Reviewerly/exHarmony huggingface dataset:
The Implementation of School Financial Management System in Managing the BOS Fund

School financial management should be managed properly. With clear and transparent financial management, it will affect operational systems and mechanisms to meet the various elementary needs of the school. Financial management also plays a determinant role for the success of education. Thus, financial management needs to be implemented by educational institutions, so that the use of school funds can be more effective and efficient and the budget misuse is able to be minimized appropriately. This study aims to find out how the education financial management system is implemented in managing BOS funds. In this study, the authors used a qualitative approach with the type of field research conducted at SMAN 17 Bombana, Southeast Sulawesi Province. The data from this study were obtained from observations, interviews and documentations. As reseach participants, this study involved the head of school, treasurer of school, committee chairs, student guardians, educators and education staffs. Then, data were analyzed and conclusions were drawn. Therefore, this study shows that the implementation of the education financial management system in managing BOS funds at SMAN 17 Bombana, Southeast Sulawesi Province has been implemented very well. This is evidenced by the steps carried out, including financial planning by holding meetings first, then financial implementation which is divided into three parts, namely acceptance, distribution and management. Another indicator is the evaluation system which is divided into two parts, consisting of the school's internal side and the education office's external side.Keywords: implementation, Financial Management, BOS Management Fund
DZ-OPINION: Algerian Dialect Opinion Analysis Model with Deep Learning Techniques

Today, many sources of unstructured information such as social networks and blogs are more or less freely available on the web, and their volume is constantly growing, which constitutes a free gold mine for collecting public opinion. Opinion plays a crucial role because it can influence the decision-making process. Sentiment or opinion analysis is a discipline that can be used to meet decision-making needs, provide feedback to new product launches and marketing campaigns, and protect a company’s reputation, especially in social networking environments with massive data by exploiting textual data generated by users. In contrast to the techniques used, we have used and adapted in this study deep learning techniques: CNN and LSTM to identify their potential in this area and apply it to a corpus of Arabic data and in particular in Algerian dialect collected from social networks (50572 Facebook comments). We obtained promising results with an 85% f-measure. This represents a good start for an opinion analysis on the Algerian Dialect.
Energy-time entanglement from a resonantly driven quantum dot three-level system

Entanglement is a major resource in advanced quantum technology, where it can enable secure exchange of information over large distances. Energy-time entanglement is particularly attractive for its beneficial robustness in fiber-based quantum communication and can be demonstrated in the Franson interferometer. We report on Franson-type interference from a resonantly driven biexciton cascade under continuous wave excitation. Our measurements yield a maximum visibility of (73 $\pm$ 2)% surpassing the limit of violation of Bell's inequality (70.7%) by more than one standard deviation. Despite being unable to satisfy a loophole free violation, our work demonstrates promising results concerning future works on such a system. Furthermore, our systematical studies on the impact of driving strength indicate that dephasing mechanisms and deviations from the cascaded emission have major impact on the degree of the measured energy-time entanglement.
Design and construction of the Tracking Written Learner Language (TRAWL) Corpus: A longitudinal and multilingual young learner corpus

This article describes the design and construction of the Tracking Written Learner Language (TRAWL) Corpus. The corpus combines several features that are all rare for learner corpora: it is longitudinal, following individual pupils over several years; it has data from young learners from school years 5 to 13 (ages 10–18); it is multilingual, containing learners’ texts in several L3s (French, German and Spanish), L2 English and L1 Norwegian; and it includes teacher comments on a number of the texts. In addition, some of the texts exist in both a first and a second revised version, all tied to a rich set of meta-data. Not only does such a corpus offer new possibilities for research on language acquisition in general; it can also be used to provide valuable insights for teachers, teacher training and policymaking within the national context of Norway. In this article, we describe the design of the TRAWL Corpus and outline its uses and benefits for the research community. We also describe the compilation process in the hope that it may inspire and enable others to build similar corpora for their own national contexts.
Data Mining Sales of Skin Care Products Using the K-Means Method

