Reviewerly/exHarmony huggingface dataset:
The Contribution of Learning Motivation and Parents’ Socio-Economy towards Students’ Learning Outcomes through Learning Independence

The issue about online learning above cause a serious problem in the learning process conducted in Indonesia during covid-19 pandemic. It is stated that there is a decrease towards students’ learning outcomes during online learning. This study aims to analyze the contribution of students’ learning motivation and parents’ socio-economy towards students’ learning outcomes through learning independence. The study population was 2716 students. The research sample was 350 students. Research using path analysis techniques. The results of the study are: there is a positive contribution between learning motivation to economic learning outcomes with a contribution of 13% to economic learning outcomes. There is a direct influence between socioeconomic conditions on economic learning outcomes with a contribution of 30% to economic learning outcomes. There is an influence between learning independence on economic learning outcomes with a contribution of 22% to economic learning outcomes. There is an influence between learning motivation on learning independence with a contribution of 22% to learning independence. There is an influence between parents' socioeconomic conditions on learning independence with a contribution of 13% to learning independence. There is an indirect influence between learning motivation on economic learning outcomes through learning independence by 5%. There is an indirect influence between learning motivation on economic learning outcomes through learning independence by 3%. Can be concluded that students’ learning motivation and parents’ socio-economy during online learning plays an important role in influencing students’ learning outcomes.
Hate Speech Detection for Banjarese Languages on Instagram Using Machine Learning Methods

Hate speech refers to verbal expression or communication that aims to provoke or discriminate against individuals. The Ministry of Communication and Information of Indonesia has encountered and dealt with 3,640 cases of hate speech transmitted through digital channels between 2018 and 2021. Particularly in South Kalimantan, hate speech in the local language, Banjarese has become increasingly prevalent in recent years. Surprisingly, there is a lack of research on using machine learning to detect hate speech in the Banjarese language, specifically on Instagram. Therefore, this study aimed to address this gap by constructing a dataset of Banjarese language hate speech and comparing various feature extraction and machine learning models to detect Banjarese language hate speech effectively. Thisresearch used several feature extraction techniques and machine learning methods to detect Banjareselanguage hate speech. The feature extraction methods used were Word N-Gram, Term Frequency- Inverse Document Frequency (TF-IDF), a combination of Word N-Gram and TF-IDF, Word2Vec, and Glove, while the machine learning methods used were Support Vector Machine (SVM), Na¨ıve Bayes, and Decision Tree. The results of this study revealed that the combination of TF-IDF for feature extraction and SVM as the model achieves exceptional performance. The average Recall, Precision, Accuracy, and F1-Score score exceeded 90%, demonstrating the model’s ability to identify Banjarese hate speech accurately.
Designing Immersive Labs and Programs for Higher Education

Immersive learning tools and technologies such as augmented and virtual reality are set to disrupt learning technology support and learning lab design for educational institutions and campuses. Campus facilities such as computer labs may not be properly equipped to utilize these new technologies to support emerging technologies as the design of these spaces, the implementation strategies, and technology requirements can be very different from more traditional computer-aided learning. This paper outlines a strategic approach to design and implement immersive learning labs in higher education settings, with a focus on virtual and augmented reality-enhanced content. It begins with a clarification of design concepts and initial program suggestions and ends with a range of setup and management tools for these labs.&#x0D; Keywords: Immersive Learning, Learning Laboratories, Collaborative Online International Learning, Virtual Reality, Augmented Reality
The Virtual Museum Rubric: A Preliminary Report

Recent developments in virtual reality (VR) have opened the doors to many unique opportunities for individuals to create virtual exhibitions and museums. However, this does not guarantee the creation of successful museums, as many factors need to be considered to provide an enjoyable visitor experience. To this end, this paper describes our preliminary attempt to construct a rubric for assessing the quality of virtual reality museums. The development stage was divided into four main areas. First, a research team was asked to reflect on their desired expectations of the exhibitions, the goals for creating the museums, and their expectations. Second, the specifics of the evaluation of the design and structure were determined with reference to the museological literature. Thirdly, the related objectives were identified and grouped, leading to the formation of the core dimensions for the rubric. Finally, the core dimensions were used to create an evaluation rubric. The rubric was divided into four categories: Exhibition Planning, Spatial Design, Display, and Exhibition Graphics. For each category, the specific dimensions were selected, and the basic concepts were discussed. Furthermore, the evaluation criteria for successful exhibitions and experiences were presented, as well as how each dimension helps in the evaluation of virtual reality museums. The rubric we presented is expected to improve the overall quality of exhibitions and experiences of virtual reality museums.&#x0D; Keywords: Museology, Virtual Reality, Metaverse, Evaluation, Gallery
Predicting Progression from Mild Cognitive Impairment to Alzheimer’s Dementia with Adversarial Attacks

