The extensive ubiquitous availability of sensors in smart devices and the Internet of Things (IoT) has opened up the possibilities for implementing sensor-based activity recognition. As opposed to traditional sensor t...
The extensive ubiquitous availability of sensors in smart devices and the Internet of Things (IoT) has opened up the possibilities for implementing sensor-based activity recognition. As opposed to traditional sensor time-series processing and hand-engineered feature extraction, in light of deep learning’s proven effectiveness across various domains, numerous deep methods have been explored to tackle the challenges in activity recognition, outperforming the traditional signal processing and traditional machine learning approaches. In this work, by performing extensive experimental studies on two human activity recognition datasets, we investigate the performance of common deep learning and machine learning approaches as well as different training mechanisms (such as contrastive learning), and various feature representations extracted from the sensor time-series data and measure their effectiveness for the human activity recognition task.
In gossip networks, a source node forwards time-stamped updates to a network of observers according to a Poisson process. The observers then update each other on this information according to Poisson processes as well...
Numerous scientific and engineering applications exist for thermofluids. The primary cause of cervical cancer is the human papillomavirus (HPV), and thermos-fluid is crucial for identifying, treating, and understandin...
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Foreground detection is a critical step for separating the moving object from the background in video processing. Tensor factorization has been used in foreground detection due to its ability to process complex high-d...
Foreground detection is a critical step for separating the moving object from the background in video processing. Tensor factorization has been used in foreground detection due to its ability to process complex high-dimensional data, such as color images and videos. However, traditional tensor factorization often lacks the ability for uncertainty quantification. Bayesian tensor factorization can measure the uncertainty by considering the distributions of the tensor factorization model parameters. Besides, domain knowledge is commonly available and could improve the accuracy of foreground detection of the Bayesian tensor factorization model if it can be appropriately incorporated. In this work, a new Bayesian tensor factorization model, named Posterior Regularized Bayesian Robust Tensor Factorization (PR-BRTF), is proposed with incorporating characteristics of dynamic foreground, as a sparsity posterior regularization term. Furthermore, the variational Bayesian inference and $L_{1}$ norm is combined for inducing sparsity with an efficient inference. The experiments in real-world case studies have shown the performance improvement of the proposed model over state-of-art methods.
In this paper we consider a colouring version of the general position problem. The gp-chromatic number is the smallest number of colours needed to colour V (G) such that each colour class has the no-three-in-line prop...
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Coronavirus disease (COVID-19)-associated coagulopathy represents a serious complication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that can lead to thromboembolic events. Given...
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We revisit here the dynamics of an engineered dimer granular crystal under an external periodic drive in the presence of dissipation. Earlier findings included a saddle-node bifurcation, whose terminal point initiated...
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The use of Green's function in quantum many-body theory often leads to nonlinear eigenvalue problems, as Green's function needs to be defined in energy domain. The GW approximation method is one of the typical...
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The unavailability of wasted energy due to the irreversibility in the process is called the entropy *** irreversible process is a process in which the entropy of the system is *** second law of thermodynamics is used ...
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The unavailability of wasted energy due to the irreversibility in the process is called the entropy *** irreversible process is a process in which the entropy of the system is *** second law of thermodynamics is used to define whether the given system is reversible or ***,our focus is how to reduce the entropy of the system and maximize the capability of the *** are many methods for maximizing the capacity of heat *** constant pressure gradient or motion of the wall can be used to increase the heat transfer rate and minimize the *** objective of this study is to analyze the heat and mass transfer of an Eyring-Powell fluid in a porous *** this,we choose two different fluid models,namely,the plane and generalized Couette *** flow is generated in the channel due to a pressure gradient or with the moving of the upper *** present analysis shows the effects of the fluid parameters on the velocity,the temperature,the entropy generation,and the Bejan *** nonlinear boundary value problem of the flow problem is solved with the help of the regular perturbation *** validate the perturbation solution,a numerical solution is also obtained with the help of the built-in command NDSolve of MATHEMATICA *** velocity profile shows the shear thickening behavior via first-order Eyring-Powell *** is also observed that the profile of the Bejan number has a decreasing trend against the Brinkman ***ηi→0(i=1,2,3),the Eyring-Powell fluid is transformed into a Newtonian fluid.
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