Reviewerly/exHarmony huggingface dataset:
Analisis Sistem Informasi Akuntansi Siklus Pendapatan dan Siklus Pengeluaran Pada PT. Cipta Mortar Utama

The purpose of this research is to analyze the accounting informationsSystem income cycle and expenditure cycle at PT. Cipta Mortar Utama. This type of research is descriptive research, namely to determine the variable value of the income cycle and expenditure cycle. Data analysis and analysis techniques used are qualitative analysis methods. Data collection techniques in writing this thesis using the method of observation, interviews, and documentation. The sample used is 4 (four) employees from the related section of the income cycle and expenditure cycle. The results of the analysis show that the income cycle in the billing function has not yet started because the work is still manual and the computer is not provided and access to view bills that have not been paid by customers. Sales returns that are still not running according to procedures so that the returned goods become damaged and cannot be reproduced. Overcoming this problem in the billing function that is already using a computer and viewing an updated database. The sales return function must carry out activities according to procedures so as not to result in transportation costs for the disposal of the returned goods. The expenditure cycle still experiences loss of documents which results in work being hampered and payments that have not been made according to the agreement between the company and the supplier so that the company is blocked and cannot get the goods when needed. Overcoming the issue of the expenditure cycle when the document is lost, the log book must be done manually so that it can be seen where the status of the document is and the payment department must make payments according to the agreed due date. Purchase returns at the company are in accordance with procedures and there is no need for changes.&#x0D; Keywords : Accounting information systems, revenue cycle, expenditure cycle, credit sales, credit purchases
maths e.g. as a learning resource

This update describes the use of the “maths e.g.” question database in enhancing any sort of learning material by easily including specific ‘Try one yourself’ links to any of the over 5000 individual (randomised) questions or allowing student selection from the numerous topics/sub-topics that span the school/university interface and selected service mathematics content. A new question type is also presented to facilitate the (partial) testing of more theoretical material.
Optimization of Decision Tree Evaluation Using SIMD Instructions

Decision forest (decision tree ensemble) is one of the most popular machine learning algorithms. To use large models on big data, like document scoring with learning-to-rank models, we need to evaluate these models efficiently. In this paper, we explore MatrixNet, the ancestor of the popular CatBoost library. Both libraries use the SSE instruction set for scoring on CPU. This paper investigates the opportunities given by the AVX instruction set to evaluate models more efficiently. We achieved 35% speedup on the binarization stage (nodes conditions comparison), and 20% speedup on the trees apply stage on the ranking model.
SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation

We propose the SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation learning framework. Unlike previous works on speech representation learning, which learns multilingual contextual speech embedding at the resolution of an acoustic frame (10-20ms), this work focuses on learning multimodal (speech-text) multilingual speech embedding at the resolution of a sentence (5-10s) such that the embedding vector space is semantically aligned across different languages. We combine state-of-the-art multilingual acoustic frame-level speech representation learning model XLS-R with the Language Agnostic BERT Sentence Embedding (LaBSE) model to create an utterance-level multimodal multilingual speech encoder SAMU-XLSR. Although we train SAMU-XLSR with only multilingual transcribed speech data, cross-lingual speech-text and speech-speech associations emerge in its learned representation space. To substantiate our claims, we use SAMU-XLSR speech encoder in combination with a pre-trained LaBSE text sentence encoder for cross-lingual speech-to-text translation retrieval, and SAMU-XLSR alone for cross-lingual speech-to-speech translation retrieval. We highlight these applications by performing several cross-lingual text and speech translation retrieval tasks across several datasets.
A tale of four parsers: methodological reflections on diagnostic evaluation and in-depth error analysis for meaning representation parsing

Abstract We discuss methodological choices in diagnostic evaluation and error analysis in meaning representation parsing (MRP), i.e. mapping from natural language utterances to graph-based encodings of semantic structure. We expand on a pilot quantitative study in contrastive diagnostic evaluation, inspired by earlier work in syntactic dependency parsing, and propose a novel methodology for qualitative error analysis. This two-pronged study is performed using a selection of submissions, data, and evaluation tools featured in the 2019 shared task on MRP. Our aim is to devise methods for identifying strengths and weaknesses in different broad families of parsing techniques, as well as investigating the relations between specific parsing approaches, different meaning representation frameworks, and individual linguistic phenomena—by identifying and comparing common error patterns. Our preliminary empirical results suggest that the proposed methodologies can be meaningfully applied to parsing into graph-structured target representations, as a side-effect uncovering hitherto unknown properties of the different systems that can inform future development and cross-fertilization across approaches.
Research Trend Analysis in Liberal Arts Education －A Systematic Review of the Literature

