Reviewerly/exHarmony huggingface dataset:
Graphormer-NAS-X: A Novel Graphormer-Based Neural Architecture Search Method

Neural architecture search (NAS) aims to search for the best neural network model for various tasks. In this paper, an improved Graphormer is used as an encoder to learn the embedding of the neural architecture, called Graphormer-NAS-X, where X is related to the specific search algorithm. Specifically, the centrality encoding is improved by adding an eigenvector centrality that can capture the importance of adjacent nodes. And the position encoding is enhanced by using the shortest distance of the directed graph, which can better reflect neural architecture’s single-directional flow of computational information. The experiments are evaluated on two public datasets with two commonly used search algorithms. Graphormer-NAS-X outperforms most existing NAS algorithms, which indicates Graphormer benefits the downstream search.
Evaluation of Remedial English using Gray Relational Analysis (GRA) method

Remedial Teaching is the instruction given to students to help them overcome their general or specific weaknesses.A transfer student is a student who does not meet the criteria for admission to college-level classes.Learn more in: Evaluating the effectiveness of a basic writing course.Alternatives include: Listening Errors, Spelling Errors, Grammar Errors, and Reconstruction Vocabulary Errors.Evaluation preferences are as follows: Student 1, Student 2, Student 3, Student 4, Student 5. From the results, it is seen that Student 4 has achieved the first rank, whereas Student 3 has the lowest rank.The GRA (Gray-related analysis) dataset for Remedial English indicates that Student 4 is ranked the highest.
Using VoiceThread to develop the speaking skills of EFL learners: A systematic literature review

Abstrak: Penggunaan teknologi dalam proses belajar mengajar membantu siswa dalam proses pembelajaran. Alat teknologi informasi dan komunikasi (TIK) digunakan seperti VoiceThread. Ulasan ini berfokus pada penelitian sebelumnya tentang penggunaan VoiceThread untuk mengembangkan keterampilan berbicara siswa di antara pelajar EFL. Tinjauan sistematis ini menyelidiki penggunaan VoiceThread untuk mengembangkan keterampilan berbicara pelajar EFL. Penelitian ini menggunakan pedoman Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) untuk mencari hasilnya. Total sepuluh artikel jurnal dari 2011 hingga 2022 diakses dari Google Scholar. Peninjauan, kriteria inklusi, dan pengecualian dalam proses pemilihan dilakukan dalam proses peninjauan. Selanjutnya, ulasan jurnal artikel menyajikan penggunaan VoiceThread menunjukkan efek positif dalam mengembangkan keterampilan berbicara siswa di antara pelajar EFL. Temuan mengungkapkan bahwa VoiceThread mengembangkan keterampilan berbicara siswa di antara pelajar EFL dengan fitur-fitur di dalamnya. Implikasi praktis dari studi ini adalah guru EFL disarankan menggunakan VoiceThread untuk mendukung proses pembelajaran berbicara siswa. Abstract: The use of technology in teaching and learning helps students in the learning process. The information communication and technology (ICT) tools are used, such as VoiceThread. This review focuses on previous research on the use of VoiceThread to develop the speaking skills of EFL learners. This systematic review investigated the use of VoiceThread to develop the speaking skills of EFL learners. This study uses Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to search for the result. A total of ten journal articles from 2011 to 2022 were accessed from Google Scholar. A review, an inclusion, and exclusion criteria in selecting process were employed in the review. Furthermore, the article journal review presents the use of VoiceThread shows a positive effect to develop the speaking skills of EFL learners. The findings reveal that VoiceThread develops the speaking skills of EFL learners with the features in it. The practical implication of this study is that EFL teachers are advised to use VoiceThread to support students' speaking learning process.
Future Directions for RDF2vec

In this chapter, we highlight a few shortcomings of RDF2vec, and we discuss possible future ways to mitigate those. Among the most prominent ones, there are the handling of literal values (which are currently not used by RDF2vec), the handling of dynamic knowledge graphs, and the generation of are explanations for systems using RDF2vec (which are currently black box models).
TINGKAT PENERAPAN TATA KELOLA TEKNOLOGI INFORMASI (IT GOVERNANCE) TERHADAP KUALITAS SDM BERDASARKAN MODEL PENGUKURAN COBIT: A SYSTEMATIC LITERATURE REVIEW

