Reviewerly/exHarmony huggingface dataset:
The Impact of the 5E Learning Model Improved with Concept Maps on Motivation

The examination of students’ motivation towards lessons is an important aspect of educational studies. The constructivist approach significantly impacts the improvement of students’ motivation. The present study aims to examine the use of the 5E learning model with concept maps to support students’ motivation and compare the 5E approach and the classical approach to teaching chemistry in terms of motivational dimensions. The main subject of this study is an assessment of students’ motivation using the 5E learning model, which promotes student-centred teaching. The study was conducted with 100 8th-grade lower secondary school students who attended a school in the Turkish Republic of Northern Cyprus (TRNC) during the spring semester of the 2018/19 school year. The study was conducted with two randomly selected groups: experimental (EG) and control (CG). The lessons of the EG were taught using the 5E Learning Model Improved with Concept Maps, while lessons of the CG were conducted using the current, conventional teaching method. The Motivated Strategies for Learning Questionnaire (MSLQ) was applied as a data collection tool in the study. Descriptive statistics and Multivariate Analysis of Variance (MANOVA) were used in data analysis. As a result, it was determined that the EG students’ motivation scores showed a significant difference from the CG students’ motivation scores. Furthermore, a significant difference was established between the EG and CG students’ intrinsic goal orientation and test anxiety post-test scores. Although the EG students’ averages for other sub-dimensions were higher than the CG students’ averages, no significant difference was found between the groups.
A Framework for the Design, Production, and Evaluation of Scientific Visualizations

Visualizations play a critical role in discovering, understanding, interpreting, synthesizing, and communicating scientific knowledge. Effective scientific visualization requires careful attention to a number of factors, in particular, a faithful translation of scientific evidence, understanding of the communication needs of the target audience, and skillful application of visualization design principles. As a result, science visualization projects require a team of contributors with specialized knowledge and technical expertise. Regardless of team size and structure, a clear definition and appreciation of the design process as well as an understanding of the responsibilities of each contributor are imperative to the success of a project. Gaps in understanding often result in conflict between visualizers and stakeholders, compromising the quality of the scientific visualization. Although many companies have developed their own process through trial and error over years of experience, to date, there is no formalized framework for scientific visualization that details the steps of the process and the contributions of each individual. Informed by our examination of case studies, frameworks, and our collective experience as practitioners, we propose a framework tailored to the design, production, and evaluation of scientific visualization that aims to support practitioners in meeting their objectives and facilitating conversations that allow others to better understand the impact of the design process on the final product. We explore underlying drivers of decision-making within the visualization design space, describe the activities and outputs that impact decisions made about the final visualization, and discuss potential applications and limitations of this framework in practice.
Tractable Closure-Based Possibilistic Repair for Partially Ordered DL-Lite Ontologies

Inconsistency in formal ontologies is usually addressed by computing repairs for the dataset. There are several strategies for selecting the repairs used to evaluate queries, with various levels of cautiousness and classes of computational complexity. This paper deals with inconsistent partially ordered lightweight ontologies. It introduces a new method that goes beyond the cautious strategies and that is tractable in the possibilistic setting, where uncertainty concerns only the data pieces. The proposed method, called C $$\pi $$ -repair, proceeds as follows. It first interprets the partially ordered dataset as a family of totally ordered datasets. Then, it computes a single data repair for every totally ordered possibilistic ontology induced from the partially ordered possibilistic ontology. Next, it deductively closes each of these repairs in order to increase their productivity, without introducing conflicts or arbitrary data pieces. Finally, it intersects the closed repairs to obtain a single data repair for the initial ontology. The main contribution of this paper is an equivalent characterization that does not enumerate all the total orders, but also does not suffer from the additional computational cost naturally incurred by the deductive closure. We establish the tractability of our method by reformulating the problem using the notions of dominance and support. Intuitively, the valid conclusions are supported against conflicts by consistent inclusion-minimal subsets of the dataset that dominate all the conflicts. We also study the rationality properties of our method in terms of unconditional and conditional query-answering.
Unified Transformer with Cross-Modal Mixture Experts for Remote-Sensing Visual Question Answering

