/pdf/lying-moral-choice-in-public-and-private-life-52gzwr5asg.pdf

Lying: Moral Choice in Public and Private Life

The hybrid media system: Politics and power

The Elements of Journalism: What Newspeople Should Know and the Public Should Expect. Bill Kovach and Tom Rosenstiel. New York: Crown Publishers, 2001. 205 pp. $20 hbk. Inherent in the behavior and process we call "journalism" is controversy. Journalism's inexact, less-than-scientific character almost assures it will occasionally enter a crisis, becoming awash in change, its self-identity threatened or its credibility challenged. Just as naturally, we can depend on someone to attempt to right the ship, calm the waters, and smooth the sails. Enter The Elements of Journalism, which simply lists and details what the authors consider journalism's ten guiding principles and purpose for the enlightenment of journalists and the general public. For the former, the book serves as a Journalism 101 moral refresher: Journalism's purpose is truth, produced by those loyal to that purpose and to the citizens (not to profits). Committee of Concerned journalists Chairman Bill Kovach and Project for Excellence in journalism Director Tom Rosenstiel assert, however, that truth is not enough: There is also verification, to which they devote a chapter that's as good a reporting primer as any textbook. The authors explain "the intellectual principles of a science of reporting" gleaned from numerous discussions with and surveys of editors, journalists, and members of the public. As a CCJ member, I applaud the effort: Journalists need to be reminded of their profession's high ethical objective. Any craft-journalism included-requires a howto inventory. For the public, the book provides a glimpse into why serious journalists behave as they do; journalism has a purpose over and above selling newspapers and airtime. The lay reader discovers there is a rationale for what journalists cover and that not only is journalism a map with which to navigate society, but that good journalism strives to be accurate and comprehensive. But it is not enough to index-as the text does-all the proper characteristics. To be sure, the authors credibly explain each item. For example, they say journalists have an obligation to monitor the powerful because-as investigative reporting gets trivialized (thanks to consumer-oriented topics such as the honesty of auto brake mechanics) and challenged-journalism's function as a public forum becomes weakened, giving way to "a new wave of journalism as assertion." But as if it were Moses coming down from the Big Newsroom In The Sky, the book comes off as so many pronouncements. This is no text for a skeptic; while each element gets its due in terms of description and analysis, there is no Burning Bush-no original explanation exists for the list as a whole. By its structure, the text expects the reader to accept the idea that journalism is a recipe. The book makes a weak attempt at synthesis in the last chapter, in which the authors claim that journalists' responsibility to conscience ties all the other elements together. On its face, this makes sense: For example, what journalist would not say his or her conscience is the reason for his or her search (via journalism) for truth? But the final chapter does not engage in such analytical application of the elements, choosing instead to elaborate on the role of conscience in newsrooms and the threat certain newsroom cultures pose against individual conscience. …

The Elements of Journalism: What Newspeople Should Know and the Public Should Expect

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.

Deep Learning for Anomaly Detection: A Survey.

http://spiral.imperial.ac.uk/bitstream/10044/1/42668/2/Arrayago%20et%20al%20%282015%29%20-%20Description%20of%20stress-strain%20curves%20for%20stainless%20steel%20alloys.pdf

Description of stress-strain curves for stainless steel alloys

Transverse impact resistance of hollow and concrete filled stainless steel columns

In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images. In the proposed method median filter is modified by adding more features. Experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE). Experimental results of magnetic resonance (MR) image and ultrasound image demonstrate that the proposed algorithm is comparable to popular image smoothing algorithms. Key words : Magnetic resonance image; Ultrasound image; PSNR; SNR; RMSE. © 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. doi:10.3329/jsr.v3i1.5544                J. Sci. Res. 3 (1), 81-89 (2011)

/pdf/a-new-method-to-remove-noise-in-magnetic-resonance-and-28s9icw2kx.pdf

A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images

Impact behaviour of pre-compressed hollow and concrete filled mild and stainless steel columns

Detection of lung cancer is the most interesting research area of researcher's in early stages. The proposed system is designed to detect lung cancer in premature stage in two stages. The proposed system consists of many steps such as image acquisition, preprocessing, binarization, thresholding, segmentation, feature extraction, and neural network detection. At first Input lung CT images to the system and then passed through the image preprocessing stage by using some image processing techniques. In first stage, Binarization technique is used to convert binary image and then compare it with threshold value to detect lung cancer. In second stage, segmentation is performed to segment the lung CT image and a strong feature extraction method has been introduced to extract the some important feature of segmented images. Extracted features are used to train the neural network and finally the system is tested any cancerous and noncancerous images. The performance of proposed system shows satisfactory results and proposed method gives 96.67% accuracy.

Detection of lung cancer from CT image using image processing and neural network

The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started following this practice. Although the wide-spread practice of clickbait makes the reader's reliability on media vulnerable, a large scale analysis to reveal this fact is still absent. In this paper, we analyze 1.67 million Facebook posts created by 153 media organizations to understand the extent of clickbait practice, its impact and user engagement by using our own developed clickbait detection model. The model uses distributed sub-word embeddings learned from a large corpus. The accuracy of the model is 98.3%. Powered with this model, we further study the distribution of topics in clickbait and non-clickbait contents.

Mohammad Abu Yousuf

Papers

Transverse impact resistance of hollow and concrete filled stainless steel columns

A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images

Impact behaviour of pre-compressed hollow and concrete filled mild and stainless steel columns

Detection of lung cancer from CT image using image processing and neural network

Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?