Reviewerly/exHarmony huggingface dataset:
Remote Data Collection During a Pandemic: A New Approach for Assessing and Coding Multisensory Attention Skills in Infants and Young Children

In early 2020, in-person data collection dramatically slowed or was completely halted across the world as many labs were forced to close due to the COVID-19 pandemic. Developmental researchers who assess looking time (especially those who rely heavily on in-lab eye-tracking or live coding techniques) were forced to re-think their methods of data collection. While a variety of remote or online platforms are available for gathering behavioral data outside of the typical lab setting, few are specifically designed for collecting and processing looking time data in infants and young children. To address these challenges, our lab developed several novel approaches for continuing data collection and coding for a remotely administered audiovisual looking time protocol. First, we detail a comprehensive approach for successfully administering the Multisensory Attention Assessment Protocol (MAAP), developed by our lab to assess multisensory attention skills (MASks; duration of looking, speed of shifting/disengaging, accuracy of audiovisual matching). The MAAP is administered from a distance (remotely) by using Zoom, Gorilla Experiment Builder, an internet connection, and a home computer. This new data collection approach has the advantage that participants can be tested in their homes. We discuss challenges and successes in implementing our approach for remote testing and data collection during an ongoing longitudinal project. Second, we detail an approach for estimating gaze direction and duration collected remotely from webcam recordings using a post processing toolkit (OpenFace) and demonstrate its effectiveness and precision. However, because OpenFace derives gaze estimates without translating them to an external frame of reference (i.e., the participant's screen), we developed a machine learning (ML) approach to overcome this limitation. Thus, third, we trained a ML algorithm [(artificial neural network (ANN)] to classify gaze estimates from OpenFace with respect to areas of interest (AOI) on the participant's screen (i.e., left, right, and center). We then demonstrate reliability between this approach and traditional coding approaches (e.g., coding gaze live). The combination of OpenFace and ML will provide a method to automate the coding of looking time for data collected remotely. Finally, we outline a series of best practices for developmental researchers conducting remote data collection for looking time studies.
Optimized Artificial Neural network models to time series

Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and the absolute mean square error were also used to measure the accuracy of the estimation for methods used. The important result obtained in this paper is that the optimal neural network was the Backpropagation (BP) and Recurrent neural networks (RNN) to solve time series, whether linear, semilinear, or non-linear. Besides, the result proved that the inefficiency and inaccuracy (failure) of RBF in solving nonlinear time series. However, RBF shows good efficiency in the case of linear or semi-linear time series only. It overcomes the problem of local minimum. The results showed improvements in the modern methods for time series forecasting.
Quantum-Inspired Interpretable AI-Empowered Decision Support System for Detection of Early-Stage Rheumatoid Arthritis in Primary Care Using Scarce Dataset

Rheumatoid arthritis (RA) is a chronic inflammatory and long-term autoimmune disease that can lead to joint and bone erosion. This can lead to patients’ disability if not treated in a timely manner. Early detection of RA in settings such as primary care (as the first contact with patients) can have an important role on the timely treatment of the disease. We aim to develop a web-based Decision Support System (DSS) to provide a proper assistance for primary care providers in early detection of RA patients. Using Sparse Fuzzy Cognitive Maps, as well as quantum-learning algorithm, we developed an online web-based DSS to assist in early detection of RA patients, and subsequently classify the disease severity into six different levels. The development process was completed in collaborating with two specialists in orthopedic as well as rheumatology orthopedic surgery. We used a sample of anonymous patient data for development of our model which was collected from Shohada University Hospital, Tabriz, Iran. We compared the results of our model with other machine learning methods (e.g., linear discriminant analysis, Support Vector Machines, and K-Nearest Neighbors). In addition to outperforming other methods of machine learning in terms of accuracy when all of the clinical features are used (accuracy of 69.23%), our model identified the relation of the different features with each other and gave higher explainability comparing to the other methods. For future works, we suggest applying the proposed model in different contexts and comparing the results, as well as assessing its usefulness in clinical practice.
Analisis Kemampuan Siswa Kelas VIII dalam Menyelesaikan Soal Literasi Numerasi