This study was conducted with the aim of comprehensively examining the research results of liberal arts education in Korea and deriving research tasks to be implemented for the revitalization of liberal arts education in the future. To this end, a total of 1,491 papers from 2011 to 2021(all of which used“liberal arts”as their theme) were used for analysis and were analyzed by an analysis framework prepared by a researcher, The research results of liberal arts education are as follows: Liberal arts education research has grown significantly over the past 10 years, and the Content Method(63.3%) has been studied the most as a research topic. Among these content methods, 612 Basic Education studies (54.2%) ranked the highest in terms of topics studied, followed by 546 in Communication Education (89.2%) and 37 in Cognitive Education(6.0%). The most common topic of study concerning the subject of literacy education was 149 in Physical Experiences (48.4%), followed by Personality 86 (27.9%) and Career 21 (6.8%). The most frequent research regarding support operation was Quality Control with 358 (67.8%), followed by 143 (27.1%) in Teaching and Learning. In order for liberal arts education research to develop further, continuous joint research activities, with various academic societies and associations in the field of liberal arts education, are required. In addition, follow-up studies involving various topics, such as research on new topics of liberal arts education pertaining to character and career, research on the professors themselves teaching liberal arts education, and performance management research to review the effectiveness of liberal arts education, should be conducted.
Driving pattern recognition using mobile telematics

Mobile Telematics is an application that enables it’s users to detect driving behavior through sensors that already exist on a common smart phone device. Sensors such as Gyroscopes, GPS, Radio-Positioning, Existing Cameras, Location APIs, etc will help detect and identify valuable pieces of information, this can then be used to create unsupervised learning models on the driver’s driving behavior and display feedback for the driver to identify and correct in order to minimize the risk of a potential accident. Current telematics models are not efficient and accurate enough to reduce the chances of road accidents, the sensors required to allow for users to access the needed data may also be expensive. Thus, we have found that our system is not only improved but also relatively inexpensive since we are using devices most of our user base already have -- Smartphones. Our framework consists of three main components -- Self Organizing Maps, Nine-Layers Deep Auto-Encoder, Partitive Clustering Algorithm. The self organizing maps simplifies complex pieces of data into consumable information, the deep auto encoder takes into consideration anomalies of said data and extracts its features, and the partitive clustering algorithms groups the data based on a set of driving behavior classifications.
DATA SECURITY IN CLOUD COMPUTING USING HYBRID ENCRYPTION ALGORITHMS

Cloud computing is an ever growing stream in computer science. The use of cloud systems has increased manifold in the last decade including growing number of opportunities and services like Microsoft Azure, Amazon Web Services, Google Cloud etc. The security of cloud systems is one of the founding pillars of cloud computing as data security issues are rampant on the internet as of late. Ensuring a high level of security is vital for any cloud service provider to make sure the network is protected, identity of user is secured, data privacy is ensured and the confidentiality of data is intact. This research paper focuses on these security measures and how they can be improved upon using encryption algorithms. The algorithm, in a virtual environment, encrypts and decrypts the said file using the AES algorithm. Keywords - Encryption, Data Confidentiality, AES, Security issues, Data Privacy
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation

Stemming from the limited availability of datasets and textual resources for low-resource languages such as isiZulu, there is a significant need to be able to harness knowledge from pre-trained models to improve low resource machine translation. Moreover, a lack of techniques to handle the complexities of morphologically rich languages has compounded the unequal development of translation models, with many widely spoken African languages being left behind. This study explores the potential benefits of transfer learning in an English-isiZulu translation framework. The results indicate the value of transfer learning from closely related languages to enhance the performance of low-resource translation models, thus providing a key strategy for low-resource translation going forward. We gathered results from 8 different language corpora, including one multi-lingual corpus, and saw that isiXhosa-isiZulu outperformed all languages, with a BLEU score of 8.56 on the test set which was better from the multi-lingual corpora pre-trained model by 2.73. We also derived a new coefficient, Nasir's Geographical Distance Coefficient (NGDC) which provides an easy selection of languages for the pre-trained models. NGDC also indicated that isiXhosa should be selected as the language for the pre-trained model.
Secured Ethereum Transactions using Smart Contracts &amp; Solidity

