Software failures inform engineering work, standards, regulations. For example, the Log4J vulnerability brought government and industry attention to evaluating and securing software supply chains. Retrospective failure analysis is thus a valuable line of software engineering research. Accessing private engineering records is difficult, so such analyses tend to use information reported by the news media. However, prior works in this direction have relied on manual analysis. That has limited the scale of their analyses. The community lacks automated support to enable such analyses to consider a wide range of news sources and incidents.

This paper explores the use of a Bayesian nonparametric topic modeling technique for the purpose of anomaly detection in video data. We present results from two experiments. The first experiment shows that the proposed technique is automatically able characterize the underlying terrain, and detect anomalous flora in image data collected by an underwater robot. The second experiment shows that the same technique can be used on images from a static camera in a dynamic unstructured environment. In the second dataset, consisting of video data from a static seafloor camera capturing images of a busy coral reef, the proposed technique was able to detect all three instances of an underwater vehicle passing in front of the camera, amongst many other observations of fishes, debris, lighting changes due to surface waves, and benthic flora.

Lab-on-a-chip technology scales down multiple laboratory processes to a chip capable of performing automated biochemical analyses. Electrowetting on dielectric (EWOD) is a digital microfluidic lab-on-a-chip technology that uses patterned electrodes for droplet manipulation. The main limitations of EWOD devices are the restrictions in volume and motion of droplets due to the fixed size, layout, and addressing scheme of the electrodes. Optoelectrowetting on dielectric (OEWOD) is a recent technology that uses optical sources and electric fields for droplet actuation on a continuous surface. We describe an open surface light-actuated OEWOD device that can manipulate droplets of multiple volumes ranging from 1 to 50 μL at voltages below 45 V. To achieve lower voltage droplet actuation than previous open configuration devices, we added a dedicated dielectric layer of high dielectric constant (Al2O3with εrof 9.1) and significantly reduced the thickness of the hydrophobic layer. The device is capable of transporting droplets at speeds as high as 12 mm/sec using a data projector as an optical source. We developed a multiple axis contact pad design to apply lateral electric fields along different axes to achieve multi-axis droplet movement. We demonstrated microfluidic operations including droplet merging, mixing, and parallel droplet motion. Further, the OEWOD device is capable of droplet transportation using a tablet computer's LCD screen as an optical source.