scholarly journals Study on Thumbnail Images and Titles Selected by Viewers in YouTuber’s Videos

2019 ◽  
Vol 18 (1) ◽  
pp. 139-145
Author(s):  
Ryosuke SATO ◽  
Ryoichi TAMURA
Keyword(s):  
Thumbnail Images

scholarly journals Artificial Intelligence Imagery Analysis Fostering Big Data Analytics

2019 ◽  
Vol 11 (8) ◽  
pp. 178 ◽  
Author(s):  
Stefan Cremer ◽  
Claudia Loebbecke
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Quality Data ◽  
Background Information ◽  
Research And Practice ◽  
Management Approaches ◽  
Thumbnail Images

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

scholarly journals Phylotastic: improving access to tree-of-life knowledge with flexible, on-the-fly delivery of trees

2018 ◽  
Author(s):  
Van D. Nguyen ◽  
Thanh H. Nguyen ◽  
Abu Saleh Md. Tayeen ◽  
H. Dail Laughinghouse ◽  
Luna L. Sánchez-Reyes ◽  
...  
Keyword(s):  
Web Services ◽  
Tree Of Life ◽  
Online Resources ◽  
Species List ◽  
Geologic Time ◽  
Current State ◽  
Custom Made ◽  
Special Knowledge ◽  
Research And Education ◽  
Thumbnail Images

Abstract(1) A comprehensive phylogeny of species, i.e., a tree of life, has potential uses in a variety of contexts in research and education. This potential is limited if accessing the tree of life requires special knowledge, complex software, or long periods of training.(2) The Phylotastic project aims to use web-services technologies to lower the barrier for accessing phylogenetic knowledge, making it as easy to get a phylogeny of species as it is to get online driving directions. In prior work, we designed an open system of web services to validate and manage species names, find phylogeny resources, extract subtrees matching a user’s species list, calibrate them, and mash them up with images and information from online resources.(3) Here we report a publicly accessible system for on-the-fly delivery of phylogenetic knowledge, developed with user feedback on what types of functionality are considered useful by researchers and educators. The system currently consists of a web portal that implements 3 types of workflows to obtain species phylogenies (scaled by geologic time and decorated with thumbnail images); 19 underlying web services accessible via a common registry; and toolbox code in R and Python so that others can create applications that leverage these services. These resources cover most of the use-cases identified in our analysis of user needs.(4) The Phylotastic system, accessible viahttp://www.phylotastic.org, provides a unique resource to access the current state of phylogenetic knowledge, useful for a variety of cases in which a tree extracted quickly from online resources (as distinct from a tree custom-made from character data) is sufficient, as it is for many casual uses of trees identified here.

scholarly journals Detection Method for Classifying Malicious Firmware

2021 ◽  
Vol 13 (6) ◽  
pp. 1-9
Author(s):  
David Noever ◽  
Samantha E. Miller Noever
Keyword(s):  
Operating Systems ◽  
Deep Neural Network ◽  
Detection Method ◽  
Forensic Analysis ◽  
Embedded Devices ◽  
Learning Methods ◽  
Digit Recognition ◽  
Image Conversion ◽  
Thumbnail Images ◽  
The Internet Of Things

A malicious firmware update may prove devastating to the embedded devices both that make up the Internet of Things (IoT) and that typically lack the same security verifications now applied to full operating systems. This work converts the binary headers of 40,000 firmware examples from bytes into 1024-pixel thumbnail images to train a deep neural network. The aim is to distinguish benign and malicious variants using modern deep learning methods without needing detailed functional or forensic analysis tools. One outcome of this image conversion enables contact with the vast machine learning literature already applied to handle digit recognition (MNIST). Another result indicates that greater than 90% accurate classifications prove possible using image-based convolutional neural networks (CNN) when combined with transfer learning methods. The envisioned CNN application would intercept firmware updates before their distribution to IoT networks and score their likelihood of containing malicious variants. To explain how the model makes classification decisions, the research applies traditional statistical methods such as both single and ensembles of decision trees with identifiable pixel or byte values that contribute the malicious or benign determination.

