DNA-Based Cryptographic Method for the Internet of Things

Internet of Things (IoT) is a set of connected smart devices providing and sharing rich data in real-time without involving a human being. However, IoT is a security nightmare because like in the early computer systems, security issues are not considered in the design step. Thereby, each IoT system could be susceptible to malicious users and uses. To avoid these types of situations, many approaches and techniques are proposed by both academic and industrial researches.DNA computing is an emerging and relatively new field dealing with data encryption using a DNA computing concepts. This technique allows rapid and secure data transfer between connected objects with low power consumption. In this paper, authors propose a symmetric cryptography method based on DNA. This method consists in cutting the message to encrypt/decrypt in blocks of characters and use a symmetric key extracted from a chromosome for encryption and decryption. Implemented on the embedded platform of a Raspberry Pi, the proposed method shows good performances in terms of robustness, complexity and attack resistance.

Computing views, remodeling environments

This article traces the development and expansion of early computer systems for analyzing views at three state-owned agencies in the United States and Great Britain: the US Forest Service, the Central Electricity Generating Board of England and Wales, and the Greater London Authority. Following the technology over four decades, from 1968 to 2012, the article traces assumptions incorporated into initial programs and propagated through to the present. These programs were designed to address questions about visual environments and proximities by numerical calculations alone, without the need for field observations. Each historical episode provides unique insights into the role of abstraction and calculation in the production of landscapes and the built environment, and shows how computer-generated view data became an important currency in planning control, not primarily for aesthetic but for financial and political reasons.

Just Kidding

Many a new word has been coined in jest. Scientist was conjured as a facetious term for those engaged in scientific research. Indianapolis was a fanciful suggestion for the name of Indiana’s capital. Software was simply a play on hardware among early computer programmers. Whimsical coinage is especially common in the cybersphere where not just software but crowdsource, blog and blogosphere resulted from insider wisecracking. Playfulness is an ill-appreciated source of neologisms in general. The linguist Allen Walker Read cited “jubilance” as a primary motivation for word creation. The widespread adoption of neologisms that originated as bon mots, punch lines, and flippant remarks generally surprise their coiners as much as anyone. This is more true than ever in a world where language is continually fertilized by whimsical bloggers, wisecracking comedians, and sundry quipsters who are less intent on expanding our vocabulary than on being amusing.

Encoded Perception

Despite her status as an unpaid “resident visitor” for most of her nearly two-decade tenure there, Lillian Schwartz created some of the most important works of early computer art at Bell Labs. This essay unravels the conceptual frameworks of “vision” as they manifest in Schwartz’s early computer films made between 1970 and 1972, with a specific emphasis on vision as “information” and “data.” It argues that these specific films in Schwartz’s oeuvre explored a newly emerging model of vision based on the rendering practices of computers and scientific instruments, while navigating the fraught question of the role of the embodied viewer. Resisting this rationalized order of vision, which would ultimately result in the emergence of information as both a commodity and an asset class, Schwartz’s films instead explore the contingencies of rendering information with the newly developing medium of the computer.

Border Detection in Skin Lesion Images Using an Improved Clustering Algorithm

The incidence of skin cancer has been increasing in recent years and it can become dangerous if not detected early. Computer-aided diagnosis systems can help the dermatologists in assisting with skin cancer detection by examining the features more critically. In this article, a detailed review of pre-processing and segmentation methods is done on skin lesion images by investigating existing and prevalent segmentation methods for the diagnosis of skin cancer. The pre-processing stage is divided into two phases, in the first phase, a median filter is used to remove the artifact; and in the second phase, an improved K-means clustering with outlier removal (KMOR) algorithm is suggested. The proposed method was tested in a publicly available Danderm database. The improved cluster-based algorithm gives an accuracy of 92.8% with a sensitivity of 93% and specificity of 90% with an AUC value of 0.90435. From the experimental results, it is evident that the clustering algorithm has performed well in detecting the border of the lesion and is suitable for pre-processing dermoscopic images.