Reviewerly/exHarmony huggingface dataset:
Financial Reporting System For Enabling Trusted Accounting Using Blockchain

Traditional financial reporting is restricted to software solutions like ERP Finance, which call for manually recording the value of the business transactions in their own systems of Information technology. It is fraught with shortcomings like human errors/oversights (due to manual feeding of transactional data) and fraud (as there is no way to authenticate the transactions and/or their true value). To counter these limitations, we need to reimagine the way a company’s financial transactions get recorded and classified. We propose a solution that would maintain a company’s financial data, while increasing trustworthiness and transparency of the system. This is done by way of integrating Cloud, Big Data and Blockchain technologies, and provide a trusted access to all its stakeholders. A blockchain, kept on Hybrid cloud infrastructure, records accounting transactions between two companies serves as the single source of truth for both the companies, thus eliminating chances of fraud. The article explores the InterWork alliance, which standardizes blockchain code for business transactions. It also explores how it can be linked to Government blockchains to make the tokens (blocks) as indelible and trustworthy. Red Hat Cloud helps pull data to build reports like Profit and Loss and Balance Sheet while extracting data from blocks kept on multiple servers and in different geographies. Meaning can be given to these financial transactions through Big data. This article explores how to bring together these concepts to build an implementable Financial Accounting solution.
Mapping Flood-Prone Areas Based Geographic Information System Using Composite Mapping Analysis

Floods are destructive, very detrimental, and require a long time to recover. To overcome the impact of flooding on the people of East Aceh Regency, research related to the mapping of flood-prone areas in East Aceh District. In the mapping process using Quantum Geographic Information System (QGIS) Desktop and the research methodology used is Composite Mapping Analysis (CMA) which consists of the process of determining the class of each parameter, determining the weight of each parameter by combining each parameter which includes the scoring process for each parameter, then overlaying the parameters used, performing calculations and producing a relative weight or spatial mean, and combining the spatial mean to produce a value for the weight of each flood parameter. The results of the study obtained the weight of rainfall of 23.85%; land slope parameter of 27.25%; the land height parameter is 24.7%5 and the flow density parameter is 24.15%, and the result is that the area of flood-prone areas has been divided into 3 classes, that is the very vulnerable class has an area of 230.099 ha (42.6%), the vulnerable class has an area of 224.732 ha (41.6%), and the bit vulnerable class has an area of 85.416 ha (15.8%).
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KECENDERUNGAN KECURANGAN AKUNTANSI

In the business of world, fraud or deviant behavior is often encountered, one of which is accounting fraud. Accounting fraud is an act that is against the law and is an act of dishonesty committed by someone inside or outside a company. The purpose of this study was to determine the influence of internal control, observance of accounting rules, and individual morality on the tendency toward accounting fraud. The sample used is non-probability sampling by applying a random sampling methodology to 100 company employee respondents in the departments of accounting, finance, purchasing, and the like in Batam City. Multiple linear regression analysis approaches were employed in this study's data analysis to both assess the dependent variable's dependency on each independent variable and the dependent variable's influence on the independent variables. The results of the study show that adherence to accounting rules and individual morality have a partially significant effect on the tendency toward accounting fraud. Internal control partially has no significant effect on the tendency for accounting fraud
THE STUDENTS’ PERSONALITY BASED ON THEIR READING ATTITUDE

This research aimed to know and analyze students’ personalities based on their reading attitudes and identify the reading strategies frequently used by students. The research was conducted at SMP PGRI 1 Palembang using a qualitative method. The data were collected through observation, surveying using two questionnaires (a personality questionnaire and a reading strategy questionnaire), interviews, and documentation. The research sample consisted of 30 students from class VIII.1 at SMP PGRI 1 Palembang in the academic year 2022–2023, even semester. Based on this research, it was found that 24% of students in class VIII.1 have a conscientiousness personality characterized by being organized, responsible, and disciplined. Furthermore, 43% of students use a global reading strategy that focuses on overall text comprehension. These findings indicate a relationship between reading attitude and students’ personalities, with conscientiousness and global reading strategy dominating. The implication is that this research provides important insights for the development of effective learning strategies to enhance students’ reading skills at SMP PGRI 1 Palembang.
Evaluation of Data Clustering Accuracy using K-Means Algorithm