Remote-sensing visual question answering (RSVQA) aims to provide accurate answers to remote sensing images and their associated questions by leveraging both visual and textual information during the inference process. However, most existing methods ignore the significance of the interaction between visual and language features, which typically adopt simple feature fusion strategies and fail to adequately model cross-modal attention, struggling to capture the complex semantic relationships between questions and images. In this study, we introduce a unified transformer with cross-modal mixture expert (TCMME) model to address the RSVQA problem. Specifically, we utilize the vision transformer (VIT) and BERT to extract visual and language features, respectively. Furthermore, we incorporate cross-modal mixture experts (CMMEs) to facilitate cross-modal representation learning. By leveraging the shared self-attention and cross-modal attention within CMMEs, as well as the modality experts, we effectively capture the intricate interactions between visual and language features and better focus on their complex semantic relationships. Finally, we conduct qualitative and quantitative experiments on two benchmark datasets: RSVQA-LR and RSVQA-HR. The results demonstrate that our proposed method surpasses the current state-of-the-art (SOTA) techniques. Additionally, we perform an extensive analysis to validate the effectiveness of different components in our framework.
GESTURE HANDLER

Hand gestures are a natural way of interactions when one person communicates with each other and therefore hand movements can be treated as a nonverbal form of communication. Hand gesture recognition is a process of understanding and classifying meaningful human hands movements. Gesture recognition was supposed to develop a new type of Human Computer Interaction system that would overcome the problems that users face with the current system. The algorithm that is being described in this technology is not based on background image subtraction and is not programmed for a specific hand type the algorithm used can process different hand types, recognizes no. of fingers, and can carry out tasks as per requirement or as described in the algorithm.
Penetration of Blockchain Technology in Healthcare Management Domain: Challenges and Opportunities

Blockchain technology (Btech) is one of the new technologies in healthcare management domain. Challenges and opportunities are closely related to the usability of the technology. The study aims to evaluate penetration of the technology in healthcare management domain. Web of Science database was utilized to obtain publications. The publications were subjected to various analyses in the R environment. The parameters were handled within the framework of the values provided with biblioshiny package. The ggplot2 package was used for data exploration and visualization. According to the findings, Btech has been discussed in the context of the management of electronic health records. Prominent key concepts and thematic map revealed that the technology could be a potential tool in the management of health records. While privacy, security, complexity, and sustainability stand out as the main challenges; active patients, management of health records, quality and reliability of clinical trials, and its use in monitoring and procurement processes in the pharmaceutical industry stand out as opportunities. In general, Btech is a brand new paradigm in the management of health data, and in this respect, it can make important contributions to healthcare management from the perspectives of patients, providers, and payers.
Development of Virtual Reality-Based Learning Media for Product-Setting Subjects in Vocational Schools

The research aims to develop learning media based on Virtual Reality for the Product Management subject at SMKN 14 Jakarta. The study uses research and development methods (Research &amp; Development) using the combined procedure reference development model between the Borg and Gall models, the Dick Carey Model), and the Hannafin and Peck Models. This model is carried out through needs analysis, design, development, testing, implementation, and maintenance. The results of this study are as follows. (In summary, the research went through several key stages: 1) The analysis stage highlighted the limitations of practical facilities for the Limited Product Arrangement subject. 2) The design phase resulted in a comprehensive design, including Virtual Reality illustrations with 3D display designs, color selection, typography, page layouts, images, videos, audio, and interface designs. 3) During the development of Virtual Reality products, the researcher processed the content into a medium using the Unity Engine application. 4) The testing phase, which included material and media testing, produced highly favorable results. Material experts rated content quality and objectives at 4.8 in the "very feasible" category, while media experts found functional suitability very doable with a percentage score of 4.67. Usability testing yielded a score of 4.7, indicating good usability. Performance efficiency met standards and effectively engaged students' learning interest. Overall, these findings demonstrate the success of the research in developing Virtual Reality-based learning materials for the Product Arrangement subject.
Comparative Study of Blockchain Consensus Algorithms in Cryptocurrency

