Group signature allows the anonymity of a real signer in a group to be revoked by a trusted party called group manager. It also gives the group manager the absolute power of controlling the formation of the group. Ring signature, on the other hand, does not allow anyone to revoke the signer anonymity, while allowing the real signer to form a group (also known as a ring) {\it arbitrarily} without being controlled by any other party. In this paper, we propose a new variant for ring signature, called {\it Revocable Ring Signature}. The signature allows a real signer to form a ring arbitrarily while allowing a set of authorities to revoke the anonymity of the real signer. This new variant inherits the desirable properties from both group signature and ring signature in such a way that the real signer will be responsible for what it has signed as the anonymity is revocable by authorities while the real signer still has the freedom on ring formation. We provide a formal security model for revocable ring signature and propose an efficient construction which is proven secure under our security model.

In this paper, we propose a secure semi-fragile watermarking technique based on integer wavelet transform with a choice of two watermarks to be embedded. A self-recovering algorithm is employed, that hides the image digest into some wavelet subbands for detecting possible illicit object manipulation undergone in the image. The semi-fragility makes the scheme tolerant against JPEG lossy compression with the quality factor as low as 70\%, and locates the tampered area accurately. In addition, the system ensures more security because the embedded watermarks are protected with private keys. The computational complexity is reduced by using parameterized integer wavelet transform. Experimental results show that the proposed scheme guarantees safety of a watermark, recovery of image and localization of tampered area.

We propose {\em short} group signature (GS) schemes which are provably secure {\em without} random oracles. Our basic scheme is about 14 times shorter than the Boyen-Waters GS scheme at Eurocrypt 2006, and 42\% shorter than the recent GS schemes due to Ateniese {\em et al}. The security proofs are provided in the Universally Composable model, which allows the proofs of security valid not only when our scheme is executed in isolation, but also in composition with other secure cryptographic primitives. We also present several new computational assumptions and justify them in the generic group model. These assumptions are useful in the design of high-level protocols and may be of independent interest.

A new private set-operation problem is proposed. Suppose there are $n$ parties with each owning a secret set. Let one of them, say $P$, be the leader, $S$ be $P$'s secret set, and $t$ (less than $n-1$) be a threshold value. For each element $w$ of $S$, if $w$ appears more than $t$ times in the rest parties' sets, then $P$ learns which parties' sets include $w$, otherwise $P$ cannot know whether $w$ appears in any party's set. For this problem, a secure protocol is proposed in the semi-honest model based on semantically secure homomorphic encryption scheme, secure sharing scheme, and the polynomial representation of sets. The protocol only needs constant rounds of communication.

Pseudo-random sequences are used extensively for their high speed and security level and less errors. As a branch, the cyclotomic sequences and the generalized ones are studied widely because of their simple mathematical structures and excellent pseudo-random properties. In 1998, Ding and Helleseth introduced a new generalized cyclotomy which includes the classical cyclotomy as a special case. In this paper, based on the generalized cyclotomy, new generalized cyclotomic sequences with order two and length {\it pq} are constructed. An equivalent definition of the sequences is deduced so that the autocorrelation values of these sequences can be determined conveniently. The construction contributes to the understanding of the periodic autocorrelation structure of cyclotomically-constructed binary sequences, and the autocorrelation function takes on only a few values.

Vegetation ecosystem simulation and visualisation are challenging topics involving multidisciplinary aspects. In this paper, we present a new generic frame for the simulation of natural phenomena through manageable and interacting models. It focuses on the functional growth of large vegetal ecosystems, showing coherence for scales ranging from the individual plant to communities and with a particular attention to the effects of water resource competition between plants. The proposed approach is based on a model of plant growth in interaction with the environmental conditions. These are deduced from the climatic data (light, temperature, rainfall) and a model of soil hydrological budget. A set of layers is used to store the water resources and to build the interfaces between the environmental data and landscape components: temperature, rain, light, altitude, lakes, plant positions, biomass, cycles, etc. At the plant level, the simulation is performed for each individual by a structural-functional growth model, interacting with the plant's environment. Temperature is spatialised, changing according to altitude, and thus locally controls plant growth speed. The competition for water is based on a soil hydrological model taking into account rainfalls, water runoff, absorption, diffusion, percolation in soil. So far, the incoming light radiation is not studied in detail and is supposed constant. However, competition for light between plants is directly taken into account in the plant growth model. In our implementation, we propose a simple architecture for such a simulator and a simulation scheme to synchronise the water resource updating (on a temporal basis) and the plant growth cycles (determined by the sum of daily temperatures). The visualisation techniques are based on sets of layers, allowing both morphological and functional landscape views and providing interesting tools for ecosystem management. The implementation of the proposed frame leads to encouraging results that are presented and illustrate simple academic cases.

3D modeling of trees in real environments is a challenge in computer graphics and computer vision, since the geometric shape and topological structure of trees are more complex than conventional artificial objects. In this paper, we present a multi-process approach that is mainly performed in 2D space to faithfully construct a 3D model of the trunk and main branches of a real tree from a single range image. The range image is first segmented into patches by jump edge detection based on depth discontinuity. Coarse skeleton points and initial radii are then computed from the contour of each patch. Axis directions are estimated using cylinder fitting in the neighborhood of each coarse skeleton point. With the help of axis directions, skeleton nodes and corresponding radii are computed. Finally, these skeleton nodes are hierarchically connected, and improper radii are modified based on plant knowledge. 3D models generated from single range images of real trees demonstrate the effectiveness of our method. The main contributions of this paper are simple reconstruction by virtue of image storage order of single scan and skeleton computation based on axis directions.

