Study of affect in speech requires suitable data, as emotional expression and perception vary across languages. Until now, no corpus has existed for natural expression of affect in spontaneous Finnish, existing data being acted or from a very specific communicative setting. This paper presents the first such corpus, created by annotating 12,000 utterances for emotional arousal and valence, sampled from three large-scale Finnish speech corpora. To ensure diverse affective expression, sample selection was conducted with an affect mining approach combining acoustic, cross-linguistic speech emotion, and text sentiment features. We compare this method to random sampling in terms of annotation diversity, and conduct post-hoc analyses to identify sampling choices that would have maximized the diversity. As an outcome, the work introduces a spontaneous Finnish affective speech corpus and informs sampling strategies for affective speech corpus creation in other languages or domains.

Bacteria and other small creatures squirming inside bodies often have to propel themselves through thick, viscous environments. For a human, this would look like someone awkwardly trying to swim their way through a pool of honey. Nature has already come up with creative solutions to this sticky problem. E.coli, for example, uses a corkscrew motion to cut through the muck while flagella contort their frames and whip themselves forward. Now, using this natural adaptation as inspiration, researchers from Tampere University and Anhui Jianzhu University have created a new doughnut-shaped micro-robot capable of autonomously navigating its way through mucus and other goopy substances.

Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing their signal morphology offers insights into their health status. Unfortunately, existing approaches are optimized for controlled environments, neglecting realistic conditions such as time-varying rotational speeds and the vibration's non-stationary nature. This paper presents a fusion of time-frequency analysis and deep learning techniques to diagnose bearing faults under time-varying speeds and varying noise levels. First, we formulate the bearing fault-induced vibrations and discuss the link between their non-stationarity and the bearing's inherent and operational parameters. We also elucidate quadratic time-frequency distributions and validate their effectiveness in resolving distinctive dynamic patterns associated with different bearing faults. Based on this, we design a time-frequency convolutional neural network (TF-CNN) to diagnose various faults in rolling-element bearings. Our experimental findings undeniably demonstrate the superior performance of TF-CNN in comparison to recently developed techniques. They also assert its versatility in capturing fault-relevant non-stationary features that couple with speed changes and show its exceptional resilience to noise, consistently surpassing competing methods across various signal-to-noise ratios and performance metrics. Altogether, the TF-CNN achieves substantial accuracy improvements up to 15%, in severe noise conditions.

The health and safety hazards posed by worn crane lifting ropes mandate periodic inspection for damage. This task is time-consuming, prone to human error, halts operation, and may result in the premature disposal of ropes. Therefore, we propose using deep learning and computer vision methods to automate the process of detecting damaged ropes. Specifically, we present a novel vision-based system for detecting damage in synthetic fiber rope images using convolutional neural networks (CNN). We use a camera-based apparatus to photograph the lifting rope's surface, while in operation, and capture the progressive wear-and-tear as well as the more significant degradation in the rope's health state. Experts from Konecranes annotate the collected images in accordance with the rope's condition; normal or damaged. Then, we pre-process the images, design a CNN model in a systematic manner, evaluate its detection and prediction performance, analyze its computational complexity, and compare it with various other models. Experimental results show the proposed model outperforms other techniques with 96.4% accuracy, 95.8% precision, 97.2% recall, 96.5% F1-score, and 99.2% AUC. Besides, they demonstrate the model's real-time operation, low memory footprint, robustness to various environmental and operational conditions, and adequacy for deployment in industrial systems.

It looks like enough of us believe in fairies after all, but it's not Tinkerbell who is flying this time. Scientists from Tampere University in Finland have developed a 0.2-inch (4 mm) robot that uses wind and light energy to soar through the air. Their'flying aero-robot based on light-responsive materials assembly' - FAIRY - weighs just 1.2 milligrams, meaning it can be blown about by even a gentle breeze. It resembles a dandelion seed or'pappus', with super-fine bristles poking from two wings which gently flap when activated with light. The'flying aero-robot based on light-responsive materials assembly' (pictured) - FAIRY - weighs just 1.2 milligrams so can be blown about by even a gentle breeze.

The maritime industry is experiencing a technological revolution that affects shipbuilding, operation of both seagoing and inland vessels, cargo management, and working practices in harbors. This ongoing transformation is driven by the ambition to make the ecosystem more sustainable and cost-efficient. Digitalization and automation help achieve these goals by transforming shipping and cruising into a much more cost- and energy-efficient, and decarbonized industry segment. The key enablers in these processes are always-available connectivity and content delivery services, which can not only aid shipping companies in improving their operational efficiency and reducing carbon emissions but also contribute to enhanced crew welfare and passenger experience. Due to recent advancements in integrating high-capacity and ultra-reliable terrestrial and non-terrestrial networking technologies, ubiquitous maritime connectivity is becoming a reality. To cope with the increased complexity of managing these integrated systems, this article advocates the use of artificial intelligence and machine learning-based approaches to meet the service requirements and energy efficiency targets in various maritime communications scenarios.

According to the US Bureau of Labour Statistics, "Employment of computer and information technology occupations is projected to grow 12% from 2018 to 2028, much faster than the average for all occupations. These occupations are projected to add about 546,200 new jobs." We are at the cusp of the Fourth Industrial Revolution, where the physical, digital and biological worlds are merging in unprecedented forms and scale. Yet, despite the number of STEM jobs flourishing, less than a third (29.3%) of those employed in scientific research and development across the world in 2016 are women. Eurostat found that in 2020, of almost 73 million persons employed in science and technology in the EU, aged from 15 to 74, nearly 37.5 million were women (51.3%) and 35.5 million men (48.7%).

Researchers at Tampere University have successfully used artificial intelligence to predict nonlinear dynamics that take place when ultrashort light pulses interact with matter. This novel solution can be used for efficient and fast numerical modeling, for example, in imaging, manufacturing and surgery. The findings were published in the prestigious Nature Machine Intelligence journal. Artificial intelligence can distinguish different types of laser pulse propagation, just as it recognizes subtle differences of expression in facial recognition. The newly found solution can make it simpler to design experiments in fundamental research and will allow algorithms to be embedded in the next generation of laser systems to ensure real-time optimization.

BEGIN ARTICLE PREVIEW: According to a new study by Tampere University in Finland, making eye contact with a robot may have the same effect on people as eye contact with another person. The results predict that interaction between humans and humanoid robots will be surprisingly smooth. With the rapid progress in robotics, it is anticipated that people will increasingly interact with so called social robots in the future. Despite the artificiality of robots, people seem to react to them socially and ascribe humane attributes to them. For instance, people may perceive different qualities – such as knowledgeability, sociability, and likeability – in robots based on how they look and/or behave. Previous surveys have been able to shed light on people’s perceptions of social robots and their characteristics, but the very central question of what kind of automatic reactions social robots evoke in us humans has remained unanswered. Does interacting wi

The European Robotics Forum 2018 (ERF2018), the most influential meeting of the robotics community in Europe, takes place in Tampere on 13-15 March 2018. ERF brings together over 900 leading scientists, companies, and policymakers for the largest robotics networking event in Europe. Under the theme "Robots and Us", the over 50 workshops cover current societal and technical themes, including human-robot-collaboration and how robotics can improve industrial productivity and service sector operations. During the opening the ERF2018, on 13 March, Juha Heikkilä, Head of unit, EC DG CNECT, explained that "the European Robotics Forum has been instrumental in breaking down silos and bringing together a strong, integrated robotics community in Europe. This year's theme, "Robots and Us", reflects the increasingly broad impact of robotics and allows discussing not just technology but also the all-important non-technological aspects of robotics."