Interactive Visualization of Video Data for Fish Population Monitoring

The recent use of computer vision techniques for monitoring ecosystems has opened new perspectives for marine ecology research. These techniques can extract information about fish populations from in-situ cameras, without requiring ecologists to watch the videos. However, they inherently introduce uncertainty since a

The Metabolism and Growth of Web Forums

We view web forums as virtual living organisms feeding on user's attention and investigate how these organisms grow at the expense of collective attention. We find that the "body mass" ($PV$) and "energy consumption" ($UV$) of the studied forums exhibits the allometric growth property, i.e., $PV_t \sim UV_t ^ \theta$. This implies that within a forum, the network transporting attention flow between threads has a structure invariant of time, despite of the continuously changing of the nodes (threads) and edges (clickstreams). The observed time-invariant topology allows us to explain the dynamics of networks by the behavior of threads. In particular, we describe the clickstream dissipation on threads using the function $D_i \sim T_i ^ \gamma$, in which $T_i$ is the clickstreams to node $i$ and $D_i$ is the clickstream dissipated from $i$. It turns out that $\gamma$, an indicator for dissipation efficiency, is negatively correlated with $\theta$ and $1/\gamma$ sets the lower boundary for $\theta$. Our findings have practical consequences. For example, $\theta$ can be used as a measure of the "stickiness" of forums, because it quantifies the stable ability of forums to convert $UV$ into $PV$, i.e., to remain users "lock-in" the forum. Meanwhile, the correlation between $\gamma$ and $\theta$ provides a convenient method to evaluate the `stickiness" of forums. Finally, we discuss an optimized "body mass" of forums at around $10^5$ that minimizes $\gamma$ and maximizes $\theta$.Comment: 6 figure

Beautiful and damned. Combined effect of content quality and social ties on user engagement

User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on Knowledge and Data Engineering (Volume: PP, Issue: 99

Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks

Image processing is a rapidly growing research area of computer science and remains as a challenging problem within the computer vision fields. For the classification of flower images, the problem is mainly due to the huge similarities in terms of colour and texture. The appearance of the image itself such as variation of lights due to different lighting condition, shadow effect on the object’s surface, size, shape, rotation and position, background clutter, states of blooming or budding may affect the utilized classification techniques. This study aims to develop a classification model for Malaysian blooming flowers using neural network with the back propagation algorithms. The flower image is extracted through Region of Interest (ROI) in which texture and colour are emphasized in this study. In this research, a total of 960 images were extracted from 16 types of flowers. Each ROI was represented by three colour attributes (Hue, Saturation, and Value) and four textures attribute (Contrast, Correlation, Energy and Homogeneity). In training and testing phases, experiments were carried out to observe the classification performance of Neural Networks with duplication of difficult pattern to learn (referred to as DOUBLE) as this could possibly explain as to why some flower images were difficult to learn by classifiers. Results show that the overall performance of Neural Network with DOUBLE is 96.3% while actual data set is 68.3%, and the accuracy obtained from Logistic Regression with actual data set is 60.5%. The Decision Tree classification results indicate that the highest performance obtained by Chi-Squared Automatic Interaction Detection(CHAID) and Exhaustive CHAID (EX-CHAID) is merely 42% with DOUBLE. The findings from this study indicate that Neural Network with DOUBLE data set produces highest performance compared to Logistic Regression and Decision Tree. Therefore, NN has been potential in building Malaysian blooming flower model. Future studies can be focused on increasing the sample size and ROI thus may lead to a higher percentage of accuracy. Nevertheless, the developed flower model can be used as part of the Malaysian Blooming Flower recognition system in the future where the colours and texture are needed in the flower identification process

The sweet spot: How people trade off size and definition on mobile devices

Mobile TV can deliver up-to-date content to users on the move. But it is currently unclear how to best adapt higher resolution TV content. In this paper, we describe a laboratory study with 35 participants who watched short clips of different content and shot types on a 200ppi PDA display at a resolution of either 120x90 or 168x128. Participants selected their preferred size and rated the acceptability of the visual experience. The preferred viewing ratio depended on the resolution and had to be at least 9.8H. The minimal angular resolution people required and which limited the up-scaling factor was 14 pixels per degree. Extreme long shots were best when depicted actors were at least 0.7° high. A second study researched the ecological validity of previous lab results by comparing them to results from the field. Image size yielded more value for users in the field than was apparent from lab results. In conclusion, current prediction models based on preferred viewing distances for TV and large displays do not predict viewing preferences on mobile devices. Our results will help to further the understanding of multimedia perception and service designers to deliver both economically viable and enjoyable experiences

The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

The Big Picture on Small Screens Delivering Acceptable Video Quality in Mobile TV

Mobile TV viewers can change the viewing distance and (on some devices) scale the picture to their preferred viewing ratio, trading off size for angular resolution. We investigated optimal trade-offs between size and resolution through a series of studies. Participants selected their preferred size and rated the acceptability of the visual experience on a 200ppi device at a 4: 3 aspect ratio. They preferred viewing ratios similar to living room TV setups regardless of the much lower resolution: at a minimum 14 pixels per degree. While traveling on trains people required videos with a height larger than 35mm