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PurposeThis study aimed to investigate how honest participants perceived an attacker to be during shoulder surfing scenarios that varied in terms of which Principle of Persuasion in Social Engineering (PPSE) was used, whether perceived honesty changed as scenarios progressed, and whether any changes were greater in some scenarios than others. Design/methodology/approachParticipants read one of six shoulder surfing scenarios. Five depicted an attacker using one of the PPSEs. The other depicted an attacker using as few PPSEs as possible, which served as a control condition. Participants then rated perceived attacker honesty. FindingsThe results revealed honesty ratings in each condition were equal during the beginning of the conversation, participants in each condition perceived the attacker to be honest during the beginning of the conversation, perceived attacker honesty declined when the attacker requested the target perform an action that would afford shoulder surfing, perceived attacker honesty declined more when the Distraction and Social Proof PPSEs were used, participants perceived the attacker to be dishonest when making such requests using the Distraction and Social Proof PPSEs and perceived attacker honesty did not change when the attacker used the target’s computer. Originality/valueTo the best of the authors’ knowledge, this experiment is the first to investigate how persuasion tactics affect perceptions of attackers during shoulder surfing attacks. These results have important implications for shoulder surfing prevention training programs and penetration tests. 

The Robotic locomotion community is interested in optimal gaits for control. Based on the optimization criterion, however, there could be a number of possible optimal gaits. For example, the optimal gait for maximizing displacement with respect to cost is quite different from the maximum displacement optimal gait. Beyond these two general optimal gaits, we believe that the optimal gait should deal with various situations for high-resolution of motion planning, e.g., steering the robot or moving in “baby steps.” As the step size or steering ratio increases or decreases, the optimal gaits will slightly vary by the geometric relationship and they will form the families of gaits. In this paper, we explored the geometrical framework across these optimal gaits having different step sizes in the family via the Lagrange multiplier method. Based on the structure, we suggest an optimal locus generator that solves all related optimal gaits in the family instead of optimizing each gait respectively. By applying the optimal locus generator to two simplified swimmers in drag-dominated environments, we verify the behavior of the optimal locus generator. 

Auditory icons are naturally occurring sounds that systems play to convey information. Systems must convey complex messages. To do so, systems can play: 1) a single sound that represents the entire message, or 2) a single sound that represents the first part of the message, followed by another sound that represents the next part of that message, etc. The latter are known as concatenated auditory icons. To evaluate those approaches, participants interpreted single and concatenated auditory icons designed to convey their message well and poorly. Single auditory icons designed to convey their message well were correctly interpreted more often than those designed to convey their message poorly; that was not true for concatenated auditory icons. Concatenated auditory icons should not be comprised of a series of sounds that each represents its piece of a message well. The whole of a concatenated auditory icon is not the sum of its parts. 

Phishing attack countermeasures have previously relied on technical solutions or user training. As phishing attacks continue to impact users resulting in adverse consequences, mitigation efforts may be strengthened through an understanding of how user characteristics predict phishing susceptibility. Several studies have identified factors of interest that may contribute to susceptibility. Others have begun to build predictive models to better understand the relationships among factors in addition to their prediction power, although these studies have only used a handful of predictors. As a step toward creating a holistic model to predict phishing susceptibility, it was first necessary to catalog all known predictors that have been identified in the literature. We identified 32 predictors related to personality traits, demographics, educational background, cybersecurity experience and beliefs, platform experience, email behaviors, and work commitment style. 

We address the problem of human action classification in drone videos. Due to the high cost of capturing and labeling large-scale drone videos with diverse actions, we present unsupervised and semi-supervised domain adaptation approaches that leverage both the existing fully annotated action recognition datasets and unannotated (or only a few annotated) videos from drones. To study the emerging problem of drone-based action recognition, we create a new dataset, NEC-DRONE, containing 5,250 videos to evaluate the task. We tackle both problem settings with 1) same and 2) different action label sets for the source (e.g., Kinectics dataset) and target domains (drone videos). We present a combination of video and instance-based adaptation methods, paired with either a classifier or an embedding-based framework to transfer the knowledge from source to target. Our results show that the proposed adaptation approach substantially improves the performance on these challenging and practical tasks. We further demonstrate the applicability of our method for learning cross-view action recognition on the Charades-Ego dataset. We provide qualitative analysis to understand the behaviors of our approaches. 

Human activities often occur in specific scene contexts, e.g. playing basketball on a basketball court. Training a model using existing video datasets thus inevitably captures and leverages such bias (instead of using the actual discriminative cues). The learned representation may not generalize well to new action classes or different tasks. In this paper, we propose to mitigate scene bias for video representation learning. Specifically, we augment the standard cross-entropy loss for action classification with 1) an adversarial loss for scene types and 2) a human mask confusion loss for videos where the human actors are masked out. These two losses encourage learning representations that are unable to predict the scene types and the correct actions when there is no evidence. We validate the effectiveness of our method by transferring our pre-trained model to three different tasks, including action classification, temporal localization, and spatio-temporal action detection. Our results show consistent improvement over the baseline model without debiasing.