In the 21st century, students are required to master the three skills, namely the quality of character, competence, and literacy. In order these skills to be achieved, the ability to think and reason is needed. One of literacy that closely related to the ability to thinks and reason is numeracy literacy. This research was conducted with the title: "Ability of Class VIII Junior High School Students in solving numeracy literacy questions". This research was conducted at St. Josef Freinadementz Junior High School, Kota District, Southwest Sumba Regency. The aim is to describe students' abilities in solving numeracy literacy problems. This research includes descriptive qualitative research, and it was carried out from July 2021 to September 2021 at SMP St. Josef Freinadementz, City District, Southwest Sumba Regency. The research subjects were 15 students of class VIII. Data were collected through numeracy literacy skills tests and interviews. The results showed that 73.3% of students were in the very poor category and 26.7% were in the less category, so it can be concluded that in general the students' ability in solving numeracy literacy questions is still low.
CCGraMi: An Effective Method for Mining Frequent Subgraphs in a Single Large Graph

In modern applications, large graphs are usually applied in the simulation and analysis of large complex systems such as social networks, computer networks, maps, traffic networks. Therefore, graph mining is also an interesting subject attracting many researchers. Among them, frequent subgraph mining in a single large graph is one of the most important branches of graph mining, it is defined as finding all subgraphs whose occurrences in a dataset are greater than or equal to a given frequency threshold. In which, the GraMi algorithm is considered the state of the art approach and many algorithms have been proposed to improve this algorithm. In 2020, the SoGraMi algorithm was proposed to optimize the GraMi algorithm and presented an outstanding performance in terms of runtime and storage space. In this paper, we propose a new algorithm to improve SoGraMi based on connected components, called CCGraMi (Connected Components GraMi). Our experiments on four real datasets (both directed and undirected) show that the proposed algorithm outperforms SoGraMi in terms of running time as well as memory requirements.
Improving Robot Perception Skills Using a Fast Image-Labelling Method with Minimal Human Intervention

Robot perception skills contribute to natural interfaces that enhance human–robot interactions. This can be notably improved by using convolutional neural networks. To train a convolutional neural network, the labelling process is the crucial first stage, in which image objects are marked with rectangles or masks. There are many image-labelling tools, but all require human interaction to achieve good results. Manual image labelling with rectangles or masks is labor-intensive and unappealing work, which can take months to complete, making the labelling task tedious and lengthy. This paper proposes a fast method to create labelled images with minimal human intervention, which is tested with a robot perception task. Images of objects taken with specific backgrounds are quickly and accurately labelled with rectangles or masks. In a second step, detected objects can be synthesized with different backgrounds to improve the training capabilities of the image set. Experimental results show the effectiveness of this method with an example of human–robot interaction using hand fingers. This labelling method generates a database to train convolutional networks to detect hand fingers easily with minimal labelling work. This labelling method can be applied to new image sets or used to add new samples to existing labelled image sets of any application. This proposed method improves the labelling process noticeably and reduces the time required to start the training process of a convolutional neural network model.
Modulating human memory for complex scenes with artificially generated images

Visual memory schemas (VMS) are two-dimensional memorability maps that capture the most memorable regions of a given scene, predicting with a high degree of consistency human observer's memory for the same images. These maps are hypothesized to correlate with a mental framework of knowledge employed by humans to encode visual memories. In this study, we develop a generative model we term 'MEMGAN' constrained by extracted visual memory schemas that generates completely new complex scene images that vary based on their degree of predicted memorability. The generated populations of high and low memorability images are then evaluated for their memorability using a human observer experiment. We gather VMS maps for these generated images from participants in the memory experiment and compare these with the intended target VMS maps. Following the evaluation of observers' memory performance through both VMS-defined memorability and hit rate, we find significantly superior memory performance by human observers for the highly memorable generated images compared to poorly memorable. Implementing and testing a construct from cognitive science allows us to generate images whose memorability we can manipulate at will, as well as providing a tool for further study of mental schemas in humans.
A Survey on Arabic Text Classification Using Deep and Machine Learning Algorithms

Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy they got. Deep Learning (DL) and Machine Learning (ML) models were used to enhance text classification for Arabic language. Remarks for future work were concluded.
Towards a Usable Ontology: The Identification of Quality Characteristics for an Ontology-Driven Decision Support System

Achieving quality in use (i.e., the higher-level quality objectives) is now widely accepted for building a usable system and software product. However, ontology engineering is a discipline for which the quality theories are not yet well developed and adapted and thus, ontology engineering still does not have an agreed methodology and standards for ontology evaluation. As a result, an ontology-driven system may consist of a badly engineered ontology that is not usable. This will in turn cause an adverse effect on the quality in use of the corresponding system. It is necessary to alleviate this problem by formulating an evaluation methodology and standards towards producing a usable ontology particularly targeting an ontology-driven system. It is evident through the literature as well as our practical experience through an agricultural ontology-driven Decision Support System (DSS) that quality in use of the system is tightly coupled with the quality of the ontology. As the first step towards this, we explored the well-established quality theories in system and software engineering to adapt and enhance the quality concepts defined so far in the ontology engineering domain. In the light of this study, we devised an ontology quality approach that guides developers to produce ontologies by avoiding quality issues to make ontology-driven DSSs usable. The proposed approach was exemplified using a use case from the agriculture domain. This research could be a foundation to inspire and assist ontology engineers to rethink about ontology quality from a broader view in developing a usable ontology.
Predicting Fraud in Mobile Money Transactions using Machine Learning: The Effects of Sampling Techniques on the Imbalanced Dataset

Mobile Money Fraud is advancing in developing countries. We propose a solution to this problem based on machine learning. Labeled data from financial transactions which include mobile money transactions are, however, skewed towards the negative class. Machine learning models built with such datasets are unreliable as the prediction algorithms will be biased towards the negative class. We investigate the performance of different sampling and weighting techniques such as Adaptive Synthetic Sampling (ADASYN) and Synthetic Minority Oversampling Technique (SMOTE). We select Logistic Regression for the experiments due to its simplicity and relatively low computational needs. The performance is evaluated with different metrics. Manually tuning the weights of the classes achieved the best results in our experiments.
Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor

Statistical machine learning has developed into integral components of contemporary scientific methodology. This integration provides automated procedures for predicting phenomena, case diagnosis, or object identification based on previous observations, uncovering patterns underlying data, and providing insights into the problem. Identification of corn plant diseases and pests using it has become popular recently. Corn (Zea mays L) is one of the essential carbohydrate-producing foodstuffs besides wheat and rice. Corn plants are sensitive to pests and diseases, resulting in a decrease in the quantity and quality of the production. Eradicate pests and diseases according to their type is a solution to overcome the problem of disease in corn plants. This research aims to identify corn plant diseases and pests based on the digital image using the Multinomial Naïve Bayes and K-Nearest Neighbor methods. The data used consisted of 761 digital images with six classes of corn plants disease and pest. The investigation shows that the K-Nearest Neighbor method has a better predictive performance than the Multinomial Naïve Bayes (MNB) method. The MNB method with two categories has an accuracy level of 92.72%, a precision level of 79.88%, a recall level of 79.24%, F1-score 78.17%, kappa 72.44%, and AUC 71.91%. Simultaneously, the K-Nearest Neighbor approach with k=3 has an accuracy of 99.54 %, a precision of 88.57%, recall 94.38%, F1-score 93.59%, kappa 94.30%, and AUC 95.45%.
DepTSol: An Improved Deep-Learning- and Time-of-Flight-Based Real-Time Social Distance Monitoring Approach under Various Low-Light Conditions

