object was positioned in front of the camera at a sequential distance and was measured directly. The distances measured in the experiment with a set of training data obtained from the image were fitted into a curve using the least-square framework to derive a non-linear function that was used for estimating the object’s distance also known as the z-coordinate. The result from the experiment showed that there was an average error of 1.33 mm between the actual distance and the estimated distance of the object. Hence, this method can be applied in many robotic and autonomous systems applications.
The Influence of Utilizing Gamification Media Wordwall on the Improvement of Pancasila Education Learning Outcomes

This research aims to investigate the impact of using wordwall gamification media on enhancing educational achievements. The research strategy used in this study was the One-Group pretest-posttest design. The participants consisted of 43 students enrolled in the Chemistry C Education program at Yogyakarta State University, taking the Pancasila Education course. The data collected from the study were analyzed using descriptive statistics. According to the findings of inferential statistical analysis using the t-test method, there was a significant improvement in students' learning outcomes, with a mean gain of 12.31 score points. Given that the t count is statistically significant at a significance level of 0.05, the null hypothesis (H0) is rejected in favor of 3400 of the alternative hypothesis (Ha). This implies a discernible impact of using wordwall gamification media on enhancing learning outcomes. Based on the findings and deliberations, it can be deduced that the utilization of Wordwall media in the context of gamified learning exhibits efficacy in enhancing the educational achievements of 43 participants enrolled in the C Chemical Education program, namely those doing Pancasila Education courses at Yogyakarta State University.
The Development Of E-Module Based On Discovery Learning In Theme 5 At Grade IV Elementary School

The research is attracted by students’ motivation to participate in learning. They only like using printed book materials and worksheets in schools through reading assignments and doing exercises in the book. It is developed by Discovery Learning-based e-modules in integrated thematic learning. It applies the ADDIE model. Data collection techniques use interview lists, validation sheets, questionnaires, and documentation. Data analysis techniques use descriptive techniques to describe validity, practicality, and effectiveness. The results showed that from the E-module feasibility test, the lecturers were subject matter experts, language expert lecturers, media/design expert lecturers, and one teacher. Based on the validation of material experts, it is obtained an average total score of 97% or classified as very valid. Then, the next validation shows valid criteria with an average score of 100% linguists. Design validation obtained a total average score of 95%, 94%, and an average score of 80%. One teacher tested the practicality of the E-module with a score of 98%. The student response test conducted on 13 grade IV students obtained a value with a proportion of 97%. E-modules in Discovery Learning-based thematic learning include features that are very practical or very feasible to use.
Mutual-Attention Net: A Deep Attentional Neural Network for Keyphrase Generation

Neural keyphrase generation (NKG) is a recently proposed approach to automatically extract keyphrase from a document. Unlike the traditional keyphrase extraction, the NKG can generate keyphrases that do not appear in the document. However, as a supervised method, NKG is hindered by noise. In order to solve the problem that the existing NKG model does not consider denoising the source document, in this work, this paper introduces a new denoising architecture mutual-attention network (MA-net). Considering the structure of documents in popular datasets, the multihead attention is applied to dig out the relevance between title and abstract, which aids denoising. To further accurate generation of high-quality keyphrases, we use multihead attention to compute the content vector instead of Bahdanau attention. Finally, we employ a hybrid network that augments the proposed architecture to solve OOV (out-of-vocabulary) problem. It can not only generate words from the decoder but also copy words from the source document. Evaluation using five benchmark datasets shows that our model significantly outperforms the state-of-the-art ones currently in the research field.
Cloze Procedure Technique on Students' Reading Comprehension of Descriptive Text

This research investigated the effectiveness of the cloze procedure technique on students' reading comprehension of descriptive texts and to find out students’ responses toward the cloze procedure technique proposed by Taylor Wilson. This research used experimental research and used quantitative data. The instrument was a reading test (pre-test, post-test) and a questionnaire with 15 questions. This study's population was all class X students, with a sample of 54 students. This research lasted for one month. In this study, it can be seen from the pre-test and post-test results that have been calculated using the Independent Sample T-test through the SPSS program that the significance was 0.001, which means that the cloze procedure technique is effective. In the questionnaire results, the answers (yes) were chosen from as many as 293 and (no) as many as 112, which meant that most students at SMA Negeri 1 Lingga Utara responded positively about the cloze procedure technique.
The Correlation between Students’ Mastery of Adverbs and Their Writing Ability of Recount Text

