data mining technology has yielded fruitful results in the area of crime discovery and intelligent decision making. Credit card is one of the most popular payment methods, providing great convenience and efficiency. H...
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We propose a community discovery method based on deep auto-encoding (DGAE_DST). Firstly, we use the pre-trained two-layer neural network and k-means algorithm to initialize the centroid vector, and then use the DNN mo...
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Existing supervised deep learning model requires large amounts of labeled training data to learn new tasks. This is a limitation for many practical applications in disaster areas as well as in many other fields such a...
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The lightweight target detection model is deployed in an environment with limited computing power and power consumption, which is widely used in many fields. Most of the current lightweight technologies only focus on ...
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Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in *** unpredictable outliers will be uncovered in pr...
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Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in *** unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey ***,it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant *** learning is also unsuitable for this purpose because designing proper training sets for unanticipated signals is *** by these challenges,we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical *** comparison,we construct three methods,which are built upon the k-nearest neighbors(KNN),Convolutional Auto-Encoder(CAE)+KNN,and CAE+KNN+Attention Mechanism(att CAE_KNN)*** sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above *** show that att CAE_KNN achieves the best recall(78%),which is 53%higher than the classical KNN method and 22%higher than CAE+*** efficiency of att CAE_KNN(10 minutes)is also superior to KNN(4 h)and equal to CAE+KNN(10 minutes)for accomplishing the same ***,we believe that it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised ***,we will apply att CAE_KNN to available survey data sets to assess its applicability and reliability.
SDN (Software-defined networking) are important for current network systems, such as cloud systems. The characteristics of flow in SDN and the impact of flow table entries and controllers on data packet transmission a...
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Estimating individual treatment effect (ITE) is a challenging task due to the need for individual potential outcomes to be learned from biased data and counterfactuals are inherently unobservable. Some researchers pro...
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How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering a...
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In order to select a composition scheme that meets user's needs and high performance from large-scale web services in the edge cloud, this paper proposes a trusted service composition optimization scheme called TS...
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Large-scale and diverse businesses based on the cloud computing platform bring the heavy network traffic to cloud data ***,the unbalanced workload of cloud data center network easily leads to the network congestion,th...
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Large-scale and diverse businesses based on the cloud computing platform bring the heavy network traffic to cloud data ***,the unbalanced workload of cloud data center network easily leads to the network congestion,the low resource utilization rate,the long delay,the low reliability,and the low *** order to improve the utilization efficiency and the quality of services(QoS)of cloud system,especially to solve the problem of network congestion,we propose MTSS,a multi-path traffic scheduling mechanism based on software defined networking(SDN).MTSS utilizes the data flow scheduling flexibility of SDN and the multi-path feature of the fat-tree structure to improve the traffic balance of the cloud data center network.A heuristic traffic balancing algorithm is presented for MTSS,which periodically monitors the network link and dynamically adjusts the traffic on the heavy link to achieve programmable data forwarding and load *** experimental results show that MTSS outperforms equal-cost multi-path protocol(ECMP),by effectively reducing the packet loss rate and *** addition,MTSS improves the utilization efficiency,the reliability and the throughput rate of the cloud data center network.
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