Practical chosen-ciphertext secure Hierarchical Identity-Based Broadcast Encryption

We focus on practical Hierarchical Identity-Based Broadcast Encryption (HIBBE) with semantic security against adaptively chosen-ciphertext attacks (CCA2) in the standard model. We achieve this goal in two steps. First, we propose a new HIBBE scheme that is secure against chosen-plaintext attacks (CPA). Compared with the existing HIBBE scheme that is built from composite-order bilinear groups, our construction is based on prime-order bilinear groups. The much better efficiency of group operations in prime-order bilinear groups makes our proposed HIBBE scheme more practical. Then, we convert it into a CCA2-secure scheme at the cost of a one-time signature. Instead of extending one user hierarchy in the Canetti–Halevi–Katz approach from CPA-secure (\(l+1\))-Hierarchical Identity-Based Encryption [(\(l+1\))-HIBE] to CCA2-secure \(l\)-HIBE, our construction merely adds one on-the-fly dummy user in the basic scheme. We formally prove the security of these two schemes in the standard model. Comprehensive theoretical analyses and experimental results demonstrate that the proposed HIBBE schemes achieve desirable performance.     

Formulating and solving a class of optimization problems for high-performance gray world automatic white balance

This paper provides new insights into methods performing automatic white balance for a digitally captured image. It is shown that automatic white balance may be formulated as an optimization problem with explicit definition of objective function, decision variables, and constraints. Three alternative methods of formulating the optimization problem are proposed. It is also shown that fuzzy inference rules, commonly utilized in existing literatures to evaluate to what degree an image satisfying the gray world assumption, may be incorporated into the objective function of the optimization problem. A two-stage adjustment law with constrained search direction is then proposed to update the decision variables. A gradient descent algorithm is employed to numerically solve the problem, which guarantees the convergence and that optimal white balance effort is achieved for most images. Experimental results and a comparative study justify that the proposed methods are preferable to existing methods with regard to the execution time, the algorithmic complexity, and the performance.     

基于网络的智能辅导模式研究与系统设计

针对目前辅导行业的现状,为解决课后辅导的自动化、实时化的问题,分析了学生课后学习掌握知识的过程,提出了一个基于学生课后学习规律的辅导模型,建立了面向问题的多层面知识辅导新模式,构建了基于此模式的网络智能家教系统的框架结构。该系统采用基于知识的自动推理技术使得推理过程得以完整保存来分析后续的多层面辅导信息,采用资源模型分解知识粒度保证抽象度,采用网络服务模式满足用户的实时使用。以学生提出的几何问题为实例,展示了多层面辅导过程,实验结果表明了该模式和系统设计的可行性。     

Invariant object recognition based on combination of sparse DBN and SOM with temporal trace rule

Multimedia Tools and Applications - This paper proposes a trace rule based self-organized map (SOM) model built upon a sparse 2-stage deep belief network (DBN). The combination of SOM and sparse...     

New receipt-free voting scheme using double-trapdoor commitment

It is considered to be the most suitable solution for large scale elections to design an electronic voting scheme using blind signatures and anonymous channels. Based on this framework, Okamoto first proposed a receipt-free voting scheme [30] for large scale elections. However, in the following paper, Okamoto [31] proved that the scheme [30] was not receipt-free and presented two improved schemes. One scheme requires the help of the parameter registration committee and the other needs a stronger physical assumption of the voting booth. In this paper, we utilize the double-trapdoor commitment to propose a new receipt-free voting scheme based on blind signatures for large scale elections. Neither the parameter registration committee nor the voting booth is required in our scheme. We also present a more efficient zero-knowledge proof for secret permutation. Therefore, our scheme is much more efficient than Okamoto’s schemes [30] and [31] with the weaker physical assumptions. Moreover, we prove that our scheme can achieve the desired security properties.     

A new neural network algorithm for planarization problems

To deal with the planarization problem widely used in many applications including routing very-large-scale integration （VLSI） circuits, this paper points out that only when its vertices are arranged in some specific order in a line can a planar graph be embedded on a line without any cross connections or cross edges. Energy function is proposed to meet the need of embedding a graph on a single line and route it correctly. A Hopfield network is designed according to the proposed energy function for such embedding and routing. The advantage of the proposed method is that it not only can detect if a graph is a planar one or not, but also can embed a planar graph or the maximal planar subgraph of a non-planar graph on a single line. In addition, simulated annealing is employed for helping the network to escape from local minima during the running of the Hopfield network. Experiments of the proposed method and its comparison with some existent conventional methods were performed and the results indicate that the proposed method is of great feasibility and effectiveness especially for the planarization problem of large graphs.     

Globally stable adaptive robust tracking control using RBF neural networks as feedforward compensators

In previous adaptive neural network control schemes, neural networks are usually used as feedback compensators. So, only semi-globally uniformly ultimate boundedness of closed-loop systems can be guaranteed, and no methods are given to determine the neural network approximation domain. However, in this paper, it is showed that if neural networks are used as feedforward compensators instead of feedback ones, then we can ensure the globally uniformly ultimate boundedness of closed-loop systems and determine the neural network approximation domain via the bound of known reference signals. It should be pointed out that this domain is very important for designing the neural network structure, for example, it directly determines the choice of the centers of radial basis function neural networks. Simulation examples are given to illustrate the effectiveness of the proposed control approaches.     

无线环境下一种新的证书状态查询方案

分析有线环境现有证书状态查询方案的基础上,提出了一种对传统的 O C S P进行了改进的方案-用户端部分缓存的ocsp方案,并将其与其他证书状态查询方案进行了比较。该方案借鉴了哈希链的思想,具有很高的安全性,可以有效减少用户端及服务器端的运算量,降低带宽负荷,适合用作无线环境下的证书状态查询方案。     

Effects of Load Direction on Performance of Tiliting Pad Journal Bearings

At the previous works for tilting journal pad bearings （TPJBs）, most TPJBs are designed to operate with the load either directly on-pad（LOP） or directly between-pad（LBP）. However, in practice, in some cases, the load direction can change dramatically in operation. With the current requirements for the analysis of dynamic characteristics of the rotor-bearing systems, the effects of the load direction on the bearing performance must be known. So, in this paper, the effects of load direction on the static and dynamic characteristics of TPJBs are obtained with theoretical analysis based on the hydrodynamic lubrication theory, and the influence of load direction on the performance of TPJBs in different structure and operating parameters is also analysed. The results show that the load direction has considerable effects on the static and dynamic characteristics of the TPJBs, especially for the TPJBs that are operating under heavy load and high rotor speed. And for the operating condition that load direction changed rapidly, TPJBs with more pads, smaller length to diameter ratio, larger clearance ratio and smaller perload ratio can reduce the performance fluctuation with the variation of load direction.