Reviewerly/exHarmony huggingface dataset:
HyPE: Attention with Hyperbolic Biases for Relative Positional Encoding

In Transformer-based architectures, the attention mechanism is inherently permutation-invariant with respect to the input sequence's tokens. To impose sequential order, token positions are typically encoded using a scheme with either fixed or learnable parameters. We introduce Hyperbolic Positional Encoding (HyPE), a novel method that utilizes hyperbolic functions' properties to encode tokens' relative positions. This approach biases the attention mechanism without the necessity of storing the $O(L^2)$ values of the mask, with $L$ being the length of the input sequence. HyPE leverages preliminary concatenation operations and matrix multiplications, facilitating the encoding of relative distances indirectly incorporating biases into the softmax computation. This design ensures compatibility with FlashAttention-2 and supports the gradient backpropagation for any potential learnable parameters within the encoding. We analytically demonstrate that, by careful hyperparameter selection, HyPE can approximate the attention bias of ALiBi, thereby offering promising generalization capabilities for contexts extending beyond the lengths encountered during pretraining. The experimental evaluation of HyPE is proposed as a direction for future research.
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions. Generalizing to unseen multi-modal distributions poses even greater difficulties due to the distinct properties exhibited by different modalities. To overcome the challenges of achieving domain generalization in multi-modal scenarios, we propose SimMMDG, a simple yet effective multi-modal DG framework. We argue that mapping features from different modalities into the same embedding space impedes model generalization. To address this, we propose splitting the features within each modality into modality-specific and modality-shared components. We employ supervised contrastive learning on the modality-shared features to ensure they possess joint properties and impose distance constraints on modality-specific features to promote diversity. In addition, we introduce a cross-modal translation module to regularize the learned features, which can also be used for missing-modality generalization. We demonstrate that our framework is theoretically well-supported and achieves strong performance in multi-modal DG on the EPIC-Kitchens dataset and the novel Human-Animal-Cartoon (HAC) dataset introduced in this paper. Our source code and HAC dataset are available at https://github.com/donghao51/SimMMDG.
An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

Despite the extensive effort to improve intelligent educational tools for smart learning environments, automatic Arabic essay scoring remains a big research challenge. The nature of the writing style of the Arabic language makes the problem even more complicated. This study designs, implements, and evaluates an automatic Arabic essay scoring system. The proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing tasks. Then, it comprises two main components: the grading engine and the adaptive fusion engine. The grading engine employs string-based and corpus-based similarity algorithms separately. After that, the adaptive fusion engine aims to prepare students’ scores to be delivered to different feature selection algorithms, such as Recursive Feature Elimination and Boruta. Then, some machine learning algorithms such as Decision Tree, Random Forest, Adaboost, Lasso, Bagging, and K-Nearest Neighbor are employed to improve the suggested system’s efficiency. The experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation values. Furthermore, in the adaptive fusion engine, the Random Forest algorithm outperforms all other machine learning algorithms using the (80%–20%) splitting method on the original dataset. It achieves 91.30%, 94.20%, 0.023, 0.106, and 0.153 in terms of Pearson’s Correlation Coefficient, Willmot’s Index of Agreement, Mean Square Error, Mean Absolute Error, and Root Mean Square Error metrics, respectively.
Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

The purpose of Human Activities Recognition (HAR) is to recognize human activities with sensors like accelerometers and gyroscopes. The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms. In this paper, we experimentally validate the HAR process and its various algorithms independently. On the base of which, it is further proposed that, in addition to the necessary eigenvalues and intelligent algorithms, correct prior knowledge is even more critical. The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object, the sampling process, the sampling data, the HAR algorithm, etc. Thus, a solution is presented under the guidance of right prior knowledge, using Back-Propagation neural networks (BP networks) and simple Convolutional Neural Networks (CNN). The results show that HAR can be achieved with 90%–100% accuracy. Further analysis shows that intelligent algorithms for pattern recognition and classification problems, typically represented by HAR, require correct prior knowledge to work effectively.
ELECTRONIC DEBUREAUCRATIZATION OF SCHOOL DOCUMENTATION AS A SYSTEMIC FACTOR FOR IMPROVING ORGANIZATION OF EDUCATION PROCESS

