Reviewerly/exHarmony huggingface dataset:
What motivated Malaysians in banking with the service provider in the post COVID-19 pandemic era?

The fierce competitive environment in Malaysian banking industry has pressured the service providers into focusing on the improvement of their customers loyalty.This study aims to examine the antecedents of customer satisfaction to promote customer loyalty. This study adapted SERVQUAL model to explain the influencing factors. Hundred and one valid and reliable responses were used for data analysis. The findings suggested positive relationships between tangibility, assurance, and empathy on customer satisfaction. The relationship between customer satisfaction and customer loyalty is significantly positive. Contrary to previous findings, responsiveness and reliability are not significant in explaining customer satisfaction. This study is one of the very few studies in Malaysia and Asia to propose effective measures to enhance customer satisfaction and loyalty post COVID-19 pandemic era.
Improving the understanding of web user behaviors through machine learning analysis of eye-tracking data

Abstract Eye-tracking techniques are widely used to analyze user behavior. While eye-trackers collect valuable quantitative data, the results are often described in a qualitative manner due to the lack of a model that interprets the gaze trajectories generated by routine tasks, such as reading or comparing two products. The aim of this work is to propose a new quantitative way to analyze gaze trajectories (scanpaths) using machine learning. We conducted a within-subjects study ( N = 30) testing six different tasks that simulated specific user behaviors in web sites (attentional, comparing two images, reading in different contexts, and free surfing). We evaluated the scanpath results with three different classifiers (long short-term memory recurrent neural network—LSTM, random forest, and multilayer perceptron neural network—MLP) to discriminate between tasks. The results revealed that it is possible to classify and distinguish between the 6 different web behaviors proposed in this study based on the user’s scanpath. The classifier that achieved the best results was the LSTM, with a 95.7% accuracy. To the best of our knowledge, this is the first study to provide insight about MLP and LSTM classifiers to discriminate between tasks. In the discussion, we propose practical implications of the study results.
Teacher’s Perception of Self-Assessment in English Writing Competency for Junior High School Students

Self-assessment provides many benefits in learning English, especially in terms of writing ability. This study aims to analyze teachers' perceptions and challenges that teachers find of the application of selfassessment in students' writing competencies at everal junior high school in Nusa Penida. This study used mixed method explanatory design as the research approach. The subjects of this study were EFL secondary teachers in three junior high school. Quantitative data in this study used a questionnaire to obtain accurate data related to teacher perceptions. Qualitative data used semistructured interviews. The interview statement was adjusted to the theory in the questionnaire. The data collection from questionnaire is analyzed by using Excel to find out the median of the questionnaire. In the interview, the data analyzed using thematic analysis. The result of this study shows that teachers have perceptions that tend to be positive towards the implementation of self-assessment in writing competency as a means for students to self-reflect, help students get feedback, develop writing ideas to be better confidently and independently. Teachers also experience obstacles in implementing self-assessment that the purpose of selfassessment is still not well understood by students.
HOW NFTs CAN REVOLUTIONIZE THE BOOK INDUSTRY

As the times are changing, physical books are also becoming old &amp; a lot of young generations are preferring digital versions of physical books. The people who are most affected by these changes are the authors of those physical books. So the authors can adopt the NFTs which are unique tokens or digital representations of ownership of their book. They essentially turn their book into an NFT. This way the book can be digitally distributed as well as the author gets a lot of control over the distribution or sales of the book. This also garners the attention of the young generation. NFTs are our future and there are a lot of applications of NFT in the future.
Perbandingan Motivasi Belajar Siswa Pada Saat Pembelajaran IPS Secara Daring dan Tatap Muka: Studi Komparatif di SMPN 1 Lembang

The outbreak of Covid-19 has significantly impacted students’ learning process and motivation levels. This study aimed to investigate students' motivation levels during face-to-face and virtual learning in social studies and compare students’ learning motivation after the policies implemented due to Covid-19. The research employed a quantitative approach with a comparative descriptive method, with a sample size of 72 students from classes VII-A and VII-B of SMPN 1 Lembang. The instrument testing was conducted by performing validity and reliability tests with a significance table of 5%. The data obtained were processed using normality tests, which showed that the data was normal and homogeneous. The data was processed through parametric testing by performing independent samples t-test. The statistical test results showed a significance value of 0.000, less than 0.05. The statistical test results concluded that there were differences in students' motivation when learning social studies in virtual and face-to-face settings. The findings of this study revealed that the difference in motivation level could be observed from the average descriptive analysis of students during virtual social studies learning in the low and high categories, while face-to-face learning was in the high and very high categories between male and female students.Keywords: Learning motivation, social studies learning, virtual and face-to-face learning.
A Driver Abnormal Behavior Detection Method Based on Improved YOLOv7 and OpenPose