This study aimed to examine the relationship between students' mastery of adverbs and their success in producing recount texts. It also examined how well the students could use adverbs in their writing. This study's methodology combined a correlational approach with pre-experimental techniques. The findings demonstrated that students had an excellent grasp of adverbs, as seen by the mean value of their scores as well as their writing scores on the pre- and post-tests. The post-test revealed a constant increase in their score. The substantial association between the two variables was caused by the fact that the r-value was higher than the r product moment's critical value. Students' success in producing recount texts and their command of adverbs were significantly positively correlated. As a result, the better the students' writing recount text, the higher their adverb mastery score will be. The determination coefficient (r2) that was obtained indicated that 41.5% of the variation in writing performance may be attributed to the students' adverb competence. This demonstrated that the mastery of adverbs by students can account for both increases and decreases in writing scores by up to 41.5% through a linear relationship using the regression equation = 49.148 + 0.417X. In other words, the students' proficiency with adverbs had a significant influence on their success in writing recounts, with a proportion of 41.5%, leaving the remaining 58.5% to other considerations.
The Effect of Blended Learning Model and Learning Motivation on Students’ Writing Ability of Public Islamic High School Students in Indonesia

The objective of the study is to investigates students’ writing ability of XII grade students in Public Islamic High School Tanjungpinang by comparing blended learning model and face-to-face learning model. This quasi-experimental study used two groups: the experimental group who were taught using blended learning and control group who were taught using face-to-face learning. The population was XII grade students. A sample of 60 students was divided into 2 groups, with 30 students as the control group and 30 students as the experimental group. The techniques for collecting the data were a test method by giving a written test and non-testing method by distributing a questionnaire. The results of study show that there is significant effect on students' writing ability and the learning model, there is significant effect on students' writing ability and learning motivation, and there is no interaction between learning models, learning motivation and writing ability.
A class of permutation quadrinomials over finite fields

AbstractLet Fq denote the finite field of order q. In this paper, we investigate the permutation property of the quadrinomial f(x)=x+a1xq(q−1)+1+a2xq+a3x2(q−1)+1 over Fq2, where q is odd and a1,a2,a3∈Fq*. More precisely, we obtain the necessary and sufficient conditions for f(x) to be a permutation polynomial over Fq2.KEYWORDS: Finite fieldpermutation polynomialpermutation quadrinomial2020 MATHEMATICS SUBJECT CLASSIFICATION: 11T0611T55 AcknowledgmentsThe authors would like to thank the anonymous reviewer for his/her valuable comments and helpful suggestions which improved both the quality and presentation of this paper.Additional informationFundingThe second author gratefully acknowledges BITS Pilani, Hyderabad campus for the financial support.
Australia’s Notifiable Data Breach Scheme: An Analysis of Risk Management Findings for Healthcare

This paper provides an overview of the first five years of data published via the Australian Governments’ Notifiable Data Breach (NDB) scheme, operated by the Office of the Australian Information Commissioner (OAIC). Applying investigative techniques including descriptive and inferential statistics, Pareto analysis, distribution analysis, and bivariate correlations it is discovered that 80% of data breach incidents are substantively caused by fives forms of human error, particularly failures in email management. A deeper investigation across each of the periods studied reveals significant correlations often involve insider-based threats, suggesting these can be an indicative predictor for other events such as phishing and ransomware attacks. The included summary of increasing privacy concerns from the public and government-led legislative amendments in Australia, further illustrates the urgency and importance of applying this knowledge to the critical infrastructure of healthcare.
A provably secure collusion-resistant identity-based proxy re-encryption scheme based on NTRU

Proxy re-encryption (PRE) technology realizes the transformation of decryption right from a delegator to a delegatee. In response to the rapid development of quantum attack technology, scholars have proposed the lattice-based PRE schemes, and the underlying encryption scheme of most lattice-based PRE schemes is the dual cryptosystem, which has the defects of large storage overhead, computational overhead and low efficiency. To address these deficiencies, we present a novel identity-based proxy re-encryption (IB-PRE) scheme based on NTRU. By means of the underlying efficient NTRU scheme, the proposed scheme reduces the storage overhead and computational overhead on key size and computational complexity, and improves efficiency. Our scheme possesses desired properties such as multi-hop property, unidirectionality and collusion-resistance. Meanwhile, based on the decisional Hermite normal form of ring learning with errors (D-RLWEHNF) hard problem, it is carefully designed and demonstrated to be secure under selective identity and chosen plaintext attacks in the standard model.
Feature space reduction method for ultrahigh-dimensional, multiclass data: random forest-based multiround screening (RFMS)

