Artificial Intelligence
The recent developments in the field of the Internet of Things (IoT) bring alongside them quite a few advantages. Examples include real-time condition monitoring, remote control and operation and sometimes even remote fault remediation. Still, despite bringing invaluable benefits, IoT-enriched entities inherently suffer from security and privacy issues. This is partially due to the utilization of insecure communication protocols such as the Open Charge Point Protocol (OCPP) 1.6. OCPP 1.6 is an application-layer communication protocol used for managing electric vehicle chargers.
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This dataset provides measurements of cerebral blood flow using Radio Frequency (RF) sensors operating in the Ultra-Wideband (UWB) frequency range, enabling non-invasive monitoring of cerebral hemodynamics. It includes blood flow feature data from two arterial networks, Arterial Network A and Arterial Network B. Statistical features were manually extracted from the RF sensor data, while autonomous feature extraction was performed using a Stacked Autoencoder (SAE) with architectures such as 32-16-32, 64-32-16-32-64, and 128-64-32-16-32-64-128.
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The rapid evolution of visual data demands compression technologies that balance theoretical expressiveness with practical deployment constraints. Current learning-based approaches face dual challenges: non-differentiable quantization operations that hinder end-to-end optimization, and rigid architectural components limiting adaptability to diverse content characteristics. This paper introduces a novel neural compression framework that integrates principles from Kolmogorov-Arnold Networks (KANs) with dynamic quantization mechanisms.
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The fitness landscape of optimization problems significantly impacts the performance of metaheuristic optimization algorithms. While no algorithm performs well on all problems, identifying the most suitable one for a specific problem can be achieved by extracting features from the landscape. However, these features are often extracted before optimization, disregarding valuable knowledge collected during the optimization process. Moreover, existing algorithm selection methods rely on a single algorithm, limiting flexibility and missing potentially better options.
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Iron Ore Powder Dataset: The mineral powder dataset is utilized to predict the moisture content present in the mineral powder. Images of the mineral powder at different moisture levels exhibit variations in texture, which are influenced by the moisture content and significantly affect the surface morphology of the powder. Specifically, this variation is evident in the changes in texture and surface features.
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110 Views
Smart Home Automation (SHA) has significantly improved homes’ convenience, comfort, security, and safety. It has gained widespread use due to its intelligent monitoring and quick response capabilities. The current state of SHA enables effective monitoring and motion detection. However, false notifications remain a significant challenge, as they can cause unnecessary alarms in intrusion detection systems. To address this, we propose an intelligent model for a smart home security system that uses computer vision techniques to detect trespasser movement near the boundary wall.
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The Metaverse Gait Authentication Dataset (MGAD) is a large-scale gait dataset designed for biometric authentication in virtual environments.
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This dataset comprises Channel State Information (CSI) data collected from WiFi signals in six indoor environments, specifically designed for research in indoor intrusion detection. The dataset captures fine-grained variations in wireless signals caused by human, which are indicative of potential intrusions. CSI data, extracted from commercial WiFi chipsets, provides detailed amplitude and phase information across subcarriers, enabling robust detection of subtle environmental changes.
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Legal analysis utilizing natural language processing and machine learning technologies is a difficult undertaking that has recently sparked the interest of many academics and industries. Using a human-annotated dataset summarized into colloquial Thai from Supreme Court decisions, this work investigates a different combination of NLP, ML, and rule-based techniques for accurate legal case analysis as per Thai law, especially property-related offences, with the intuition to imitate the lawyer's cognitive process.
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The process of allocating the intestate inheritance among the statutory heirs is sophisticated yet occurs regularly. Many scholars have attempted to develop automated allocation systems to tackle this High task. However, most amply existing systems rely on conventional form-based input, which may overwhelm the general users. Furthermore, no existing system concerning intestate inheritance allocation according to the Civil and Commercial Code of Thailand is publicly available.
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