Data clustering is one of the methods in data science that is often used in data analysis. This method is used in making groupings from a collection of datasheets. Data clustering is done to find patterns or relationships between data. This research aims to evaluate the accuracy of data clustering using K-Means algorithm on wine datasheet. Wine datasheet has 13 features that describe the chemical characteristics of three types of wine. The clustering process must produce the best clustering evaluation metrics. The evaluation metric is done through comparison between the clustering results of K-Means algorithm with Davies Bouldin and Silhouette. The research steps involved data standardization, selection of the optimal number of clusters, and assessment of clustering accuracy. The research method uses KDD which consists of pre-processing, transformation, model building and model evaluation. Experimental results show that appropriate parameters and cluster initialization can improve clustering evaluation metrics. The clustering results show that the normalized datasheet produces evaluation metrics for Davies Bouldin 2 groups and Silhouette produces 3 groups. Before normalization, Davies Boulidin results in 7 groups and Silhouette results in 2 groups. In conclusion, this study produced different evaluation metrics between normalized and non-normalized datasheets. The selection of the number of groups chosen depends on the context of the data analysis performed and is selected into 3 groups which can be labelled "Superior Variety", the second group "Intermediate Variety" and the third group "Standard Variety".
Functional Systemic Linguistic Approach in Translation Studies of Taxis Markers in "WoY"

&lt;em&gt;&lt;span lang="EN-US"&gt;Waiting on You (WoY) is the work of Kristan Higgins, a New York Times bestselling author whose novel was also translated into Indonesian by Nina Andiana. Higgins consistently uses complex clauses in writing novels. As a result, long complex clauses are difficult to translate. This research refers to the Functional Systemic Linguistics approach which aims to: explain the interdependence and semantic logic of complex clauses in source language text are translated into interdependence and semantic logic of complex clauses in recipient language text in novel WoY; Taxis marker translation technique used in translating from source language text to recipient language; the shifts found in the translated novel Penantian Terpanjang; the quality of translation of complex clauses in target language. This method is descriptive qualitative with taxis as an object in the novel WoY, sourced from source language texts and recipient language texts from the novel entitled Penantian Terpanjang, and obtained from informants who are linguistic and translation experts. The technique is purposive sampling. Techniques of collecting data are document analysis and focus group discussions. The data validity is triangulation. The results are after breaking down sentences into clauses and analyzing them, researchers find that the most dominant form (interdependence) is paratactic, and the meaning (semantic logic) that is most widely used is the extension. Meanwhile, the most widely used technique is common. The types of shifts found are form (interdependence) and meaning (semantic logic). This shift has a positive effect on readability so that it can improve the quality of the novel translation.&lt;/span&gt;&lt;/em&gt;
An efficient framework for secure data transmission using blockchain in IoT environment

&lt;p&gt;The secure and efficient sharing of data has been recognised as a significant concern in Internet of Things (IoT)-enabled smart systems, including smart cities, smart agriculture, and smart health applications. Smart systems utilise a substantial quantity of IoT devices, which in turn generate a significant volume of data. Internet of Things (IoT) devices typically possess constrained storage and processing capacities, making the implementation of security measures on such devices a difficult task. This paper presents a framework for secure data transmission using blockchain (SDTUB) for blockchain-based IoT systems, with a focus on enhancing data security. The use of clustered authorization aims to enhance the interoperability of IoT authorization. The central blockchain is employed for permission purposes concerning cluster management nodes, whereas the regional blockchain suffices for authorization of regular nodes. The proposed mechanism is implemented using MATLAB, and the performance is analysed using performance metrics such as energy consumption and objective value. In the proposed mechanism, the energy consumption is low compared to the AuBWSN technique.&lt;/p&gt;
Digital Devices Used in a Secondory School: A Case Study of Access and Use in Learning Mathematics

This research investigates the types of digital devices used by students at a Secondary School in Kiritpur Municipality, Nepal, and their relationship with grade level, focusing on integrating technology in teaching mathematics. The case is urban school phenomena. A survey was conducted with 420 students, using a questionnaire for data collection. Statistical tools used in analysis included univariate (frequency and percentage) and bivariate (Chi-Square) methods. Findings show that mobile devices are the most widely used for attending online classes. Around 25% of students lack access to necessary technology at least mobile too. Attendance rates vary between lower and higher grade levels. Importantly, the study reveals limited technology integration at an urban School, with teachers lacking awareness of how to effectively adapt and incorporate technologies in the classroom, specifically for mathematics teaching. These results underscore the need to improve digital device availability and the types for teaching science, mathematics like subjects, which demand dynamic, interactive tools use. Similarly, access to devices particularly at the lower secondary level is lower, and over reliance on parents' device needs reducing reliance on parental devices. Additionally, there is a call for enhanced professional development to support teachers in integrating technology into mathematics instruction. Addressing these challenges will contribute to enhancing the learning experience and overall effectiveness of technology integration in the classroom.
Breast cancer detection in pathological imaging using deep learning methods

