Designated Verification of Non-invertible Watermark
Digital watermark has been proposed as the means for copyright protection of multimedia data in network-based distribution framework. However, existing digital watermarking mechanisms have some problems that they cannot resolve the rightful ownership alone. Specially, someone can provide counterfeited watermark which can be performed on a watermarked image to allow multiple claims of rightful ownership. In this paper, we use designated verification method for undeniable signatures in the watermarking process. The confirmer of watermark can sure that only the intended verifier(s) can be convinced about the validity or invalidity of the signature.

title: High Quality Perceptual Steganographic Techniques
abstract: Recently, several steganographic algorithms for two-color binary images have been proposed [16,1,14,2]. In this paper, we propose steganographic algorithms which embeds a secret message into bitmap images and palette-based images. To embed a message, the suggested algorithm divides a bitmap image into bit-plane images from LSB-plane to MSB-plane for each pixel, and considers each bit-plane image as a binary one. The algorithm splits each bit-plane image into m × n blocks, and embeds a r-bit(\(r = \lfloor {\rm log_2}(mn + 1) \rfloor - 1\)) message into the block. And our schemes embed a message to every bit-plane from LSB to MSB to maximize the amount of embedded message and to minimize the degradation. The schemes change at most two pixels in each block. Therefore, the maximal color changes of the new algorithm are much smaller than other bit-plane embedding schemes’ such as the sequential substitution schemes [10,7,11].

title: Estimating network proximity and latency
abstract: Network proximity and latency estimation is an important component in discovering and locating services and applications. With the growing number of services and service providers in the large-scale Internet, accurately estimating network proximity/latency with minimal probing overhead becomes essential for scalable deployment. Although there exist a number of network distance estimation schemes, they either rely on extensive infrastructure support, require the IP address of the potential targets, falsely cluster distant nodes, or perform poorly with even few measurement errors. We propose Netvigator, a scalable network proximity and latency estimation tool that uses information obtained from probing a small number of landmark nodes and intermediate routers (termed milestones) that are discovered en route to the landmarks, to identify the closest nodes. With very little additional probing overhead, Netvigator uses distance information to the milestones to accurately locate the closest nodes. We developed a Netvigator prototype and report our performance evaluation on PlanetLab and in the intranet of a large enterprise. Netvigator is a running service on PlanetLab as a part of HP Labs' S3 (Scalable Sensing Service).

title: An Efficient Neighbor Knowledge Based Broadcasting for Mobile Ad Hoc Networks
abstract: Although a flooding is the most commonly used method for one-to-all communications, it often results in huge traffic in mobile ad hoc networks. Several optimization schemes have been proposed to alleviate this problem. However, none of them offers a complete solution and has their own limitations, such as, lack of reliability, high latency or excessive control packet overhead. In this paper, we propose the FONIAH (Flooding based on One- hop Neighbor Information and Adaptive Holding) scheme to overcome these limitations.

title: Drug risk assessment with determining the number of sub-populations under finite mixture normal models
abstract: In some biological experiments, it is quite common that laboratory subjects may be different in their patterns of susceptibility to a treatment. In these situations, finite mixture model analysis becomes a useful tool. Under a finite mixture normal model, drug risk assessment problems are studied under the assumption that the population at risk consists of multiple sub-types of different susceptibilities. The EM algorithm and its extension are utilized to obtain the maximum likelihood estimates of the model. Particular discussion is given to the model selection problem based on Bayes factor, which is approximated by the Schwarz criterion. Meantime, the practical significance of the proposed method is illustrated with an actual dose–response data set.

title: Updatability in Federated Database Systems
abstract: It is important to support updates in federated database systems. However, not all updates on the federated schema are possible because some may violate certain constraints in the local databases which are involved in the federation. In this paper, we give a formal framework which characterizes the conditions under which a federated schema object type is updatable. We study the steps involved to systematically map an update request on an external view of a federated schema into the equivalent update(s) on the local databases. We also consider the situation where semantically equivalent object types may not model exactly the same set of objects in the real world. We ensure that the set constraints (EQUAL, SUBSET, DISJOINT) between the instance sets of equivalent object types are not violated after an update.

title: Merging Back-propagation and Hebbian Learning Rules for Robust Classifications
abstract: By imposing saturation requirements on hidden-layer neural activations, a new learning algorithm is developed to improve robustness on classification performance of a multi-layer Perceptron. Derivatives of the sigmoid functions at hidden-layers are added to the standard output error with relative significance factors, and the total error is minimized by the steepest-descent method. The additional gradient-descent terms become Hebbian, and this new algorithm merges two popular learning algorithms, i.e., error back-propagation and Hebbian learning rules. Only slight modifications are needed for the standard back-propagation algorithm, and additional computational requirements are negligible. This saturation requirement effectively reduces output sensitivity to the input, which results in improved robustness and better generalization for classifier networks. Also distributed representations at hidden-layers are successfully suppressed to accomplish efficient utilization of hidden neurons. Computer simulations demonstrates much faster learning convergence as well as improved robustness for classifications and hetero-associations of binary patterns.