Global illumination effects are crucial for virtual plant rendering. Whereas real-time global illumination rendering of plants is impractical, ambient occlusion is an efficient alternative approximation. A tree model with millions of triangles is common, and the triangles can be considered as randomly distributed. The existing ambient occlusion methods fail to apply on such a type of object. In this paper, we present a new ambient occlusion method dedicated to real time plant rendering with limited user interaction. This method is a three-step ambient occlusion calculation framework which is suitable for a huge number of geometry objects distributed randomly in space. The complexity of the proposed algorithm is $O(n)$, compared to the conventional methods with complexities of $O(n^2)$. Furthermore, parameters in this method can be easily adjusted to achieve flexible ambient occlusion effects. With this ambient occlusion calculation method, we can manipulate plant models with millions of organs, as well as geometry objects with large number of randomly distributed components with affordable time, and with perceptual quality comparable to the previous ambient occlusion methods.

This paper presents a novel approach for human identification at a distance using gait recognition. Recognition of a person from their gait is a biometric of increasing interest. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis (PCA), to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. Fourier transform is performed to achieve translation invariant for the gait patterns accumulated from silhouette sequences which are extracted from different circumstances. Kernel PCA is then used to extract higher order relations among the gait patterns for future recognition. A fusion strategy is finally executed to produce a final decision. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles.

Mining causality is essential to provide a diagnosis. This research aims at extracting the causality existing within multiple sentences or EDUs (Elementary Discourse Unit). The research emphasizes the use of causality verbs because they make explicit in a certain way the consequent events of a cause, e.g., Aphids su\pmb ck the sap from rice leaves. Then leaves will shrink. Later, they will become yellow and dry.'. A verb can also be the causal-verb link between cause and effect within EDU(s), e.g., Aphids suck the sap from rice leaves causing leaves to be shrunk' (``causing' is equivalent to a causal-verb link in Thai). The research confronts two main problems: identifying the interesting causality events from documents and identifying their boundaries. Then, we propose mining on verbs by using two different machine learning techniques, Naïve Bayes classifier and Support Vector Machine. The resulted mining rules will be used for the identification and the causality extraction of the multiple EDUs from text. Our multiple EDUs extraction shows 0.88 precision with 0.75 recall from Naïve Bayes classifier and 0.89 precision with 0.76 recall from Support Vector Machine.

Fiducial marker based Augmented Reality has many applications. So far the inner pattern of the fiducial marker is always used to encode the markers. Thus a large portion of the fiducial marker image is used for encoding instead of providing corresponding feature points for pose accuracy. This paper presents a novel method which utilizes directly the projective invariant contained in the positional relation of the corresponding feature points to encode the marker. The proposed method does not require the region of pattern image for encoding any more and can provide more corresponding feature points so that higher pose accuracy can be achieved easily. Many related approaches such as cumulative distribution function, reprojection verification and robust process are proposed to overcome the problem of sensibility of the projective invariant. Experimental results show that the proposed fiducial marker system is reliable and robust, and can provide higher pose accuracy than that achieved by existing fiducial marker systems.

A new variant of HEAPSORT is presented in this paper. The algorithm is not an internal sorting algorithm in the strong sense, since extra storage for $n$ integers is necessary. The basic idea of the new algorithm is similar to the classical sorting algorithm HEAPSORT, but the algorithm rebuilds the heap in another way. The basic idea of the new algorithm is it uses only one comparison at each node. The new algorithm shift walks down a path in the heap until a leaf is reached. The request of placing the element in the root immediately to its destination is relaxed. The new algorithm requires about $n \log n-0.788928n$ comparisons in the worst case and $n\log n-n$ comparisons on the average which is only about $0.4n$ more than necessary. It beats on average even the clever variants of QUICKSORT, if $n$ is not very small. The difference between the worst case and the best case indicates that there is still room for improvement of the new algorithm by constructing heap more carefully.

Protein folding is a relevant computational problem in Bioinformatics, for which many heuristic algorithms have been proposed. This work presents a methodology for the application of differential evolution (DE) to the problem of protein folding, using the bi-dimensional hydrophobic-polar model. DE is a relatively recent evolutionary algorithm, and has been used successfully in several engineering optimization problems, usually with continuous variables. We introduce the concept of genotype-phenotype mapping in DE in order to provide a mapping between the real-valued vector and an actual folding. The methodology is detailed and several experiments with benchmarks are done. We compared the results with other similar implementations. The proposed DE has shown to be competitive, statistically consistent and very promising.

We investigate the problem of listing combinations using a special class of operations, {\it prefix shifts}. Combinations are represented as bitstrings of 0's and 1's, and prefix shifts are the operations of rotating some prefix of a bitstring by one position to left or right. We give a negative answer to an open problem asked by F. Ruskey and A. Williams (Generating combinations by prefix shifts, In Proc. 11th Annual International Computing and Combinatorics Conference 2005, LNCS 3595, Springer, 2005, pp.570$\sim$576), that is whether we can generate combinations by only using three very basic prefix shifts on bitstrings, which are transposition of the first two bits and the rotation of the entire bitstring by one position in either direction (i.e., applying the permutations $\sigma_2$, $\sigma_n$ and $\sigma_n^{-1}$ to the indices of the bitstrings).