The Knowledge Based View of Embedding Strategy for Platforms Enterprises

Dialectically linking with heterogenous suppliers, manufacturers, consumers and resources, emerging e-commerce platforms achieve and maintain competitive advantage. Based on different levels of innovation network content and structure, YEATION and YOUPIN, two typical ecommerce platforms, adopt different strategies involving the promotion of value-seeking. Many decisions concern how companies perceive the innovation environment and how to engage in the innovation network. Knowledge base view provides useful theoretical lens to understand the network embedding approach for electric business platform enterprises. To put forward, enterprises with wide breadth should adopt structural strategy give priority to relational embeddedness, on the other hand, enterprises of which the depth of knowledge base is deep and width of insufficient shall adopt strategy give priority to the embedding of structure. It is also proposed that platform enterprises should not adopt a single embeddedness strategy but should adopt auxiliary embeddedness strategy in a timely manner.

High-Capacity and High-Quality Reversible Data Hiding Method Using Recurrent Round-Trip Embedding Strategy in the Quotient Image

In previous research, scholars always think about how to improve the information hiding algorithm and strive to have the largest embedding capacity and better image quality, restoring the original image. This research mainly proposes a new robust and reversible information hiding method, recurrent robust reversible data hiding (triple-RDH), with a recurrent round-trip embedding strategy. We embed the secret message in a quotient image to increase the image robustness. The pixel value is split into two parts, HiSB and LoSB. A recurrent round-trip embedding strategy (referred to as double R-TES) is designed to adjust the predictor and the recursive parameter values, so the pixel value carrying the secret data bits can be first shifted to the right and then shifted to the left, resulting in pixel invariance, so the embedding capacity can be effectively increased repeatedly. Experimental results show that the proposed triple-RDH method can effectively increase the embedding capacity up to 310,732 bits and maintain a certain level of image quality. Compared with the existing pixel error expansion (PEE) methods, the triple-RDH method not only has a high capacity but also has robustness for image processing against unintentional attacks. It can also be used for capacity and image quality according to the needs of the application, performing adjustable embedding.

An Embedding Strategy Using Q-Ary Convolutional Codes for Large and Small Payloads

Matrix embedding (ME) code is a commonly used steganography technique, which uses linear block codes to improve embedding efficiency. However, its main disadvantage is the inability to perform maximum likelihood decoding due to the high complexity of decoding large ME codes. As such, it is difficult to improve the embedding efficiency. The proposed q-ary embedding code can provide excellent embedding efficiency and is suitable for various embedding rates (large and small payloads). This article discusses that by using perforation technology, a convolutional code with a high embedding rate can be easily converted into a convolutional code with a low embedding rate. By keeping the embedding rate of the (2, 1) convolutional code unchanged, convolutional codes with different embedding rates can be designed through puncturing.

Dual discriminative auto-encoder network for zero shot image recognition

Zero Shot learning (ZSL) aims to use the information of seen classes to recognize unseen classes, which is achieved by transferring knowledge of the seen classes from the semantic embeddings. Since the domains of the seen and unseen classes do not overlap, most ZSL algorithms often suffer from domain shift problem. In this paper, we propose a Dual Discriminative Auto-encoder Network (DDANet), in which visual features and semantic attributes are self-encoded by using the high dimensional latent space instead of the feature space or the low dimensional semantic space. In the embedded latent space, the features are projected to both preserve their original semantic meanings and have discriminative characteristics, which are realized by applying dual semantic auto-encoder and discriminative feature embedding strategy. Moreover, the cross modal reconstruction is applied to obtain interactive information. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method.

The Embedded Strategy of Enterprise Innovation Network Based on Knowledge Base

Constructing the innovation network is an important means of breakthrough in the traditional manufacturers for the emerging e-commerce platform, based on two different levels of innovation network node, content and structure, YEATION and YOUPIN were chosen to analyse and research the relationship and between knowledge base and the embedding strategy of electric business platform enterprises to create innovation network. To put forward the knowledge base width wider, insufficient depth of the enterprise should adopt structural strategy give priority to relational embeddedness, on the other hand, enterprises of which the depth of knowledge base is deep and width of insufficient shall adopt strategy give priority to the embedding of structure. At the same time, it is proposed that platform enterprises should not adopt a single embeddedness strategy, but should adopt auxiliary embeddedness strategy in a timely manner.

A Cas-embedding strategy for minimizing off-target effects of DNA base editors

DNA base editors, typically comprising editing enzymes fused to the N-terminus of nCas9, display off-target effects on DNA and/or RNA, which have remained an obstacle to their clinical applications. Off-target edits are typically countered via rationally designed point mutations, but the approach is tedious and not always effective. Here, we report that the off-target effects of both A > G and C > T editors can be dramatically reduced without compromising the on-target editing simply by inserting the editing enzymes into the middle of nCas9 at tolerant sites identified using a transposon-based genetic screen. Furthermore, employing this Cas-embedding strategy, we have created a highly specific editor capable of efficient C > T editing at methylated and GC-rich sequences.