&lt;p&gt;Early diagnosis of Alzheimer's disease plays a crucial role in treatment planning that might slow down the disease's progression. This problem is commonly posed as a classification task performed by machine learning and deep learning techniques. Although data-driven techniques set the state-of-the-art in many domains, the scale of the available datasets in Alzheimer's research is not sufficient to learn complex models from patient data. This study proposes a simple yet promising framework to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). The proposed framework comprises a shallow neural network for binary classification and a single-step gradient-based adversarial attack to find an adversarial progression direction in the input space. The step size required for the adversarial attack to change a patient's diagnosis from MCI to AD indicates the distance to the decision boundary. The patient's diagnosis at the next visit is predicted by employing this notion of distance to the decision boundary. We also present a potential application of the proposed framework to patient subtyping. Experiments with two publicly available datasets for Alzheimer's disease research provide evidence that the proposed framework can address predicting MCI-to-AD conversion and subtyping by only training a shallow neural network.&lt;/p&gt;
Range Space and Similarity Transformation using Symbolic Mathematics in Python

&lt;p&gt; Linear systems involved in engineering and scientific calculations can be analyzed more easily using the mathematical tool of similarity transformation. However, understanding the numerous abstract linear algebra theorems associated with this transformation can be quite challenging. In this paper, we present computational methods for systematizing these theorems using SymPy (a symbolic mathematics library) in the Python programming language. The paper covers numerous theorems, including the basis of vector space, range space, null space, rank and nullity, eigenvalues, generalized eigenvectors, eigenspace, and diagonal-Jordan canonical form transformations. These theorems are transformed into abstract data models, which are then represented as programmatic objects using Object-Oriented Programming (OOP) for processing input and output data. The aim of this paper is to provide a more efficient and effective means of displaying and applying these theorems through the use of computers. A Python programming language module was eventually created from the developed programmatic objects. It can be used for programming both offline on PC and online through the web application "SymPy Live," producing satisfactory computational results compared to MATLAB, even when used on mobile devices. &lt;/p&gt;
Implementation of K-Means and SAW Methods in Determining Non-Cash Food Aid Recipients

Determination of prospective non cash food assistance recipients, especially in Air Talas village, still uses a manual system so that in the process of determining the recipient there is a risk that the recipient will be inaccurate, so that the village government needs a system that can assist the process of determining prospective non cash food assistance recipients. This study aims to implement the K-Means and SAW methods in determining recipients of non cash food assistance in Air Talas village. The benefits of this research can help the Air Talas village government in determining and recommending prospective non cash food assistance recipients in accordance with established criteria, making it easier to filter, group, and rank appropriate population data according to criteria. In addition, this research is also useful for providing convenience to the community through data collection, clustering, and ranking in a transparent, real, and fast and accurate manner using decision support system software. The K-Means clustering method and the Simple Additive Weighting Ranking method were used in this study with data collection techniques through interviewing sources, in this case the village government, the social section of the community, and through collecting village archive data and relevant journals. The research location is Air Talas village with 316 data used. The results of the study are clustering data as much as 77 data obtained from feasible clusters. The cluster data was then tested using the accuracy value and obtained a value of 80%. Then the research is also in the form of ranking data using clustered data which obtains an accuracy value of 64%.
A Study on the Effect of Incentives on Employees Performance

The aim of this study is to research how different incentives affect the employees performance and job satisfaction. In the study , the effects of recognition, participation, feedback, monetary incentives, non-monetary tangible incentives and benefits on performance and job satisfaction are researched. It is posited that different incentives have different effects on performance and job satisfaction. Effects of incentives on performance and job satisfaction are researched through critical literature review and Questionnaire.
Identification and Classification for Diagnosis of Malaria Disease using Blood Cell Images