The article analyzes the relationship between the process of electronic debureaucratization of school documentation in Ukraine and the development of school education, including the freeing of teachers' time for creative work and the formation of prerequisites for the development of school leadership. The electronic debureaucratization of school documentation process was supported at the level of the Ministry of Education and Science of Ukraine and implemented in the form of the publicly available (free for users) e-Journal application program in the environment, supported by the national educational management information system of SHC «AICEM». The practice of using e-Journal shows that the main stakeholders in the educational process receive significant benefits and free up their time for creative work. In particular, the school administration is able to promptly generate educational reports, achievement charts by class and by subject, analyze teachers' performance, and monitor the progress of students, classes, and the school as a whole. Teachers have easy access to the current сlass schedule on the computer, access to teaching materials, division of classes into groups without paper journals, automatic generation of complex reports based on the results of the semester or school year, and effective communication with students and their parents. The parents quickly receive information about their children’s progress and attendance, monitor their learning and homework, and communicate directly with teachers. For a deep understanding of the needs of teachers, administrators, students, and parents working with the e-Journal platform, feedback channels have been created that allow the systematization of user requests regarding the features of the service's functioning, collection of statistical data based on which developers can analyze user wishes for service development, identify problematic areas requiring improvement, and promptly address them. Proposals for improving the electronic school debureaucratization process are presented.
Design Objectives for Evolvable Knowledge Graphs

Knowledge graphs (KGs) structure knowledge to enable the development of intelligent systems across several application domains. In industrial maintenance, comprehensive knowledge of the factory, machinery, and components is indispensable. This study defines the objectives for evolvable KGs, building upon our prior research, where we initially identified the problem in industrial maintenance. Our contributions include two main aspects: firstly, the categorization of learning within the KG construction process and the identification of design objectives for the KG process focusing on supporting industrial maintenance. The categorization highlights the specific requirements for KG design, emphasizing the importance of planning for maintenance and reuse.
Learning development in a time of disruption

The Covid-19 Pandemic had (and continues to have) a significant worldwide impact on Higher Education (Watermeyer et al., 2021; Sharaievska et al., 2022). How Learning Development responded to this challenge varied considerably across the sector.&#x0D; Many of the challenges and solutions for Learning Development are represented in the Compendium of Innovative Practice: Learning Development in a Time of Disruption, a special issue of the Journal of Learning Development in Higher Education (JLDHE, 2021). The Compendium issue of JLDHE contained 102 peer-reviewed reflections, identifying numerous challenges and responses to teaching in Covid-19.&#x0D; This conference paper will share the findings of a research project that has systematically analysed all 102 contributions to the compendium. Each reflection was analysed using structural, topic and thematic coding to identify common responses and challenges to pandemic teaching.&#x0D; The findings of this study have identified a range of individual and shared challenges for both students and third space professionals. One of the core findings relates to the diversity of responses that have been designed to meet these challenges, with over 100 distinct pedagogic and technical solutions to pandemic teaching. From these, five core themes have emerged: emergency remote teaching; reflective practice and evaluation; pedagogy and technology support; collaboration and shared practice; and, course design for the long-term.&#x0D; &#x0D; This paper will reflect on implications for future practice in times of disruption and provide delegates with the opportunity to consider how it relates to their institutions.
Is it necessary to use two-way items? Testing the effects of using favourable and unfavourable items

The topic of ethnicity is a topic that is often discussed in research, especially psychology research. Considering that Indonesia is a country with many ethnicities, therefore, ethnicity is a very important and relevant topic to be discussed in many studies. In this regard, robust tests measuring ethnicity are needed to collect data and further the data can be validly interpreted. The purpose of this study was to psychometrically evaluate the effects of the method of using two-way items (favorable and unfavorable) on two scales, Ethnic Experience and Ethnic Identity. This evaluation will lead to an evaluation of possible new factors that may arise due to the use of two-way items. The approach used is the Multitrait Multimethod (MTMM), with Confirmatory Factor Analysis (CFA), to test the four alternative models used. The results of this study indicate that two (CTCM, and CTUM) of the four proposed models have a satisfactory fit. This proves that there is indeed an effect from the method of using two-way items (favorable and unfavorable).
DESIGN AND BUILD A WEBSITE-BASED FOREIGNER SERVICE APPLICATION APPLICATION AT THE CLASS 1 IMMIGRATION OFFICE SPECIFICALLY FOR NON-TPI WEST JAKARTA

