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The advances in Internet of things lead to an increased number of devices generating and streaming data. These devices can be useful data sources for activity recognition by using machine learning. However, the set of available sensors may vary over time, e.g. due to mobility of the sensors and technical failures. Since the machine learning model uses the data streams from the sensors as input, it must be able to handle a varying number of input variables, i.e. that the feature space might change over time. Moreover, the labelled data necessary for the training is often costly to acquire. In active learning, the model is given a budget for requesting labels from an oracle, and aims to maximize accuracy by careful selection of what data instances to label. It is generally assumed that the role of the oracle only is to respond to queries and that it will always do so. In many real-world scenarios however, the oracle is a human user and the assumptions are simplifications that might not give a proper depiction of the setting. In this work we investigate different interactive machine learning strategies, out of which active learning is one, which explore the effects of an oracle that can be more proactive and factors that might influence a user to provide or withhold labels. We implement five interactive machine learning strategies as well as hybrid versions of them and evaluate them on two datasets. The results show that a more proactive user can improve the performance, especially when the user is influenced by the accuracy of earlier predictions. The experiments also highlight challenges related to evaluating performance when the set of classes is changing over time.

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings’ status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

Fig. 7 and Fig. 8 have incorrect legends in the images and have been updated.

In rural areas with low demand, demand responsive transport (DRT) can provide an alternative to the regular public transport bus lines, which are expensive to operate in such conditions. With simulation, we explore the potential effects of introducing a DRT service that replaces existing bus lines in Lolland municipality in Denmark, assuming that the existing demand remains unchanged. We set up the DRT service in such a way that its service quality (in terms of waiting time and in-vehicle time) is comparable to the replaced buses. The results show that a DRT service can be more cost efficient than regular buses and can produce significantly less CO2 emissions when the demand level is low. Additionally, we analyse the demand density at which regular buses become more cost efficient and explore how the target service quality of a DRT service can affect operational characteristics. Overall, we argue that DRT could be a more sustainable mode of public transport in low demand areas.

The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user’s privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user’s privacy in a busy office environment.

Protandry is a widespread life‐history phenomenon describing how males precede females at the site or state of reproduction. In migratory birds, protandry has an important influence on individual fitness, the migratory syndrome, and phenological response to climate change. Despite its significance, accurate analyses on the dynamics of protandry using data sets collected at the breeding site, are lacking. Basing our study on records collected during two time periods, 1979 to 1988 and 2006 to 2016, we aim to investigate protandry dynamics over 38 years in a breeding population of willow warblers (Phylloscopus trochilus). Change in the timing of arrival was analyzed in males and females, and protandry (number of days between male and female arrival) was investigated both at population level and within breeding pairs. Our results show advancement in the arrival time at the breeding site in both sexes, but male arrival has advanced to a greater extent, leading to an increase in protandry both at the population level and within breeding pairs. We did not observe any change in sex ratio that could explain the protandry increase, but pronounced temperature change has occurred and been reported in the breeding area and along the migratory route. Typically, natural selection opposes too early arrival in males, but given warmer springs, this counteracting force may be relaxing, enabling an increase in protandry. We discuss whether our results suggest that climate change has induced sex‐specific effects, if these could be evolutionary and whether the timing of important life‐history stages such as arrival at the breeding site may change at different rates in males and females following environmental shifts. Protandry is a widespread phenomenon in nature, influential on several aspects of life history in migratory birds. Despite its significance, accurate analyses are lacking on its dynamics. Here, using a unique data set spanning 38 years, we show how protandry has increased over time during a period of temperature warming, suggesting that climate change may have cause sex‐specific effects in the willow warbler.

Although the availability of sensor data is becoming prevalent across many domains, it still remains a challenge to make sense of the sensor data in an efficient and effective manner in order to provide users with relevant services. The concept of virtual sensors provides a step towards this goal, however they are often used to denote homogeneous types of data, generally retrieved from a predetermined group of sensors. The DIVS (Dynamic Intelligent Virtual Sensors) concept was introduced in previous work to extend and generalize the notion of a virtual sensor to a dynamic setting with heterogenous sensors. This paper introduces a refined version of the DIVS concept by integrating an interactive machine learning mechanism, which enables the system to take input from both the user and the physical world. The paper empirically validates some of the properties of the DIVS concept. In particular, we are concerned with the distribution of different budget allocations for labelled data, as well as proactive labelling user strategies. We report on results suggesting that a relatively good accuracy can be achieved despite a limited budget in an environment with dynamic sensor availability, while proactive labeling ensures further improvements in performance.

Self-driving cars enable dynamic shared mobility, where customers are independent of schedules and fixed stops. This study aims to investigate the potential effects shared mobility can have on future transportation. We simulate multiple scenarios to analyze the effects different service designs might have on vehicle kilometers, on the required number of shared vehicles, on the potential replacement of private cars, and on service metrics such as waiting times, travel times, and detour levels. To demonstrate how simulation can be used to analyze future mobility, we present a case study of the city of Gothenburg in Sweden, where we model travel demand in the morning hours of a workday. The results show that a significant decrease of vehicle kilometers can be achieved if all private car trips are replaced by rideshare and that shared vehicles can potentially replace at least 5 private cars during the morning peak.

A simple generic method for optimizing membrane protein overexpression in Escherichia coli is still lacking. We have studied the physiological response of the widely used \"Walker strains\" C41(DE3) and C43(DE3), which are derived from BL21(DE3), to membrane protein overexpression. For unknown reasons, overexpression of many membrane proteins in these strains is hardly toxic, often resulting in high overexpression yields. By using a combination of physiological, proteomic, and genetic techniques we have shown that mutations in the lacUV5 promoter governing expression of T7 RNA polymerase are key to the improved membrane protein overexpression characteristics of the Walker strains. Based on this observation, we have engineered a derivative strain of E. coli BL21(DE3), termed Lemo21(DE3), in which the activity of the T7 RNA polymerase can be precisely controlled by its natural inhibitor T7 lysozyme (T7Lys). Lemo21(DE3) is tunable for membrane protein overexpression and conveniently allows optimizing overexpression of any given membrane protein by using only a single strain rather than a multitude of different strains. The generality and simplicity of our approach make it ideal for high-throughput applications.