Machine Learning is a subfield of artificial intelligence that focuses on developing intelligent algorithms capable of learning from available data without requiring constant programming, enabling them to adapt to different environments based on current scenarios. These algorithms are crucial in making intelligent decisions and conducting thorough analyses to uncover intricate patterns concealed within the data. This study used multiple machine-learning classification algorithms to analyze patients' data based explicitly on input images containing parasite-infected and uninfected Malaria samples. AI techniques were utilised to measure the presence of parasites in the images. The image classification system was designed to accurately identify malaria parasites in blood images by generating image features related to color, texture, and cell and parasite geometry. A classifier based on SVM (Support Vector Machine) provided by Weka was employed to differentiate between parasite-infected and non-infected blood images. Through extensive experimentation, it was determined that SVM strategies exhibited significant relevance, achieving a cross-validation accuracy of 99.4% in the basic diagnosis of malaria fever. This finding holds great potential in assisting clinicians with accurate infection diagnoses.
Emotion identification in human faces through ensemble of deep learning models

Investigation of human face images forms an important facet in affective analysis. The work, a DL-based ensemble is proposed for this purpose. Seven pre-trained models namely Facenet, Facenet2018, VGG16, Resnet-50, Senet-50, Arcface and Openface that have been developed for face verification have been exploited and customized for emotion identification. To each of these models, each all over interaction with softmax method to classification groups are augmented and entire network is then trained completely for emotion recognition. After training all the models individually, the probabilities for each of the class by each of the model are summed to derive at the final value. The class that holds the highest of this value is finalized as the predicted emotion. Thus, the proposed methodology involves image collection, image pre-processing comprising of contrast enhancement, face detection and extraction, face alignment, image augmentation facilitating rotation, shifting, flipping and zooming transformations and appropriate resizing and rescaling, feature extraction and classification through ensemble of customized afore-mentioned pre-trained convolutional neural networks, evaluation and evolving of best weights for emotion recognition from face images with enhanced accuracy. The proposed methodology is evaluated on the well-established FER-2013 dataset. The methodology achieves a validation accuracy of 74.67% and test accuracy of 76.23%. Further, similar images of another dataset (Face Expression Recogniton dataset) are included for training the models and the impact of extra training is assessed to see if there is improvement in performance. The experiments reveal marked improvement in face emotion identification performance reaching values of 94.98% for both validation and test set of FER-2013 dataset and 94.99% on validation set of Face Expression Recognition dataset.
Correction: Ion and water adsorption to graphene and graphene oxide surfaces

Correction for 'Ion and water adsorption to graphene and graphene oxide surfaces' by Amanda J. Carr, et al., Nanoscale, 2023, https://doi.org/10.1039/d3nr02452k.
Node Importance Estimation with Multiview Contrastive Representation Learning

Node importance estimation is a fundamental task in graph analysis, which can be applied to various downstream applications such as recommendation and resource allocation. However, existing studies merely work under a single view, which neglects the rich information hidden in other aspects of the graph. Hence, in this work, we propose a Multiview Contrastive Representation Learning (MCRL) model to obtain representations of nodes from multiple perspectives and then infer the node importance. Specifically, we are the first to apply the contrastive learning technique to the node importance analysis task, which enhances the expressiveness of graph representations and lays the foundation for importance estimation. Moreover, based on the improved representations, we generate the entity importance score by attentively aggregating the scores from two different views, i.e., node view and node-edge interaction view. We conduct extensive experiments on real-world datasets, and the experimental results show that MCRL outperforms existing methods on all evaluation metrics.
Deep learning-based decoding of spatial information from limbic-cortical local field potentials reveal drifting spatial representations with increasing stability

Neural representations of space in the hippocampus and related brain areas change over timescales of daysweeks, even when there are no apparent behavioural changes.This 'representational drift' occurs even after animals are fully familiar with a given context.Many qualities of this phenomenon are unknown, yet few tools exist to aid analysis.Here we present a novel deeplearning approach for robust quantification and analysis of ensemble level representational drift.Using this method, we analyse a longitudinal dataset of 0.5-475Hz broadband local field potential (LFP) data taken from Hippocampal, Prefrontal-Cortex and Parietal-Cortex of rats collected over multiple days, before and after a contextual rule change in a spatial navigation learning task.First, we observed clear spatial representations in all considered brain regions, despite the low frequency LFP data used.Second, we show statistically significant drift in these representations in all brain regions.Lastly, we show a statistically significant increase in the stability of representations for all considered brain regions as time and experience increases.Our general strategy for using deep neural networks to quantify drift in broadband LFP data opens up new possibilities for flexibly dissecting the features of drift in large-scale neural recordings, and how they relate to animal behaviour.
A Cross-Sectional Study on Student Experience and Contentment of Online Learning During Covid 19 Lockdown

