Deep neural networks (DNNs) offer plenty of challenges in executing efficient computation at edge nodes, primarily due to the huge hardware resource demands. The article proposes HYDRA, hybrid data multiplexing, and r...
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Background: Machine learning (ML) prepares and trains a model through supervised or unsupervised learning methods. Sputum, a respiratory tract secretion, is a common laboratory specimen that aids in diagnosing respira...
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Cybersecurity threats highlight the need for robust network intrusion detection systems to identify malicious behaviour. These systems rely heavily on large datasets to train machine learning models capable of detecti...
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ISBN:
(数字)9798331506209
ISBN:
(纸本)9798331506216
Cybersecurity threats highlight the need for robust network intrusion detection systems to identify malicious behaviour. These systems rely heavily on large datasets to train machine learning models capable of detecting patterns and predicting threats. In the past two decades, researchers have produced a multitude of datasets, however, some widely utilised recent datasets generated with CICFlowMeter contain inaccuracies. These result in flow generation and feature extraction inconsistencies, leading to skewed results and reduced system effectiveness. Other tools in this context lack ease of use, customizable feature sets, and flow labelling options. In this work, we introduce HERA, a new open-source tool that generates flow files and labelled or unlabelled datasets with user-defined features. Validated and tested with the UNSW-NB15 dataset, HERA demonstrated accurate flow and label generation.
Labeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical...
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Malware Crimes are ubiquitous nowadays. One of the challenges faced by Cyber Forensics Investigators is to retrieve the details of a suspicious program, which might be malware, from a computer. Another challenge is th...
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Multinational companies (MNC) take care of employees by taking feedback, in which each day's mood counts as a trivial factor. Traditional and common methods of collecting data are manual and take up employees prod...
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Brain-Computer Interface (BCI) systems are the leading technology in the world related to Neurosciences. Human intelligence and imagination have no bounds and this has led to vast advancement in BCI systems related to...
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Brain-Computer Interface (BCI) systems are the leading technology in the world related to Neurosciences. Human intelligence and imagination have no bounds and this has led to vast advancement in BCI systems related to Neurological and allied sciences. This system measures the activity of the Central Nervous System (CNS) using biosignals and output is called Electroencephalography (EEG). The main purpose of BCI is to acquire EEG brain signals, identify patterns, extract features, and produce resultant actions. This process communicates with a modest electronic system designed for movements of physically challenged or paralyzed people. The purpose of this BCI system is to design a model to check the attention level of body movement. The movements are based on the EEG signals captured from the 19-electrode EEG headset. This allows gaining control over optimized real-time feature selection for EEG signals. The dataset of 30 subjects’ sample EEG signals is recorded for classification and analysis purpose. The EEG signals are classified using Logistic Regression, Decision Tree, and Random Forest algorithms. It is shown that Random Forest is the most efficient classifier with the highest accuracy of 99.47%.
Spin-orbit torques (SOTs) caused by spin currents generated in a ferromagnetic electrode enable a fast and deterministic magnetization switching. One SOT (y-SOT), polarized orthogonal to both electric current flowing ...
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Spin-orbit torques (SOTs) caused by spin currents generated in a ferromagnetic electrode enable a fast and deterministic magnetization switching. One SOT (y-SOT), polarized orthogonal to both electric current flowing in the electrode and easy axis of a ferromagnetic free layer, causes fast magnetization instability. The other SOT (z-SOT) points to the easy-axis direction and leads to the deterministic switching. Here, we evaluate the magnetization switching probability by these SOTs for various values of electric current density and the ratio of two SOTs from numerical simulation of the Landau-Lifshitz-Gilbert (LLG) equation. It is found that the switching probability is maximized when the electric current density is close to a critical value for the magnetization destabilization solely by the y-SOT. The origin of such a current dependence is investigated by analyzing temporal dynamics, spectra of the magnetization distribution, and a steady-state solution of the LLG equation. We reveal that the maximization of the switching probability originates due to two different switching behaviors. In the low current region, the magnetization in some trials remains near the initial state because of the weak y-SOT, and thus, the switching error occurs. The number of such trials, which represents the switching error, decreases as the electric current density increases because both y and z-SOTs prompt the switching. In the large current region, the large y-SOT immediately tilts the magnetization toward the switched direction. However, since the y-SOT prefers an in-plane magnetized state, the switching error due to a probabilistic return to the initial state after turning off the electric current density occurs. The switching error in this region tends to increase with increasing current density. As a result, the switching probability is optimized when the electric current density is close to the critical value.
Gait recognition is a biometric technology that identifies individuals in a video sequence by analysing their style of walking or limb movement. However, this identification is generally sensitive to appearance change...
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1 Introduction Time seriesaugmentationis an essential approachto solvethe overfitting problem on the time series classification(TSC)task[1,2].Although existing approaches perform better in mitigating this problem,none...
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1 Introduction Time seriesaugmentationis an essential approachto solvethe overfitting problem on the time series classification(TSC)task[1,2].Although existing approaches perform better in mitigating this problem,none of them focus on protecting saliency regions on time *** key informative shapelets contained in these regions are the core basis for distinguishing categories(e.g.,upward spikes in ECG and high amplitude in Sensor).
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