The study explores the role of Learning Management Systems (LMS) in enhancing academic teamwork and ambidextrous learning in Indonesian higher education. It examines how different uses of LMS, both explorative and exp...
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This study aims to establish the transformative power of Social Network Applications (SNA) on organisational learning and knowledge sharing within Indonesian organisations. This study conducted a quantitative business...
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While most previous work pays attention on extracting dense subgraphs, such as k-cores, we argue that augmenting the graph to maximize the size of dense subgraphs is also very important and finds many applications. Th...
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An organisation's success relies more on dynamic knowledge management (KM). A successful knowledge management system is inextricably linked to employee behaviour, namely intra-organisational knowledge sharing. Ind...
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Global technological improvements are accelerating and influencing numerous industries, including education. Using machine learning, technological innovations can be utilized in the education industry. Machine learnin...
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Objectives: As individuals age, cognitive abilities such as working memory (WM), decline. In the current study, we investigated the effect of age on WM, and elucidated sources of errors. Method: A total of 102 healthy...
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Machine learning has recently seen a significant upsurge in its influence across diverse scientific domains. Among the array of machine learning techniques, the support vector machine (SVM) has emerged as a powerful s...
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Students 'attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long ...
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Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the a...
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Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d
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