Abstract In recent years, several screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features, many of which are irrelevant or redundant. However, most of these methods cannot handle data with thousands of classes. Prediction models built to authenticate users based on multichannel biometric data result in this type of problem. In this study, we present a novel method known as random forest-based multiround screening (RFMS) that can be effectively applied under such circumstances. The proposed algorithm divides the feature space into small subsets and executes a series of partial model builds. These partial models are used to implement tournament-based sorting and the selection of features based on their importance. This algorithm successfully filters irrelevant features and also discovers binary and higher-order feature interactions. To benchmark RFMS, a synthetic biometric feature space generator known as BiometricBlender is employed. Based on the results, the RFMS is on par with industry-standard feature screening methods, while simultaneously possessing many advantages over them.
Risk and Return of NFT, Metaverse, Cryptocurrency, and NFT’s Relation with IP

This research discusses the Risk and Return of NFT, IP, Metaverse, and cryptocurrency. To investigate the factors affecting the risk and return of Cryptocurrency, Metaverse &amp; NFT, and the connection between them. Being aware of the risk and return factors will help investors make the right decision and avoid these risk factors. The research studies the link between NFT and other digital assets. As well as, the relation between NFT and IP. Furthermore, explores the probability of NFT taking place in the Middle East. Qualitative Method was used to collect the primary data in the completion of this study. Data were collected using a survey strategy by interviewing investors, creators, and blockchain technology specialists through semi-structured interviews. The research time horizon across-sectional study. As well as secondary sources to help explain the concepts and support the primary data. After having analyzed the information, that has been gathered regarding the topic of discussion, it has come to the result that the NFT is digital proof of ownership, that is used in digital or real assets if legally approved. Besides, risk and return factors affecting NFT, Metaverse, and cryptocurrency are very close with small differences in cryptocurrencies as it is a wide market. Furthermore, there is a huge potential for taking place in the Middle East. Which creates new investment opportunities.
Home Self-medication Question-Answering System for the Elderly Based on Seq2Seq Model and Knowledge Graph Technology

With the deepening of aging, chronic diseases of the elderly are the main burden of disease in most countries in the world. The prevalence of chronic diseases in urban areas in China is as high as 75%. Many elderly people use multiple drugs for a long time. Home self-medication problems occur frequently. In order to alleviate this problem to a certain extent, knowledge graph technology and a deep learning model are used to design a home self-medication question-answering system for the elderly and their caregivers. Explore a feasible way of providing automated online consultation intelligent services. In this paper, we have collected medication as well as professional Q&A (question and answer) data in the field of aging health, and constructed a knowledge graph that meets the characteristics of medication use in the elderly. Based on the matching rules in the question judging module, the problems entered by users are classified. For professional knowledge related to diseases and medications of the elderly, the question-answering system uses the knowledge graph to search for answers. For other basic knowledge related to elderly health, the system uses the BERT model to vectorize its users’ questions, then matches the questions by calculating cosine similarity, thus finding the corresponding answers. The system adds the Seq2Seq model as a supplement to the answer retrieval method of the knowledge graph. The testing results shows that the system provides online consultation services more accurately and efficiently for home self-medication for the elderly and their caregivers.
PELATIHAN PENGGUNAAN CRAB BOX PADA PEMBESARAN KEPITING BAKAU DESA KAJUARA KABUPATEN BONE

The method of cultivating mud crabs in Kajuara Village is still carried out traditionally and uses natural food, thus affecting the production of crabs which are not uniform in terms of size and weight. Apart from that, there is still a lack of partner knowledge regarding the behavior and habits of mud crabs as well as appropriate methods for cultivating mud crabs. The aim of this service is to increase the knowledge and skills of cultivators in cultivating mud crabs using crab boxes. The stages of implementing service activities include: 1) Survey and interview stage, 2) preparation stage, 3) implementation stage and 4) evaluation stage. Based on the achievements of the service activities, participants have knowledge and skills in the mud crab cultivation method using a crab box.
Constructing Multi-constrained Cognitive Diagnostic Tests: An Improved Ant Colony Optimization Algorithm

