Crowdsensing is becoming a hot topic because of its advantages in the field of smart city. In crowdsensing, task allocation is a primary issue which determines the data quality and the cost of sensing tasks. In this paper, on the basis of the sweep covering theory, a novel coverage metric called 't-sweep k-coverage' is defined, and two symmetric problems are formulated: minimise participant set under fixed coverage rate constraint (MinP) and maximise coverage rate under participant set constraint (MaxC). Then based on their submodular property, two task allocation methods are proposed, namely double greedy (dGreedy) and submodular optimisation (SMO). The two methods are compared with the baseline method linear programming (LP) in experiments. The results show that, regardless of the size of the problems, both two methods can obtain the appropriate participant set, and overcome the shortcomings of linear programming.
Multiply-distorted stereoscopic images are common in real-world applications.The mixture of multiple distortions results in complex binocular visual behavior of multiply-distorted stereoscopic images, making it challenging for existing blind singly-distorted stereoscopic image quality assessment (IQA) methods to obtain satisfactory results on multiply-distorted stereoscopic images.Because binocular rivalry caused by different distortions in the left and right views greatly influences the final stereoscopic image quality, we propose a registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image in this paper.First, we employ a registration-based distortion representation to characterize the distortion in the stereoscopic image.Then we represent the binocular rivalry by merging the left and right views into a cyclopean image.Considering that the color and intensity of pixels in the RGB image can better reflect the information of the distorted image, then a grayscale cyclopean image is further converted to the color binocular representation using tone mapping.Finally, a multiply-distorted stereoscopic IQA method based on a double-stream convolutional neural network is proposed.The two subnetworks are used to extract quality features from the registration-based distortion representation and color binocular representation, respectively.Experimental results demonstrate that the proposed model outperforms the state-of-theart models on the multiply-distorted stereoscopic image databases.
The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker-chosen ones. In this paper, we propose a novel backdoor defense method to mark and purify the infected neurons in the backdoored neural networks. Specifically, we first define a new metric, called benign salience. By combining the first-order gradient to retain the connections between neurons, benign salience can identify the infected neurons with higher accuracy than the commonly used metric in backdoor defense. Then, a new Adaptive Regularization (AR) mechanism is proposed to assist in purifying these identified infected neurons via fine-tuning. Due to the ability to adapt to different magnitudes of parameters, AR can provide faster and more stable convergence than the common regularization mechanism in neuron purifying. Extensive experimental results demonstrate that our method can erase the backdoor in neural networks with negligible performance degradation.
In the cloud computing environment with massive information services and decision-making resources, the accuracy and reliability of information are more important than previous single closed systems. Therefore, ensuring the reliability of information and the stable operation of the system are the core problems in the research fields such as the Internet Plus and the Internet of Things. The connectivity and diagnosability are two important measures for the fault tolerance of multiprocessor systems. Theg-good-neighbor conditional connectivity (Rg-connectivity) is the minimum number of nodes that make the graph disconnected, and each node has at leastgneighbors in every remaining component. Theg-good-neighbor conditional diagnosability (g-GNCD) is the maximum number of faulty processors that has been correctly identified in a system, and any fault-free processor has no less thangfault-free neighbors. ExchangedX-cubes are a class of irregular networks, obtained by deleting links from hypercubes and some variant networks of hypercubes (X-cubes). They not only combine the advantages ofX-cubes but also reduce the interconnection complexity. ExchangedX-cubes classify its nodes into two different classes clusters with a unique connecting rule. In this paper, we propose the generalized exchangedX-cubes framework so that architecture can be constructed by different connecting rules. Furthermore, we study theRg-connectivity andg-GNCD of generalized exchangedX-cubes under the PMC and MM∗models. As applications, theRg-connectivity andg-GNCD of generalized exchanged hypercubes, dual-cube-like networks, generalized exchanged crossed cubes, and locally generalized exchanged twisted cubes are determined, respectively.
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