No abstract

Online gaming is a multi-billion dollar industry that entertains a large, global population. One unfortunate phenomenon, however, poisons the competition and the fun: cheating. The costs of cheating span from industry-supported expenditures to detect and limit cheating, to victims' monetary losses due to cyber crime.This paper studies cheaters in the Steam Community, an online social network built on top of the world's dominant digital game delivery platform. We collected information about more than 12 million gamers connected in a global social network, of which more than 700 thousand have their profiles flagged as cheaters. We also collected in-game interaction data of over 10 thousand players from a popular multiplayer gaming server. We show that cheaters are well embedded in the social and interaction networks: their network position is largely undistinguishable from that of fair players. We observe that the cheating behavior appears to spread through a social mechanism: the presence and the number of cheater friends of a fair player is correlated with the likelihood of her becoming a cheater in the future. Also, we observe that there is a social penalty involved with being labeled as a cheater: cheaters are likely to switch to more restrictive privacy settings once they are tagged and they lose more friends than fair players. Finally, we observe that the number of cheaters is not correlated with the geographical, real-world population density, or with the local popularity of the Steam Community.This analysis can ultimately inform the design of mechanisms to deal with anti-social behavior (e.g., spamming, automated collection of data) in generic online social networks.

Peer-to-peer technologies have proven their strength in large-scale resource sharing and data transfer. Such systems, however, still need to address a variety of issues, including efficient routing, security, quality of service, incentives, and reputation. Recent research started leveraging social information to develop new and effective techniques to improve the performance of peer-to-peer systems. However, using social information is a double-edged sword, which can bring benefits as well as new challenges. This survey presents and classifies the types of social information that have been used so far in the design of peer-to-peer systems, how the social fabric has been used to facilitate transactions in the system, and some challenges caused by using social information.

Improving the accuracy and efficiency of bridge structure damage detection is one of the main challenges in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic gyroscope and applying the deep-learning algorithm to perform structural damage detection. With a scale-down bridge model, three types of damage scenarios and an intact benchmark were simulated. A supervised learning model based on the deep convolutional neural networks was proposed. After the training process under ten-fold cross-validation, the model accuracy can reach 96.9% and significantly outperform that of other four traditional machine learning methods (random forest, support vector machine, k-nearest neighbor, and decision tree) used for comparison. Further, the proposed model illustrated its decent ability in distinguishing damage from structurally symmetrical locations.

Location-based mobile applications such as Foursquare and Jiepang help bridge the gap between offline and online. People that we encounter and connect with around physical resources such as conferences, provide opportunities for extending our social networks from offline to online. We give a preliminary study on the relationship between the online and physical social networks in terms of the online and offline interactions (O2O) established during the conference, using a mobile social proximity-based platform called Find & Connect. We propose metrics of overlap fraction, network scale, and O2O transfer amount to quantify the O2O behavior of conference attendees. Our results demonstrate that more than half of the online interactions are included in the offline interaction network, and the offline to online transfer holds the dominant position in this bidirectional O2O transition between online and offline interaction networks. To the best of our knowledge, this is the first work to study the influence of offline to online, and vice versa, in a restricted environment using a social proximity-based system.