This paper addresses the problem of anomaly detection for high-dimensional sensing data. The one-class support vector machine (OCSVM) is one of the most popular unsupervised methods for anomaly detection. When data are high dimensional and large scale, however, the efficiency of OCSVM-based methods in anomaly detection suffers. Although dimensionality-reduction tools, such as deep belief networks, can be applied to compress the high-dimensional data to alleviate the problem, the accuracy and timely detection are still hard to improve due to the inherent features of OCSVM. In this paper, we propose a new form of OCSVM model based on the structure of the compressed data and the characteristics of OCSVM. Based on the new model, we design both optimal and approximate methods for model training and testing. We evaluate the performance of our methods with extensive experiments on four real-world datasets. The experimental results demonstrate that our new methods, both optimal and approximate ones, not only significantly outperform the state-of-the-art in accuracy and efficiency, but also achieve the good performance without the need of manual parameter tuning. In addition, our approximate training and testing mechanism can reduce the computing time by three orders of magnitude with a negligible loss in accuracy.

For agricultural disease image identification, obtained images are typically unclear, which can lead to poor identification results in real production environments. The quality of an image has a significant impact on the identification accuracy of pre-trained image classifiers. To address this problem, we propose a generative adversarial network with dual-attention and topology-fusion mechanisms called DATFGAN. This network can effectively transform unclear images into clear and high-resolution images. Additionally, the weight sharing scheme in our proposed network can significantly reduce the number of parameters. Experimental results demonstrate that DATFGAN yields more visually pleasing results than state-of-the-art methods. Additionally, treated images are evaluated based on identification tasks. The results demonstrate that the proposed method significantly outperforms other methods and is sufficiently robust for practical use.

The growth of the most significant field crops such as rice, wheat, maize, and soybean are influenced because of various pests. And crop production is decreased due to various categories of insects. Deep learning technologies significantly increased the efficiency of identifying and controlling agricultural pests attack. However, agricultural pests images obtained are often obscure and unclear because of the sparse density of cameras deployed in the real farmland. This always makes pests difficult to recognize and monitor. Additionally, the existing classification and segmentation methods are not satisfying for the identification of low-resolution images because they are pre-trained on the clear and high-resolution datasets. Therefore, it is crucial to restore and upscale the obtained low-resolution pest images in order to improve classification accuracy and the recall rate of the instance segmentation. In this paper, we propose a generative adversarial network (GAN) with quadra-attention and residual and dense fusion mechanisms to transform low-resolution pest images. Compared with previous state-of-the-art PSNR-oriented super-resolution methods, our proposed method is more powerful in image reconstruction and achieves the state of the art performance. The experiment results show that after reconstructing with our proposed gan, the recall rate increased by 182.89% and classification accuracy also improved a lot. Besides, our proposed method could decrease the density of the camera layout in the agricultural Internet of Things (IOT) monitor systems and the cost of infrastructure, which is practical for real-world applications.

Understanding the pattern of end-to-end traffic flows in datacenter networks (DCNs) is essential to many DCN designs and operations (e.g., traffic engineering and load balancing). However, little research work has been done to obtain traffic information efficiently and yet accurately. Researchers often assume the availability of traffic tracing tools (e.g., OpenFlow) when their proposals require traffic information as input, but these tools may have high monitoring overhead and consume significant switch resources even if they are available in a DCN. Although estimating the traffic matrix (TM) between origin-destination pairs using only basic switch SNMP counters is a mature practice in IP networks, traffic flows in DCNs show totally different characteristics, while the large number of redundant routes in a DCN further complicates the situation. To this end, we propose to utilize resource provisioning information in public cloud datacenters and the service placement information in private datacenters for deducing the correlations among top-of-rack switches, and to leverage the uneven traffic distribution in DCNs for reducing the number of routes potentially used by a flow. These allow us to develop ATME as an efficient TM estimation scheme that achieves high accuracy for both public and private DCNs. We compare our two algorithms with two existing representative methods through both experiments and simulations; the results strongly confirm the promising performance of our algorithms.

This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called "deep belief network online quarter-sphere support vector machine," which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%-5%.