Data mining is a form of method advancement in computerization that can dig past data into very valuable information. The problem in this study is that the sale of beauty and skincare carried out by Toko Hayati Store is still done manually so that it can cause it to not match the stock in the storage warehouse with changing market demand. Data mining with the K-Means method is one solution to this problem by grouping similar data, in this study grouping into two, namely best-selling and unsold products. The purpose of this study is that the store can provide stock of products in the warehouse according to market demand. Using a sample of 30 data resulted in 18 data as skin care products were not selling well and 12 data as skin care products were not selling well. With the results of a 100% similarity between manual calculations and using the rapid miner application, it can be concluded that the K-Means algorithm can be used as a solution to the problems that exist in the Toko Hayati Store.
Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines

Floods can cause negative impacts in various aspects, starting from economic, social, to health aspects. Even quoted from the site gis.bnpb.go.id, during 2022, there have been 1031 cases of flood disasters in Indonesia. Meanwhile in Jakarta, in 2022 there have been 14 cases of floods that caused hundreds of people to lose their homes. Several approaches can be taken to determine public opinion about flooding, one of which is text mining with an analysis of community sentiment. Sentiment analysis aims to determine public opinion regarding flood management in the capital city of Jakarta based on positive, neutral, and negative categories. To get the public sentiment, researchers carried out several stages, including the preprocessing stage. After obtaining public sentiment regarding flood management through the preprocessing stage, then classification is carried out based on public opinion so that it can be used by related parties for evaluation material in flood handling. In this study, the classification method used is the SVM method which is one of the supervised learning methods in machine learning. After classification, the next stage is the testing process using the K-Fold Cross Validation method. From the various sentiments obtained from Twitter data, it can be concluded that there are around 414 positive sentiments and 2464 negative sentiments related to flood handling in DKI Jakarta, while the results obtained from the test results show that the accuracy reaches 88.6%, the precision reaches 88.6% and recall reached 89.4%.
An innovative method for enhancing advanced encryption standard algorithm based on magic square of order 6

This paper introduces an improved advanced encryption standard (AES) cipher algorithm by proposing a new algorithm based on magic square to decrease the AES execution time. This is done by replacing Mixcolumn function with magic square of order 6. This paper raises the security level of AES cryptosystem by using another key, which is generated using magic square while decreasing the execution time. For application of encrypting a colouring image, visual studio and MATLAB programs have been used as means for computing results. The results of complexity, time execution, National Institute of Standards and Technology (NIST) tests, histogram, differential attacks and peak signal to noise ratio (PSNR) are computed and compared with the original AES cryptosystem, the original and the proposed algorithms. The proposed algorithm results in reasonable findings under several evaluation metrics. For instance, the complexity of our proposed algorithm is higher than the basic AES while decreases the time execution. The experimental results show that the suggested algorithm provides an efficient and secure way for image encryption. The suggested algorithm leads to leverage the complexity of cipher process as well as to make the linear and differential cryptanalysis harder by pre-process the input image initial step of the proposed AES.
Explainable Machine Learning

Machine learning methods are widely used in commercial applications and in many scientific areas [...]
Developing the Computational Building Blocks for General Intelligent in SOAR

Cognitive architecture's purpose is to generate artificial agents with capacities similar to the human mind. Soar Cognitive Architecture is to produce the fixed computational building blocks needed for generally intelligent agents— agents that can outright a variety of tasks and encode, use, and learn all types of knowledge to realize the broad cognitive abilities present in humans. This paper introduced an arithmetic agent that does multicolumn, two-digit addition in SOAR. Here, we show the entire calculating procedure, including all of its operators. We are using episodic memory assistance to enhance the set of cognitive abilities that let the agent learn and reason.
Digital Mangatoon to Born Out New Peace in English Classroom

Digital comics are proven to be an effective learning tool for both teachers and students. At the university level, this study was set out to find the views of second-semester students on the use of Mangatoon digital comics in reading instruction. A quantitative study was performed, involving second-semester students at the Universitas of Al Asyariah Mandar (n=30). This study found, that on average, students find Mangatoon helpful in assisting them in reading since it has practical features preserving their motivation. Based on the student’s perception, the reading material presented in Mangatoon is easily understood, hence improvement in their reading skills can be maximised. &#x0D; &#x0D; Keywords: Digital Comic; Reading; ELT; and Pedagogical Innovation.
Content-Based Image Retrieval System using Color Moment and Bag of Visual Words with Local Binary Pattern

