Reviewerly/exHarmony huggingface dataset:
Analisis Strategi Penelusuran Informasi di Perpustakaan pada Mahasiswa Tingkat Akhir FIS UINSU

In general, one of the final tasks of students who want to earn a bachelor's degree is to compile scientific papers or what is commonly called a thesis. Writing thesis, students are required to have independence in the process through the guidance process carried out by the supervisor. The process of writing a thesis requires adequate information support as a source of writing. The research method used in this research is a qualitative research method to describe the nature of the problem under investigation. Sulistyo-Basuki (2006: 78) describes qualitative research intending to get a complete reflection of things for the thoughts of people being watched. This study resulted in final year students of the faculty of social sciences at the UINSU Library. Based on the results of the discussions that have been carried out, seven information search strategies were found in this study. That is Ellis' information seeking behavior strategy.&#x0D; Keywords: Behavior, Information Retrieval, Final Year Student&#x0D;
Problems Of Online Learning In The Perspective Of English Education Students Ikip Budi Utomo Malang

The Covid-19 pandemic has prompted the government to make regulatory changes in several aspects, including education aspect in Indonesia. The government implements a new policy, namely online learning, including at the university level. Although it is effective in stopping the spread of Covid-19, it turns out that there are still many problems that students must face in its implementation. This study used descriptive qualitative method. Collecting data using questionnaires and interviews. The data collected will be analyzed using descriptive statistics. The subjects in this study were students of English Education IKIP Budi Utomo. Through this research, the problems faced by students can be recorded. The results showed that the problem faced by students was the decreased in academic motivation caused by the inability of students to participate in online learning. Many students admit that they are less focused, constrained by the internet connection and quota, and inadequate learning media.
Attitudes of EFL Learners on Using Mall in English Online Classroom

In the era of easiness because of the development of modern things correlated with language, almost all human life aspects should include those. Nowadays almost nothing who do not know how to access technology, including EFL learners. Besides this research aims to identify EFL learners’ attitudes toward using MALL for online English classes. Technology role and learners’ ability to utilize it are considered significant after the global pandemic was announced, which drove all education activities to be fully online. One of the subjects that can greatly benefit from technology is English, since almost everyone worldwide enhances its technology. To achieve the goals of this study, a descriptive qualitative method was used, with a questionnaire adopted from Jebur and interviews conducted with 52 EFL learners at Tidar University who had completed a two-year English online course as a result of the pandemic outbreak. The result shows that the positive attitudes of most EFL learners are visible toward the use of mobile phones in English online classes. It can be a benefit in some cases, yet need another media where it could be depending on each learner’s preference. In conclusion, teachers should be aware of each learner to provide authentic media for particular class purposes. However, this research would benefit for the newest data to the readers or researchers in this certain field to make further improvements.
Research on High Precision Scanning Reconstruction Algorithm for Robot with Line Laser Scanner

In order to perform high precision 3D reconstruction it is necessary to acquire high precision point cloud data. Line laser scanners are increasingly used in the field of high-precision point cloud measurement due to their high accuracy and good implementability. However, the traditional method is to fix the line laser scanner above the conveyor belt and scan by the movement of the object. This method has low flexibility in measurement and is more limited. It tends to produce blind areas in the field of view. In addition, it brings new challenges to data fusion because only a single line data can be obtained for each scan. And with the high degree of freedom, the robot has high flexibility. Therefore, we established a high-precision scanning system for robot based on line laser scanner and studied the related high-precision reconstruction algorithm. The hand-eye calibration of the line laser scanner is performed by a simple flat-panel piece. Using the calibration results and the proposed robot scanning strategy, the data fusion problem of a single line point cloud is solved and high precision reconstruction of complex object surfaces is achieved.
Providing a Hybrid and Symmetric Encryption Solution to Provide Security in Cloud Data Centers