Blockchain technology serves as a cornerstone for various cryptocurrencies, with current research primarily focusing on its security aspects due to its inherent qualities such as stability, immutability, security, and decentralization. Similar to other distributed systems, Blockchain relies on consensus algorithms to achieve agreement and safeguard its network. In recent years, diverse consensus algorithms have emerged within the Blockchain ecosystem, playing a pivotal role in upholding the security and integrity of distributed networks in blockchain technology. These algorithms are fundamental in maintaining trust within the blockchain technology realm. They can be categorized into two types: proof-based and voting-based. This paper presents some of the prominent consensus algorithms from these two categories while scrutinizing their respective strengths, weaknesses, and the specific types of blockchains to which these algorithms are applicable. Keywords—Blockchain, consensus algorithm, PoS, Pow, DPoS, PBFT
eScience Serverless Data Storage Services in the Edge-Fog-Cloud Continuum

MeshStore, a fault-tolerant serverless storage model for edge-fog-cloud continuum systems, enables organizations to integrate distributed heterogeneous storage resources into a single unified storage service for the sharing of data through serverless functions deployed on edge-fog-cloud environments to create continuum dataflows. This unified service automatically and transparently manages the input/output data of serverless functions by coupling storage structures, including load-balancing and data allocation/location algorithms. Organizations also can add non-functional requirement properties (e.g., either reliability or security) to the storage structures when managing sensitive data.
frances: Cloud-Based Historical Text Mining with Deep Learning and Parallel Processing

frances is an advanced cloud-based text mining digital platform that leverages information extraction, knowledge graphs, natural language processing (NLP), deep learning, and parallel processing techniques.It has been specifically designed to unlock the full potential of historical digital textual collections, such as those from the National Library of Scotland, offering cloud-based capabilities and extended support for complex NLP analyses and data visualizations.frances enables realtime recurrent operational text mining and provides robust capabilities for temporal analysis, accompanied by automatic visualizations for easy result inspection.In this paper, we present the motivation behind the development of frances, emphasizing its innovative design and novel implementation aspects.We also outline future development directions.Additionally, we evaluate the platform through two comprehensive case studies in history and publishing history.Feedback from participants in these studies demonstrates that frances accelerates their work and facilitates rapid testing and dissemination of ideas.
Multi-View Transfer with Marginal and Conditional Alignment for Many-Task Optimization

Many-task optimization problem (MaTOP) is a kind of problem in that more than three optimization tasks are required to be solved simultaneously. How to utilize multiple different kinds of similarities among many tasks for knowledge transfer (KT) is a challenging issue in solving MaTOP. To this end, this paper proposes a multi-view transfer (MVT)-based differential evolution (MVTDE) for effectively transferring knowledge in solving MaTOP. In particular, a perspective of task alignment is regarded as a view of performing KT. By combining different views including marginal and conditional distribution alignment, MVT can better fully utilize various task similarities from different aspects to solve MaTOP efficiently. This paper is with two contributions. First, we propose the MVT method that incorporates multiple KT methods from the views of marginal and conditional distribution alignment to enhance the search efficiency on the target tasks. Second, we propose to control the occurrence of different KT methods in MVT according to probability parameters and propose a parameter update strategy to adaptively adjust the probability parameters according to the feedback of different KT methods. In the experimental study, we carry out comparative experiments on three widely used MaTOP benchmarks to validate the effectiveness of MVTDE. The results show that MVTDE obtains competitive performance compared to several state-of-the-art many-task optimization algorithms.
An Assessment of Machine Learning Algorithms and Models for Prediction of Change-Prone Java Methods

Identifying which parts of code are prone to change during software evolution allows developers to prioritize and allocate resources efficiently. Having as focus a smaller scope makes easier change management and allows monitoring the type of modification and its impact. However, existing change-proneness prediction approaches are focused mainly on system classes. But the problem is that classes contain many characteristics of different software attributes and some software behaviors are more granular and better captured at the method-level. Motivated by these facts, in this paper, we empirically assess the performance of four machine learning algorithms for change-prone method prediction in seven open-source software projects. We derived and compared models obtained with three sets of independent variables (features): a set composed of structural metrics, a second set composed of evolution-based metrics, and a third that includes a combination of both kinds of metrics. The results show that, Random Forest presents the best general performance, independently of the used indicator and set of features. The model composed by both sets of metrics outperforms the other two. Two features based on the frequency of changes that happened in the evolution history of the method are point out as the most important for our problem.
Extended Object Tracking Based on Distributed Sensor Network

