A novel reversible robust watermarking algorithm based on chaotic system

Reversible watermarking can be applied to the protection for important digital media, such as medical and military image, it allows the watermark to be extracted and the original image to be restored completely, but reversible watermarking with stronger robustness is seldom discussed in existing literature. In this paper, a novel reversible watermarking algorithm based on chaotic system is proposed; chaotic system is not only used to search space of reversibility of the scheme, but also used to randomly select the position of watermarking embedding. Consequently, the proposed scheme achieves larger threshold space of reversibility and better performance of security. For some specific thresholds, the proposed algorithm is not only reversible, but also has stronger robustness against image compression. The experimental results show that the ergodicity and sensitivity to initial values of chaotic system play an important role in reversible watermarking algorithm, and the proposed scheme can achieve balance between reversibility and robustness with the help of chaotic system.     

Uncertain term structure model of interest rate

Term structure models describe the evolution of the yield curve through time, without considering the influence of risk, tax, etc. Recently, uncertain processes were initialized and applied to option pricing and currency model. Under the assumption of short interest rate following uncertain processes, this study investigates the term-structure equation. This equation is first derived for valuing zero-coupon bond. Finally, analytic solutions of the uncertain interest rate equation are given when the process of interest rate is assumed to be the uncertain counterparts of the Ho-Lee model and Vasicek model, respectively.     

On the security and improvement of a two-factor user authentication scheme in wireless sensor networks

User authentication is a basic security requirement during the deployment of the wireless sensor network (WSN), because it may operate in a rather hostile environment, such as a military battlefield. In 2010, Khan and Alghathbar (KA) found out that Das’s two-factor user authentication scheme for WSNs is vulnerable to the gateway node (GW-node) bypassing attack and the privileged-insider attack. They further presented an improved scheme to overcome the security flaws of Das’s scheme. However, in this paper, we show that KA’s scheme still suffers from the GW-node impersonation attack, the GW-node bypassing attack, and the privileged-insider attack. Hence, to fix the security flaws in KA’s scheme, we propose a new user authentication scheme for WSNs. The security of the user authentication session in the proposed scheme is reduced by the model of Bellare and Rogaway. The security of partial compromise of secrets in the proposed scheme is reduced and analyzed by our adversarial model. Based on the performance evaluation, the overall cost of the proposed scheme is less than that of KA’s scheme. Hence, we believe that the proposed scheme is more suitable for real security applications than KA’s scheme.     

Spectral clustering with eigenvector selection

The task of discovering natural groupings of input patterns, or clustering, is an important aspect of machine learning and pattern analysis. In this paper, we study the widely used spectral clustering algorithm which clusters data using eigenvectors of a similarity/affinity matrix derived from a data set. In particular, we aim to solve two critical issues in spectral clustering: (1) how to automatically determine the number of clusters, and (2) how to perform effective clustering given noisy and sparse data. An analysis of the characteristics of eigenspace is carried out which shows that (a) not every eigenvectors of a data affinity matrix is informative and relevant for clustering; (b) eigenvector selection is critical because using uninformative/irrelevant eigenvectors could lead to poor clustering results; and (c) the corresponding eigenvalues cannot be used for relevant eigenvector selection given a realistic data set. Motivated by the analysis, a novel spectral clustering algorithm is proposed which differs from previous approaches in that only informative/relevant eigenvectors are employed for determining the number of clusters and performing clustering. The key element of the proposed algorithm is a simple but effective relevance learning method which measures the relevance of an eigenvector according to how well it can separate the data set into different clusters. Our algorithm was evaluated using synthetic data sets as well as real-world data sets generated from two challenging visual learning problems. The results demonstrated that our algorithm is able to estimate the cluster number correctly and reveal natural grouping of the input data/patterns even given sparse and noisy data.     

Coordinated ordering decisions for products with short lifecycle and variable selling price

The supplier–buyer coordination is an important policy in the supply chain management. The buyer in the two-echelon inventory system with regular selling season has to face the uncertainty of customer demand, supplier’s delivery time and variable price change. At the same time, the supplier has to consider the inventory holding and delay cost. The objective of this study is to develop an integrated supply chain strategy for products with short lifecycle and variable selling price to entice cooperation. The strategy must provide a win–win situation for both the supplier and the buyer. A numerical case example, sensitivity analysis and compensation mechanism are given to illustrate the model.     

Supervised and Reinforcement Evolutionary Learning for Wavelet-based Neuro-fuzzy Networks

This study presents a wavelet-based neuro-fuzzy network (WNFN). The proposed WNFN model combines the traditional Takagi–Sugeno–Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions as wavelet neural network bases. A novel supervised evolutionary learning, called WNFN-S, is proposed to tune the adjustable parameters of the WNFN model. The proposed WNFN-S learning scheme is based on dynamic symbiotic evolution (DSE). The proposed DSE uses the sequential-search-based dynamic evolutionary (SSDE) method. In some real-world applications, exact training data may be expensive or even impossible to obtain. To solve this problem, the reinforcement evolutionary learning, called WNFN-R, is proposed. Computer simulations have been conducted to illustrate the performance and applicability of the proposed WNFN-S and WNFN-R learning algorithms.     

Stabilizing Redox-Active Hexaazatriphenylene in a 2D Conductive Metal–Organic Framework for Improved Lithium Storage Performance

Organic redox-active materials are promising electrode candidates for lithium-ion batteries by virtue of their designable structure and cost-effectiveness. However, their poor electrical conductivity and high solubility in organic electrolytes limit the device's performance and practical applications. Herein, the π-conjugated nitrogen-containing heteroaromatic molecule hexaazatriphenylene (HATN) is strategically embedded with redox-active centers in the skeleton of a Cu-based 2D conductive metal–organic framework (2D c-MOF) to optimize the lithium (Li) storage performance of organic electrodes, which delivers improved specific capacity (763 mAh g⁻¹ at 300 mA g⁻¹), long-term cycling stability (≈90% capacity retention after 600 cycles at 300 mA g⁻¹), and excellent rate performance. The correlation of experimental and computational results confirms that this high Li storage performance derives from the maximum number of active sites (CN sites in the HATN unit and CO sites in the CuO₄ unit), favorable electrical conductivity, and efficient mass transfer channels. This strategy of integrating multiple redox-active moieties into the 2D c-MOF opens up a new avenue for the design of high-performance electrode materials.     

Aggregation-induced emission luminogen-assisted stimulated emission depletion nanoscopy for super-resolution mitochondrial visualization in live cells

Nano Research - Aggregation-induced emission luminogens (AIEgens) are fluorescent agents that are ideal for bioimaging and have been widely used for organelle targeting, cellular mapping, and...