One of the most crucial components of information technology infrastructure in the modern world is cloud data centers. Customers have access to these data centers' infrastructure and software, which enable them to store and process massive amounts of data. However, the security and protection of private data in cloud data centers is a serious problem that needs effective and c solutions. Security and privacy issues exist because cloud computing outsources the processing of sensitive data. Consumer worries about cloud infrastructure security remain, particularly those related to data privacy. A thorough analysis of research efforts in the area of cloud security is the main objective of this study. In order to do this, a variety of models were evaluated, their advantages and disadvantages were identified, and a viable security solution based on symmetric algorithms was put forth. The original text in the proposed solution (Hybrid encryption algorithm) is first encrypted using the faster symmetric key method AES, and then its key is encrypted using the faster asymmetric key scheme RSA. This increases efficiency and speed. This method will shorten the time required for data encryption while enhancing its security. The final step was implementing the desired solution in the Eclipse software environment and comparing it against the Blowfish and RSA algorithms. The evaluation's findings indicate that the solution is more advantageous, which has resulted in a nearly two-fold decrease in execution time and a marked increase in throughput when compared to the RSA algorithm. Additionally, the execution time has shrunk, and throughput has been vastly improved compared to the Blowfish method.
"The significance of cooperative pedagogy in forming characteristics of humanity "

The article describes the stage-by-stage training of cooperative pedagogy in the system of continuous education. At present, humanitarian pedagogy is interpreted as a scientific and theoretical system that considers the student as an active, conscious, equal participant in the educational process and develops him based on his own capabilities.
"The need to implement a competency-based approach, educational standards and principles"

This article examines the development of volitional qualities and its significant aspects in future physical education teachers on the basis of a competency approach. At the same time, the task of the development of volitional qualities in teachers of physical education in the framework of pedagogical sciences, the methodology of training personnel were analyzed.
"Pedagogical conditions for improving the process of developing methodological competence of future technology teachers"

Today, modern schools need teachers with promising teaching technologies, and on the other hand, innovative and interactive methods of teaching students studying in higher education, and sufficient knowledge on the practical application of modern technologies. Is explained by the fact that they do not have qualifications. Methodological competence of a teacher is related to the experience of using teaching technology, innovative teaching methods and methods in university practice, and is a set of psychological, pedagogical and methodological knowledge, skills and abilities. Pedagogical conditions for improving the process of developing methodical competence of future teachers of technology and ways of effectively organizing lessons are covered in the article.
Using video materials in developing communicative competence of students

The use of video in the classroom is very effective in the formation of communicative competence of students. The purpose of the study is to analyse the issues of using authentic video materials in developing students’ communicative competence. The methods of scientific research as analysis, synthesis, deduction and other theoretical research methods were applied. Results. The research work comprises the developing students’ communicative competence (speaking skills and listening skills) through watching authentic films. Conclusion. Using video material facilitates for developing of learning process. It will be helpful in formation of professional competence of future specialists.
Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

This paper presents a novel approach to fingerspelling recognition in real-time, utilizing a two-dimensional Convolutional Neural Network (2D CNN). Existing recognition systems often fall short in real-world conditions due to variations in illumination, background, and user-specific characteristics. Our method addresses these challenges, delivering significantly improved performance. Leveraging a robust 2D CNN architecture, the system processes image sequences representing the dynamic nature of fingerspelling. We focus on low-level spatial features and temporal patterns, thereby ensuring a more accurate capture of the intricate nuances of fingerspelling. Additionally, the incorporation of real-time video feed enhances the system's responsiveness. We validate our model through comprehensive experiments, showcasing its superior recognition rate over current methods. In scenarios involving varied lighting, different backgrounds, and distinct user behaviors, our system consistently outperforms. The findings demonstrate that the 2D CNN approach holds promise in improving fingerspelling recognition, thereby aiding communication for the hearing-impaired community. This work paves the way for further exploration of deep learning applications in real-time sign language interpretation. This research bears profound implications for accessibility and inclusivity in communication technology.
Analysis and application of knowledge points in English network course teaching by using PageRank

The research uses PageRank algorithm to calculate the R value of English network teaching knowledge points, and analyses its changes through experiments to give the focus of teaching knowledge points. The results show that learners' enthusiasm for learning English online courses fluctuates significantly, and their final scores are affected by the number of days of study, with the highest pass rate reaching 76.6%. Learners hit the most at the beginning of learning English, which decreased over time, up to 8,650 times. At the same time, the accuracy and recall of PageRank algorithm in knowledge point analysis are at a high level, with the accuracy reaching 88.1%. Using PageRank algorithm to calculate the R value of knowledge points can enable teachers to adjust teaching methods and strategies according to their changes, and learners can also master the learning focus, which is highly practical in the analysis of knowledge points.
"Scientific analysis of the organization and development of the educational process based on digital technologies in the conditions of educational cluster"

