Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction

Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.Comment: Preprint of the paper accepted at the 1st Workshop on Affect and Sentiment in Multimedia (ASM), in ACM MultiMedia 2015. Brisbane, Australi

Analysis of Security Threats in Voice Over Internet Protocol (VOIP)

The VoIP system is build on the IP network, so it is affected by the IP network security problem. It has many security problems because of the security mechanism of VoIP system and other external factors. These effects relate to the following three aspects: confidentiality, integrity and availability. This paper makes a detailed analysis discussed several security potential threats by dividing it into several categories like social, eavesdropping, service abuse, etc. and finally shows how this threats are harmful to VoIP. Keywords-VoIP; Security threat

Pricing, Investment, and Network Equilibrium

Despite rapidly emerging innovative road pricing and investment principles, the development of a long run network dynamics model for necessary policy evaluation is still lagging. This research endeavors to fill this gap and models the impacts of road financing policies throughout the network equilibration process. The manner in which pricing and investment jointly shape network equilibrium is particularly important and explored in this study. The interactions among travel demand, road supply, revenue mechanisms and investment rules are modeled at the link level in a network growth simulator. After assessing several measures of effectiveness, the proposed network growth model is able to evaluate the short- and long-run impacts of a broad spectrum of road pricing and investment policies on large-scale road networks, which can provide valuable information to decision-makers such as the implications of various policy scenarios on social welfare, financial situation of road authorities and potential implementation problems. Some issues hard to address in theoretical analysis can be examined in the agent-based simulation model. As a demonstration, we apply the network growth model to assess marginal and average pricing scenarios on a sample network. Even this relatively simple application provides new insights into issues around road pricing that have not previously been seriously considered. For instance, the results disclose a potential problem of over-investment when the marginal cost pricing scheme is adopted in conjunction with a myopic profit-neutral investment policy.Transportation network equilibrium; Road growth; Pricing; Congestion toll; Investment; Transport policy analysis.

Imputation of missing network data:Some simple procedures

Analysis of social network data is often hampered by non-response and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ignored and only complete cases were analyzed. To overcome the problems created by missing data, several treatment methods are proposed in the literature: model-based methods within the framework of exponential random graph models, and im- putation methods. In this paper we focus on the latter group of methods, and investigate the use of some simple imputation procedures to handle missing network data. The results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network. Missing data treatment based on simple imputation procedures, however, does also have large negative effects and simple imputations can only successfully correct for non-response in a few specific situations

Imputation of missing network data:Some simple procedures

Analysis of social network data is often hampered by non-response and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ignored and only complete cases were analyzed. To overcome the problems created by missing data, several treatment methods are proposed in the literature: model-based methods within the framework of exponential random graph models, and im- putation methods. In this paper we focus on the latter group of methods, and investigate the use of some simple imputation procedures to handle missing network data. The results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network. Missing data treatment based on simple imputation procedures, however, does also have large negative effects and simple imputations can only successfully correct for non-response in a few specific situations