The current driver’s abnormal behavior detection is interfered with by complex backgrounds, body self-obscuring, and other factors leading to low accuracy of small object detection and large error of human node detection. In this paper, we propose a driver abnormal behavior detection method based on improved YOLOv7 and OpenPose. Firstly, we add an attention mechanism to the backbone network of YOLOv7, increase the feature information contained in the shallow feature map, introduce Wise-IoU loss function to improve the detection accuracy of small and long objects (e.g., cigarettes, water glasses). Secondly, we calculate the IoU values between the driver’s hand and the confidence frame of small objects to detect the abnormal behavior of driver-object interaction (e.g., smoking, drinking). Thirdly, we improve the OpenPose network model by replacing convolutional kernels and adjusting the connection of convolutional kernels to achieve two-dimensional node detection of the driver’s driving posture. Finally, we use the FastDTW algorithm, and calculate the similarity of the driver’s two-dimensional nodal information to detect the abnormal posture of the driver. The experimental results show that the method in this paper has high accuracy of small object detection and low nodal error, and can effectively detect abnormal behaviors such as smoking, drinking and abnormal posture of drivers during driving.
Hessian Non-negative Hypergraph

Hypergraph is a vital relationship modeling tool applied to many learning tasks. Compared with the common graph, the hypergraph can model the high-order relationship among more than two data samples. Thus, hypergraph provides more flexibility and the capability of modeling complex data correlations. However, real-world image data inevitably lie on or close to a thin layer of a manifold while containing the noise component. The practical image data influence the hypergraph learning procedure, subsequently resulting in a notable performance deterioration. To this end, we propose a novel Hessian non-negative hypergraph model. Specifically, the Hessian energy regularized non-negative constrained data reconstruction is used to generate manifold-respecting and noise resistant hyperedges. The extensive experiments on the benchmark image datasets demonstrate that the proposed hypergraph model can significantly enhance the learning performance of the hypergraph-based image clustering and classification.
TaxoSBERT: Unsupervised Taxonomy Expansion Through Expressive Semantic Similarity

Knowledge graphs are crucial resources for a large set of document management tasks, such as text retrieval and classification as well as natural language inference. Standard examples are large-scale lexical semantic graphs, such as WordNet, useful for text tagging or sentence disambiguation purposes. The dynamics of lexical taxonomies is a critical problem as they need to be maintained to follow the language evolution across time. Taxonomy expansion, in this sense, becomes a critical semantic task, as it allows for an extension of existing resources with new properties but also to create new entries, i.e. taxonomy concepts, when necessary. Previous work on this topic suggests the use of neural learning methods able to make use of the underlying taxonomy graph as a source of training evidence. This can be done by graph-based learning, where nets are trained to encode the underlying knowledge graph and to predict appropriate inferences. This paper presents TaxoSBERT as a simple and effective way to model the taxonomy expansion problem as a retrieval task. It combines a robust semantic similarity measure and taxonomy-driven re-rank strategies. This method is unsupervised, the adopted similarity measures are trained on (large-scale) resources out of a target taxonomy and are extremely efficient. The experimental evaluation with respect to two taxonomies shows surprising results, improving far more complex state-of-the-art methods.
UMLDesigner: An Automatic UML Diagram Design Tool

Software design encompasses a range of activities, starting with the request for the computerization of a process and progressing through various phases. Among these phases, modeling is particularly crucial and can sometimes be challenging. This paper presents an approach to automatically analyze software specifications and generate the corresponding UML class diagram. We use Natural Language Processing tools, namely Stanza and NLTK, and a rule-based method for data extraction. We have developed UMLDesigner, a tool that includes a text-to-diagram editor enabling users to create UML diagrams based on textual descriptions of management rules. Our model has been trained on French language data specific to the UML class diagram domain. To facilitate end-user adoption, we have containerized the model into an API and developed a web application that interfaces with the API to process text and generate diagram images. The conducted experiments demonstrate the effectiveness of UMLDesigner.
Automatic Model Selection Algorithm Based on BYY Harmony Learning for Mixture of Gaussian Process Functional Regressions Models

