Typically, a supervised learning model is trained using passive learning by randomly selecting unlabelled instances to annotate. This approach is effective for learning a model, but can be costly in cases where acquiring labelled instances is expensive. For example, it can be time-consuming to manually identify spam mails (labelled instances) from thousands of emails (unlabelled instances) flooding an inbox during initial data collection. Generally, we answer the above scenario with uncertainty sampling, an active learning method that improves the efficiency of supervised learning by using fewer labelled instances than passive learning. Given an unlabelled data pool, uncertainty sampling queries the labels of instances where the predicted probabilities, p, fall into the uncertainty region, i.e., $p \approx 0.5$. The newly acquired labels are then added to the existing labelled data pool to learn a new model. Nonetheless, the performance of uncertainty sampling is susceptible to the area of unpredictable responses (AUR) and the nature of the dataset. It is difficult to determine whether to use passive learning or uncertainty sampling without prior knowledge of a new dataset. To address this issue, we propose bell curve sampling, which employs a bell curve weight function to acquire new labels. With the bell curve centred at p=0.5, bell curve sampling selects instances whose predicted values are in the uncertainty area most of the time without neglecting the rest. Simulation results show that, most of the time bell curve sampling outperforms uncertainty sampling and passive learning in datasets of different natures and with AUR.

The development of new methods and approaches to the rapid diagnosis of stress is an urgent task, taking into account the current epidemiological (Covid-19) situation [1]. Psychological stress plays a key role in the development of many physical and neurological diseases. The term "stress" is usually used to denote both a strong adverse physical and / or psychogenic external environmental impact, and for a state of psychophysiological stress that develops under their influence, initially serving to adapt a person to new environmental conditions. Stress, as a chronic psychophysiological overstrain, can provoke the manifestation or exacerbation of symptoms of the disease, serve as one of the risk factors or aggravate the severity of the disease. Emotional overstrain reduces the productivity and quality of work performed by a person.

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IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. Their designs draw on concepts from the human brain and significantly outperform conventional computers in comparative studies. They report on their recent findings in the Journal of Applied Physics. Today's computers are built on the von Neumann architecture, developed in the 1940s. Von Neumann computing systems feature a central processer that executes logic and arithmetic, a memory unit, storage, and input and output devices.

Every week, we talk about important data and analytics topics with top data scientists on Facebook Live. In our upcoming #DataTalk, we're talking with Dr. Lindsey Zuloaga about how recruitment teams are using artificial intelligence to help them with their work. Get the video link and reminder to watch the Facebook Live event. To suggest future data science topics or guests, please contact Mike Delgado. Dr. Lindsey Zuloaga is the Director of Data Science at HireVue.