Reviewerly/exHarmony huggingface dataset:
Role of Artificial Intelligence in Enhancing Efficiency of Accounting Information System and Non-Financial Performance of the Manufacturing Companies

The current study launched from the main objective of examining the impact of artificial intelligence (AI) and its role in supporting and improving the efficiency of AIS on one hand, and non-financial performance standards on the other. In order to achieve this goal and indicate the extent of its conformity with reality; quantitative approach was used and a questionnaire were adopted as a study tool, the questionnaire was distributed electronically to a sample of (409) managers, heads of departments and accountants in industrial establishments operating in Jordan during the fiscal year 2020/2021. By analyzing the primary data based on SPSS, the study came to the conclusion that AI techniques played a significant role in enhancing efficiency of AIS outcomes through focusing on outcomes&amp;#39; understandability, reliability, credibility and comparability, on another level, AI techniques also proved its ability to influence non-financial performance through focusing on feeding organization with the needed information that locates weak points and develop them, and strength points to exploit them. Study recommended the need to link the operations of intelligent systems to the goals of the organization as a whole and ensure the complete interdependence between the AIS systems and the accounting information in the systems.
Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations

Photovoltaic (PV) generation is potentially uncertain. Probabilistic PV generation forecasting methods have been proposed with prediction intervals (PIs) to evaluate the uncertainty quantitively. However, few studies have applied PIs to geographically distributed PVs in a specific area. In this study, a two-step probabilistic forecast scheme is proposed for geographically distributed PV generation forecasting. Each step of the proposed scheme adopts ensemble forecasting based on three different machine-learning methods. When individual PV generation is forecasted, the proposed scheme utilizes surrounding PVs’ past data to train the ensemble forecasting model. In this case study, the proposed scheme was compared with conventional non-multistep forecasting. The proposed scheme improved the reliability of the PIs and deterministic PV forecasting results through 30 days of continuous operation with real data in Japan.
Assessment of English Oral Reading Fluency Rates for Grade seven English Second Language Learners in Tanzania

This study aimed at assessing reading rates for learners in grade seven English second language (ESL) in Morogoro region, Tanzania. A total of 220 participants were randomly selected and assigned two grade appropriate English texts to read so as determine their reading rates. Fluency scale test was used to assess pupils’ reading fluency rates and the data was statistically analysed using SPSS software. It was necessary to asses pupils’ reading rates in the study context because there are no established fluency rate norms set for grade seven pupils in the country. In this case the current study serves as a reference for improvement. Results show that grade seven pupils in Morogoro region had an average reading fluency rate of 101words per minute (WPM) for fiction text and 95WPM for non-fiction text. The difference in the two texts can be due to the length of words and difficulty level in non-fiction text against fiction text. The scored rate is below the adopted benchmarks which implies that these pupils are at risk in reading fluency, consequently, calling for immediate interventions.
Portrait Quality Assessment using Multi-Scale CNN

In this paper, we propose a novel and standardized approach to the problem of camera-quality assessment on portrait scenes.Our goal is to evaluate the capacity of smartphone front cameras to preserve texture details on faces.We introduce a new portrait setup and an automated texture measurement.The setup includes two custom-built lifelike mannequin heads, shot in a controlled lab environment.The automated texture measurement includes a Region-of-interest (ROI) detection and a deep neural network.To this aim, we create a realistic mannequins database, which contains images from different cameras, shot in several lighting conditions.The ground-truth is based on a novel pairwise comparison technology where the scores are generated in terms of Just-Noticeable-differences (JND).In terms of methodology, we propose a Multi-Scale CNN architecture with random crop augmentation, to overcome overfitting and to get a low-level feature extraction.We validate our approach by comparing its performance with several baselines inspired by the Image Quality Assessment (IQA) literature.
Telling stories, performing operations as a design method

Exploration of stories in architectural discourse has yet to generate a structured discussion in the methods of its operations as part of a design endeavour. This issue of ARSNET presents a collection of articles that demonstrates a variety of everyday stories emerging from the experience of space and objects, and outlines the methodologies of their corresponding spatial operations. Through the stories of occupying, manoeuvring, navigating, dispersing, and sensing, this issue highlights various methods of spatial operations. With operations ranging from tracing as a way of revealing space, reduction as manoeuvring strategy, connecting the virtual and the real as a way of finding new meaning and use of space, and creating a spatial atmosphere based on trajectories of senses; the issue expands the discussion of stories-driven architectural design methods.
Modelling of Change Propagation to Support the Project Management of a Large-Scale Engineering System