Researchers have recently focused their attention on Content-Based Image Retrieval (CBIR).It has emerged as one of the most fascinating areas in image processing and computer vision.With CBIR, the most comparable pictures that match the query image are pulled from an image database.As a result, it necessitates feature extraction (Local / Global) and similarity calculation.This paper uses a CBIR technique to determine the images that best match the image query by utilizing both global and local image features.A color moment is used for global features to describe the complete image.Local Binary Pattern (LBP) as a local feature, on the other hand, extracts interest points by building a Bag of Visual Words (BoVW).The distance between the query and database image features is computed using the Euclidean distance.Precision and recall are computed on the Corel-1K dataset to assess the retrieval performance.
The Use of Artificial Neural Networks in Agricultural Plants

Artificial Neural Networks use high-performance computing and big data technology, opportunities for science to create new opportunities in agriculture. The purpose of writing this article is to analyze the use of artificial neural networks on (a) plant diseases based on plant leaf diseases, (b) plant pests, (c) growth or quality, and (d) agricultural products. The writing method used is a literature study of the research that has been done. The keywords used in the search for references include ANN, plant, diseases, pests, growth or quality, and agricultural products. Publishers for the reference in this article are ScienceDirect and IEEE. The years of publication of the references are restricted from 2015 to 2022. Based on the literature study results, it was concluded that Artificial Neural Networks' deep learning models are accurate for detecting and classifying leaf diseases and pests, detecting growth, and application to agricultural plant products.
Modern university education and a new perception of the world

Today, education is experiencing radical changes, and its mission is changing dramatically too. The pandemic has significantly spurred this process, forced to take distance education for granted and transfer many individuals to the virtual space. For a very long time the institute of education has acted as the most conservative field of human activity in the modern society. But the current situation is changing rapidly. It is obvious that the next twenty years will be the era of the most radical changes. And the main source of these changes are the scientific, technological, information and communication revolutions, the epidemiological situation, which, like a tsunami, generate changes and determine the nature and content of a new civilization. In this situation, education acts as a center that fills the ongoing changes with content, becomes a value matrix of a different reality. The issues of stability and development of civilization directly depend on education, and education becomes the foundation of a new civilization. There is an understanding in the world that the old cannot be returned, everything will be different. Modern university education for a networked, post-information society can be created only on the basis of the development of new basic values that form a new perception of the world. However, there is a danger that modern education, adopting new values, will turn into a sphere that only imitates its high quality, prestige and success.
Data Augmentation for RFID-based 3D Human Pose Tracking

Interest in Radio Frequency (RF) based 3D human pose tracking has skyrocketed in the age of Artificial Intelligence of Things (AIoT). Compared to Computer Vision (CV) based methods, RF-based approaches are more resilient to lighting and non-line-of-sight conditions, and can better preserve user privacy. However, the majority of the current RF-based methods rely on a vision-aided multi-modal learning approach. An extensive amount of paired training data, i.e., Radio-Frequency Identification (RFID) data and vision data, must be collected, to achieve an adequate performance with the supervised-learning network. In order to mitigate such time-consuming and costly tasks, we propose a data augmentation method based on Generative Adversarial Network (GAN), named RFPose-GAN, to generate synthesized RFID data to alleviate the complications of using commodity RFID tags and receivers. In this paper, a forward kinematic layer is incorporated to generate simulated vision pose data, thus eliminating the need of using a Kinect 2.0 device in RFPose-GAN. Experiments conducted demonstrate that the synthesized data achieves accurate pose estimation performance.
DDPLA: A Dynamic Differential Privacy Algorithm for Social Network Based on Local Community

&lt;p&gt;Social networks contain a large number of privacy information. Personal privacy will be jeopardized if network data without privacy protection is released directly. In view of the current privacy protection technology to protect the social network, there are some problems such as low maintenance of network structure or low accuracy of network data. In order to solve these problems, this paper proposes a dynamic differential privacy algorithm for social network based on local community (DDPLA). The algorithm can divide the social network into different communities, dynamically generate privacy budgets for different communities, and then generate uncertainty graphs. Experiments show that compared with other algorithms, the social network processed by DDPLA algorithm can better balance data utility and privacy protection. Furthermore, the algorithm can better protect important nodes.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Indoor Localization Method of Mobile Educational Robot Based on Visual Sensor