This article describes the advanced pedagogical experiences of developed countries in organizing and developing the educational process based on digital technologies. Moreover, the features, aspects and signs of the research conducted on the organization and development of the educational process based on digital technologies in developed countries are discussed. In this article, you can get acquainted with the opinions and experiences of people rich in foreign experience, such as Alexey Polovinkin, Michael Treasure, Sang Min Lee and Higgins.
Digital currency - role of cryptocurrency in the new financial era

Innovations in technology have rendered the financial world more virtual and digitised, but in the new financial era, we are still not able to grasp the cryptocurrency market dynamically. The tools used for predicting and modelling the cryptocurrency market need proper improvisation so that the distrust of the public for cryptocurrency as a pioneering financial asset can be duly managed. The cryptocurrency market is considered to be a volatile market, also substantial portions of purchase of cryptocurrency are considered as speculations. With the rising number of cryptocurrencies and increase in market capitalisation of the cryptocurrency market, many studies have been conducted to understand the cryptocurrency markets in terms of its volatility. The present study has proposed models to understand the volatility of the cryptocurrency market to understand its popularity among investors.
Advanced DEEPCNN breast cancer mammogram image detection and classification with butterfly optimisation algorithm

A major aspect influencing human health is breast cancer. Mammography, fine needle aspiration, and surgical biopsy are some of the evolving diagnosis methods for this problem. Pathology images are used to diagnose breast cancer. Breast tumour surgery allows doctors to microscopically study breast tissue. Traditional methods use a cuckoo-optimised radial basis neural network. Earlier RBN algorithms handled feature extraction and reduction differently. To reduce unneeded complexity, outperform convolutional neural network (CNN) for feature extraction and classification. The Butterfly optimisation technique suggests a CNN. Zernike moments' scale, interpretation, and rotation similarity lets us bypass numerous pre-processing steps. The picture dataset was created from tumour treatment archives. The Butterfly optimisation method feeds the DCNN training data. DCNN removes, reduces, and classifies features. By determining the number of historical periods and training images for Deep CNN, optimisation improves efficiency and reduces error rates. This approach projects normal, benign, and malignant. The model achieves sensitivity, accuracy, specificity, F1 score, and recall by contrasting RBF with cuckoo search.
Study on active sleeping node detection method in sensor network based on multi-dimensional sliding window

To overcome the problems of low coverage and detection accuracy in traditional detection methods, a multidimensional sliding window based active sleep node detection method for sensor networks is proposed. Firstly, we set up an active sleep node simulator and controller in the sensor network space to determine the active sleep range. Secondly, we design a multidimensional sliding window algorithm to determine anomalies in the transmission link by calculating the standard deviation of sensing information in the sliding window. Finally, the total length of data transmission is dimensionally transformed to achieve reliable detection of active sleep nodes. The experimental results show that the coverage rate of the detection results of this method is closer to 1, and its detection accuracy remains between 94.84% -97.32%, and the detection process time remains between 1.72 s-232 s. It has the advantages of strong reliability and high efficiency in applications.
Role of artificial intelligence in knowledge management: an empirical study of industry experts using stepwise multiple regression

Knowledge management (KM) is considered the lifeblood of growth and sustainability in modern organisations. The emergence of various information technology-based solutions has thoroughly transformed the KM system by making it efficient, swift, and robust. Artificial intelligence is one such IT evolution. The study has considered experts from different industries like IT and ITEs, research, banking, FinTech, and retail. The data were collected from 217 industry experts through a structured questionnaire containing a five-point Likert scale for measurement, containing 20 items as independent variables and one as the dependent variable. Data analysis was done through 'exploratory factor analysis (EFA)' and multiple regression analysis. The most important factor contributing to knowledge management was found to be 'knowledge distribution' with an effect size of standardised beta (.806), followed by 'amplifying efficiency' (0.307), real-time engagement and collaboration (0.108), and 'AI as an artificial neural network' (0.080).
Blockchain-Based Distributed Collaborative Computing for Vehicular Digital Twin Network

