Reviewerly/exHarmony huggingface dataset:
Perceptual annotation of local distortions in videos: tools and datasets

To assess the quality of multimedia content, create datasets, and train objective quality metrics, one needs to collect subjective opinions from annotators. Different subjective methodologies exist, from direct rating with single or double stimuli to indirect rating with pairwise comparisons. Triplet and quadruplet-based comparisons are a type of indirect rating. From these comparisons and preferences on stimuli, we can place the assessed stimuli on a perceptual scale (e.g., from low to high quality). Maximum Likelihood Difference Scaling (MLDS) solver is one of these algorithms working with triplets and quadruplets. A participant is asked to compare intervals inside pairs of stimuli: (a,b) and (c,d), where a,b,c,d are stimuli forming a quadruplet. However, one limitation is that the perceptual scales retrieved from stimuli of different contents are usually not comparable. We previously offered a solution to measure the inter-content scale of multiple contents. This paper presents an open-source python implementation of the method and demonstrates its use on three datasets collected in an in-lab environment. We compared the accuracy and effectiveness of the method using pairwise, triplet, and quadruplet for intra-content annotations. The code is available here: https://github.com/andreaspastor/MLDS_inter_content_scaling.
Selection of the MOORA Method in Motorcycle Lending Decisions

Nowadays, many people need vehicles for their daily activities, so motorcycle loans are an alternative. Motorcycle credit is a credit facility that aims to finance the purchase of motorcycles. In granting motorcycle credit, a system is needed that is able to select customers who are entitled to receive their applications according to predetermined criteria. This study uses a decision support system (DSS) as a problem solver in decision making. A decision support system (DSS) is a system that can provide the ability to solve problems in semi-structured and unstructured situations. One of the DSS methods used to solve decision-making problems This decision is the MOORA method. The MOORA method is a method whose calculations are made as simple as possible
Decision Support System for Choosing Online Learning Platforms Using the Complex Proportional Assessment Method

Online learning platforms can be an alternative to learning outside of school that can help improve student academic achievement. To determine the right online learning platform, decision makers must seek information one by one about the services provided by the platform as well as its weaknesses and strengths. This makes it difficult to make a choice and takes a long time. This research aims to build a Decision Support System (DSS) for choosing an online learning platform by applying the Complex Proportional Assessment (COPRAS) approach to get the right decision and not take long. The COPRAS approach can produce optimal alternatives based on alternative analysis using alternative assumptions by offering utility judgments so that the attributes of each alternative are sorted by interval. Based on the results of the case study, Zenius obtained the highest utility score with a score of 100, followed by Ruangguru with a score of 86.19, Pahamify with a score of 84.34, and Quipper with a score of 68.67. The calculation results obtained from a system built by calculating manually displays the same acquisition value, this means that the system has produced an accurate calculation. On the results of usability testing using aspects including understanding, learnability, operability and interesting, the average score is 91% and is in the good category. This demonstrates how user-friendly and implementable the created system is.
EYE-DIRECTION-BASED SAFETY NAVIGATION SYSTEM FOR ELDERLY AND PHYSICALLY CHALLENGED PERSONS

This research paper includes the eye-direction-based safety automated navigation system that was implemented for the elderly and physically challenged people. The purpose of this navigation system is to avoid the assistance required for physically challenged people. This system, which controls the motorised wheelchair navigation, depends on pupil detection. The sequential images were captured via Bluetooth specs glass using an image processing technique. The system navigates in the directions specified by the user, such as "move to the left," "move to the right," "move forward," and "stop." Additionally, a sensor is fixed in front of the wheelchair to detect objects and avoid faulty navigation. A centralised wireless detector device is also available in a wheelchair for emergency purposes. A Raspberry Pi Model B is a high-speed detection kit controlled by the whole system.
Nowcasting the Number of Chinese Students Studying Abroad in Times of COVID-19 Using Baidu Index