Abstract In this paper, an extended Kalman filtering algorithm based on coupled velocity model (CVM) is proposed for extended object tracking under distributed sensor networks. This algorithm tracks an object using measurements collected by multiple sensor nodes, and then obtains a global solution based on weighted Kullback-Leibler divergence. Simulation results display that the improved method can effectively track the extended object.
PENGEMBANGAN E-MODUL MENGGUNAKAN FLIPBOOK PADA SUBTEMA BENDA TUNGGAL DAN CAMPURAN

E-Module Development Using Single and Mixed SubThemes Flipbooks aims todetermine the feasibility of developing teaching materials. The researchmethodology used was research and development with the ADDIE model (Analyze,Design, Development, Implementation, Evaluation) with research subjects fromclass V-A SDN Dukuh 01. The instruments used in this research were validationsheets from media experts, linguists, and expert material with three experts, studentresponse instruments, and pre-test and post-test instruments. The validation resultsyielded an average of 96.3% with the criteria of "Very Eligible", then the results ofthe pre-test and post-test produced an N-Gain score of 0.59 with the criteria of"Moderate" and obtained 89% of student responses with the criteria of "Very Good".Based on the results obtained, the development of e-modules using flipbooks onsingle and mixed object sub-themes meets valid criteria.
Development and Prospect of Satellite Remote Sensing Technology for Ocean Dynamic Environment

Covers advancements in spacecraft and tactical and strategic missile systems, including subsystem design and application, mission design and analysis, materials and structures, developments in space sciences, space processing and manufacturing, space operations, and applications of space technologies to other fields.
Hyper-entanglement between pulse modes and frequency bins

Hyper-entanglement between two or more photonic degrees of freedom (DOF) can enhance and enable new quantum protocols by allowing each DOF to perform the task it is optimally suited for. Here we demonstrate the generation of photon pairs hyper-entangled between pulse modes and frequency bins. The pulse modes are generated via parametric downconversion in a domain-engineered crystal and subsequently entangled to two frequency bins via a spectral mapping technique. The resulting hyper-entangled state is characterized and verified via measurement of its joint spectral intensity and non-classical two-photon interference patterns from which we infer its spectral phase. The protocol combines the robustness to loss, intrinsic high dimensionality and compatibility with standard fiber-optic networks of the energy-time DOF with the ability of hyper-entanglement to increase the capacity and efficiency of the quantum channel, already exploited in recent experimental applications in both quantum information and quantum computation.
DH-Net: Dynamic Head for Object Detection Network

Object detection performs a vital function in numerous domains. In contemporary object detection frameworks, multiple detection heads are a crucial element. Multi-performance head improvement is mostly attributable to the employment of many heads to detect objects of different sizes, but this also results in significant computing usage. Moreover, not all heads need to participate in the detecting process for every image. We first investigate the correlation between the number of heads and computing consumption, explore how to determine whether a detection head is in charge of detecting an object, and propose a dynamic head network for detection (DH-Net), which according to the input photographs, adaptively pick which heads to make predictions. As a result, the technique can accomplish more effective dynamic reasoning and a better balance between detection accuracy and computing expense. DH-Net allows the model to lower the number of parameters by up to 34% while still maintaining comparable accuracy, according to a series of controlled trials in the TT100K datasets. Moreover, it has the effect of speeding up training for redundant models.
PELUANG DAN TANTANGAN PENYIMPANAN CLOUD STORAGE PADA DOKUMEN DIGITAL

Cloud storage is a digital document storage application which is the result of the development of a cloud computing system, this system utilizes the internet network in carrying out its business processes. Utilization of cloud storage applications on digital documents provides various conveniences for users, not only professional users who work for their organizations but can be used by individual personal users who use it to store personal documents. With the existence of a remote work system must be implemented, in disruption era which is slowly shifting conventional patterns, it also affects the process of storing digital documents into things that need to be implemented in organizations. Cloud storage supports collaboration which is easy to implement with colleagues, access can be done anywhere and anytime, to have advanced security. Online document storage provides benefits for business processes. The benefits of cloud storage are supporting paperless programs, making it easier to preserve digital documents, helping organizations in controlling data, mitigating disasters against unpredictable events, saving expenses, and having an advanced data security system. In addition to the benefits/conveniences offered by the service provider. User readiness needs to be considered. This will be a challenge aspect in implementing cloud storage, such as education and socialization regarding the use of cloud storage systems which are still not optimal, vulnerabilities in the misuse of document data stored in the cloud and requires superior human resources in the use of cloud storage.