The visual analysis of pheripheral blood samples is an important test in the procedures for the diagnosis of leukemia. Automated systems based on artificial vision methods can speed up this operation and they can increase the accuracy of the response also in telemedicine applications. Unfortunately, there are not available public image datasets to test and compare such algorithms. In this paper we propose a new public dataset of blood samples, specifically designed for the evaluation and the comparison of algorithms for segmentation and classification. For each image in the dataset, the classification of cell is given, and it is provided a specific set of figures of merits to be processed in order to fairly compare different algorithms when working with the proposed dataset. We hope that this initiative could give a new test tool to the image processing and pattern matching communities, aiming at stimulate new studies in this important field of research.

The goal of the Advanced Encryption Standard (AES) is to achieve secure communication. The use of AES does not, however, guarantee reliable communication. Prior work has shown that even a single transient error occurring during the AES encryption (or decryption) process will very likely result in a large number of errors in the encrypted/decrypted data. Such faults must be detected before sending to avoid the transmission and use of erroneous data. Concurrent fault detection is important not only to protect the encryption/decryption process from random faults. It will also protect the encryption/decryption circuitry from an attacker who may maliciously inject faults in order to find the encryption secret key. In this paper, we first describe some studies of the effects that faults may have on a hardware implementation of AES by analyzing the propagation of such faults to the outputs. We then present two fault detection schemes: The first is a redundancy-based scheme while the second uses an error detecting code. The latter is a novel scheme which leads to very efficient and high coverage fault detection. Finally, the hardware costs and detection latencies of both schemes are estimated

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Touchless palmprint recognition systems enable high-accuracy recognition of individuals through less-constrained and highly usable procedures that do not require the contact of the palm with a surface. To perform this recognition, methods based on local texture descriptors and Convolutional Neural Networks (CNNs) are currently used to extract highly discriminative features while compensating for variations in scale, rotation, and illumination in biometric samples. In particular, the main advantage of CNN-based methods is their ability to adapt to biometric samples captured with heterogeneous devices. However, the current methods rely on either supervised training algorithms, which require class labels (e.g., the identities of the individuals) during the training phase, or filters pretrained on general-purpose databases, which may not be specifically suitable for palmprint data. To achieve a high recognition accuracy with touchless palmprint samples captured using different devices while neither requiring class labels for training nor using pretrained filters, we introduce PalmNet, which is a novel CNN that uses a newly developed method to tune palmprintspecific filters through an unsupervised procedure based on Gabor responses and Principal Component Analysis (PCA), not requiring class labels during training. PalmNet is a new method of applying Gabor filters in a CNN and is designed to extract highly discriminative palmprint-specific descriptors and to adapt to heterogeneous databases. We validated the innovative PalmNet on several palmprint databases captured using different touchless acquisition procedures and heterogeneous devices, and in all cases, a recognition accuracy greater than that of the current methods in the literature was obtained.

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The increasing popularity of Cloud computing as an attractive alternative to classic information processing systems has increased the importance of its correct and continuous operation even in the presence of faulty components. In this paper, we introduce an innovative, system-level, modular perspective on creating and managing fault tolerance in Clouds. We propose a comprehensive high-level approach to shading the implementation details of the fault tolerance techniques to application developers and users by means of a dedicated service layer. In particular, the service layer allows the user to specify and apply the desired level of fault tolerance, and does not require knowledge about the fault tolerance techniques that are available in the envisioned Cloud and their implementations.

Abstract-The privacy protection of the biometric data is an important research topic, especially in the case of distributed biometric systems. In this scenario, it is very important to guarantee that biometric data cannot be steeled by anyone, and that the biometric clients are unable to gather any information different from the single user verification/identification. In a biometric system with high level of privacy compliance, also the server that processes the biometric matching should not learn anything on the database and it should be impossible for the server to exploit the resulting matching values in order to extract any knowledge about the user presence or behavior.Within this conceptual framework, in this paper we propose a novel complete demonstrator based on a distributed biometric system that is capable to protect the privacy of the individuals by exploiting cryptosystems. The implemented system computes the matching task in the encrypted domain by exploiting homomorphic encryption and using Fingercode templates. The paper describes the design methodology of the demonstrator and the obtained results. The demonstrator has been fully implemented and tested in real applicative conditions. Experimental results show that this method is feasible in the cases where the privacy of the data is more important than the accuracy of the system and the obtained computational time is satisfactory.

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