Frequent language exposure and use are among the most important conditions for successful language learning whether in classrooms, during study abroad, or in other informal contexts. Research probing exposure and usage often relies on one-off self-report questionnaires in which participants estimate their typical level of language exposure over extended periods of time, often long after it occurre

Assertions are one of the most useful automated techniques for checking program’s behaviour and hence have been used for different verification and validation tasks. We provide an overview of the last two decades of research involving ‘assertions’ in software testing. Based on a term–based search, we filtered the inclusion of relevant papers and synthesised them w.r.t. the problem addressed, the s

Ensuring functional safety is a critical challenge for autonomous robots, as they must operate reliably and predictably despite uncertainty. However, existing safety measures can over-constrain the system, limiting the robot’s availability to perform its assigned task. To address this problem, we propose a more flexible strategy that equips robots with theability to adapt to system failures and re

Autonomous driving has become an important research area for road traffic, whereas testing of autonomous driving systems to ensure a safe and reliable operation remains an open challenge. Substantial real-world testing or massive driving data collection does not scale since the potential test scenarios in real-world traffic are infinite, and covering large shares of them in the test is impractical

It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions. This paper proposes a method to adapt camera parameters according to a normalizing flow-based out-of-distibution detector. A small-scale study is conducted which shows that adapting camera parameters according to this out-of-distibution detector leads to an average increase of 3 to 4% poin

Context: DevOps has recently become a mainstream solution for bridging the gaps between development (Dev) and operations (Ops) enabling cross-functional collaboration. The DevOps concept of continuous monitoring may bring a lot of benefits to development teams such as early detection of run-time errors and various performance anomalies. Objective: We aim to explore deep learning (DL) solutions for

We built machine learning and image analysis tools in order to forecast winter wheat yield based on a rich multi dimensional tensor of agricultural information spanning different scales. This information consists of satellite multi-band images, local soil samples obtained from national databases, local weather as well as field data from 23 farms cultivating winter wheat in southern Sweden. This is

Continuous experimentation (CE) is a software development approach where product decisions are data-driven. Large global internet-facing companies such as Microsoft, Google, and Facebook apply the practice by leveraging their massive user bases to obtain high statistical significance in experimentation results. However, companies with smaller user bases, such as small- and medium-sized enterprises

[Background] The game industry faces fierce competition and games are developed on short deadlines and tight budgets. Continuously testing and experimenting with new ideas and features is essential in validating and guiding development toward market viability and success. Such continuous experimentation (CE) requires user data, which is often limited in early development stages. This challenge is

In the rapidly advancing domain of autonomous vehicle technology, the management of intersection traffic emerges as a demanding challenge. This paper describes the development and implementation of a novel testbed designed for the investigation of an autonomous intersection management system (AIMS). The research primarily focuses on the complex coordination required among autonomous vehicles for e

Battery-free IoT devices are becoming increasingly interesting for environmental and practical reasons, with ambient backscatter as one of the candidate technologies. We present a class of balanced transmission patterns for ambient backscatter communication (BSC) and evaluate their performance under different signal-to-interference ratios and fading conditions. These patterns allow for bit synchro

Animal migration has fascinated scientists and the public alike for centuries, yet migratory animals are facing diverse threats that could lead to their demise. The Anthropocene is characterised by the reality that humans are the dominant force on Earth, having manifold negative effects on biodiversity and ecosystem function. Considerable research focus has been given to assessing anthropogenic im

Coming from a disadvantaged background can have negative impact on an individual’s educational trajectory. Some people however seem unaffected and cope well with the demands and challenges posed by school education, despite growing up in adverse conditions, a phenomenon termed academic resilience. While it is uncertain which underlying factors make some people more likely to circumvent unfavorable

mmWave communication has come up as the unexplored spectrum for 5G services. With new standards for 5G NR positioning, more off-the-shelf platforms and algorithms are needed to perform indoor positioning. An object can be accurately positioned in a room either by using an angle and a delay estimate or two angle estimates or three delay estimates. We propose an algorithm to jointly estimate the ang

Distributed multiple-input multiple-output (D-MIMO) is a promising technology for simultaneous communication and positioning. However, phase synchronization between multiple access points in D-MIMO is challenging and methods that function without the need for phase synchronization are highly desired. Therefore, we present a method for D-MIMO that performs direct positioning of a moving device base

We propose a novel hybrid approach that integrates Neural Ordinary Differential Equations (NODEs) with Bayesian optimization to address the dynamics and parameter estimation of a modified time-delay-type Susceptible-Infected-Removed (SIR) model incorporating immune memory. This approach leverages a neural network to produce continuous multi-wave infection profiles by learning from both data and th

Ex vivo working porcine heart models allow for the study of a heart’s function and physiology outside the living organism. These models are particularly useful due to the anatomical and physiological similarities between porcine and human hearts, providing an experimental platform to investigate cardiac disease or assess donor heart viability for transplantation. This chapter presents an in-depth

Background: In attention-deficit/hyperactivity disorder (ADHD), poor inhibitory control is one of the main characteristics, with oculomotor inhibition impairments being considered a potential biomarker of the disorder. While auditory white noise has demonstrated the ability to enhance working memory in this group, visual white noise is still unexplored and so are the effects of both types of white