Eye tracking has been used as part of software engineering and computer science research for a long time, and during this time new techniques for machine learning (ML) have emerged. Some of those techniques are applicable to the analysis of eye-tracking data, and to some extent have been applied. However, there is no structured summary available on which ML techniques are used for analysis in different types of eye-tracking research studies.

As one of the biggest contributors to road accidents and fatalities, drink driving is worthy of significant research attention. However, most existing systems on detecting or preventing drink driving either require special hardware or require much effort from the user, making these systems inapplicable to continuous drink driving monitoring in a real driving environment. In this paper, we presentDetectDUI, a contactless, non-invasive, real-time system that yields a relatively highly accurate drink driving monitoring by combining vital signs (heart rate and respiration rate) extracted from in-car WiFi system and driver’s psychomotor coordination through steering wheel operations. The framework consists of a series of signal processing algorithms for extracting clean and informative vital signs and psychomotor coordination, and integrate the two data streams using a self-attention convolutional neural network (i.e., C-Attention). In safe laboratory experiments with 15 participants,DetectDUIachieves drink driving detection accuracy of 96.6% and BAC predictions with an average mean error of$2\sim 5mg/dl$. These promising results provide a highly encouraging case for continued development.

Duplicate Address Detection (DAD) is an essential part of the Neighbor Discovery Protocol (NDP), which determines whether the IPv6 address of a node conflicts with those of other nodes. Due to the lack of verification of NDP messages, DAD is vulnerable to DoS attacks. Existing solutions suffer from high complexity, need to modify the NDP, or have a single point of failure.To solve the above problems, we propose P4DAD, a secure DAD mechanism described by P4. By creating and maintaining binding entries between IPv6 address and link-layer property of host, P4DAD can filter spoofed NDP messages in an in-network manner to prevent DoS attacks on DAD without modifications to the NDP or host stack. We implement a prototype of P4DAD and evaluate it in terms of functionality, performance, and scalability. Evaluation results show that P4DAD can prevent DoS attacks on DAD successfully with negligible overhead, and has satisfactory scalability.