No abstract

Survey literature on mobile money and its contribution in promoting financial inclusion and development, with a focus on sub‐Saharan Africa. We use taxonomic, descriptive and analytical methods to evaluate the state of knowledge in the area. We analyse how mobile technology in general may contribute to economic development and financial inclusion in theory and practise. We explain the mechanics of mobile money using Kenya's M‐Pesa as a canonical example; and consider whether the literature has fully established the potential economic impact of mobile money especially its contribution to financial inclusion. We also consider market structure, pricing and regulatory implications of mobile money. We conclude by highlighting issues that require further investigation: the take‐up of mobile money; mobile money and financial inclusion; substitutability between mobile money and conventional finance; and regulatory structures for institutions providing mobile money services.

No abstract

No abstract

No abstract

Structured output prediction aims to learn a predictor to predict a structured output from a input data vector. The structured outputs include vector, tree, sequence, etc. We usually assume that we have a training set of input-output pairs to train the predictor. However, in many real-world applications, it is difficult to obtain the output for a input, thus for many training input data points, the structured outputs are missing. In this paper, we discuss how to learn from a training set composed of some input-output pairs, and some input data points without outputs. This problem is called semisupervised structured output prediction. We propose a novel method for this problem by constructing a nearest neighbor graph from the input space to present the manifold structure, and using it to regularize the structured output space directly. We define a slack structured output for each training data point, and proposed to predict it by learning a structured output predictor. The learning of both slack structured outputs and the predictor are unified within one single minimization problem. In this problem, we propose to minimize the structured loss between the slack structured outputs of neighboring data points, and the prediction error measured by the structured loss. The problem is optimized by an iterative algorithm. Experiment results over three benchmark data sets show its advantage.

This paper proposes an improved method of reversible data hiding in encrypted images (RDH-EI). Three parties constitute the proposed system: the image owner, the remote server and the recipient. To preserve privacy, an image owner encrypts the original image using a stream cipher algorithm and uploads the ciphertext to a remote server. On server side, a data-hider is allowed to embed additional message into the encrypted image using a swapping/shifting based algorithm. After downloading the marked encrypted image from the server and implementing the decryption, a recipient can extract the hidden messages and losslessly recover the original image. Experimental results show that the proposed method achieves a larger payload than the related works. Meanwhile, a limitation in the related works that few bits can be embedded into the encrypted medical images is also eliminated in the proposed method. Index Terms-Reversible data hiding, image encryption, image recovery IntroductionReversible data hiding (RDH) is a technique to embed additional message into a cover media, such as military or medical images, using a reversible manner such that the original cover content can be perfectly restored after the extraction of hidden messages [1] [2]. Reversible data hiding in encrypted images (RDH-EI) is a new topic of reversible data hiding [3]. This technique allows a service provider to embed additional messages into encrypted images without accessing the original contents, and guarantees that the original images can be losslessly recovered on the recipient side. RDH-EI technique can be used in many applications [4][5][6][7][8][9][10][11][12]. For example in medical systems, the medical images can be encrypted before uploading to a server if a patient does not allow his/her privacy to be revealed to outsiders. Meanwhile, for a better management, the database administrator can embed the medical records or the patient's information into the encrypted image. This way, the storage payload can be saved, and the profile management is more convenient. On the other hand, when a doctor downloads the encrypted images containing additional information from the medical server, he/she can extract the patient's information exactly and recover the original medical images for diagnosis without any error.Some works have been done in the field of RDH-EI. Generally, there are two kinds of RDH-EI approaches: the joint RDH-EI and the separable RDH-EI. In the former technique, hidden messages are extracted from the marked encrypted image by the user who has the decryption key, which is realized together with image recovery. While in the latter technique, hidden messages can be extracted by the one who has not the decryption key, in which data extraction and image recovery are separated.Compared with separable RDH-EI methods, joint RDH-EI methods always estimate the spatial correlation of pixels inside the original image. As a result, the marked image should be decrypted before extracting the hidden message and recovering the original contents.RDH-...

No abstract