With the rapid development of Internet technology and the advent of the era of big data, more and more cyber security texts are provided on the Internet. These texts include not only security concepts, incidents, tools, guidelines, and policies, but also risk management approaches, best practices, assurances, technologies, and more. Through the integration of large-scale, heterogeneous, unstructured cyber security information, the identification and classification of cyber security entities can help handle cyber security issues. Due to the complexity and diversity of texts in the cyber security domain, it is difficult to identify security entities in the cyber security domain using the traditional named entity recognition (NER) methods. This paper describes various approaches and techniques for NER in this domain, including the rule-based approach, dictionary-based approach, and machine learning based approach, and discusses the problems faced by NER research in this domain, such as conjunction and disjunction, non-standardized naming convention, abbreviation, and massive nesting. Three future directions of NER in cyber security are proposed: (1) application of unsupervised or semi-supervised technology; (2) development of a more comprehensive cyber security ontology; (3) development of a more comprehensive deep learning model.
Quality control is of vital importance in compressing three-dimensional (3D) medical imaging data. Optimal compression parameters need to be determined based on the specific quality requirement. In high efficiency video coding (HEVC), regarded as the state-of-the-art compression tool, the quantization parameter (QP) plays a dominant role in controlling quality. The direct application of a video-based scheme in predicting the ideal parameters for 3D medical image compression cannot guarantee satisfactory results. In this paper we propose a learning-based parameter prediction scheme to achieve efficient quality control. Its kernel is a support vector regression (SVR) based learning model that is capable of predicting the optimal QP from both video-based and structural image features extracted directly from raw data, avoiding time-consuming processes such as pre-encoding and iteration, which are often needed in existing techniques. Experimental results on several datasets verify that our approach outperforms current video-based quality control methods.
The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system. With the help of deep neural networks, the convolutional neural network or residual neural network, which can be applied only to regular grids, is adopted to capture the spatial dependence for flow prediction. However, the obtained regions are always irregular considering the road network and administrative boundaries; thus, dividing the city into grids is inaccurate for prediction. In this paper, we propose a new model based on multi-graph convolutional network and gated recurrent unit (MGCN-GRU) to predict traffic flows for irregular regions. Specifically, we first construct heterogeneous inter-region graphs for a city to reflect the relationships among regions. In each graph, nodes represent the irregular regions and edges represent the relationship types between regions. Then, we propose a multi-graph convolutional network to fuse different inter-region graphs and additional attributes. The GRU is further used to capture the temporal dependence and to predict future traffic flows. Experimental results based on three real-world large-scale datasets (public bicycle system dataset, taxi dataset, and dockless bike-sharing dataset) show that our MGCN-GRU model outperforms a variety of existing methods.
We propose a joint feature and metric learning deep neural network architecture, called the associative affinity network (AAN), as an affinity model for multi-object tracking (MOT) in videos. The AAN learns the associative affinity between tracks and detections across frames in an end-to-end manner. Considering flawed detections, the AAN jointly learns bounding box regression, classification, and affinity regression via the proposed multi-task loss. Contrary to networks that are trained with ranking loss, we directly train a binary classifier to learn the associative affinity of each track-detection pair and use a matching cardinality loss to capture information among candidate pairs. The AAN learns a discriminative affinity model for data association to tackle MOT, and can also perform single-object tracking. Based on the AAN, we propose a simple multi-object tracker that achieves competitive performance on the public MOT16 and MOT17 test datasets.
Optimal transportation plays a fundamental role in many fields in engineering and medicine, including surface parameterization in graphics, registration in computer vision, and generative models in deep learning. For quadratic distance cost, optimal transportation map is the gradient of the Brenier potential, which can be obtained by solving the Monge-Ampère equation. Furthermore, it is induced to a geometric convex optimization problem. The Monge-Ampère equation is highly non-linear, and during the solving process, the intermediate solutions have to be strictly convex. Specifically, the accuracy of the discrete solution heavily depends on the sampling pattern of the target measure. In this work, we propose a self-adaptive sampling algorithm which greatly reduces the sampling bias and improves the accuracy and robustness of the discrete solutions. Experimental results demonstrate the efficiency and efficacy of our method.
This paper studies the transfer path planning problem for safe transfer of an aircraft on the aircraft carrier flight deck under a poor visibility condition or at night. First, we analyze the transfer path planning problem for carrier-based aircraft on the flight deck, and define the objective to be optimized and the constraints to be met. Second, to solve this problem, the mathematical support models for the flight deck, carrier aircraft entity, entity extension, entity posture, entity conflict detection, and path smoothing are established, as they provide the necessary basis for transfer path planning of the aircraft on the aircraft carrier. Third, to enable automatic transfer path planning, we design a multi-habitat parallel chaos algorithm (called KCMPSO), and use it as the optimization method for transfer path planning. Finally, we take the Kuznetsov aircraft carrier as a verification example, and conduct simulations. The simulation results show that compared with particle swarm optimization, this method can solve the transfer path planning problem for an aircraft on the aircraft carrier flight deck better.
Soft sensors are widely used to predict quality variables which are usually hard to measure. It is necessary to construct an adaptive model to cope with process non-stationaries. In this study, a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables. Specifically, a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity measurement algorithm. The proposed soft sensing method tries to find a general approach for non-stationary processes via qualityrelated information where the concepts of local similarities and window confidence are explained in detail. The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column. It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.
Heterogeneous vehicular clustering integrates multiple types of communication networks to work efficiently for various vehicular applications. One popular form of heterogeneous network is the integration of long-term evolution (LTE) and dedicated short-range communication. The heterogeneity of such a network infrastructure and the non-cooperation involved in sharing cost/data are potential problems to solve. A vehicular clustering framework is one solution to these problems, but the framework should be formally verified and validated before being deployed in the real world. To solve these issues, first, we present a heterogeneous framework, named destination and interest-aware clustering, for vehicular clustering that integrates vehicular ad hoc networks with the LTE network for improving road traffic efficiency. Then, we specify a model system of the proposed framework. The model is formally verified to evaluate its performance at the functional level using a model checking technique. To evaluate the performance of the proposed framework at the micro-level, a heterogeneous simulation environment is created by integrating state-of-the-art tools. The comparison of the simulation results with those of other known approaches shows that our proposed framework performs better.
This paper presents a methodology of designing an amplifier integrated with a microstrip filter using an active coupling matrix. The microstrip filter is directly coupled to the active device, and the integrated filter amplifier can achieve filtering as well as matching functionalities, simultaneously, eliminating the need for separate matching networks. The filter amplifier is represented by an active coupling matrix, with additional columns and rows in the matrix corresponding to the active transistor. The matrix can be used to calculate the S-parameter responses (i.e., the return loss and the gain) and the initial dimensions of the integrated device. Moreover, the integration of a filter and an amplifier leads to a reduced loss and a more compact architecture of the devices. An X-band microstrip filter amplifier has been designed and demonstrated as an example. Microstrip technology has been chosen because of its appealing advantages of easy fabrication, low cost, and most importantly, easy integration with active devices.
A tunable stepped-impedance resonator using liquid crystal is demonstrated. Two resonant frequencies at 3.367 and 7.198 GHz are realized and can be continuously tuned by external applied voltages. Continuous tunable ranges of 52 and 210 MHz have been achieved at a particularly low driving voltage of 14 V, which shows good agreement with the simulation results. The voltage-induced hysteresis phenomenon is also investigated. This device also has a low insertion loss of −2.9 and −4 dB for the two resonant frequencies and the return losses are less than −21.5 dB. This work provides a new protocol to realize a tunable frequency for communication systems.