Artificial Intelligence
Nowadays, more and more machine learning models have emerged in the field of sleep staging. However, they have not been widely used in practical situations, which may be due to the non-comprehensiveness of these models' clinical and subject background and the lack of persuasiveness and guarantee of generalization performance outside the given datasets. Meanwhile, polysomnogram (PSG), as the gold standard of sleep staging, is rather intrusive and expensive. In this paper, we propose a novel automatic sleep staging architecture calle
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Dataset captured in an experimental setup using four human cadaveric hip specimens by exciting the vertebra of interest with a sine sweep vibration at the spinous process and attaching a custom highly-sensitive piezo contact microphone to the screw head to capture the propagated vibration characteristic.
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Due to the smaller size, low cost, and easy operational features, small unmanned aerial vehicles (SUAVs) have become more popular for various defense as well as civil applications. They can also give threat to national security if intentionally operated by any hostile actor(s). Since all the SUAV targets have a high degree of resemblances in their micro-Doppler (m-D) space, their accurate detection/classification can be highly guaranteed by the appropriate deep convolutional neural network (DCNN) architecture.
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In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory.
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The dataset of satellite images we use comes from Satellite Tool Kit (STK), which is the world's top satellite simulation software produced by AGI Company of the United States. We mainly use STK to provide a high-precision visual simulation module, which can provide users with high-fidelity visual support in space. We collected 18 video sequences as our training set and 6 video sequences as our verification set. A video sequence contains a satellite, and each video sequence contains 40 to 60 satellite images.
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The preview of the road surface states is essential for improving the safety and the ride comfort of autonomous vehicles. This dataset consists of 1 million (240 x 360 pixels) road surface images captured under a wide range of road and weather conditions in China. The original pictures are acquired with a vehicle-mounted camera and then the patches containing only the road surface area are cropped. The images are classified into 27 categories, containing both the friction level, material, and unevenness properties.
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In recent years, many agent-based models of human groups have implemented a mechanism of emotion contagion, yet empirical validation is lagging behind. The aim of the present paper is to validate an agent-based model of emotion contagion at the level of group emotion, by comparing simulations against the emotional development of real people in small groups. To study the effect of emotion contagion, the participants interacted via a video call, where they were virtually placed in different social environments while they played a quiz.
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SCVIC-CIDS-2021 was created using the raw data in CIC-IDS-2018*, while this new dataset, SCVIC-CIDS-2022 is formed from NDSec-1** meta-data by following a similar procedure.
This dataset has been used in the following work:
J. Liu, M. Simsek, B. Kantarci, M. Bagheri, P. Djukic, "Bridging Networks and Hosts via Machine Learning-Based Intrusion Detection"; under review in IEEE Transactions on Dependable and Secure Computing.
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