This research aims to support the engineering change management for a large scale engineering system from the perspective of project management. To achieve the goal, we suggests a method to systemize the tacit process of the skilled managers by using a network-based information model. The model assumed that the direction of the propagation is changeable by engineer’s decision. It enables to explore various propagation paths in accordance with the current project status. The explored paths are evaluated from the project management perspective. Although further study requires, we verified the effectiveness of the proposed method on a case study of an FPSO.
Stages of a Mathematical Model Developing of the Educational Trajectory

The model is based on the use of interactive teaching methods. A characteristic feature of the use of interactive technologies is the organization of training that takes into account the inclusion of all the students of a group without exception in the learning process. Joint activity means that each participant makes his or her own individual contribution, whereby in the course of work there is an exchange of knowledge, ideas, and methods of activity. An environment of educational communication is created that is characterized by openness, interaction of participants, equality of their arguments, accumulation of common knowledge, and the possibility of mutual evaluation and control. The use of neural networks to study and predict educational assets will provide research and development organizations and teams with innovative and effective ways of conducting research in the field of educational theory, modeling of the cognitive processes related to formation of different student competencies, and devising more appropriate methods for estimating student educational outcomes.
PENERAPAN METODE STORYTELLING UNTUK MENINGKATKAN PERCAYA DIRI DAN KEMAMPUAN BERBICARA SISWA (Studi Pada Kelas X Mata Pelajaran Bahasa Inggris di SMA Negeri 1 Pagar Alam)

The aims of this study to: (1) Describe the application of Storytelling method to improve students’ self confidence in SMA Negeri 1 Pagar Alam, (2) Describe the application of Storytelling method can improve students’ speaking skill at SMA Negeri 1 Pagar Alam. The subjects of this study are students of X Social 4 class SMAN 1 Pagaralam. This study was conducted in two cycles by following the procedure of Classroom Action Research, i,e. Planning, Implementing, Observing and Reflecting. Each cycle of the study has two meetings and one test. The data of this study were obtained by using test to measure speaking skill. And Observation sheets to measure students’ self confidence. This research was conducted in class X Social 4 as a PTK class. This study produces conclusions: (1) the application of storytelling method can improve students’ self confidence, (2) the application of storytelling method can improve students’ achievement in speaking, (3) the application of storytelling method effectively increases students’ ability in speaking and their self confidence
Ensuring the QoE-Related Fairness to Reduce the User Abandonment Ratio

Nowadays, it is quite a challenge for app owners to keep users engaged with an app. Currently, the level of user abandonment is one of the key parameters that application owners are interested in. To meet these challenges, we conduct an extended study of a previously proposed solution that significantly reduces the abandonment rate of a given application. The investigated solution is based on the methods of fairness using the QoE and QoS approach. This paper shows that application abandonment ratios can be reduced by using an appropriate approach to fair bandwidth allocation. Adjusting the bandwidth allocation to users, taking into account the quality of the user experience, has a more effective effect on reducing app abandonment ratios than if quality of service is taken into account. This is because the users make the decision to abandon the application based on their feelings rather than technical parameters. In order to effectively reduce application abandonment ratios, a suitable bandwidth allocation algorithm must be used. This paper presents the impact of using different algorithms on the abandonment ratio and compares the popularly used algorithms and the previously proposed bandwidth allocation algorithm.
A Lightweight Hierarchical Model with Frame-Level Joints Adaptive Graph Convolution for Skeleton-Based Action Recognition

In skeleton-based human action recognition methods, human behaviours can be analysed through temporal and spatial changes in the human skeleton. Skeletons are not limited by clothing changes, lighting conditions, or complex backgrounds. This recognition method is robust and has aroused great interest; however, many existing studies used deep-layer networks with large numbers of required parameters to improve the model performance and thus lost the advantage of less computation of skeleton data. It is difficult to deploy previously established models to real-life applications based on low-cost embedded devices. To obtain a model with fewer parameters and a higher accuracy, this study designed a lightweight frame-level joints adaptive graph convolutional network (FLAGCN) model to solve skeleton-based action recognition tasks. Compared with the classical 2s-AGCN model, the new model obtained a higher precision with 1/8 of the parameters and 1/9 of the floating-point operations (FLOPs). Our proposed network characterises three main improvements. First, a previous feature-fusion method replaces the multistream network and reduces the number of required parameters. Second, at the spatial level, two kinds of graph convolution methods capture different aspects of human action information. A frame-level graph convolution constructs a human topological structure for each data frame, whereas an adjacency graph convolution captures the characteristics of the adjacent joints. Third, the model proposed in this study hierarchically extracts different levels of action sequence features, making the model clear and easy to understand; further, it reduces the depth of the model and the number of parameters. A large number of experiments on the NTU RGB + D 60 and 120 data sets show that this method has the advantages of few required parameters, low computational costs, and fast speeds. It also has a simple structure and training process that make it easy to deploy in real-time recognition systems based on low-cost embedded devices.
Determination of Appropriate Thresholding Method in Segmentation Stage in Detecting Breast Cancer Cells