The delivery mode of the lessons was transitioned from face-to-face to online/e-learning in response to the Covid-19 lockdown across the Middle East, particularly in Oman. The University of Technology and Applied Sciences, Al Musannah (UTASA), also adopted this approach, which brought forth both opportunities and challenges for the academic community, including teachers and students. However, no systematic studies were conducted across various departments at the university to gain insights into the implications of full-time online/e-learning. Therefore, this study was designed to comprehend the perceptions of cross-sectional UTASA students regarding the effectiveness of e-learning, encompassing their experiences and satisfaction with participating in it. The study employed a combination of quantitative and qualitative data collection methods, utilizing a survey questionnaire and a descriptive question. The participants included both male and female learners (N = 212) from departments such as IT (Information Technology), Business, Engineering, and ELC (English Language Centre). The analysis encompassed both descriptive and inferential statistical analyses of the quantitative data, as well as a descriptive thematic analysis of the qualitative data. The results revealed that over half of the participants held a clearly positive impression of their e-learning experience and satisfaction during the Covid-19 lockdown. Furthermore, the analysis of qualitative data shed light on the reasons behind both negative and positive sentiments towards e-learning, along with suggestions for potential enhancements. The diverse reactions of the participants to the survey questions have assisted researchers and interested parties in gaining a comprehensive understanding of both the favorable and unfavorable aspects of the procedure. A subset of the participants held a pessimistic view of online learning due to factors such as receiving low grades, encountering inadequate technical assistance, and observing a lack of commitment. In contrast, a different group perceived online learning as advantageous, citing its provision of a convenient and adaptable learning environment, along with convenient access to recorded lectures. Additionally, certain survey respondents put forth recommendations for enhancing online learning, including the need for better training, improved Internet connectivity, and enhanced interaction between teachers and students, as well as among fellow students. In summary, the study yielded valuable insights into the experiences and contentment levels of learners engaged in the online teaching and learning process. The findings and ensuing discussion provide essential recommendations for stakeholders and future researchers alike.
Recognition of Object’s Opening Features Using Machine Learning

Machine learning is utilized to identify for the objects’ opening number with 100% accuracy. For objects with up to 36 openings, 1° resolution is achieved in all wedge angles and directions with $\gt88.86$% accuracy.
Analysis and Comparison of Waterfall Model and Agile Approach in Software Projects

Looking at the history of civilization from past to present, the concepts of project and project management are frequently encountered. Thanks to project management, businesses can work target-oriented, provide high motivation, facilitate internal control and provide a significant increase in quality. The concept of software, born with the development of technology, penetrates our lives more and more day by day. The globalizing world has brought together the concepts of software and project management and integrated them. Software projects require special management techniques because of their content. For this reason, new methods have emerged over time in the management of software projects. In this study, the concept of the project, the development of project management from its birth over the years, project management in software projects, software development life cycles, and the Waterfall Model and Agile Approach, which are two methods used in software projects, are discussed. The perception of the Waterfall Model and Agile Approach was evaluated with the survey study created with the participation of 145 employees from the software industry. Thus, it is aimed to support managers to choose an effective and efficient method at the point of deciding which method to proceed in software project management.
Hubungan Dukungan Sosial Terhadap Motivasi Belajar Mahasiswa: Studi Literatur

Students play a crucial role as learners who constantly interact with various learning tasks assigned to them. Positive social support from various sources, such as close friends, parents, teachers, and individuals around them, can help students develop the ability to enhance their learning motivation. This study aims to examine the relationship between social support and learning motivation. It adopts a literature library research approach, utilizing samples derived from previous studies, both quantitative and qualitative in nature. The research data demonstrates a correlation between social support from various sources and learning motivation, encompassing the students. Despite having a relatively high level of low learning motivation, this can be attributed to insufficient social support. Among the findings, peer and parental support emerged as the most influential forms of social support based on the existing research overview.