Memory demand is associated with increased mental workload. The objective of the present study was to examine the effects of visuospatial memory secondary tasks on driving performance. Memory tasks for the unknown word-figure pairs and recognition tasks for word-figure pairs at two-level difficulties were employed separately to represent working memory’s process and long-term memory’s process. A simulator study was conducted based on the simulation of the standard environment of Lane change test (LCT). The performance of lane keeping, lane change, and secondary tasks was measured by statistical methods. The comprehensive appraisal model was constructed to quantify total driving performance. The results showed that the mean path deviation, steering angle, and lane excursion times increased, and the proportion of correct lane change decreased, with the perceived workload increasing and the total driving performance decreasing in dual-task driving condition. Compared with the simple working memory group, as the difficulty of tasks increased in difficult working memory group, lane change performance degraded and the perceived workload increased. In contrast to difficult working memory group, the performance of lane keeping and lane change increased, while the perceived workload decreased and the total performance increased by about 50% in difficult recognition group. There were few differences between the simple working memory group and simple recognition group. The difficult working memory group had the lowest total driving performance. The results indicate that as the secondary task’s difficulty increases, driving performance will degrade. Performance improves significantly when the working memory process is converted to the recognition process. This trend is more obvious when the memory task assumes to be more difficult.

Zoomable user interfaces are widely used in static visualizations and have many benefits. However, they are not well supported in animated visualizations due to problems such as change blindness and information overload. We propose the spot-tracking lens, a new zoomable user interface for animated bubble charts, to tackle these problems. It couples zooming with automatic panning and provides a rich set of auxiliary techniques to enhance its effectiveness. Our preliminary user studies suggested that, besides allowing users to examine detail information, it can be an engaging approach to exploratory analysis for dynamic data.

Cyber threat intelligence sharing is emerging as an important tool for network security as it can identify evolving threat patterns and prevent attackers from replicating their early success across the Internet. However the types of information sharing being practiced today are at the tactical level focusing on specific attacks, e.g. characteristics of a piece of malware, and black listed IP addresses and domains. In this paper we argue sharing cyber intelligence at a more strategic level is needed. By strategic information we mean information about salient common features of groups of attacks and attackers. Strategic information allows us to take actions that are much closer to the source of the attacks. For example instead of block an IP address as opposed to shutting down the botnet. We propose at set of strategic cyber threat indicators and show how they can be derived using an IDS log from a large commercial enterprise.

Existing research efforts into tennis visualization have primarily focused on using ball and player tracking data to enhance professional tennis broadcasts and to aid coaches in helping their students. Gathering and analyzing this data typically requires the use of an array of synchronized cameras, which are expensive for non-professional tennis matches. In this paper, we propose TenniVis, a novel tennis match visualization system that relies entirely on data that can be easily collected, such as score, point outcomes, point lengths, service information, and match videos that can be captured by one consumer-level camera. It provides two new visualizations to allow tennis coaches and players to quickly gain insights into match performance. It also provides rich interactions to support ad hoc hypothesis development and testing. We first demonstrate the usefulness of the system by analyzing the 2007 Australian Open men's singles final. We then validate its usability by two pilot user studies where two college tennis coaches analyzed the matches of their own players. The results indicate that useful insights can quickly be discovered and ad hoc hypotheses based on these insights can conveniently be tested through linked match videos.

Previous studies on E-transaction time-series have mainly focused on finding temporal trends of transaction behavior. Interesting transactions that are time-stamped and situation-relevant may easily be obscured in a large amount of information. This paper proposes a visual analytics system, Visual Analysis of E-transaction Time-Series (VAET), that allows the analysts to interactively explore large transaction datasets for insights about time-varying transactions. With a set of analyst-determined training samples, VAET automatically estimates the saliency of each transaction in a large time-series using a probabilistic decision tree learner. It provides an effective time-of-saliency (TOS) map where the analysts can explore a large number of transactions at different time granularities. Interesting transactions are further encoded with KnotLines, a compact visual representation that captures both the temporal variations and the contextual connection of transactions. The analysts can thus explore, select, and investigate knotlines of interest. A case study and user study with a real E-transactions dataset (26 million records) demonstrate the effectiveness of VAET.

This paper proposes a novel visualization approach, which can depict the variations between different human motion data. This is achieved by representing the time dimension of each animation sequence with a sequential curve in a locality-preserving reference 2D space, called the motion track representation. The principal advantage of this representation over standard representations of motion capture data - generally either a keyframed timeline or a 2D motion map in its entirety - is that it maps the motion differences along the time dimension into parallel perceptible spatial dimensions but at the same time captures the primary content of the source data. Latent semantic differences that are difficult to be visually distinguished can be clearly displayed, favoring effective summary, clustering, comparison and analysis of motion database.