This paper presents the design and analysis of a data science competition on a problem of time series regression from aeronautics data. For the purpose of performing predictive maintenance, aviation companies seek to create aircraft “numerical twins”, which are programs capable of accurately predicting strains at strategic positions in various body parts of the aircraft. Given a number of input parameters (sensor data) recorded in sequence during the flight, the competition participants had to predict output values (gauges), also recorded sequentially during test flights, but not recorded during regular flights. The competition data included hundreds of complete flights. It was a code submission competition with complete blind testing of algorithms. The results indicate that such a problem can be effectively solved with gradient boosted trees, after preprocessing and feature engineering. Deep learning methods did not prove as efficient.

Evolutionary computation is used to construct undetectable computer attack scripts. Using a simulated operating system, we show that scripts can be evolved to cover their tracks and become difficult to detect from log file analysis.

Interactive evolutionary design, a powerful technique where one marries the exploratory capabilities of evolutionary computation with the aesthetic skills and domain knowledge of the human as selective agent, has been demonstrated to be an extremely powerful exploratory design method. One of interactive evolutions most promising uses is in discovering individual-level rules of behavior and interaction that will produce a desired collective pattern in a group of human or non-human agents. The problem of finding micro-rules that produce interesting macro-behavior poses significant challenges, all the more so when what constitutes "interesting" macro-behavior may not be known ahead of time. Here, the system at our disposal is a real-time crowd polling and display system, whose potential for generating interesting group behavior remains largely untapped. Additionally, because the system is capable of polling large crowds of people in real time, it presents an ideal framework within which to take advantage of a relatively unexplored form of interactive evolution - collective evolution, where the opinions of the entire group are taken into account in the design of the next generation. Collective evolution has a broad range of potential applications, including marketing research and logo and brand name design.

A method for automatically classifying facial images is proposed. Faces are represented using elastic graphs labelled with with 2D Gabor wavelet features. The system is trained from examples to classify faces on the basis of high-level attributes, such as sex, "race", and expression, using linear discriminant analysis (LDA). Use of the Gabor representation relaxes the requirement for precise normalization of the face: approximate registration of a facial graph is sufficient. LDA allows simple and rapid training from examples, as well as straightforward interpretation of the role of the input features for classification. The algorithm is tested on three different facial image datasets, one of which was acquired under relatively uncontrolled conditions, on tasks of sex, "race" and expression classification. Results of these tests are presented. The discriminant vectors may be interpreted in terms of the saliency of the input features for the different classification tasks, which we portray visually with feature saliency maps for node position as well as filter spatial frequency and orientation.

A method for automatically extracting symbolic-level information from single facial images is proposed. We combine a 2D Gabor wavelet labelled elastic graph representation with Fisher discriminant analysis. Use of this representation relaxes the requirement for exact normalization of the face. The algorithm is used to extract information about the sex, "race" and emotional expression of the face in the image. A visual interpretation of the discriminant vectors is provided.

We propose a method for automatically classifying facial images based on labeled elastic graph matching, a 2D Gabor wavelet representation, and linear discriminant analysis. Results of tests with three image sets are presented for the classification of sex, "race", and expression. A visual interpretation of the discriminant vectors is provided.