In this paper, a new steganography scheme with high embedding payload and good visual quality is presented. Before embedding process, secret information is encoded as block using Reed-Muller error correction code. After data encoding and embedding into the low-order bits of host image, modulus function is used to increase visual quality of stego image. Since the proposed method is able to embed...

In this paper, a high capacity reversible data hiding technique using parametric binary tree labeling scheme is proposed. The proposed used to label plaintext image’s pixels as two different categories, regular and irregular pixels, through symmetric or asymmetric process. Regular are only utilized for secret payload embedding whereas not utilized. efficiently exploits intra-block correlation, ...

<span lang="EN-US">With the rising number of accidents in Indonesia, it is still necessary to evaluate and analyze accident data. The categorization traffic data has been developed using word embedding, however additional work needed achieve better results. Several informative named entities are frequently sufficient differentiate whether or not information on a exists. Named-entities inf...

In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is timeconsuming, whereas approaches not relying on human supervision are scalable but less effective. To reduce the amount of human supervision for label noise cleaning, we...

We propose a method for domain adaptation on graphs. Given sufficiently many observations of the label function on a source graph, we study the problem of transferring the label information from the source graph to a target graph for estimating the target label function. Our assumption about the relation between the two domains is that the frequency content of the label function, regarded as a ...

We consider the problem of active learning when the categories are represented as a tree with leaf nodes as outputs and internal nodes as clusters of the outputs at multiple granularity. Recent work has improved the traditional techniques by moving beyond ”flat” structure through incorporation of the label hierarchy into the uncertainty measure. However, these methods have two major limitations...

Network embedding methods have garnered increasing aŠention because of their e‚ectiveness in various information retrieval tasks. Œe goal is to learn low-dimensional representations of vertexes in an information network and simultaneously capture and preserve the network structure. Critical to the performance of a network embedding method is how the edges/vertexes of the network is sampled for ...

Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection. Existing methods are largely focused on finding distributed representations for unsigned networks and are unable to discover embeddings that respect ...

Background There is a growing trend for patients to seek the least invasive treatments with less risk of complications and downtime for facial rejuvenation. Thread embedding acupuncture has become popular as a minimally invasive treatment. However, there is little clinical evidence in the literature regarding its effects. Methods This single-arm, prospective, open-label study recruited partic...