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The development of neuroimaging instrumentation has boosted neuroscience researches. Consequently, both the fineness and the cost of data acquisition have profoundly increased, leading to the main bottleneck of this field: limited sample size and high dimensionality of neuroimaging data. Therefore, the emphasis of ideas of data pooling and research collaboration has increased over the past decade. Collaborative analysis techniques emerge as the idea developed. In this paper, we present NEURO-LEARN, a solution for collaborative pattern analysis of neuroimaging data. Its collaboration scheme consists of four parts: projects, data, analysis, and reports. While data preparation workflows defined in projects reduce the high dimensionality of neuroimaging data by collaborative computation, pooling of derived data and sharing of pattern analysis workflows along with generated reports on the Web enlarge the sample size and ensure the reliability and reproducibility of pattern analysis. Incorporating this scheme, NEURO-LEARN provides an easy-to-use Web application that allows users from different sites to share projects and processed data, perform pattern analysis, and obtain result reports. We anticipate that this solution will help neuroscientists to enlarge sample size, conquer the curse of dimensionality and conduct reproducible studies on neuroimaging data with efficiency and validity.

The aim of steganography is to put a secret into carriers and only be seen by participants. This paper proposes a layered steganography model which helps to simplify the steganography design. The steganography model enables the two communication parties to interact with each other, which enables the receiving end to get data by data prediction, and optimizes the steganography mechanism to take full advantage of the existing methods to implement multi-carriers steganography. Herein, the interactive steganography action is decomposed into three basic interactive procedures, and the embedding and extracting procedures are implemented via these basic interactions, in which texts, pictures, voices or videos are used as carriers. With the features of language diversification, common media, real-time in an instant message communication, the proposed steganography in this paper is flexible, secure and reliable.

This article describes the application of IoT in logistics management systems. The global structured IoT based on the EPC and its application in the logistics system has been studied and analyzed. Moreover one view the combination of IoT based on EPC and WSN for complementary application is proposed. And also discussed the feasibility of integrating RFID and WSN technology and logistics management applications' advantages and business detailed design are discussed.

The service-driven NGN means that the NGN should be constructed from the perspective of satisfying customers' demands. So controlling the network based on users' willingness is an emerging demand. Based on the psychophysical laws, the paper proposes a dynamically priority queueing system, which employs a greedy admission control algorithm to schedule the services, built on a user patience on the waiting time. The analysis and simulation show that the scheduling policy has more advantages on the system throughput, the waiting time and the degree of a user's satisfaction over other three methods under a heavy load in a multi-service environment. This scheduling policy can meet a multi-service overload control requirements in NGN.