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Using deep learning to detect depression from speech

Artificial intelligence (AI) tools have achieved promising results on numerous tasks and could soon assist professionals in various settings. In recent years, computer scientists have been exploring the potential of these tools for detecting signs of different physical and psychiatric conditions.

Depression is one of the most widespread psychiatric disorders, affecting approximately 9.5% of American adults every year. Tools that can automatically detect signs of depression might help to reduce suicide rates, as they would allow doctors to promptly identify people in need of psychological support.

Researchers at Jinhua Advanced Research Institute and Harbin University of Science and Technology have recently developed a deep learning algorithm that could detect depression from a person’s speech. This model, introduced in a paper published in Mobile Networks and Applications, was trained to recognize emotions in by analyzing different relevant features.

Deep learning for quantum sensing

Quantum sensing represents one of the most promising applications of quantum technologies, with the aim of using quantum resources to improve measurement sensitivity. In particular, sensing of optical phases is one of the most investigated problems, considered key to developing mass-produced technological devices.

Optimal usage of quantum sensors requires regular characterization and calibration. In general, such calibration is an extremely complex and resource-intensive task—especially when considering systems for estimating multiple parameters, due to the sheer volume of required measurements as well as the computational time needed to analyze those measurements. Machine-learning algorithms present a powerful tool to address that complexity. The discovery of suitable protocols for algorithm usage is vital for the development of sensors for precise quantum-enhanced measurements.

A particular type of machine-learning algorithm known as “reinforcement learning” (RL) relies on an intelligent agent guided by rewards: Depending on the rewards it receives, it learns to perform the right actions to achieve the desired optimization. The first experimental realizations using RL algorithms for the optimization of quantum problems have been reported only very recently. Most of them still rely on prior knowledge of the model describing the system. What is desirable is instead a completely model-free approach, which is possible when the agent’s reward does not depend on the explicit system model.

The Future Is Here — Healenium — A self-healing Selenium test automation library

Year 2022 Basically this can be used with Python for self healing networks and software testing.


Have you ever found yourself in a situation where you realized a small change of the UI broke your E2E test again? Well, it happens to me very often.

Recently I read an article from on medium.com about an interesting library called Healineum which can come to the rescue.

Healenium is an AI-powered open-source library that improves the stability of Selenium-based tests, handles changes of updated web elements automatically and helps to overcome the problem of UI autotests instability using a self-healing mechanism…

Microsoft launches the new Bing, with ChatGPT built in

“It’s a new day for search,” Microsoft CEO Satya Nadella said today. For 13 years now, Microsoft has tried to get you to use Bing, but you didn’t want to, so its global market share remains in the low single digits. Now, the company is pulling out all the stops in an effort to better compete with Google. Today, at a press event in Redmond, Washington, Microsoft announced its long-rumored integration of OpenAI’s GPT-4 model into Bing, providing a ChatGPT-like experience within the search engine.

The company is also launching a new version of its Edge browser today, with these new AI features built into the sidebar.

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