The use of information technology has now been widely applied in the process of immigration services and supervision carried out by the Directorate General of Immigration. One of them is return of immigration documents and transferring foreigner data using a Google form. However, in its implementation there are still some problems. This study aims to determine the current business process for document return services and foreigner data mutations at West Jakarta Immigration Office and to provide an overview of designing a web-based application for immigration services for foreigner. The design of this application uses the Waterfall Model System Development Life Cycle (SDLC) method. The results of this study are a website-based application prototype for foreign citizen services with various features provided such as checking application status, information on requirements for submitting requests for return of immigration documents and mutation of foreigner data, submission of ERP applications, submission of EPO applications, submission of transfer requests address, and submission of a passport mutation application which can later be used as a reference to be applied to immigration document return services and foreigner data mutations at the Special Class 1 Immigration Office for non-TPI West Jakarta.
ANALYSIS OF FOREIGNER REPORTING APPLICATIONS WITH DELONE AND MCLEAN MODELS (CASE STUDY OF CLASS I IMMIGRATION OFFICE NON TPI PEMALANG)

The development of information and communication technology is happening so very fast and is driven by the human need to help get the job done.Directorate General of Immigration is one of the government agencies that implement the use of information and communication technology, the use of such technology is expected to be able to help the duties and functions of Immigration. One of the functions of Immigration is as a state security, in that it is realized by supervision.In this case, the supervision in question is for foreign nationals not only the process of entry and exit, but concerns the existence and activity while in the territory of Indonesia.In 2015 the Directorate General of Immigration released a Foreign Reporting Application (APOA) with the aim of facilitating the reporting of foreigners to the Immigration office. The use of the application has been running in all Immigration offices, but at the Imigrasi Office Class I Non TPI The use of APOA encounters obstacles in use.Therefore, the authors analyzed APOA using the DeLone and McLean models to measure the success of APOA and findout the obstacles that exist through the variables that have been determined.The results of this study concluded that the application can run well and proven to help surveillance and variables in modeling have relationships with each other.
DEVELOPMENT OF AN ANDROID APPLICATION-BASED ACADEMIC INFORMATION SYSTEM AT IMMIGRATION POLYTECHNIC

The Academic Information System or commonly called SIAKAD is a system created to provide easy services for students in online administrative activities. This system was built to assist the Immigration Polytechnic academics, lecturers and students in finding out information related to the academics of students and as a database of student information and cumulative grade point scores. Currently the Academic Information System (SIAKAD) is still a website-based system. The features on the website are also quite adequate. However, with current technological developments, students more often access learning media through applications than websites, which causes the Immigration Polytechnic's academic information system to be developed into an android-based application.&#x0D; The purpose of this research is to develop and build an Academic Information System (SIAKAD) application using Android Studio software. The research method used is the Waterfall system development method. The research results obtained are how to develop this academic information system into an android-based application, and the flow of using the Immigration Polytechnic academic information system application, as well as an android- based application product that has gone through the trial stage. The target of this android version of the SIAKAD application is to be used by students in accessing academic information online through the mobile application. Recommendations that can be given are to carry out regular maintenance and evaluation as well as carry out further development related to other operating systems as well as from the interface of applications that have been made.
The Effects of Self-development, Self-therapy, and Self-presentation Motivations on Quality of Life, Visitor Loyalty, and Omnivorous Cultural Engagement through Visitor Satisfaction in Art Museums: Examining the Moderation Effect of Age and Attitude toward SNS Posting

This study investigates the fulfillment of self-motivation including self-development, self-therapy, and self-presentation on the outcomes such as subjective quality of life, omnivorous cultural engagement, and visitor loyalty via visitor satisfaction in art museums. The analyses on 285 valid survey responses demonstrated that fulfillment of self-development and self-therapy needs have significant positive influence on visitor satisfaction. The effect of fulfilling self-presentation need on visitor satisfaction was found to be moderated by attitude toward SNS posting, while the age factor, based on generations, did not show a significant moderation effect. It was also found that visitor satisfaction positively influences visitor loyalty, including intentions of revisit and recommendation. Increased satisfaction also enhances subjective QOL and omnivorous cultural engagement among art museum visitors, implying promising collaboration among cultural arts institutions. The findings have both theoretical and practical implications for enhancing visitor experiences and promoting diverse cultural engagement. Limitations and future research directions are also discussed.
Formation of Innovative Personality Traits in the Course of Teaching English

The purpose of the article is to present relevant tasks for the formation and development of an innovative personality key qualities (creativity, mobilizing potential, reasonable risk-taking, responsibility achievement, motivation, reflexivity etc.) within the framework of teaching English to students of the communication direction. Practical approbation of the above exercises showed a significant increase in the qualities of students, necessary for an innovative personality in the modern world. The originality of the work consists in the development of new exercises aimed at developing innovative personality traits through teaching English. As a methodology we used works of M. G. Evdokimova and M. A. Krasilshchikova V.E. Klochko and E.V. Galazhinsky, O. B. Mikhailova. The results obtained can be used as a basis for new approaches to teaching innovative personality in the framework of teaching English in a non-linguistic university.
Bearings-Only Tracking With Speed and Range Constraints