The transformative power of the thumbnail image: Media logistics and infrastructural aesthetics

First Monday ◽  
2017 ◽  
Author(s):  
Nanna Thylstrup ◽  
Stina Teilmann-Lock
Keyword(s):  
Cultural History ◽  
Visual Information ◽  
Digital Economy ◽  
Digital Information ◽  
Transformative Power ◽  
Functional Image ◽  
History Of ◽  
Thumbnail Images ◽  
Inherent Part

Thumbnail images are discreet, yet central navigational tools in increasingly complex visual information environments. Indeed, without thumbnail images there would be no image search: they are an inherent part of the information architecture of most digital information platforms. Yet, how might we understand the role of the thumbnail as an attention technology in the digital economy? And what kind of aesthetic does it produce? This paper examines the legal negotiations of the thumbnail image and the ensuing decision to conceptualize the thumbnail as a functional image against the cultural history of visual attention technologies and the aesthetics of their connective function. Such an endeavour, we propose, allows us to understand and appreciate the significant digital economy and particular aesthetic of the thumbnail image despite its apparent subtlety.

scholarly journals Thumbnail Images Using Resampling Method

2013 ◽  
Vol 3 (5) ◽  
pp. 23-27
Author(s):  
Lavanya Digumarthy ◽  
Keyword(s):  
Resampling Method ◽  
Thumbnail Images

scholarly journals Thumbnail images: uncertainties, infrastructures and search engines

2020 ◽  
pp. 25-42
Author(s):  
Nanna Bonde Thylstrup ◽  
Stina Teilmann
Keyword(s):  
Search Engines ◽  
Thumbnail Images

scholarly journals Deep Learning for Identifying Malicious Firmware

2021 ◽  
Author(s):  
David Noever ◽  
Samantha E. Miller Noever
Keyword(s):  
Deep Learning ◽  
Operating Systems ◽  
Deep Neural Network ◽  
Forensic Analysis ◽  
Embedded Devices ◽  
Learning Methods ◽  
Digit Recognition ◽  
Image Conversion ◽  
Thumbnail Images ◽  
The Internet Of Things

A malicious firmware update may prove devastating to the embedded devices both that make up the Internet of Things (IoT) and alsothat typically lack the same security verifications now applied to full operating systems. This work converts the binary headers of 40,000 firmware examples from bytes into 1024-pixel thumbnail images to train a deep neural network. The aim is to distinguish benign and malicious variants using modern deep learning methods without needing detailed functional or forensic analysis tools. One outcome of this image conversion enables contact with the vast machine learning literature already applied to handle digit recognition (MNIST). Another result indicates that greater than 90% accurate classifications prove possible using image-based convolutional neural networks (CNN) when combined with transfer learning methods. The envisioned CNN application would intercept firmware updates before their distribution to IoT networks and score their likelihood of containing malicious variants.

Checking the Integrity of Images with Signed Thumbnail Images

2020 ◽  
Vol 2020 (4) ◽  
pp. 118-1-118-7
Author(s):  
Martin Steinebach ◽  
Sebastian Jörg ◽  
Huajian Liu
Keyword(s):  
Image Processing ◽  
Social Media ◽  
Everyday Life ◽  
Digital Images ◽  
Great Effort ◽  
Social Media Platforms ◽  
Thumbnail Images ◽  
Processing Steps

The integrity of images is an important and interesting field of research, since digital images are constantly encountered in everyday life today. The availability of image processing programs makes it possible for almost anyone to manipulate images without great effort. With the help of social media platforms, the hurdle for their distribution to a very large number of viewers has also been lowered. As a result, confidence in the integrity and authenticity of images, which was even stronger at the time of analogue photography, is dwindling. The aim of this work is to develop and investigate a concept that counteracts the lost trust and creates an opportunity to check the integrity of processed images. The concept is based on a combination of signed thumbnails and the logging of possible processing steps. We show that this combination has advantages compared to the existing approaches.

Sign in / Sign up

Export Citation Format

Share Document