Since the beginning of the COVID-19 pandemic, Ministry of Education of the People’s Republic of China has not released the annual number of students studying abroad. This paper first proposes a monthly indicator, network public opinion factors for studying abroad, by the Principal Components Analysis (PCA) method using the daily search data of Baidu Index of study abroad keywords from 2011 to 2022. Then Mixed Data Sampling (MIDAS) regression is implemented for nowcasting the annual growth rate of the number of Chinese students studying abroad from 2020 to 2022. Finally, we evaluated the effects of COVID-19 pandemic on the annual number of students studying abroad by comparing the prediction results of the MIDAS model and the Grey model. According to the results of MIDAS model, the predicted number of Chinese students studying abroad in 2020, 2021 and 2022 are respectively 756600, 737171, 803022, and the annual growth rate are respectively 7.548%, - 2.568%, 8.933%. The sharp decline in 2021 means the COVID-19 pandemic has a serious adverse effect on international student mobility, and the rapid rebound in 2022 shares one sign of a possible rapid growth of the number of Chinese students studying abroad comeback.
TOWARDS SELF-ADAPTIVE LEARNING PLATFORM IN FUTURE CLASSROOM BASED ON EMOTION COMPUTING

The accurate recognition of the emotion of learners is the basis of harmonious emotional interaction in the intelligent learning environment of the future classroom. This project focuses on the lack of research and practical exploration on harmonious emotional interaction in the future classroom, and constructs the database of learning pictures and expression pictures of learners by employing Convolution Neural Network. Endowing the future classroom with a self-adaptive interaction property at the emotional level by emotion computing can promote to learn effectively. The proposed platform can self-adaptively adjust the visual characteristics of learning picture according to the expression of learners, visual emotion preference and learning picture emotion, and then present the adapted learning picture to learners in real time, so as to realize the adaptive interaction at the emotional level in the future classroom.
Cognitive Information Processing Approach To History Learning

The human thought process is a process carried out by an influencing condition. Utilization of capacity in the cognitive domain of humans has basically started to run since humans utilize motor and sensory capacities. In learning history for students using the cognitive domain becomes very difficult, because in this domain there needs to be processing of information that is absorbed in each lesson. Processing can be in the form of various learning models that can stimulate memory to process information. Because basically the view of cognitive information management pays attention to how sensory input is transformed, reduced, described, stored, retrieved, and used. Thus any learning strategy in history learning if using a cognitive information processing approach will very likely be more effective and efficient.
A Study on Differences in Leisure Activities and Stress According to Big5 Personality Types of University Students in a Pandemic Environment

This study focuses on the personal characteristics of university students as predictors of perceived stress and leisure activities during the pandemic. The goal is to provide information on leisure activities and coping with COVID-19 stress based on individual personality types. To achieve this, an online survey was conducted from November 10th to 16th, 2020, targeting university students. A total of 504 participants were analyzed using an MNL model to examine the causal relationships between personality type, demographic characteristics, pandemic stress, and leisure activity type. The results of the cluster analysis showed that personality types could be divided into five groups: agreeableness and creativity, extraversion, conscientiousness, non-specificity, and neuroticism. The study found that individual personality traits take into account differences in pursuing relevant leisure activities and accepting stress due to social constraints. Pandemic stress was found to be more sensitive in extraversion, conscientiousness, and neuroticism groups, while the agreeableness and creativity group was less affected by pandemic stress. In addition, the study identified differences in leisure activities according to individual personality traits, and the level of leisure activities to be pursued varied depending on the individual's personality type. Based on the results of this study, suggestions for coping with pandemic stress and leisure activities according to personality type were proposed.
Analisis Butir Soal Pilihan Ganda Menggunakan Anates pada Penilaian Tengah Semester Kelas VII D SMP Negeri 1 Ngajum Kabupaten Malang

Evaluation or assessment of learning is one part of a series of learning processes. A quality assessment device is able to measure what will be measured. In fact not all teachers do an analytical test first before the manuscript test is used. Analysis of question items is a series of activities carried out in assessing the quality of question items. Validity provides an idea which the assessment tool can measure exactly what is to be measured. The reliability of an assessment tool is to measure the extent to which the measurement results with the assessment tool are stable or consistent. Anates is an application or software developed to help carry out the analysis of question items quickly and practically. A study was carried out with the title "Analysis of Multiple Choice Test Items Using Anates in the Midterm Assessment of Class VII D of SMP Negeri 1 Ngajum, Malang". The use of the Anates application version 4.0.9 has proven effective in making it easier for educators to analyze multiple choice test quickly. Based on the results of the analysis, it was concluded that the correlation of question number 4 compared to the total score is very significant, while the correlation of questions number 6, 7, and 8 is significant. The differentiating power of questions number 1, 4, 6, 7, 8 and 10 is very good, acceptable while question number 9 the differentiating power needs to be improved. Questions number 1, 2, and 5 are very easy, questions number 3 are easy, questions number 7, 8, 10 are medium, questions number 4, 6, and 9 are difficult. Distractors numbered 1B, 1D, 3B, 3D, 4D, 5D, 6D, 7B, 8B should be replaced, while distractors numbers 1C, 6B, 7A, and 7C need to be revised.
WEAPON DETECTION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Limiting gun violence has become a major concern in recent years. Deep Learning models can help reduce gun violence by automatically detecting weapons from security cameras. In this project, we will be implementing the use of Deep learning algorithms, to detect any firearms / weapons to improve response time and reduce potential harm. The proposed system in this project is a weapon detection system based on CNN. The project will involve the use of various resources for implementation, such as: Tensorflow, OpenCV, Python, CNN, Google Colab, Numpy, Pandas
Vid2Act: Activate Offline Videos for Visual RL