Social distancing is an utmost reliable practice to minimise the spread of coronavirus disease (COVID-19). As the new variant of COVID-19 is emerging, healthcare organisations are concerned with controlling the death and infection rates. Different COVID-19 vaccines have been developed and administered worldwide. However, presently developed vaccine quantity is not sufficient to fulfil the needs of the world’s population. The precautionary measures still rely on personal preventive strategies. The sharp rise in infections has forced governments to reimpose restrictions. Governments are forcing people to maintain at least 6 feet (ft) of safe physical distance to stay safe. With summers, low-light conditions can become challenging. Especially in the cities of underdeveloped countries, where poor ventilated and congested homes cause people to gather in open spaces such as parks, streets, and markets. Besides this, in summer, large friends and family gatherings mostly take place at night. It is necessary to take precautionary measures to avoid more drastic results in such situations. To support the law and order bodies in maintaining social distancing using Social Internet of Things (SIoT), the world is considering automated systems. To address the identification of violations of a social distancing Standard Operating procedure (SOP) in low-light environments via smart, automated cyber-physical solutions, we propose an effective social distance monitoring approach named DepTSol. We propose a low-cost and easy-to-maintain motionless monocular time-of-flight (ToF) camera and deep-learning-based object detection algorithms for real-time social distance monitoring. The proposed approach detects people in low-light environments and calculates their distance in terms of pixels. We convert the predicted pixel distance into real-world units and compare it with the specified safety threshold value. The system highlights people violating the safe distance. The proposed technique is evaluated by COCO evaluation metrics and has achieved a good speed–accuracy trade-off with 51.2 frames per second (fps) and a 99.7% mean average precision (mAP) score. Besides the provision of an effective social distance monitoring approach, we perform a comparative analysis between one-stage object detectors and evaluate their performance in low-light environments. This evaluation will pave the way for researchers to study the field further and will enlighten the efficiency of deep-learning algorithms in timely responsive real-world applications.
Spatio-temporal monitoring of humidity induced 3D displacements and strains in mounted and unmounted parchments

Abstract The high hygroscopicity and heterogeneity of parchment make it particularly sensitive to changes in environmental conditions. The proper housing of a historical parchment, including mounting should mitigate the impact of the changes on the object. However, finding an appropriate mounting method requires detailed knowledge of the mechanical response of a parchment on the combined influence from environmental changes and forces introduced by the mounting. In this paper we present the capabilities of 3D digital image correlation (3D DIC) method implemented for full-field, spatio-temporal monitoring of displacements and strains in parchment exposed to environmental changes. We combine the measurement concept with the particular, critical task of evaluating the effectiveness of stabilising a single sheet of parchment mounted using elastic polyester strips. 3D DIC is implemented to record in non-invasive and non-contact way the full-field response of parchment mock-ups to fluctuations of the relative humidity (RH) without interfering with the climatic chamber processes. We measure and compare the impact of fluctuating RH on unmounted and mounted parchment samples, through the full-field 3D measurements of dimensional changes at selected humidity levels, and through the local and global analysis of the samples’ response (displacement/strains) in time. We had proven in quantitative and detailed way that the applied mount fully fulfils its task, namely reduce significantly the out-of-plane displacements and strains occurring in the parchment during RH changes in the range from 39 to 80%. Our study also demonstrates the possibilities of using 3D DIC for the efficient support of conservation scientists in the development of storage, mounting, and treatment protocols for historical parchment.
Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM)

Technological developments continue to encourage the creation of various innovations in almost all aspects of human life. One of the innovations that is becoming a worldwide phenomenon today is the presence of cryptocurrency as a digital currency that is able to replace the role of conventional currency as a means of payment. Currently, the number of cryptocurrency investors in Indonesia has reached 4.45 million people as of March 2021, an increase of 78% compared to the end of the previous year. Very volatile price movements make cryptocurrency investments considered speculative so the risks faced are also very high. The purpose of this study is to build a predictive model that is able to forecast prices on the cryptocurrency market. The algorithm used to build the prediction model is Long Short Term Memory (LSTM). LSTM is the development of the Recurrent Neural Network (RNN) algorithm to overcome problems in the RNN in managing data for a long period. LSTM is considered superior to other algorithms in managing time series data. The data in this study were taken from the Yahoo Finance website using the Pandas Datareader library through Google Collaboratory. The entire prediction model development process is carried out through Google Collaboratory tools. To improve the accuracy of the model, the Nadam optimization algorithm was used and three testing sessions were carried out with the number of Epochs of 1, 10, and 20 in each session. The final test results show that the best prediction performance occurs when testing the DOGE coin type with the number of Epoch 20 which gets an RMSE value of 0.0630.