As in all cancer types, the early detection of breast cancer is vital in terms of patients holding on to life. Today, computer-aided image processing systems play an important role in the detection of diseases. Analyzing the images with accurate image processing methods is very important for professionals to interpret the images and to develop the treatment methods for diseases appropriately. The images contain-ing cancer cells (tumoroid) used in this study were obtained from the mini-Opto tomography device that creates 3D images by reconstruction of 2D images taken from different angles. It is an electronic, mechan-ical, and software-based device capable of 3D imaging of tumoroids up to 1 cm in diameter in size. Ob-serving an entire tumor spheroid that has the size of several centimeters in size in a single square image with a microscope is not possible, but with mini-Opto tomography it is possible. In our study, a few layers of 3D images of the tumoroid produced by MCF-7 breast cancer cells obtained on the different days from the mini-Opto device were used. Image thresholding offers many advantages at the segmenta-tion stage in order to distinguish the target objects. In this study, the determination of the most appropriate thresholding method for detecting the main tumor masses in the layered images was investigated. Moreo-ver, the contours of the tumoroid were determined in the original images based on applying the outcomes of thresholding. While various thresholding methods have been applied on diverse images in the literature, we have applied a few thresholding methods to small tumors up to 2 mm in size. As a result of the quali-tative assessment based on the results of the contour drawings on the thresholded images, the global thresholding and adaptive thresholding methods gave the best results.
Makna Di Balik Gerakan Literasi

The purpose of this study is to ascertain the meaning of the literacy movement in Buru district, where the movement's stated objective is to increase human resources. The issue is focused on the implementation process, which has been underway for three years but has yet to demonstrate the program's significance and impact on the community's real conditions. The approach taken in this study is qualitative, and the number of informants interviewed will be 15. These informants will be chosen purposefully, with the understanding that respondents will be considered related parties in order to accomplish the research objectives. The research was conducted in Namlea village, Buru district, with informants that included village officials, BPD members, representatives of community leaders, traditional leaders, and religious leaders, as well as community representatives with competencies relevant to the research objectives. This study collects data about the literacy movement's implementation through observation and in-depth interviews. In this research, I employ qualitative data analysis in accordance with Miles, Huberman, and Spradley's concepts. This study concludes that the program-based literacy movement has not yet fully grasped the meaning; people's awareness of literacy has focused on participation levels but not on activities that promote relaxation, protect the brain, prevent disease, and prolong life.
A method to improve the BB84 protocol

Quantum cryptography, the principles of which are based on classical mechanics laws, solves exceptionally the issue of key distribution in classical cryptography. BB84, the first quantum key distribution created by Charles Bennett and Gilles Brassard in 1984 offers unconditional security and allows the transmission of a key with the length equal to the length of the message. According to Vernom, using the key with the above feature once together with an encryption algorithm leads to the formation of a most secure cryptographic system. The paper presents a method for improving the BB84 quantum protocol, using ten states of polarization, quantum memory and direct communication in both directions. The implementation of both the proposed method and the BB84 protocol was done through a C# application.
Automated Detection of Bilingual Obfuscated Abusive Words on Social Media Forums: A Case of Swahili and English Texts

The usage of social media has exponentially grown in recent years leaving the users with no limitations on misusing the platforms through abusive contents as deemed fit to them. This exacerbates abusive words exposure to innocent users, especially in social media forums, including children. In an attempt to alleviate the problem of abusive words proliferation on social media, researchers have proposed different methods to help deal with variants of the abusive words; however, obfuscated abusive words detection still poses challenges. A method that utilizes a combination of rule based approach and character percentage matching techniques is proposed to improve the detection rate for obfuscated abusive words. The evaluation results achieved F1 score percentage ratio of 0.97 and accuracy percentage ratio of 0.96 which were above the significance ratio of 0.5. Hence, the proposed approach is highly effective for obfuscated abusive words detection and prevention.&#x0D; Keywords: Rule based approach, Character percentage matching techniques, Obfuscated abuse, Abuse detection, Abusive words, Social media
Exploring Sources for Seeking Cultural Heritage Information