Pretraining RL models on offline video datasets is a promising way to improve their training efficiency in online tasks, but challenging due to the inherent mismatch in tasks, dynamics, and behaviors across domains. A recent model, APV, sidesteps the accompanied action records in offline datasets and instead focuses on pretraining a task-irrelevant, action-free world model within the source domains. We present Vid2Act, a model-based RL method that learns to transfer valuable action-conditioned dynamics and potentially useful action demonstrations from offline to online settings. The main idea is to use the world models not only as simulators for behavior learning but also as tools to measure the domain relevance for both dynamics representation transfer and policy transfer. Specifically, we train the world models to generate a set of time-varying task similarities using a domain-selective knowledge distillation loss. These similarities serve two purposes: (i) adaptively transferring the most useful source knowledge to facilitate dynamics learning, and (ii) learning to replay the most relevant source actions to guide the target policy. We demonstrate the advantages of Vid2Act over the action-free visual RL pretraining method in both Meta-World and DeepMind Control Suite.
How the Language Competence of International Students is Culturally Oriented When Learning Indonesian Language

Language learning is often viewed as a task focused on the acquisition of linguistic skills, disregarding the cultural context in which the language is spoken. This approach to language learning often leads to ineffective communication and misunderstandings that arise due to cultural differences. The article discusses the challenges that BIPA students in West Sumatra face when attempting to develop their communicative abilities and cross-cultural understanding. The primary objective of this article is to explore the relationship between language learning and cultural awareness, particularly within the framework of BIPA instruction. The article aims to highlight the importance of integrating cultural knowledge with language learning to develop effective communication skills in a foreign language. The authors also seek to demonstrate how the Minangkabau tradition can be used as a foundation for fostering intercultural understanding and communication among BIPA learners. They conducted research using two instructional and conditional features of BIPA learners' knowledge to explore the impact of learning Indonesian with a focus on culture and the ability to communicate across cultures. They also used a qualitative approach to highlight strategies and methods for fostering intercultural understanding and communication grounded in the Minangkabau tradition. The data for this study were collected by using interviews with BIPA educators and students in West Sumatra. The study demonstrated that integrating cultural knowledge with language learning is essential for the development of effective communication skills in a foreign language. The research shows that BIPA students who are exposed to cultural elements along with language instruction demonstrated a higher level of communicative ability and cross-cultural understanding. It also pictures out various strategies and methods for fostering intercultural understanding and communication grounded in the Minangkabau tradition. However, the authors also note the serious challenges that West Sumatra's BIPA educators and students face in gaining this crucial understanding. In conclusion, the article emphasizes the importance of integrating cultural knowledge with language learning to develop effective communication skills in a foreign language. The research also demonstrates the positive impact of cultural elements in language instruction, particularly within the framework of BIPA instruction. It provides valuable insights for BIPA educators and students who seek to enhance their communicative abilities and cross-cultural understanding.
MATRIX EQUATIONS IN DEEP LEARNING RESOLUTION FOR M DATA HAS N PARAMETERS

This article on the vectorization of learning equations by neural network aims to give the matrix equations on [1-3]: first on the Z [8, 9] model of the perceptron[6] which calculates the inputs X, the Weights W and the bias, second on the quantization function [10] [11], called loss function [6, 7] [8]. and finally thegradient descent algorithm for maximizing likelihood and minimizing Z errors [4, 5].
Perancangan Sistem Informasi Akuntansi Persediaan Pada Cv Bintang Karya Nusantara Menggunakan Microsoft Excel