Today cryptocurrency, like Bitcoin, is gaining popularity in the middle people and we cannot deny its role in the global economy. Although cryptocurrency does not instead of fiat money in the near future, it will take a significant part of the market. Despite the fact that it has no special laws in many countries yet, there are already ways people can use cryptocurrency. While the crypto markets have been banned in China, Japan, on the contrary, officially allows people to pay differently services or goods via Bitcoin. Currently, there are a large number of different cryptocurrencies but this thesis describes a service that works with Ether (Eth) in its blockchain - Ethereum. In contrast Bitcoin, the Ethereum blockchain is not just a cryptocurrency support tool for it, it is a a powerful platform dedicated to a country that brings several development opportunities: new markets, loans, registrations and other resources. This makes Ethereum a good replacement by known payment methods. However, the current method of transmitting eth is still difficult for ordinary people and requires a certain technical background. This is one of the current issues for Ethereum platform. The process of creative creation is very complex moment; therefore, most people do not understand or are afraid to use it. That is slow reduces the process of eth integration in society and makes it less enjoyable to compare in standard payment systems. In addition, there is another important factor to consider - risk of loss of property. In order to create a job, the sender must know public address, 42- character long hexadecimal character unit, recipient. If any a error in any characters, money will be sent to the wrong address and can not to be downloaded. As the blockchain network has no centralized management in the system, there are no conditions that can help recover the lost money. The platform on which the service operates is called Ethereum. It can be defined as a a world-class powerful machine that uses different systems - smart contracts - using a custom blockchain. These applications work as planned and cannot checked, closed, rigged or interrupted. Created by Vitalik Buterin in 2015 an open source forum helps to streamline Blockchain (BC) technology process. integration. Therefore, it attracts interests from a variety of sources and major one’s companies such as Microsoft and IBM. The Ethereum platform can be used in a variety of business or financial sectors. It ensures security and prevents any intrusion into the system. Companies and services based on Ethereum they can do business with other companies and services that they do not knowing without the risk of fraud. Ethereum allows you to register any type of trade for any asset without the need to use trial. This makes it easier compared to current methods of trade registration
Faculty Members’ Attitudes towards the Objectives of Cognitive Behaviour

Knowledge management is one of the issues that closely touches the core of organizations, and its application greatly contributes to improving the performance of academic institutions and achieving a competitive advantage. In addition, it is also so important to know the cognitive behavior, as human behavior is affected in general by environmental conditions and variables, and the needs differ according to different age stages. Thus, the behavior knowledge helps to deal accurately with others and choose the appropriate approaches to do so. Added to that, the study of behavior must not be limited to the individual behavior, but it should necessarily include group behavior as well. Accordingly, and standing upon the previous premises, this study seeks to identify the cognitive behavior, as we will define the concept of the cognitive behavior, its most important objectives, its characteristics, the factors that affect it, its forms, in addition to knowing the trends of faculty members in the Department of Information Science at King University Abdul Aziz in Jeddah (Female Section) towards the cognitive behavior goals. The study relied on the descriptive survey method, and to achieve the objectives of the study, a questionnaire was designed that includes two parts where the first part deals with general data (personal), and the second part stresses questions related to the objectives of cognitive behavior. The researcher used the Likert quadruple scale (strongly agree, agree, disagree, strongly disagree). This study reached a set of results, the most prominent of which were that it became clear through the study the extent of the cognitive awareness possessed by the sample members. In fact, most of the results indicated the extent to which the sample members realize the importance of practicing cognitive behavior and achieving its goals. The current study reached a set of recommendations, including the necessity of advance planning and direct use of the latent energies within the faculty members (study community), and encouraging them to manage knowledge and adhere to its behavior through providing the appropriate environment for that.
MEDIA EDUCATION AND MEDIA COMPETENCE

Media education and media competence are concepts that have entered our education and play an important role in it. A huge flow of information requires its critical understanding, and this requires skills that allow you to distinguish fake information from the truth. One of the main tasks of media education is to lay the foundations of the main competencies of a lifetime. The modern model of education presupposes education during the period of readiness for retraining, a culture of knowledge renewal
Klasifikasi Usaha Mikro Kecil Menengah Menggunakan Jaringan Syaraf Tiruan Backpropagation

Nowadays, the development of Usaha Mikro Kecil Menengah (UMKM) is quite rapid. Updating data is very necessary to find out how far the development of UMKM is every year.UMKM are divided into three criteria, namely: micro, small, and medium. The problem is to determine the criteria for an UMKM based on several attributes such as: No, District, Kelurahan, Company Name, Owner's Name, Address, Telephone/HP, Type of Business, Number of Employees, Assets, Turnover, Year of Establishment, and criteria as labels. This takes a long time for the Government to determine the criteria for UMKM. This study uses data from the 2018 UMKM in the city of Bandung. ThisUMKM data will be used for classification so that criteria data can be obtained faster. The classification method used is backpropagation. The data used in this study amounted to 5219 data with 12 attributes and 1 criteria label. The 12 existing attributes are then selected into 4 attributes according to the attribute ranking. Data testing using 3-fold cross validation resulted in an accuracy of 98.4294% with the most optimum network architecture: 30 neurons, using two hidden layers, logsig activation function, trainlm training function, input layer 4 nodes and output layer 2 nodes.
Voxel-wise Adversarial Semi-supervised Learning for Medical Image Segmentation