The increasingly advanced developments have helped human activities. There are still many companies that fail to use technology effectively. Therefore, it needs IT Governance. Effective management of information systems can be done by utilizing a framework at the agency to evaluate the domain of COBIT, one of which is Manage Human Resources (APO07). Two models are often used in research related to APO07, Capability Level and Maturity Level. This study aims to measure the effectiveness of the application of the model as an evaluation medium, especially in the field of Human Resources (APO07), so that readers can find out the level of effectiveness of the influence of the model on the progress of the agency. Thirty-six (36) articles were used to search for keywords about the influence of information technology governance in the field of human resources. In conducting this research, two selections were made. The first selection was 32 articles, and the second selection was 16 articles. From the results of the study, it can be seen that of the 16 articles, 3 of them have implemented IT Governance effectively, while the other 13 have not implemented it effectively.&#x0D; Keywords: IT Governance; COBI; Maturity Level; Capability Level; Human Resource&#x0D;
Extended Knowledge Graph using Relation Modeling between Heterogeneous Data for Personalized Recommender Systems

Many researchers have investigated ways to enhance recommender systems by integrating heterogeneous data to address the data sparsity problem. However, only a few studies have successfully integrated heterogeneous data using knowledge graph. Additionally, most of the knowledge graphs built in these studies only incorporate explicit relationships between entities and lack additional information. Therefore, we propose a method for expanding knowledge graphs by using deep learning to model latent relationships between heterogeneous data from multiple knowledge bases. Our extended knowledge graph enhances the quality of entity features and ultimately increases the accuracy of predicted user preferences. Experiments using real music data demonstrate that the expanded knowledge graph leads to an increase in recommendation accuracy when compared to the original knowledge graph.
Irrigation Performance Index Model (Case Study in IPDMIP)

Each irrigation area has its own unique characteristics, depending on its geographical location, topography, population, climate, soil, and others.Based on government regulation, since 2015, all irrigation schemes in Indonesia have to be measured with the irrigation system performance index (IKSI or Indeks Kinerja Sistem Irigasi) every year.Several studies have been conducted since that year, but the IKSI model has not been evaluated yet, especially for surface irrigation.An increasing value of IKSI indicates an increase in the quality of irrigation system services.Conversely, the decreasing value of IKSI means that the efficiency and effectiveness of irrigation system management need further review and improvement.Therefore, this research aims to develop a mathematical model of the irrigation performance index as an evaluation using six parameters that examine the quality of irrigation system management.The research locations are in 947 Central Government Authorized Irrigation Areas, which was chosen considering that the irrigation performance index model had not been evaluated.The methodology consists of a SmartPLS (partial least square) application to analyze and narrow down the variables in this study, the analysis using the GRG (generalized reduced gradient) method to calculate nonlinear equations.In the initial equation, two variables, 12 dimensions, 24 indicators were analyzed.After analyzing the results using PLS-SEM, three indicators were eliminated so that the composition of the new equation became two variables, 12 dimensions, and 21 indicators that were interrelated with one another.Furthermore, GRG analysis using Microsoft Excel solver showed changes in variable weights so that the new model equation for IKSI-Bakti was 0.744 x primary irrigation network + 0.256 x tertiary irrigation network, which applies to the irrigation area of the Central Authority, especially for surface irrigation.
Soft-Prompt Tuning to Predict Lung Cancer Using Primary Care Free-Text Dutch Medical Notes

We examine the use of large Transformer-based pretrained language models (PLMs) for the problem of early prediction of lung cancer using free-text patient medical notes of Dutch primary care physicians. Specifically, we investigate: 1) how soft prompt-tuning compares to standard model fine-tuning; 2) whether simpler static word embedding models (WEMs) can be more robust compared to PLMs in highly imbalanced settings; and 3) how models fare when trained on notes from a small number of patients. All our code is available open source in https://bitbucket.org/aumc-kik/prompt_tuning_cancer_prediction/ .
Development of a methodology for calculating carbon units of heterogeneous territories based on machine learning

The paper describes a method for calculating carbon units of heterogeneous territories based on machine learning. The hierarchical structure of areal territories and the structure of the interconnection of multi-scale images are described. An approach is given to identify and classify terrain objects in order to more accurately calculate the carbon reserve of the territory.
Automatic Classification of Scanned Electronic University Documents using Deep Neural Networks with Conv2D Layers

This paper proposes a novel approach for scanned document categorization using a deep neural network architecture.The proposed approach leverages the strengths of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract features from the scanned documents and model the dependencies between words in the documents.The pre-processed documents are first fed into a CNN, which learns and extracts features from the images.The extracted features are then passed through an RNN, which models the sequential nature of the text.The RNN produces a probability distribution over the predefined categories, and the document is classified into the category with the highest probability.The proposed approach is evaluated on a dataset of scanned documents, where each document is categorized into one of four predefined categories.The experimental results demonstrate that the proposed approach achieves high accuracy and outperforms existing methods.The proposed approach achieves an overall accuracy of 97.3%, which is significantly higher than the existing methods' accuracy.Additionally, the proposed approach's performance was robust to variations in the quality of the scanned documents and the OCR accuracy.The contributions of this paper are twofold.Firstly, it proposes a novel approach for scanned document categorization using deep neural networks that leverages the strengths of CNNs and RNNs.Secondly; it demonstrates the effectiveness of the proposed approach on a dataset of scanned documents, highlighting its potential applications in various domains, such as information retrieval, data mining, and document management.The proposed approach can help organizations manage and analyze large volumes of data efficiently.
Tracer for Estimation of the Data Changes Delivered and Permalinks