Breast cancer is a disease that affects many women globally and is associated with a significant fatality rate. Worldwide nearly 12% of women are affected by breast cancer and the number is still increasing. To enhance breast cancer detection and patient survival rates, early and accurate identification of the disease is essential. Hence, there is a need for diagnostic models based on medical imaging which will help medical practitioners in diagnosing and treating the patients with minimum error and greater accuracy. Artificial intelligence (AI) and machine learning (ML) techniques can be used in medical imagining detecting complex relationship between different data elements so that disease detection and prognosis can be made easy. Deep learning (DL) techniques can be used to detect the most influencing features from images so that most serious diseases such as breast cancer can be treated in-time. In this chapter, a systematic evaluation of prior work based on breast cancer identification and prognosis using images like mammography, MRI, etc. along with DL and ML is carried out. Based on this the work proposes a design of three advanced DL methods: convolution neural network, ResNet, and U-Net to classify breast cancer mammography images. The models were trained to identify histopathology images into to two classes: malignant and benign. The method is implemented in two steps. The first step comprises of data selection and pre-processing and, in the second step, the implementation of three networks and performance measurement of each network. The results indicate U-net model outperformed by achieving 97.12% of accuracy compared to other networks.
A Formal Metamodel for Software Architectures with Composite Components

Formal component-based modeling has been shown to be invaluable for verifying the compatibility of specified components, discovering flaws early in design stages, and enabling the reuse of components, across multiple projects and teams. However, complex system specifications are large and difficult to reason with which has limited the adoption of formal approaches. In this paper, we use a formal language to build a metamodel to represent software architectures consisting of composite components. First, we propose a metamodel to describe the high-level concepts of software architectures in a component-port-connector fashion. We focus on providing hierarchical modeling capabilities by considering the construction of composite components from existing ones. Second, using Alloy as a tooled formal language, we formalize the metamodel concepts to build a reusable framework for modeling complex systems consisting of composite component structures that can be automatically constructed and checked for architectural conformance. We use a smart metering system to demonstrate the use our formal metamodel.
Galaxy Morphological Classification of the Legacy Surveys with Deformable Convolutional Neural Networks

Abstract The ongoing and forthcoming surveys will result in an unprecedented increase in the number of observed galaxies. As a result, data-driven techniques are now the primary methods for analyzing and interpreting this vast amount of information. While deep learning using computer vision has been the most effective for galaxy morphology recognition, there are still challenges in efficiently representing spatial and multi-scale geometric features in practical survey images. In this paper, we incorporate layer attention and deformable convolution into a convolutional neural network (CNN) to bolster its spatial feature and geometric transformation modeling capabilities. Our method was trained and tested on seven classifications of a data set from Galaxy Zoo DECaLS, achieving a classification accuracy of 94.5%, precision of 94.4%, recall of 94.2%, and an F1 score of 94.3% using macroscopic averaging. Our model outperforms traditional CNNs, offering slightly better results while substantially reducing the number of parameters and training time. We applied our method to Data Release 9 of the Legacy Surveys and present a galaxy morphological classification catalog including approximately 71 million galaxies and the probability of each galaxy to be categorized as Round, In-between, Cigar-shaped, Edge-on, Spiral, Irregular, and Error. The code detailing our proposed model and the catalog are publicly available in doi: 10.5281/zenodo.10018255 and GitHub ( https://github.com/kustcn/legacy_galaxy ).
Photonic integrated processor for structured light detection and distinction

Abstract Integrated photonic devices are pivotal elements across research fields that involve light-based applications. Particularly versatile platforms are programmable photonic integrated processors, which are employed in applications like communication or photonic computing. Free-space distributions of light can be coupled to such processors, which subsequently control the coupled light on-chip within meshes of programmable optical gates. This enables access to the spatial properties of free-space light, particularly its relative phase, which is usually challenging to measure. Here, we discuss and show the detection of amplitude and phase distributions of structured higher-order light beams using a multipurpose photonic processor. This can be used to directly distinguish light’s orbital angular momentum without including additional elements interacting with the free-space light. We envision applications in a range of fields that rely on the spatial distributions of light’s properties, such as microscopy or communications.
Android-Based Personal Hygiene Educational Game Application for Children

