Abstract machine modelling is a technique used frequently in developing the retargetable compilers. By translating the abstract machine operations into target machine instructions, we can construct retargetable compilers. However, such a technique will cause two problems. First, the code produced by the compilers is inefficient. Next, in order to emit the efficient code, the compilation time is too long. In view of these two disadvantages, we apply PO (peephole optimizer) in our retargetable compilers to do code optimization. Peephole optimizer searches for the adjacent instruction candidates in the intermediate code, and then replaces them with equivalent instructions of less cost. Furthermore, the peephole description table consists of simple tree-rewriting rules which are easily retargeted into different machines. At the same time, we have proposed a simple peephole pattern matching algorithm to reduce the peephole pattern matching time. The experiment indicates that the machine code generated by our compiler runs faster than that by GNU c compiler (gcc).

A modified cryptographic key assignment scheme for the dynamic access control in a group-oriented user hierarchy is presented. In the partially ordered set (poset, for short) user hierarchy (GjGi) embedded in a group-oriented (t, n) threshold cryptosystem, the source group Gi has higher security clearance to access the information items held in the target group Gj. If a target group Gj has multipe paths reachable from a source group Gi, we must choose the least cost path to rapidly resolve the dynamic access control problem Furthermore, multiple threshold values are also considered in order to meet the different security requirements.

A novel cryptographic key assignment scheme for dynamic access control in a user hierarchy is presented. Based on Rabin's public key system and Chinese remainder theorem, each security class SCi is assigned a secret key Ki and some public parameters. In our scheme, a secret key is generated in a bottom-up manner so as to reduce the computation time for key generation and the storage size for public parameters. We also show that our proposed scheme is not only secure but also efficient.

According to the grey data generating techniques in grey system theory, we propose a novel cryptosystem, whose applications can develop a new direction in the field of information security. In this paper, we present the concepts of sum-lock, difference-lock, sum-ladder, and difference-ladder. By using these concepts, we can obtain a cryptosystem with lock generation and sum-difference replacement ladder. In addition, we provide the encryption and decryption algorithms of our cryptosystem and adopt an illustrative example to verify it.

An attention residual network with triple feature as input is proposed to predict the remaining useful life (RUL) of bearings. First, the channel attention and spatial attention are connected in series into the residual connection of the residual neural network to obtain a new attention residual module, so that the newly constructed deep learning network can better pay attention to the weak changes of the bearing state. Secondly, the “triple feature” is used as the input of the attention residual network, so that the deep learning network can better grasp the change trend of bearing running state, and better realize the prediction of the RUL of bearing. Finally, The method is verified by a set of experimental data. The results show the method is simple and effective, has high prediction accuracy, and reduces manual intervention in RUL prediction.

In this letter, we introduce a novel keys distribution optimization scheme for CP-ABE based access control. This scheme integrates roles, role hierarchies and objects grouping to accelerate keys distribution, meanwhile the CP-ABE encrypting overhead is reduced by adopting deterministic cryptographic function. Experiments show that our scheme obtains noticeable improvement over the original one, especially when the number of objects is much greater than that of users.

This study employs secret codes and secret keys based on the elliptic curve to construct an elliptic curve cryptosystem with a dynamic access control system. Consequently, the storage space needed for the secret key generated by an elliptic curve dynamic access control system is smaller than that needed for the secret key generated by exponential operation built on the secure filter (SF) dynamic access control system. Using the elliptic curve to encrypt/decrypt on the secure filter improves the efficiency and security of using exponential operation on the secure filter in the dynamic access control system. With the proposed dynamic elliptic curve access control system, the trusted central authority (CA) can add/delete classes and relationships and change the secret keys at any time to achieve an efficient control and management. Furthermore, different possible attacks are used to analyze the security risks. Since attackers can only obtain the general equations for the elliptic curve dynamic access control system, they are unable to effectively perform an elliptic curve polynomial (ECP) conversion, or to solve the elliptic curve discrete logarithm problem (ECDLP). Thus, the proposed elliptic curve dynamic access control system is secure.

Video text detection (VTD) aims to localize text instances in videos, which has wide applications for downstream tasks. To deal with the variances of different scenes and text instances, multiple models and feature fusion strategies were typically integrated in existing VTD methods. A VTD method consisting of sophisticated components can efficiently improve detection accuracy, but may suffer from a limitation for real-time applications. This paper aims to achieve real-time VTD with an adaptive lightweight end-to-end framework. Different from previous methods that represent text in a spatial domain, we model text instances in the Fourier domain. Specifically, we propose a scale-aware Fourier Contour Embedding method, which not only models arbitrary shaped text contours of videos as compact signatures, but also adaptively select proper scales for features in a backbone in the training stage. Then, we construct VTD-FCENet to achieve real-time VTD, which encodes temporal correlations of adjacent frames with scale-aware FCE in a lightweight and adaptive manner. Quantitative evaluations were conducted on ICDAR2013 Video, Minetto and YVT benchmark datasets, and the results show that our VTD-FCENet not only obtains the state-of-the-arts or competitive detection accuracy, but also allows real-time text detection on HD videos simultaneously.

After exploring the classic Lambertian reflectance model, we proposed an effective illumination estimation model to extract illumination invariants for face recognition under complex illumination conditions in this paper. The estimated illumination by our method not only meets the actual lighting conditions of facial images, but also conforms to the imaging principle. Experimental results on the combined Yale B database show that the proposed method can extract more robust illumination invariants, which improves face recognition rate.

Computer aided design (CAD) technology is widely used for architectural design, but current CAD tools still require high-level design specifications from human. It would be significant to construct an intelligent CAD system allowing automatic architectural layout parsing (AutoALP), which generates candidate designs or predicts architectural attributes without much user intervention. To tackle these problems, many learning-based methods were proposed, and benchmark dataset become one of the essential elements for the data-driven AutoALP. This paper proposes a new dataset called SCUT-AutoALP for multi-paradigm applications. It contains two subsets: 1) Subset-I is for floor plan design containing 300 residential floor plan images with layout, boundary and attribute labels; 2) Subset-II is for urban plan design containing 302 campus plan images with layout, boundary and attribute labels. We analyzed the samples and labels statistically, and evaluated SCUT-AutoALP for different layout parsing tasks of floor plan/urban plan based on conditional generative adversarial networks (cGAN) models. The results verify the effectiveness and indicate the potential applications of SCUT-AutoALP. The dataset is available at https://github.com/designfuturelab702/SCUT-AutoALP-Database-Release.

This paper introduces a 1-inkdot two-way alternating pushdown automaton which is a two-way alternating pushdown automaton (2apda) with the additional power of marking at most 1 tape-cell on the input (with an inkdot) once. We first investigate a relationship between the accepting powers of sublogarithmically space-bounded 2apda's with and without 1 inkdot, and show, for example, that sublogarithmically space-bounded 2apda's with 1 inkdot are more powerful than those which have no inkdots. We next investigate an alternation hierarchy for sublogarithmically space-bounded 1-inkdot 2apda's, and show that the alternation hierarchy on the first level for 1-inkdot 2apda's holds, and we also show that 1-inkdot two-way nondeterministic pushdown automata using sublogarithmic space are incomparable with 1-inkdot two-way alternating pushdown automata with only universal states using the same space.