Semi-supervised learning for medical image segmentation is an important area of research for alleviating the huge cost associated with the construction of reliable large-scale annotations in the medical domain. Recent semi-supervised approaches have demonstrated promising results by employing consistency regularization, pseudo-labeling techniques, and adversarial learning. These methods primarily attempt to learn the distribution of labeled and unlabeled data by enforcing consistency in the predictions or embedding context. However, previous approaches have focused only on local discrepancy minimization or context relations across single classes. In this paper, we introduce a novel adversarial learning-based semi-supervised segmentation method that effectively embeds both local and global features from multiple hidden layers and learns context relations between multiple classes. Our voxel-wise adversarial learning method utilizes a voxel-wise feature discriminator, which considers multilayer voxel-wise features (involving both local and global features) as an input by embedding class-specific voxel-wise feature distribution. Furthermore, we improve our previous representation learning method by overcoming information loss and learning stability problems, which enables rich representations of labeled data. Our method outperforms current best-performing state-of-the-art semi-supervised learning approaches on the image segmentation of the left atrium (single class) and multiorgan datasets (multiclass). Moreover, our visual interpretation of the feature space demonstrates that our proposed method enables a well-distributed and separated feature space from both labeled and unlabeled data, which improves the overall prediction results.
Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift

In living organisms, homeostasis is the natural regulation of internal states aimed at maintaining conditions compatible with life. Typical artificial systems are not equipped with comparable regulatory features. Here, we introduce an artificial neural network that incorporates homeostatic features. Its own computing substrate is placed in a needful and vulnerable relation to the very objects over which it computes. For example, artificial neurons performing classification of MNIST digits or Fashion-MNIST articles of clothing may receive excitatory or inhibitory effects, which alter their own learning rate as a direct result of perceiving and classifying the digits. In this scenario, accurate recognition is desirable to the agent itself because it guides decisions to regulate its vulnerable internal states and functionality. Counterintuitively, the addition of vulnerability to a learner does not necessarily impair its performance. On the contrary, self-regulation in response to vulnerability confers benefits under certain conditions. We show that homeostatic design confers increased adaptability under concept shift, in which the relationships between labels and data change over time, and that the greatest advantages are obtained under the highest rates of shift. This necessitates the rapid un-learning of past associations and the re-learning of new ones. We also demonstrate the superior abilities of homeostatic learners in environments with dynamically changing rates of concept shift. Our homeostatic design exposes the artificial neural network's thinking machinery to the consequences of its own "thoughts", illustrating the advantage of putting one's own "skin in the game" to improve fluid intelligence.
Towards controlling mouse through Hand Gestures: A novel and efficient approach

Abstract In today’s world, we tend to see a lot of development happening in the field of Technology. Today’s technology is combined with the technique known as computer science. This paper is additionally supported little a part of AI. This paper presents a Hand gesture-based virtual mouse on our computer’s window exploitation camera &amp; handling the complete system by simply moving your fingers. Using finger detection ways for fast camera access and easy computer program makes it additional simply accessible. The system is employed to implement a motion tracking mouse. this technique reduces the employment of any physical mouse which saves time and additionally reduces effort.
Missing Data Imputation for Solar Radiatıon by Deep Neural Network

The quality of observations is fundamental issue in natural sciences. Here, the accurate and complete data is required to accomplish satisfactory estimations. There are several factors impairing the quality of measurements, such as a broken or mis-calibrated device and error in reading the measurements. Thus, this study primarily aims the imputation of the missing values in measurement of solar radiation data. Deep Neural Network (DNN) method was used to handle the missing data, and benchmarked with the classical approaches, i.e., Mean Imputation (MI), which one of the most frequently adopted data imputation method in the pertinent literature, the Linear Interpolation (LI) and Spline Interpolation (SI). The overall results highlighted that the DNN method outperformed its counterparts in terms of missing value handling through providing a greater accuracy according to the various performance metrics compared to the classical methods. It is believed that the proposed approach could make valuable contribution to the body of knowledge as well as providing significant overview to the interested researchers by filling the important gap exists in the pertinent literature.