Presently, there remains a deficiency in educational games aimed at instilling in children an awareness of personal hygiene, and numerous health issues stem from a lack of adherence to good hygiene practices. This study seeks to develop an Android-based educational game catering to the hygiene needs of young children, with a focus on assessing the application's feasibility. The research collected system-building data through observations and interviews, employing the Black Box testing method to evaluate the application's usability. Field and library research contributed to the acquisition of comprehensive data. The study's outcome is the creation of an Android-based educational game dedicated to teaching personal hygiene to children. Utilizing the UML design method, the author constructed and integrated the game into the Unity 3D application, enabling children to grasp the significance of cleanliness and environmental hygiene through engaging gameplay. Evaluation results indicate the suitability of the personal hygiene education game, with a learning menu percentage of 78% and a play menu percentage of 77%, as determined by questionnaire-based tests, affirming the system's appropriateness for use.
Dissolving distances: Designing close-to-body experiences for remote settings

I aim to offer embodied intimacy for people who are close by heart but physically apart. Specifically, I explore designing close-to-body experiences at a distance through intimate bio-rings, rings made of natural ingredients. Intimate bio-rings are highly customizable, can be biodegraded, and start dissolving when exposed to humidity e.g.: rain, sweat etc. The idea of creating a non-lasting object to be worn on the body—that required care, that was ambiguous and tangible—was a result of a prior user study of cultural probing and embodied design ideation. I further experimented with intimate bio-rings by making the ring and wearing it in everyday life together with my father, whom I live far away from. In this paper, I showcase a user study with 3 pairs (6 participants) that made intimate bio-rings at their homes while self-reported and self-documented their personal experiences. They then further shared their meaning-makings with me through an interview. Overall, participants found intimate bio-rings to be supporting new understandings about intimacy at a distance. As a result, their experience of "distance" alters slightly or changes completely by i) embarking on a journey, ii) creating time and space to be together, and iii) carrying each other through a tangible object.
Mapping the Evolving Landscape of Cloud Computing Research: A Bibliometric Analysis

This research paper presents an in-depth bibliometric analysis of cloud computing literature, aiming to uncover underlying patterns, assess citation levels, and identify key trends shaping this rapidly evolving field. The study employs robust bibliometric techniques, analyzing data collected from reputable academic databases, including Scopus. The methodology involves comprehensive data acquisition, preprocessing, analysis, and interpretation, using advanced tools like VOSviewer and Biblioshiny for visualization and statistical analysis.Our findings reveal significant growth in cloud computing research from 2019 to 2023, with a fluctuating yet ascending trajectory of scholarly output. Key contributors include China and India, underscoring their roles in advancing cloud computing research. The analysis of keyword frequency highlights 'cloud', 'data', 'computing', 'security', and 'block chain' as dominant themes, indicating a strong emphasis on data integrity, security, and emerging technologies in cloud computing.Sentiment analysis of research abstracts reveals a predominantly positive yet cautiously optimistic tone, reflecting the field's optimistic outlook towards advancements and potential applications, balanced by an awareness of inherent challenges. The study also delves into the impact of authors and organizations, identifying influential contributors and collaborative networks within the academic community.In conclusion, this paper provide a comprehensive overview of the cloud computing research landscape, offering valuable insights for researchers, policymakers, and industry leaders. It underscores the significance of cloud computing in modern society and highlights the need for continued exploration and innovation in this critical technology domain.
Two Years of PhysChem: Current Status and Future Developments

Two years ago, PhysChem was launched as a new physical chemistry journal publishing original research articles, reviews and perspectives, and communications focusing on physics-based description of chemical phenomena and their applications [...]
The The Relationship between Intensity of Social Media and Learning with Learning Outcomes of Sociology in Ngawi Regency

Teenagers, one of Indonesia's primary consumers, mainly access social media using smartphones. As a student, they use it freely, even when the teachers are in classes. Schools fear unlimited access to social media because the distraction could affect their scores. However, it also could bring positive change. 2019 is the year when the global pandemic crippled the world. Schools are forced to operate online with no direct supervision of the teachers. Social media could be used as an alternative teaching method in such events. This study aimed to determine the relationship between the intensity of social media use and learning related to learning outcomes. The use of social media carried out by students regardless of time affects student learning outcomes, both positively and negatively. Teachers use social media to support the value obtained by students to the maximum through utilization in learning. This study uses correlational quantitative methods with problem characteristics in the form of relationships between 2 or more correlations. The data collection techniques used were questionnaires and interviews. The analysis techniques used are normality test, linearity test, double linear regression test, and Pearson correlation test. The results obtained are: (1) a positive and significant relationship exists between the intensity of social media use and student sociology learning outcomes. (2) there is a positive and significant relationship between learning and student sociology learning outcomes. (3) there is a positive and significant relationship between the intensity of social media and learning with the sociology learning outcomes of grade XI social studies students of SMA N 1 Ngawi. The use of social media can be beneficial for students in learning and learning outcomes of student sociology