We introduce an online anomaly detection algorithm that processes data in a sequential manner. At each time, the algorithm makes a new observation, produces a decision, and then adaptively updates all its parameters to enhance its performance. The algorithm mainly works in an unsupervised manner since in most real-life applications labeling the data is costly. Even so, whenever there is a feedback, the algorithm uses it for better adaptation. The algorithm has two stages. In the first stage, it constructs a score function similar to a probability density function to model the underlying nominal distribution (if there is one) or to fit to the observed data. In the second state, this score function is used to evaluate the newly observed data to provide the final decision. The decision is given after the well-known thresholding. We construct the score using a highly versatile and completely adaptive nested decision tree. Nested soft decision trees are used to partition the observation space in a hierarchical manner. We adaptively optimize every component of the tree, i.e., decision regions and probabilistic models at each node as well as the overall structure, based on the sequential performance. This extensive in-time adaptation provides strong modeling capabilities; however, it may cause overfitting. To mitigate the overfitting issues, we first use the intermediate nodes of the tree to produce several subtrees, which constitute all the models from coarser to full extend, and then adaptively combine them. By using a real-life dataset, we show that our algorithm significantly outperforms the state of the art.

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Computers have been integrated into almost all areas of our daily lives. Serious problems stem from the fact that elementary and secondary school students in general have taken none or inadequate computer classes and as such, their awareness of efficient computer usage has not been adequately addressed. During university education, students have been observed experiencing hardships using computers for research and document preparation, due to their prior use of computers having consisted mostly of accessing social media and games. The current study investigated 2013-2014 high school graduates' attitudes regarding technology, the computer programs they used and their usage frequency, reasons for why they use the Internet and social media and their usage frequencies, and their basic computer skills at university level. The participants of the current study were students who had just entered various departments and language preparation levels at Fatih University, Istanbul, Turkey. 250 Participants were randomly selected. Data for the current study were collected through a Basic Computer Skills Questionnaire, an application for determining the levels of participants' basic computer skills. Significant benefits are foreseen for students entering universities as helpful computer users with awareness attained during elementary or secondary education. Additionally, particularly for students who prefer non-social majors, it will be very useful to have learned a programming language and an algorithm at basic levels. The Internet as a medium for potentially leading to a diverse range of problems today requires conscious users.

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