Target motion analysis for bearings-only tracking is a challenging task and while estimating the range and velocity of the target, estimation error may diverge. To improve the estimation accuracy and minimize the track divergence, in this paper, we assume that by hearing the sound, a sonar operator is capable of saying the upper and lower limit of range and velocity of the target and we incorporate such information along with the traditional state estimation methods. To do so, after obtaining the posterior estimate a constrained optimization problem which minimizes weighted error square has been solved. The developed method is applied to two bearings-only tracking scenarios. It has been observed that the proposed technique delivers more accurate results in terms of root mean square error and track loss percentage than that obtained from only nonlinear filters.
A Model-Based Machine Learning Approach for Assessing the Performance of Blockchain Applications

The recent advancement of Blockchain technology consolidates its status as a viable alternative for various domains. However, evaluating the performance of blockchain applications can be challenging due to the underlying infrastructure's complexity and distributed nature. Therefore, a reliable modelling approach is needed to boost Blockchain-based applications' development and evaluation. While simulation-based solutions have been researched, machine learning (ML) model-based techniques are rarely discussed in conjunction with evaluating blockchain application performance. Our novel research makes use of two ML model-based methods. Firstly, we train a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> nearest neighbour ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k\text{NN}$</tex> ) and support vector machine (SVM) to predict blockchain performance using predetermined configuration parameters. Secondly, we employ the salp swarm optimization (SO) ML model which enables the investigation of optimal blockchain configurations for achieving the required performance level. We use rough set theory to enhance SO, hereafter called ISO, which we demonstrate to prove achieving an accurate recommendation of optimal parameter configurations; despite uncertainty. Finally, statistical comparisons indicate that our models have a competitive edge. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k\text{NN}$</tex> model outperforms SVM by 5% and the ISO also demonstrates a reduction of 4% inaccuracy deviation compared to regular SO.
Research on Anomaly Detection in Time Series: Exploring United States Exports and Imports Using Long Short-Term Memory

This survey aims to offer a thorough and organized overview of research on anomaly detection, which is a significant problem that has been studied in various fields and application areas. Some anomaly detection techniques have been tailored for specific domains, while others are more general. Anomaly detection involves identifying unusual patterns or events in a dataset, which is important for a wide range of applications including fraud detection and medical diagnosis. Not much research on anomaly detection techniques has been conducted in the field of economic and international trade. Therefore, this study attempts to analyse the time-series data of United Nations exports and imports for the period 1992 – 2022 using LSTM based anomaly detection algorithm. Deep learning, particularly LSTM networks, are becoming increasingly popular in anomaly detection tasks due to their ability to learn complex patterns in sequential data.&amp;nbsp;This paper presents a detailed explanation of LSTM architecture, including the role of input, forget, and output gates in processing input vectors and hidden states at each timestep. The LSTM based anomaly detection approach yields promising results by modelling small-term as well as long-term temporal dependencies.&amp;nbsp;
Retracted: Diagnosis of Breast Cancer Using Computational Intelligence Models and IoT Applications

[This retracts the article DOI: 10.1155/2022/2143510.].
Retracted: Enhanced Invisibility and Robustness of Digital Image Watermarking Based on DWT-SVD

[This retracts the article DOI: 10.1155/2022/5271600.].
Retracted: Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

[This retracts the article DOI: 10.1155/2021/4321131.].
Retracted: Computed Tomography Image Characteristics before and after Interventional Treatment of Children’s Lymphangioma under Artificial Intelligence Algorithm

[This retracts the article DOI: 10.1155/2021/2673013.].
Retracted: Efficacy of Moxibustion in the Treatment of Parkinson’s Disease Based on Meta-Analysis under Intelligent Medical Treatment

[This retracts the article DOI: 10.1155/2022/8168152.].
Geoscientists Around the Globe

Coordinated by members of SEG's Justice, Equity, Diversity, and Inclusion (JEDI) Committee, Geoscientists Around the Globe features geoscientists from technically, geographically, and culturally diverse backgrounds. In this installment, JEDI Committee Vice Chair Eileen Martin interviews Yunyue Elita Li, associate professor of earth and planetary sciences and (by courtesy) mathematics at Purdue University.
Retracted: Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN-GA)

[This retracts the article DOI: 10.1155/2021/2000159.].