Most application scenarios of cognitive diagnostic tests (CDTs) are confronted with multiple constraints. Some existing well-developed algorithms for constructing CDTs transforms the test-construction problem into an optimization problem to improve the statistical performance of tests. However, tackling optimization problems with multiple constraints, particularly nonlinear ones, through previous optimization methods may not yield satisfactory outcomes. To address the multi-constrained CDT construction with greater efficiency, this paper introduces a method based on ant colony optimization (ACO-mTC). The method uses pheromone and heuristic information to guide the test-construction optimization. Pheromone signifies past construction experiences, while heuristic information integrates item discrimination indices and test constraint information. To facilitate the search to approximate feasible optima, each construction iteration is evaluated by item discrimination indices and test constraint information collectively. The effectiveness of the proposed method is demonstrated through a series of simulation experiments under various conditions. Comparative analysis with the random algorithm illustrates that ACO-mTC is more proficient in producing feasible tests with increased speed and enhanced statistical performance.
PENERAPAN POHON LITERASI WEEKDAY BERBASIS KARYA SISWA DI SD NEGERI 03 WATES

The implementation of online learning is one of the obstacles in implementing the School Literacy Movement (GLS). One of the affected schools is SDN 3 Wates, which previously implemented the School Literacy Movement (GLS) but did not run optimally during the pandemic. The Weekday Literacy Tree Program is based on student work at SDN 3 Wates as a form of follow-up to the GLS carried out by the school by reading 15 minutes before learning. The purpose of this study was to describe how the literacy tree was implemented during a pandemic in class V students. The research method used descriptive qualitative research with class V students with 24 students, data collection techniques used observations and interviews. Based on the results of the study, the implementation of the weekday literacy tree based on student work at SDN 3 Wates showed that the implementation of the weekday literacy tree was limited face-to-face, fifth grade students were divided into two groups on different days, Monday-Friday and Tuesday-Saturday after the activity. learning in the library. On Fridays and Saturdays students collect their work by hanging on the literacy tree that has been provided in the library.
Blockchain-based Identity and Access Management in a Community Cloud

Collaboration through resource sharing (hardware and software) offers many advantages to companies in terms of scalability and economic growth. The community cloud provides a platform for organizations to share and cooperate. However, the reliability of interactions within this distributed and heterogeneous infrastructure is linked to an efficient management of identities of the actors. It requires a control and an autonomy of each entity in the management of its users and the means of identification of its peers. Several works have proposed identity and access management systems for distributed systems, the most recent of which are based on distributed ledger technologies such as the blockchain. In addition to identity, collaboration within a community requires mutual trust, evidence of voluntary commitment and validation of consensual member integration. In this paper, we propose a decentralized identity and access management system based on smart contracts and a network of blockchain oracles. Our model uses decentralized identifiers and digital signature aggregation through the Boneh, Lynn and Shacham (BLS) signature algorithm for identification and registration of an organization in the community. Furthermore, verifiable credentials are used for authentication and resources access authorization. In addition, oracles serve as an interface for transmitting information necessary for smart contracts for evaluation, trust monitoring and exchange within the community.
Twitter and the Ukraine-Russia War: An Examination

On February 23, 2022, the conflict between Ukraine and Russia broke out, shockingthe entire world and creating a heated debate on social media. This paper focuses on understanding the sentiments of Twitter users regarding the Russo-Ukrainian war and classifying them into appropriate categories. In this project, an eight-part dataset was utilized, consisting of various hashtags. Multiple classification techniques were applied, utilizing TF-IDF and Bag of Words vectorization. The dataset consisted of over 1.3 million tweets sourced from Kaggle, with a specific focus on hashtags like ‘Russian Troops’ and ‘Ukrainian War,’ among others. Preprocessing steps involved removing symbols and numbers, converting the text to lowercase, and applying stemming. After completing the preprocessing, conducting the analysis, and training the models, an accuracy of 96% was achieved on both the Logistic Regression and Decision Tree models.