&lt;p&gt;This article aims to study the mobile positioning method of mobile educational robots indoors. In order for robots to be able to unblocked indoors, they can avoid obstacles well. Vision sensors are the direct source of information for the entire machine vision system, and are mainly composed of one or two graphics sensors, sometimes accompanied by light projectors and other auxiliary equipment. This paper presents an indoor positioning method for mobile educational robots based on visual sensors. Build some models to compare which algorithm is more in line with the positioning of indoor mobile educational robots. The experimental results in this paper show that the positioning accuracy of the optical flow meter and the odometer on the short-haired carpet is equivalent (both are less than the index 4.52%); the positioning error of the optical flowmeter on the long-haired carpet is the largest 7%, and the positioning error of the odometer is the largest it reached 83%. The error of the algorithm positioning method after the visual odometer fusion is obviously smaller than that of the optical flow method. This shows that the algorithm after visual process fusion is more suitable for indoor mobile educational robot positioning than this optical flow method.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Empirical Study of the Performance of Object Detection Methods on Road Marking Dataset

Due to the rising need for research into vehicle automation, many traffic-related datasets for the problem of object detection in traffic images were introduced. Specifically, road marking detection is one of the most essential tasks involved in vehicle automation. In this research, we looked into the problem of road markings detection and conducted experiments, evaluation and comparisons of the performance of different state-of-the-art object detection methods on the CeyMo Road Marking dataset, a recently published benchmark that poses many challenges. Experiment results show that recent state-of-the-art object detectors obtained very competitive performance on the benchmark. Particularly, YOLOv7 performed remarkably better compared to other methods, with AP@.5, AP@.75 and mAP scores at 90.7%, 76.2% and 69.5%, respectively, making it the current best method on the dataset. The substantial accuracy and time advantage of YOLOv7 also demonstrate the rapid improvements of recent research into object detection.
Thermal Environment Effect of Landscape Ecological Pattern and Dynamic Analysis of Pattern Evolution

The obvious characteristics of urban thermal environment and heat island effect have brought a series of negative effects on urban ecosystem structure and residents' health. Scholars at home and abroad have conducted basic researches on urban thermal environment, but there is a lack of researches on areas with rapid urbanization and many interference factors. There are few researches on measures or suggestions to improve urban thermal environment and heat island effect from the perspective of ecological sustainable development. To this end, this paper conducts a study on thermal environment effect of landscape ecological pattern and dynamic analysis of pattern evolution. This paper, based on the radiative transfer equation method, carries out back calculation and back analysis on the parameters of urban surface temperature to obtain the dynamic characteristics of urban thermal ecological landscape pattern. By combining the obtained urban surface temperature with the landscape index, this paper analyzes the internal change of urban surface thermal ecological landscape pattern. Through the spatial characteristics of the barycenter of urban thermal environment effect, the expansion and development trend of urban thermal environment effect is revealed. Then based on Moran'sI coefficient, the whole spatial correlation of urban thermal environment effect and the degree of spatial aggregation in local areas are analyzed. Finally, the corresponding experimental results and analytical results are given in combination with the experiments.
Swabbing Dogs and Chauffeuring Pizza Boxes

This paper discusses the alignment work that Swedish crime scene technicians perform at the crime scene. It takes as its point of departure the understanding that the criminal justice system is a collaboration of very different epistemic cultures with at times different understandings of “the same” forensic evidence and its production. Nonetheless, the collaboration and the legal security of forensic evidence depends on knowledge in the form of forensic evidence(-to-be) moving easily and stably through it, despite epistemic differences. One way of attaining such stable movement, the article argues, is the crime scene technicians’ alignment work when they recover and package traces from the crime scene – for example body fluids, fingerprints, and fibers – for transport to the forensic science laboratory. Their crime scene alignment work, it shows, is not only a core part of the crime scene technicians’ contribution to the collaborative production of forensic evidence, it is also a source of professional pride, identity, and community for them. Thus, the crime scene technicians’ alignment work is not only important for the movement of knowledge through the Swedish criminal justice system, but is also an integral part of their professional self-understanding.