In the field of software engineering, Source Code Management is always challenging and difficult to cope in certain stages. Source Code Management (SCM) plays an important role in organizing, managing, and controlling the changes delivered by the developers to the source code, documents, and other entities. During the Software Development Life Cycle (SDLC) of a software product, many stakeholders of different teams also from different geographical locations are involved, including developers, testers, and analysts. With the involvement of a number of stakeholders, organizing, managing, and controlling the changes plays an important role. Software Configuration Management (SCM) is a process that helps methodically organize, control, and manage the changes in documents, code, and other entities during the SDLC of the software product. Currently, there is no system within LGSI that can track the changes made to the code by the various stakeholders involved. This study aims to overcome this problem; the LGSI proposed a new system called Change Tracker. The study finds that an individual developer can track the changes delivered to his/her interested files and creates permalinks delivered by LGSI developers then the permalinks will be reviewed by the review task team. In this paper, the model notifies the respective stakeholders when the changes are made and thus overcome the current system’s flaws.
Fractional Diffusion Equation for Medical Image Denoising using ADI Scheme

This paper proposes a fractional order total vari-ation model for additive noise removal which uses a different fractional order of the regularization term of the objective function.The denoising model based on space and time fractional derivatives on a finite domain is discretized with effective applications of Gru¨nwald-Letnikov(G-L) and Caputo derivatives.This model has been adopted to solve Alternative Direction Implicit (ADI) scheme to denoise medical images.The advantage of this model is for smooths the homogeneous regions and enhance edge information revealing more details of the image.The results show that the proposed model has desirable feedback for enhancing medical images, revealing more detailed information than ROF(Rudin, Osher and Fatemi), TV -L1 (Total Variation L1 space) and fourth order partial differential equation based models.
Improving Task Scheduling on The Cloud Through Low Latency and Cost Effective Technique

Offloading is emerging as a promising idea to allow handheld devices to access intensive applications without incurring performance or power costs.This is especially useful for businesses that want to run line-of-business applications on handhelds..However, developing applications using cloud computing resources is a challenge because supporting the low-latency and scalability needs of applications requires highly dynamic orchestration of heterogeneous resources at different levels of the network hierarchy.It Is difficult.To reduce this complexity, this application provides a programming model that provides simplified programming abstractions and supports applications that scale dynamically at runtime.The goal of offloading is to decide for or against offloading.Code offload can make decisions in a number of ways.Much of the research on code swapping focuses on more sophisticated if-else conditions.Code offloading machine learning is currently an important research topic.Machine learning methods are employed to significantly improve the currently recommended methods and to perform robustly when tackling a wide range of learning tasks
My Responsibility Is to Continue Researching Speech/Audio Coding to Provide Highest Quality Services and High Reliability

Overview
Trolling, Cyber-Stalking, Cyber-Bullying, and Identity Theft in OSNs

Before the development of information and communication technology, social connections were confined by narrow cultural borders. User-generated multimodal information, online social networks, and rich data related to human behavior have all undergone a revolution as a result of these social technologies. However, the abuse of social technology, such as social media platforms, has given rise to a brand-new type of cyber-crimes such as cyberbullying, cyberstalking, cyber trolling, identity theft, etc. Such crimes result in a breach of privacy, a security lapse, financial fraud, and harm to public mental health and property. This chapter discusses various automated methods and systems driven by machine learning, deep learning, and fuzzy-logic-based algorithms for tackling various types of cybercrimes on various social media platforms. It then highlights various issues and challenges pertaining to the existing methods, which offer new study avenues for researchers to investigate.
Learning From Small Samples in the Age of Big Data

Humans learn new concepts from a few observations with strong generalisation ability. Discovering patterns from small samples is complicated and challenging in machine learning (ML) and deep learning (DL). The ability to successfully learn and generalise from relatively short data is a glaring difference between human and artificial intelligence. Because of this difference, artificial intelligence models are impractical for applications where data is scarce and limited. Although small sample learning is challenging, it is crucial and advantageous, particularly for attaining rapid implementation and cheap deployment costs. In this context, this chapter examines recent advancements in small-sample learning. The study discusses data augmentation, transfer learning, generative and discriminative models, and meta-learning techniques for limited data problems. Specifically, a case study of convolutional neural network training on a small dataset for classification is provided. The chapter also highlights recent advances in many extensional small sample learning problems.
Fundamentals

