There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

The increase of the cloud file sharing storage as infrastructure for serving large amounts of images over the internet inspires new data analytics paradigms. In this paper, we sketch the idea of expanding the cloud file sharing capabilities from only storing images to also performing encryption and analytics by moving and executing user defined programs near the data inside of an object storage cloud. In the big data context such as cloud file sharing storage, the arbitrary separation of storage and computation increases latency and decreases performance. The philosophy behind this approach is to package applications then move the application to the data, rather than moving data to where the application is located. In this paper, the encoding of the image is done using the P-Fibonacci transform of Discrete Cosine Coefficients “PFCC” algorithm. This paper describes the Docker Machine and Docker Swarm, an environment to support containerized user defined applications running remotely inside the cloud storage. Furthermore detailed simulations have been carried out to test the encryption service on cloud file sharing environment such as OpenStack Object Storage.

Secure image processing inside cloud file sharing environment using lightweight containers

A highly studied problem in image processing and the field of electrical engineering in general is the recovery of a true
signal from its noisy version. Images can be corrupted by noise during their acquisition or transmission stages. As noisy
images are visually very poor in quality, and complicate further processing stages of computer vision systems, it is
imperative to develop algorithms which effectively remove noise in images. In practice, it is a difficult task to
effectively remove the noise while simultaneously retaining the edge structures within the image. Accordingly, many
de-noising algorithms have been considered attempt to intelligent smooth the image while still preserving its details.
Recently, a non-local means (NLM) de-noising algorithm was introduced, which exploited the redundant nature of
images to achieve image de-noising. The algorithm was shown to outperform current de-noising standards, including
Gaussian filtering, anisotropic diffusion, total variation minimization, and multi-scale transform coefficient thresholding.
However, the NLM algorithm was developed in the spatial domain, and therefore, does not leverage the benefit that
multi-scale transforms provide a framework in which signals can be better distinguished by noise. Accordingly, in this
paper, a multi-scale NLM (MS-NLM) algorithm is proposed, which combines the advantage of the NLM algorithm and
multi-scale image processing techniques. Experimental results via computer simulations illustrate that the MS-NLM
algorithm outperforms the NLM, both visually and quantitatively.

A multi-scale non-local means algorithm for image de-noising

Thermal image quality assessment is essential in evaluating the performance of thermal imaging systems. The existing no-reference methods can be roughly categorized using quantification measures as follows: a) Human Visual System measures, b) information measures, and c) image distribution measures. All the above classes of measures are not appropriate for thermal imaging because the structure of thermal images is quite different from visible light images. In thermal images, the temperature of the background is high, temperature difference between objects and the background is small, and it is difficult to determine the link between objective and subjective image quality assessment. There is a need in new thermal image quality measures which can connect objective representation performance and thermal image characteristics. This paper presents new no-reference thermal-imaging measures and elaborates on their applications. They integrate advantages of above-mentioned conventional of measures. Simulation results demonstrate effectiveness of the presented measures in evaluating thermal image qualities in comparison with other well-known assessment tools. Finally, conclusions on more general applicability of these measures are presented.

Thermal-image quality measurements

Efficiency in terms of both accuracy and speed is highly important in any system, especially when it comes to
image processing. The purpose of this paper is to improve an existing implementation of multi-scale retinex
(MSR) by utilizing the fast Fourier transforms (FFT) within the illumination estimation step of the algorithm
to improve the speed at which Gaussian blurring filters were applied to the original input image. In addition,
alpha-rooting can be used as a separate technique to achieve a sharper image in order to fuse its results with
those of the retinex algorithm for the sake of achieving the best image possible as shown by the values of the
considered color image enhancement measure (EMEC).

Fast Fourier transform-based Retinex and alpha-rooting color image enhancement

There are several security issues tied to multimedia when implementing the various applications in the cellular phone
and wireless industry. One primary concern is the potential ease of implementing a steganography system. Traditionally,
the only mechanism to embed information into a media file has been with a desktop computer. However, as the cellular
phone and wireless industry matures, it becomes much simpler for the same techniques to be performed using a cell
phone. In this paper, two methods are compared that classify cell phone images as either an anomaly or clean, where a
clean image is one in which no alterations have been made and an anomalous image is one in which information has
been hidden within the image. An image in which information has been hidden is known as a stego image. The main
concern in detecting steganographic content with machine learning using cell phone images is in training specific
embedding procedures to determine if the method has been used to generate a stego image. This leads to a possible flaw
in the system when the learned model of stego is faced with a new stego method which doesn't match the existing
model. The proposed solution to this problem is to develop systems that detect steganography as anomalies, making the
embedding method irrelevant in detection. Two applicable classification methods for solving the anomaly detection of
steganographic content problem are single class support vector machines (SVM) and Parzen-window. Empirical
comparison of the two approaches shows that Parzen-window outperforms the single class SVM most likely due to the fact that Parzen-window generalizes less.

Sos S. Agaian

Papers

Secure image processing inside cloud file sharing environment using lightweight containers

A multi-scale non-local means algorithm for image de-noising

Thermal-image quality measurements

Fast Fourier transform-based Retinex and alpha-rooting color image enhancement

Steganography anomaly detection using simple one-class classification