The purpose of this study is to identify different sources used for seeking cultural heritage information. The paper aims to study the most vital sources used to seek cultural heritage information and examine the significant difference between sources and demographic variables. The study is conducted among youth of the Tangkhul tribe from Manipur state, India. The study used a structured questionnaire to collect data. Data screening, examining validity and reliability were conducted before analysis. SPSS version 20 was used for data analysis. An independent samples t-test and one-way ANOVA test were carried out to check the significant differences. The study reveals that elders are the most prominent sources used for seeking cultural heritage information. The findings also expose that there is a significant difference between gender and Internet sources (p-value=0.040), similarly between marital status and electronic sources (p-value=0.028), likewise between education qualification and electronic sources (p-value=0.005), also between education qualification and audiovisual source (p-value=0.042) and between the current place of residence and audiovisual sources (p-value=0.049). The findings revealed that youth used different sources for seeking cultural heritage information and the most desired sources are elders (mean score=4.02), followed by the Internet (mean score=3.48) and social media (mean score=3.46). The authors reflect the study’s unique in conducting the research design. It acknowledged the gaps in the literature and the study proposed to fill the existing holes. The study also identified the most sought source used for seeking cultural heritage information, thereby adding research value.
A multi-frame track-before-detect algorithm based on root label clustering for multiple targets

In this paper, a novel multi-frame track-before-detect algorithm is proposed, which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase. A criterion of track extrapolation is used to construct state transition set, root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space, then measurement plots of multi-frame are divided into several clusters, and finally multi-frame track-before-detect algorithm is implemented in each cluster. The computational complexity can be reduced by employing the proposed algorithm. Simulation results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks.
Formation of priorities for the public education management system development

In the context of global challenges, the problems of the education system become much more acute. Access to quality education for a number of categories of the population is limited due to financial, political, religious and social problems. The solutions to these problems lie within the competence of the governing bodies of the education system at the state level. The article identifies the duality of the problem of ensuring the quality of education. The article presents the results of a questionnaire survey to ensure the quality of training in the system of public education management. Accordingly, the respondents defined their position on the following issues: assessment of the current system of training for public administration, determination of basic qualifications and characteristics that should be possessed by specialists in the public administration system specializing in education, identification of priorities and problems in education.
Role of Artificial Intelligence in Social Media and Human Behaviour

The paper contains the simple idea of Artificial Intelligence on social media and human behaviour. The major concern of this paper is to show the changing behaviour of human being and Artificial Intelligence usefulness in human life as well as social media. Social media popularity came into high during the last decade due to smart technologies used in mobile and internet. In last 10 years research on AI shows its impact on human life as well as in media marketing.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI GEOGRAFIS PEMETAAN HAMA TANAMAN PADI

Pringsewu Regency is one of the districts in Lampung Province which is the result of the expansion of Tanggamus Regency. Pringsewu Regency consists of 8 (eight) subdistrict areas including Pardasuka Subdistrict, Ambarawa Subdistrict, Pagelaran Subdistrict, Pringsewu Subdistrict, Gading Rejo Subdistrict, Sukoharjo Subdistrict, Banyumas Subdistrict, and Adiluwih Subdistrict. Most of pringsewu regency is also used as rice fields, but the area of land used as rice fields is still under moorland. The area of land used for rice fields is 12,197 ha or 19.51% but the pringsewu agricultural service often receives reports of pest outbreaks, but pest monitoring is not controlled. These pests cause many farmers to lose money / fail to harvest. To overcome the above problems, an information system is needed that can map pest-prone areas based on the subdistrict area in Pringsewu Regency. This geographic information system is designed and created using PHP and MySQL programming languages and with google map plugins intended to the pringsewu agricultural service to help monitor cases of rice crop pests in pringsewu. The geographical information system of pest mapping in rice plants in pringsewu, it can be concluded that pest data information on rice plants in pringsewu can be seen in the form of geographical information systems by searching for pest data on google maps of fire available on the geographical information system of pest mapping on rice plants in pringsewu