CV Bintang Karya Nusantara is a service and trading company that provides consumer needs in the field of information technology. Based on the results of interviews, it is known that the incoming and outgoing inventory is not directly inputted and the company uses software application excel only as a recording medium without using several functions (formulas). As a result, information on the amount of inventory cannot be known to be up to date errors input so that there is a difference between the physical amount and the recording. To overcome these problems, researchers designed an inventory accounting information system to produce inventory information more quickly and accurately so that CV Bintang Karya Nusantara could operate optimally. The research method used in this research is system design. The result of this system is an Excel for Accounting software that can be used by companies to collect inventory data, customer data, supplier data, incoming goods, outgoing goods, sales returns, purchase returns and can be used to generate more detailed reports on merchandise inventory efficient, fast and accurate.
HEAVY METAL CONTENTS OF ONDA TANTALITE MINING AREA, NASARAWA STATE, NIGERIA

Heavy metal contents in soil from Onda tantalite mining area were studied. Thirty and ten soil samples were collected from the mining area and control site, respectively, using a hand auger. The soil samples were analysed for heavy metals; Cd, Co, Cr, Cu, Fe, Pb, Mn, Ni and Zn using atomic absorption spectrophotometer, AAS. The results obtained showed that the mean concentrations for the respective metals above were 9.38±2.98, 18.25±5.32, 40.29±8.47, 34.40±8.77, 1590.57±403.46, 53.10±11.65, 188.57±41.13, ND and 620.44±91.47 mg/kg in soils from the mining area and 6.40±1.99, 21.00±3.68, ND, 36.36±5.26, 1195.95±314.52, 31.24±6.93, 143.23±28.26, ND and 564.15±120.82 mg/kg for soils from the control site. The mean metal contents from the mining site were all higher than those from the control site due to mining activities, except for Co and Cu but were all lower compared to the recommended permissible limits by WHO except for Cd and Zn. The EF, CF, PLI, ERI and PERI evaluated for metals from the mining site show no imminent danger. However, there is the need for constant and regular monitoring of the level of heavy metals in soil from the site because of accumulation overtime, and soil remediation should be employed to take care of cadmium and zinc.
Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers

Healthcare organizations rely on patients’ feedback and experiences to evaluate their performance and services, thereby allowing such organizations to improve inadequate services and address any shortcomings. According to the literature, social networks and particularly Twitter are effective platforms for gathering public opinions. Moreover, recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors, including healthcare. The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus. The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to healthcare organizations in Saudi Arabia from 1 January 2008 to 29 January 2022. The tweets were labeled according to sentiment (positive or negative) and sector (public or private), and thereby the Hospital Patient Experiences in Saudi Arabia (HoPE-SA) dataset was produced. A simple statistical analysis was conducted to examine differences in patient views of healthcare sectors. The authors trained five models to distinguish sentiments in tweets automatically with the following schemes: a transformer-based model fine-tuned with deep learning architecture and a transformer-based model fine-tuned with simple architecture, using two different transformer-based embeddings based on Bidirectional Encoder Representations from Transformers (BERT), Multi-dialect Arabic BERT (MARBERT), and multilingual BERT (mBERT), as well as a pre-trained word2vec model with a support vector machine classifier. This is the first study to investigate the use of a bidirectional long short-term memory layer followed by a feedforward neural network for the fine-tuning of MARBERT. The deep-learning fine-tuned MARBERT-based model—the authors’ best-performing model—achieved accuracy, micro-F1, and macro-F1 scores of 98.71%, 98.73%, and 98.63%, respectively.
Analysis of Changes in the Main Content Lines in Construction of an Alternative Course of Mathematics Based on Digital Resources

The article analyzes the changes in the teaching of mathematics at school associated with the development of the digital educational environment. The theoretical analysis is accompanied by a discussion of basic examples of changes in the main content course lines, such as the algebraic line, the function-theoretic line, the line of equations and inequalities, the geometric line, the stochastic line, the line of discrete mathematics and theoretical computer science. In the process of constructive analysis, such types of changes are considered as: a shift in emphasis from operational activities to modeling, which is associated with the development of “computational thinking” (computational thinking); the use of digital representations of mathematical concepts for the formation of mental images of mathematical concepts and increasing attention to the operation of mental images; studying the material at various levels of complexity through the use of computer models; study of algorithms that are used in computer mathematics systems; the development of horizontal connections both in the methodological aspect through the increasing role of integrative plots that combine various sections of mathematics and computer science, and in the aspect of general pedagogy through the construction of common information spaces for the interaction of educational communities and the expansion of their participants.