For finite mixture models, determining the number of components is referred to as model selection. This paper puts forward an automatic model selection algorithm based on Bayesian Ying-Yang (BYY) harmony learning for mixture of Gaussian process functional regressions (mix-GPFR) models. BYY harmony learning has been successfully applied to the model selection problem of Gaussian mixture models (GMMs), but it cannot be directly used for that of mix-GPFR models. We find out the cause of this problem and propose a coping mechanism of curve reconstruction based on Gaussian process (GP) models, through which, we transform a mix-GPFR model into a GMM. Thus, we can make model selection for mix-GPFR models via BYY harmony learning. Experimental results show that our proposed automatic model selection algorithm can find the optimal number of components in a multi-source curve dataset.
Platform Policies Versus Human Rights Standards

Abstract This chapter empirically examines how five social media platforms—Facebook, Instagram, Twitter, TikTok and YouTube—deal with the content governance dilemma and the question of which human rights standard to apply when moderating user content. It builds on previous chapters’ analyses of relevant human rights standards in international law and civil society-issued documents to elucidate to what extent substantial and procedural demands are met by the platforms. After an analysis of platform policies—specifically the human rights commitments included in them, the chapter examines substantive content moderation trends in a comparative way. Thereafter, procedural practices of content moderation including transparency reporting and automated content moderation are comparatively discussed. The chapter finds a relatively high degree of convergence among the platforms on a number of practices.
Towards Exploring Adversarial Learning for Anomaly Detection in Complex Driving Scenes

One of the many Autonomous Systems (ASs), such as autonomous driving cars, performs various safety-critical functions. Many of these autonomous systems take advantage of Artificial Intelligence (AI) techniques to perceive their environment. But these perceiving components could not be formally verified, since, the accuracy of such AI-based components has a high dependency on the quality of training data. So Machine learning (ML) based anomaly detection, a technique to identify data that does not belong to the training data could be used as a safety measuring indicator during the development and operational time of such AI-based components. Adversarial learning, a sub-field of machine learning has proven its ability to detect anomalies in images and videos with impressive results on simple data sets. Therefore, in this work, we investigate and provide insight into the performance of such techniques on a highly complex driving scenes dataset called Berkeley DeepDrive.
The International Law of Content Governance

Abstract This chapter considers the extent to which international law offers guidance to the governance of digital platforms. Recent years have seen a growing attention in the potential of international law in offering normative guidance to address human rights concerns in content governance. The chapter considers whether the recent turn to international law for content governance standards is a worthwhile exercise. It finds that, beset by a host of design and structural constraints, international law does not offer meaningful normative guidance to the governance of and in digital platforms. The chapter then highlights how emergent standards are moving past the regulatory limits of international law.
Evaluating Prototypes and Criticisms for Explaining Clustered Contributions in Digital Public Participation Processes

Abstract We examine the use of prototypes and criticisms for explaining clusterings in digital public participation processes of the e-participation domain. These processes enable people to participate in various life areas such as landscape planning by submitting contributions that express their opinions or ideas. Clustering groups similar contributions together. This supports citizens and public administrations, the main participants in digital public participation processes, in exploring the submitted contributions. However, explaining clusterings remains a challenge. For this purpose, we consider the use of prototypes and criticisms. Our work generalizes the idea of applying the $$k$$ -medoids algorithm for computing prototypes on raw data sets. We introduce a centroid-based clusterings method that solely considers clusterings. It allows the retrieval of multiple prototypes and criticisms per cluster. We conducted a user study with 21 participants to evaluate our centroid-based clusterings method and the MMD-critic algorithm for finding prototypes and criticisms in clustered contributions. We examined whether these methods are suitable for text data. The related contributions originate from past, real-life digital public participation processes. The user study results indicate that both methods are appropriate for clustered contributions. The results also show that the centroid-based clusterings method outperforms the MMD-critic algorithm regarding accuracy, efficiency, and perceived difficulty.
Conclusion

Abstract The choice on the law of online content moderation is a conundrum composed of three options, none of which is fully satisfying. International human rights law is not the solution to the content governance dilemma, but its contribution cannot be excluded from the resolution of this issue. A multi-level approach is proposed where multiple actors are instrumental in translating human rights norms into rules that speak to the context of online content governance. This work focuses on the role of civil society organisations. By comparing the demands advanced by civil society actors with platforms’ policies we reconstruct an image of the process of social media constitutionalisation ‘in motion’.
CSR &amp; Sentiment Analysis: A New Customized Dictionary