Being the main starting point for intelligent transportation systems, internet of vehicles has entered a rapid development period. This motivates a large number of novel vehicular applications with diversified and personalized demands. Distributed collaborative computing provides a promising solution to meet these applications by on-demand resource management, but is faced with tremendous challenges in achieving trusted resource cooperation and accurate decision making. In this paper, we propose a blockchain-based vehicular digital twin architecture for distributed collaborative computing. In this architecture, blockchain facilitates the opening and sharing of vehicular resources, while digital twin enables the accurate management decision. Then, we investigate the computation offloading in blockchain-based vehicular digital twin networks to demonstrate the effectiveness of the proposed architecture. Finally, several important open issues are discussed in detail for further investigation on blockchain-based vehicular digital twin.
Rule-guided Counterfactual Explainable Recommendation

To empower the trust of current recommender systems, the counterfactual explanation (CE) method is adopted to generate the counterfactual instance for each input and take their changes causing the different outcomes as the explanation. Although promising results have been achieved by existing CE-based methods, we propose to generate the attribute-oriented counterfactual explanation. Different from them, we aim to generate the counterfactual instance by performing the intervention on the attributes, and then build an attribute-oriented counterfactual explainable recommender system. Considering the correlation and categorical values of attributes, how to efficiently generate the reliable counterfactual instances on the attributes challenges us. To alleviate such a problem, we propose to extract the decision rules over the attributes to guide the attribute-oriented counterfactual generation. Specifically, we adopt the gradient boosting decision tree (GBDT) to pre-build the decision rules over the attributes and develop a Rule-guided Counterfactual Explainable Recommendation model ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RCER</b> ) to predict the user-item interaction and generate the counterfactual instances for the user-item pairs. We finally conduct extensive experiments on four publicly datasets, including NYC, LON, Amazon, and Movielens datasets. Experimental results have qualitatively and quantitatively justified the superiority of our model over existing cutting-edge baselines. We release the code: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/quxiaoyang0zero/RCER</uri> .
Locality-Aware Tail Node Embeddings on Homogeneous and Heterogeneous Networks

While the state-of-the-art network embedding approaches often learn high-quality embeddings for high-degree nodes with abundant structural connectivity, the quality of the embeddings for low-degree or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tail</i> nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embeddings. In this article, we formulate the goal of learning tail node embeddings as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">few-shot regression</i> problem, given the few links on each tail node. In particular, since each node resides in its own local context, we personalize the regression model for each tail node. To reduce overfitting in the personalization, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">locality-aware</i> meta-learning framework, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">meta-tail2vec</i> , which learns to learn the regression model for the tail nodes at different localities. Moreover, to address the heterogeneity in nodes and edges on heterogeneous information networks (HINs), we further extend the proposed model and formulate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">meta-tail2vec+</i> , which is based on a dual-adaptation mechanism to facilitate the locality-aware tail node embeddings on HINs. Finally, we conduct extensive experiments and demonstrate the promising results of both meta-tail2vec and its extension meta-tail2vec+.
Achieving Cost Optimization for Tenant Task Placement in Geo-Distributed Clouds

Cloud infrastructure has gradually displayed a tendency of geographical distribution in order to provide anywhere, anytime connectivity to tenants all over the world. The tenant task placement in geo-distributed clouds comes with three critical and coupled factors: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">regional diversity in electricity prices</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">access delay for tenants</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">traffic demand among tasks</i> . However, existing works disregard either the regional difference in electricity prices or the tenant requirements in geo-distributed clouds, resulting in increased operating costs or low user QoS. To bridge the gap, we design a cost optimization framework for tenant task placement in geo-distributed clouds, called TanGo. However, it is non-trivial to achieve an optimization framework while meeting all the tenant requirements. To this end, we first formulate the electricity cost minimization for task placement problem as a constrained mixed-integer non-linear programming problem. We then propose a near-optimal algorithm with a tight approximation ratio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1-1/e)$</tex-math> </inline-formula> using an effective submodular-based method. Results of in-depth simulations based on real-world datasets show the effectiveness of our algorithm as well as the overall 10%-30% reduction in electricity expenses compared to commonly-adopted alternatives.