Lossless Authentication Watermarking Based on Adaptive Modular Arithmetic

Reversible watermarking schemes based on modulo-256 addition may cause annoying salt-and-pepper noise. To avoid the salt-and-pepper noise, a reversible watermarking scheme using human visual perception characteristics and adaptive modular arithmetic is proposed. First, a high-bit residual image is obtained by extracting the most significant bits (MSB) of the original image, and a new spatial visual perception model is built according to the high-bit residual image features. Second, the watermark strength and the adaptive divisor of modulo operation for each pixel are determined by the visual perception model. Finally, the watermark is embedded into different least significant bits (LSB) of original image with adaptive modulo addition. The original image can be losslessly recovered if the stego-image has not been altered. Extensive experiments show that the proposed algorithm eliminates the salt-and-pepper noise effectively, and the visual quality of the stego-image with the proposed algorithm has been dramatically improved over some existing reversible watermarking algorithms. Especially, the stegoimage of this algorithm has about 9.9864 dB higher PSNR value than that of modulo-256 addition based reversible watermarking scheme

Lost in Translation: What Linguistic Measurements Best Measure Text Quality of Online Listings

Ecommerce websites are filled with international sellers. Product descriptions on these sites are often written in English by non-native speakers. Linguistic imperfections in these descriptions confuse consumers, which may further attenuate their purchase intentions. How descriptive quality/efficacy can be defined and then improved shall be of great interest to all sellers and their consumers. In this research, we attempt to evaluate online product description quality using lexical measurements from linguistics studies. Linguistics measurements of writing quality were mostly developed in pure academic settings. We test and analyze these measurements\u27 applicability in defining and contrasting business description quality using Amazon.com data. Modern classification techniques in the artificial intelligence and machine learning field are deployed in identifying measurement applicability and assessing computational efficiency. Our findings enable automatic identification of descriptive efficacy through artificial intelligence methods on real ecommerce text data

Thermodynamics of percolation in interacting systems

Interacting systems can be studied as the networks where nodes are system units and edges denote correlated interactions. Although percolation on network is a unified way to model the emergence and propagation of correlated behaviours, it remains unknown how the dynamics characterized by percolation is related to the thermodynamics of phase transitions. It is non-trivial to formalize thermodynamics for most complex systems, not to mention calculating thermodynamic quantities and verifying scaling relations during percolation. In this work, we develop a formalism to quantify the thermodynamics of percolation in interacting systems, which is rooted in a discovery that percolation transition is a process for the system to lose the freedom degrees associated with ground state configurations. We derive asymptotic formulas to accurately calculate entropy and specific heat under our framework, which enables us to detect phase transitions and demonstrate the Rushbrooke equality (i.e., $\alpha+2\beta+\gamma=2$) in six representative complex systems (e.g., Bernoulli and bootstrap percolation, classical and quantum synchronization, non-linear oscillations with damping, and cellular morphogenesis). These results suggest the general applicability of our framework in analyzing diverse interacting systems and percolation processes

Cross-Scale Cost Aggregation for Stereo Matching

Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an inter-scale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.Comment: To Appear in 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2014 (poster, 29.88%

A way of relating instantaneous and finite screws based on the screw triangle product

It has been a desire to unify the models for structural and parametric analyses and design in the field of robotic mechanisms. This requires a mathematical tool that enables analytical description, formulation and operation possible for both finite and instantaneous motions. This paper presents a method to investigate the algebraic structures of finite screws represented in a quasi-vector form and instantaneous screws represented in a vector form. By revisiting algebraic operations of screw compositions, this paper examines associativity and derivative properties of the screw triangle product of finite screws and produces a vigorous proof that a derivative of a screw triangle product can be expressed as a linear combination of instantaneous screws. It is proved that the entire set of finite screws forms an algebraic structure as Lie group under the screw triangle product and its time derivative at the initial pose forms the corresponding Lie algebra under the screw cross product, allowing the algebraic structures of finite screws in quasi-vector form and instantaneous screws in vector form to be revealed.

Investigation of relaxation factor in landweber iterative algorithm for electrical capacitance tomography

It is crucial to select a suitable relaxation factor in iterative image reconstruction algorithms (e.g. Landweber iterative algorithm) for electrical capacitance tomography (ECT) because it affects the convergence. By simulation, it is found notably that the relaxation factor should be selected adaptively according to the sensor structure (e.g. the number of electrodes) and the permittivity distribution in capacitance measurements. With different number of electrodes and four typical permittivity distributions, the relaxation factor and the related convergence are investigated in consideration of the change in relative image error. It is shown that the relaxation factor can be chosen based on the upper boundary of all relaxation factors. The conclusions in this paper can be used for practical industrial processes, regarding the adaptive selection of relaxation factor and the number of iterations needed

Information Evolution in Complex Networks

Many biological phenomena or social events critically depend on how information evolves in complex networks. A seeming paradox of the information evolution is the coexistence of local randomness, manifested as the stochastic distortion of information content during individual-individual diffusion, and global regularity, illustrated by specific non-random patterns of information content on the network scale. The current research pursues to understand the underlying mechanisms of such coexistence. Applying network dynamics and information theory, we discover that a certain amount of information, determined by the selectivity of networks to the input information, frequently survives from random distortion. Other information will inevitably experience distortion or dissipation, whose speeds are shaped by the diversity of information selectivity in networks. The discovered laws exist irrespective of noise, but the noise accounts for their intensification. We further demonstrate the ubiquity of our discovered laws by applying them to analyze the emergence of neural tuning properties in the primary visual and medial temporal cortices of animal brains and the emergence of extreme opinions in social networks