In this chapter, we first briefly review the dynamics model of robot manipulators, for both rigid joint and flexible joint robots. Then, we describe the fundamentals of collision detection, which are very important when handling the collision detection problem but have not been organized for easy identification in the existing literature.
Secure authentication protocols to resist off-line attacks on authentication data table

In text-based authentication, the passwords along with user names are maintained in the Authentication Data Table (ADT). It is necessary to preserve the privacy of passwords in ADT to avoid offline attacks like brute force attacks, lookup table attacks, etc. In this paper, three password protection schemes, namely Encrypted Image Password (EIP), Dynamic Authentication Data Table (D-ADT), and Extended Encrypted Image Password (EEIP) are proposed for secure authentication. In EIP, the input passwords are first converted to hashed passwords and then transformed into images. Next, these image passwords are encrypted using a novel image password encryption system using chaos functions and confusion-diffusion mechanisms. In D-ADT, the hashed passwords are encrypted using a random key. The major highlight of this scheme is that during every log, the hashed password is encrypted with a new random key while keeping the plain password same as it is. So, during each login of the user, the old encrypted password is replaced with a new encrypted password in the authentication data table. The EEIP scheme combines both approaches. Passwords are converted to images and image passwords are encrypted with the new random key at every login. Performance and security analysis are carried out for the proposed algorithm concerning correlation analysis, differential analysis, entropy analysis, computation time, keyspace, and offline attack analysis.
Blockchain and Governance Structure

The rapid growth in the advancement of blockchain technology and its development has brought its usage in every field. Traditional legal frameworks, which depend on establishing central points of accountability and responsibility, have difficulties as a result of the characteristics of blockchain, such as decentralisation, transparency, integrity, and immutability, among others. A key challenge behind the adoption of blockchain is understanding the dynamics of blockchain governance. With the promise of an efficient network owing to the elimination of intermediaries, governance in blockchain may be understood as addition of standards and culture, laws and codes, people, and institutions that promote coordination and jointly decide a particular organisation. As a result, having a headquarters is not necessary; instead, development can rely on a globally dispersed network of programmers who create the software protocol, giving rise to the idea of a DAO (decentralized autonomous system).
Language Report Finnish

Abstract During the last ten years, digitalisation has changed the way we interact with the world creating an increasing demand for language-based AI services. In the field of language technology, the Finnish language is still only moderately equipped with products, technologies and resources. The situation has improved in recent years, but still support for automated translation leaves room for ample improvement, as the general support for spoken language is modest in industry applications although some recent research results are encouraging. We take stock of the existing resources for Finnish and try to identify some remaining gaps.
Construction of the Integration Mode of Mass Entrepreneurship and Innovation Teaching and Practice in Local Universities

This paper discusses the construction effects and strategies of the integration model of mass entrepreneurship and innovation teaching and practice, taking local universities as the background.Against the backdrop of economic development entering a new era, local universities are facing new challenges in talent cultivation.As an innovative talent training mode, the integration of innovation and entrepreneurship teaching and practice has attracted wide attention, but the problems and countermeasures in its implementation still need further discussion.This paper first selects 10 local colleges and universities of different sizes to conduct a questionnaire survey.The results show that the integration model of entrepreneurship and innovation teaching and practice can improve students' innovation awareness and practical skills.Specific data shows that out of the 10 universities surveyed, 7 universities have achieved over 80% improvement in students' innovation awareness and practical skills.Among them, the highest proportion of students' innovation awareness and practical skills reached 93%, indicating that the school's practical project design and implementation plan have been highly recognized by students.It is worth noting that the size of the school and the number of practical projects do not become the decisive factors affecting the effect of the integration model of entrepreneurship and innovation teaching and practice.In response to the problems encountered in the practical process, this article finally explores corresponding strategic suggestions.Local universities should increase cooperation with society and enterprises, establish a mechanism for industry university research cooperation through various methods such as project docking, technology transfer, and innovation and entrepreneurship incubation, so that students can be exposed to actual market demand and industry development trends.Schools should strengthen the management of practical projects, establish a scientific evaluation system, and ensure the effectiveness and quality of projects.International Journal of Educational Curriculum Management and Research 20 While providing students with sufficient practical opportunities, guidance and supervision should also be strengthened to enhance their independent innovation and self-management abilities.The integration mode of mass entrepreneurship and innovation teaching and practice is an effective way for local colleges and universities to cultivate innovative talents, which can improve students' innovative awareness and practical skills.