Detection of Deception in a Virtual World

This work explores the role of multimodal cues in detection of deception in a virtual world, an online community of World of Warcraft players. Case studies from a five-year ethnography are presented in three categories: small-scale deception in text, deception by avoidance, and large-scale deception in game-external modes. Each case study is analyzed in terms of how the affordances of the medium enabled or hampered deception as well as how the members of the community ultimately detected the deception. The ramifications of deception on the community are discussed, as well as the need for researchers to have a deep community knowledge when attempting to understand the role of deception in a complex society. Finally, recommendations are given for assessment of behavior in virtual worlds and the unique considerations that investigators must give to the rules and procedures of online communities.</jats:p

Overlapping Community Detection in Networks: the State of the Art and Comparative Study

This paper reviews the state of the art in overlapping community detection algorithms, quality measures, and benchmarks. A thorough comparison of different algorithms (a total of fourteen) is provided. In addition to community level evaluation, we propose a framework for evaluating algorithms' ability to detect overlapping nodes, which helps to assess over-detection and under-detection. After considering community level detection performance measured by Normalized Mutual Information, the Omega index, and node level detection performance measured by F-score, we reached the following conclusions. For low overlapping density networks, SLPA, OSLOM, Game and COPRA offer better performance than the other tested algorithms. For networks with high overlapping density and high overlapping diversity, both SLPA and Game provide relatively stable performance. However, test results also suggest that the detection in such networks is still not yet fully resolved. A common feature observed by various algorithms in real-world networks is the relatively small fraction of overlapping nodes (typically less than 30%), each of which belongs to only 2 or 3 communities.Comment: This paper (final version) is accepted in 2012. ACM Computing Surveys, vol. 45, no. 4, 2013 (In press) Contact: [email protected]

Exploring a Potential Bias in Detection of Mesopredators by Cameras

Mesopredators, such as the raccoon (Procyon locor), Virginia opossum (Didpelphis virginiana), and striped skunk (Mephitis mephitis) play crucial ecological roles as predators, prey, and disease vectors across much of the United States. Because of their importance and the way that populations of these mesopredators can dramatically increase due to human-subsidized resources, it is imperative that studies attempting to quantify mesopredator community composition are accurate and unbiased. However, it has recently been suggested that not all mammals trigger motion-activated wildlife game cameras at the same rate and for some species detection probability may be biased. My goals for this thesis were to 1) conduct a field experiment to explore potential detection bias of motion-triggered game cameras in relation to common mesopredators and 2) understand how reported results in the game camera literature may be influenced by this potential bias. I did this through a two-step approach. First, I simultaneously deployed side by side infrared motion-triggered game cameras and time-lapse cameras to compare the detections of mammals acquired by each. If certain species fail to reliably trigger motion cameras, I predicted that those species would be missed by the game camera while at the same time they would be documented by the time-lapse camera that is set to take photographs at 5 second intervals with no motion-trigger. Next, I conducted a systematic review of published game camera literature and compared community composition of mesopredators as determined by three approaches: by nonbaited game cameras, by baited game cameras, or by traditional research methods (track plates, trapping, roadkill surveys, hair snares, etc). This comparative analysis explored the potential detection biased quantified in experiment 1 over a larger spatial scale and across additional species. Analysis for experiment 1 yielded animal size as the only driving factor for motion detection probability, while there were no significant factors driving timelapse detection. Conducting analysis on the literature for experiment 2 yielded modest results; out of the 9 mesopredators collected with each paper, only opossum and coyote were affected by capture method. The findings of this study suggest that smaller animals could require bait for infrared detection, while larger mesopredators are generally unaffected by detection method

DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments

Multi-tenancy in resource-constrained environments is a key challenge in Edge computing. In this paper, we develop 'DYVERSE: DYnamic VERtical Scaling in Edge' environments, which is the first light-weight and dynamic vertical scaling mechanism for managing resources allocated to applications for facilitating multi-tenancy in Edge environments. To enable dynamic vertical scaling, one static and three dynamic priority management approaches that are workload-aware, community-aware and system-aware, respectively are proposed. This research advocates that dynamic vertical scaling and priority management approaches reduce Service Level Objective (SLO) violation rates. An online-game and a face detection workload in a Cloud-Edge test-bed are used to validate the research. The merits of DYVERSE is that there is only a sub-second overhead per Edge server when 32 Edge servers are deployed on a single Edge node. When compared to executing applications on the Edge servers without dynamic vertical scaling, static priorities and dynamic priorities reduce SLO violation rates of requests by up to 4% and 12% for the online game, respectively, and in both cases 6% for the face detection workload. Moreover, for both workloads, the system-aware dynamic vertical scaling method effectively reduces the latency of non-violated requests, when compared to other methods

Exploring a Potential Bias in Detection of Mesopredators by Cameras

Mesopredators, such as the raccoon (Procyon locor), Virginia opossum (Didpelphis virginiana), and striped skunk (Mephitis mephitis) play crucial ecological roles as predators, prey, and disease vectors across much of the United States. Because of their importance and the way that populations of these mesopredators can dramatically increase due to human-subsidized resources, it is imperative that studies attempting to quantify mesopredator community composition are accurate and unbiased. However, it has recently been suggested that not all mammals trigger motion-activated wildlife game cameras at the same rate and for some species detection probability may be biased. My goals for this thesis were to 1) conduct a field experiment to explore potential detection bias of motion-triggered game cameras in relation to common mesopredators and 2) understand how reported results in the game camera literature may be influenced by this potential bias. I did this through a two-step approach. First, I simultaneously deployed side by side infrared motion-triggered game cameras and time-lapse cameras to compare the detections of mammals acquired by each. If certain species fail to reliably trigger motion cameras, I predicted that those species would be missed by the game camera while at the same time they would be documented by the time-lapse camera that is set to take photographs at 5 second intervals with no motion-trigger. Next, I conducted a systematic review of published game camera literature and compared community composition of mesopredators as determined by three approaches: by nonbaited game cameras, by baited game cameras, or by traditional research methods (track plates, trapping, roadkill surveys, hair snares, etc). This comparative analysis explored the potential detection biased quantified in experiment 1 over a larger spatial scale and across additional species. Analysis for experiment 1 yielded animal size as the only driving factor for motion detection probability, while there were no significant factors driving timelapse detection. Conducting analysis on the literature for experiment 2 yielded modest results; out of the 9 mesopredators collected with each paper, only opossum and coyote were affected by capture method. The findings of this study suggest that smaller animals could require bait for infrared detection, while larger mesopredators are generally unaffected by detection method