Communication concerning the CSR pillars is key to sustainable corporate development. Sentiment analysis (SA) is a sub-area of natural language processing for studying communication through the classification of negative or positive opinions. Measuring sentiment is characterized by pitfalls related to: a) the context, where the polarity classification depends on the domain; b) the methods, if lexicon-based, machine learning, or their combination; c) the language, where the lack of resources (different from English) in literature was observed. Strategic communication based on CSR has no domain resources for investigating sentiment, neither in English nor in other languages. Our contribution is placed within the methodological setting of SA for the sustainability framework. We combined lexicon-based methods with machine-learning ones to build a customized lexicon for analyzing the CSR. The innovation concerns: 1) a domain corpus-based approach for improving a general pre-constructed dictionary; 2) the application for Italian; and 3) the performance assessment through machine learning. We developed an algorithm characterized by a multi-stage model that combines text analysis with network analysis and captures semantic concordances through an index of keyword content in the text. To validate our model from a machine learning perspective, we divided our data collection into five random samples: one sample was utilized as a train set or baseline for the lexicon’s implementation, and four were used as test sets. The study showed a notable increase in performance metrics across all samples, demonstrating the effectiveness of our proposal in building a customized lexicon for analyzing CSR in the Italian context.
Exploring ASR Models in Low-Resource Languages: Use-Case the Macedonian Language

We explore the use of Wav2Vec 2.0, NeMo, and ESPNet models trained on a dataset in Macedonian language for the development of Automatic Speech Recognition (ASR) models for low-resource languages. The study aims to evaluate the performance of recent state-of-the-art models for speech recognition in low-resource languages, such as Macedonian, where there are limited resources available for training or fine-tuning. The paper presents a methodology used for data collection and preprocessing, as well as the details of the three architectures used in the study. The study evaluates the performance of each model using WER and CER metrics and provides a comparative analysis of the results. The findings of the research showed that Wav2Vec 2.0 outperformed the other models for the Macedonian language with a WER of 0.21, and CER of 0.09, however, NeMo and ESPNet models are still good candidates for creating ASR tools for low-resource languages such as Macedonian. The research presented provides insights into the effectiveness of different models for ASR in low-resource languages and highlights the potentials for using these models to develop ASR tools for other languages in the future. These findings have significant implications for the development of ASR tools for other low-resource languages in the future, and can potentially improve accessibility to speech recognition technology for individuals and communities who speak these languages.
Using Artificial Intelligence to Reduce the Risk of Transfusion Hemolytic Reactions

The monocyte monolayer assay is a cellular assay, an in-vitro procedure that mimics extravascular hemolysis. It was developed to predict the clinical significance of red blood cell antibodies in transfusion candidates with intent to determine whether the patient needs to receive the expensive, rare, antigen-negative blood to avoid an acute hemolytic transfusion reaction that could lead to death. The assay requires a highly trained technician to spend several hours evaluating a minimum of 3,200 monocytes on a glass slide under a microscope in a cumbersome process of repetitive counting. Using the YOLO neural network model, we automate the process of identifying and categorizing monocytes from slide images, a significant improvement over the manual counting method. With this technology, blood bank technicians can save time and effort while increasing accuracy in the evaluation of blood transfusion candidates, leading to faster and better medical diagnosis. The trained model was integrated into an application that can locate, identify, and categorize monocytes, separating them from the background and noise on the images acquired by an optical microscope camera. Experiments involving a real-world data set demonstrate that F1-score, mAP scores, precision and recall are above 90%, indicating that this workflow can ease and accelerate the medical laboratory technician’s repetitive, cumbersome, and error-prone counting process, and therefore contributes to the accuracy of medical diagnosis systems.
Explainable Abnormal Time Series Subsequence Detection Using Random Convolutional Kernels

To identify anomalous subsequences in time series, it is a common practice to convert them into a set of features prior to the use of an anomaly detector. Feature extraction can be accomplished by manually designing the features or by automatically learning them using a neural network. However, for the former, significant domain expertise is required to design features that are effective in accurately detecting anomalies, while in the latter, it might be complex to learn useful features when dealing with unsupervised or one-class classification problems such as anomaly detection, where there are no labels available to guide the feature extraction process. In this paper, we propose an alternative approach for feature extraction that overcomes the limitations of the two previously mentioned approaches. The proposed method involves calculating the similarities between subsequences and a set of randomly generated convolutional kernels and the use of the One-Class SVM algorithm. We tested our approach on voltage signals acquired during circular welding processes in hot water tank manufacturing, the results indicate that the approach achieves higher accuracy in detecting welding defects in comparison to commonly used methods. Furthermore, we introduce in this work an approach for explaining the anomalies detected by making use of the random convolutional kernels, which addresses an important gap in time series anomaly detection.