Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

A new color image encryption scheme based on DNA encoding and spatiotemporal chaotic system

Abstract: In this paper, a new color image encryption scheme based on DNA operations and spatiotemporal chaotic system is presented. Firstly, to hide the distribution information of the plain image, we convert the plain image into three DNA matrices based on the DNA random encoding rules. Then, the DNA matrices are combined into a new matrix and is permutated by a scramble matrix generated by mixed linear-nonlinear coupled map lattices (MLNCML) system. In which, the key streams are associated with the secret keys and plain image, which can ensure our cryptosystem plain-image-dependent and improve the ability to resist known-plaintext or chosen-plaintext attacks. Thereafter, to resist statistical attacks, the scrambled matrix is decomposed into three matrices and diffused by DNA deletion-insertion operations. Finally, the three matrices are decoded based on DNA random decoding rules and recombined to three channels of the cipher image. Simulation results demonstrate that the proposed image cryptosystem has good security and can resist various potential attacks.

An extension of technology acceptance model: Analysis of the adoption of mobile data services in China

Abstract: Mobile data services are entering into all aspects of people's life and are expected to be an important revenue source of telecom carriers. Nevertheless, the acceptance pace of mobile data services is slower than the expected level projected by the telecommunication industry. This research investigated the reason that people use mobile data services in China. Combined with subscribers' experience factors, an extended technology acceptance model (TAM) for mobile data services was presented. The model was tested using data collected from 802 mobile subscribers. The findings of the study indicate that mobile voice service and innovation experience of mobile data services affect subscribers' consumption intention greatly; subscribers' perceived ease-of-use and brand experience influence subscribers' attitude towards mobile data services largely. Promotion of mobile data services is most effective when it is promoted with perfect voice service experience. Additionally, our findings suggest that mobile service carriers need to enhance their investments in mobile data services' R&D. Collaboration among the value chain members of mobile data services will promise cutting-edge new products and services. Using system methodology, a mobile data service infusion system model was constructed to explain the general adoption rules of mobile data services. The adoption of mobile data services in mobile logistics area was also discussed. Copyright © 2009 John Wiley & Sons, Ltd.

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

Abstract: Knowledge graph (KG) embedding is a fundamental problem in data mining research with many real-world applications. It aims to encode the entities and relations in the graph into low dimensional vector space, which can be used for subsequent algorithms. Negative sampling, which samples negative triplets from non-observed ones in the training data, is an important step in KG embedding. Recently, generative adversarial network (GAN), has been introduced in negative sampling. By sampling negative triplets with large scores, these methods avoid the problem of vanishing gradient and thus obtain better performance. However, using GAN makes the original model more complex and harder to train, where reinforcement learning must be used. In this paper, motivated by the observation that negative triplets with large scores are important but rare, we propose to directly keep track of them with cache. However, how to sample from and update the cache are two important questions. We carefully design the solutions, which are not only efficient but also achieve good balance between exploration and exploitation. In this way, our method acts as a "distilled" version of previous GAN-based methods, which does not waste training time on additional parameters to fit the full distribution of negative triplets. The extensive experiments show that our method can gain significant improvement on various KG embedding models, and outperform the state-of-the-arts negative sampling methods based on GAN.