Loneliness is a growing public health concern, particularly among older adults, and has been linked to adverse physical and mental health outcomes. This study presents a machine learning approach to predict levels of loneliness using behavioural and emotional data collected from 124 participants through a mobile phone application over a 71-day period. The dataset includes 27 features derived from self-logged information such as wellbeing scores, mood fluctuations, and time spent in various home locations. Feature selection was applied to identify the most discriminative indicators, with classification and regression models evaluated using both Support Vector Machine (SVM), and Random Forest (RF). We applied feature selection to identify the most discriminative indicators and evaluated both Support Vector Machine (SVM) and Random Forest (RF) models for classification and regression. The highest classification accuracy—69.19% on a 7-point loneliness scale—was achieved using a five-fold SVM with the top 13 features. In the regression task, the best performance was observed using 26 features, resulting in a minimum Mean Squared Error (MSE) of 0.6752. These findings indicate that a selected subset of behavioural and emotional features can offer a meaningful estimation of loneliness levels. This has potential to inform the design of real-time, personalised digital tools aimed at identifying and supporting individuals at risk of loneliness.

Large language models (LLMs) have enabled new tools in requirements engineering (RE), often in the form of intelligent agents or virtual assistants. These tools can transform how software engineers perform RE tasks and interact with stakeholders. However, existing research primarily focuses on showcasing the capabilities of these tools rather than their design and evaluation in RE-specific contexts. This limits our understanding of their practical value and hinders broader adoption. To address this gap, we propose a reference model to guide the design, use, and evaluation of intelligent RE agents. Our work introduces new RE use cases, along with evaluation metrics for intelligent RE agents. We present a study design to support systematic development and share early findings demonstrating the feasibility of our approach. The use cases show how agents can add value for RE practitioners, while our synthesized catalogue supports tool evaluation. Finally, our analysis of commercial agents reveals that these tools already support certain aspects of the envisioned RE use cases.

There are many design techniques to support the co-design of tangible technologies. However, few of these design methods allow the involvement of users at scale and across diverse geographic locations. While popular in psychology, the story completion method (SCM) has only recently started to be adopted within the HCI community. We explore whether SCM can generate meaningful design insights from large, diverse study populations for the design of Tangible User Interfaces (TUIs). Based on the results of two questionnaire studies using SCM, we conclude that the method can be used to generate meaningful design insights. Drawing on a systematic review of 870 TUI papers, we then contextualise the strengths and weaknesses of SCM against commonly used design methods, before reflecting on our experience of using the method across two distinct domains. We discuss the advantages of the method (particularly in terms of the scale and diversity of participation) and the challenges (particularly around constructing meaningful story stems, and developing the correct level of scaffolding to support creativity). We conclude that SCM is particularly suitable to be used in the early stages of the design process to understand the socio-cultural context of deployment.

Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this article, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by 13.6%.

We explore the role of reciprocity in code review processes. Reciprocity manifests itself in two ways: 1) reviewing code for others translates to accepted code contributions, and 2) having contributions accepted increases the reviews made for others. We use vector autoregressive (VAR) models to explore the causal relation between reviews performed and accepted contributions. After fitting VAR models for 24 active open-source developers, we found evidence of reciprocity in 6 of them. These results suggest reciprocity does play a role in code review, that can potentially be exploited to increase reviewer participation.

Security-critical systems typically place some requirements on the behaviour of their users, obliging them to follow certain instructions when using those systems. Security vulnerabilities can arise when users do not fully satisfy their obligations. In this paper, we propose an approach that improves system security by ensuring that attack scenarios are mitigated even when the users deviate from their expected behaviour. The approach uses structured transition systems to present and reason about user obligations. The aim is to identify potential vulnerabilities by weakening the assumptions on how the user will behave. We present an algorithm that combines iterative abstraction and controller synthesis to produce a new software specification that maintains the satisfaction of security requirements while weakening user obligations. We demonstrate the feasibility of our approach through two examples from the e-voting and e-commerce domains.

The global population is ageing, leading to shifts in healthcare needs. Home healthcare monitoring systems currently focus on physical health, but there is an increasing recognition that psychological wellbeing also needs support. This raises the question of how to design devices that older adults can interact with to log their feelings. We designed three tangible prototypes, based on existing paper-based scales of affect. We report findings from a lab study in which participants used the prototypes to log the emotion from standardised emotional vignettes. We found that the prototypes allowed participants to accurately record identified emotions in a reasonable time. Our participants expressed a perceived need to record emotions, either to share with family/carers or for self-reflection. We conclude that our work demonstrates the potential for in-home tangible devices for recording the emotions of older adults to support wellbeing.

The world is facing an ageing population phenomenon, coupled with health and social problems, which affect older people’s ability to live independently. This situation challenges the viability of health and social services. Smart home technology can play a significant role in easing the pressure on caregivers, as well as reduce the financial costs of health and social services. Activity of Daily Living (ADL) recognition is an essential step to translate sensor data into activities at high semantic levels. Supervised Machine Learning (ML) algorithms are the most commonly used techniques for this application. However, a common problem is a lack of availability of enough annotated data to train these algorithms. Collecting annotated data is expensive, time consuming, and may violate people’s privacy. Intra- and inter-personal variation in performing complex activities is another challenge for an ML-based activity recognition approach. In this paper, a multi-layered knowledge-based architecture for recognising ADL in real-time is proposed. At the first stage, sensor data is pre-processed; events that describe changes in the environment are detected at the second stage, in which the sequence of events is used to recognise more semantically complex activities at the third stage. A new ADL ontology is proposed to model the knowledge related to the sensor platform and the targeted activities as the previously proposed ontologies were either designed to deal with specific sensor data, or they ignored the context environment information which is important in recognising complex activities.

As the number, complexity, and heterogeneity of connected devices in the Internet of Things (IoT) increase, so does our need to secure these devices, the environment in which they operate, and the assets they manage or control. Collaborative security exploits the capabilities of these connected devices and opportunistically composes them to protect assets from potential harm. By dynamically composing these capabilities, collaborative security implements the security controls that satisfy both security and non-security requirements. However, this dynamic composition is often hampered by the heterogeneity of the devices available in the environment and the diversity of their behaviours. In this article, we present a systematic, tool-supported approach for collaborative security where the analysis of requirements drives the opportunistic composition of capabilities to realise the appropriate security control in the operating environment. This opportunistic composition is supported through a combination of feature modelling and mediator synthesis. We use features and transition systems to represent and reason about capabilities and requirements. We formulate the selection of the optimal set of features to implement adequate security control as a multi-objective constrained optimisation problem and use constraint programming to solve it efficiently. The selected features are then used to scope the behaviours of the capabilities and thereby restrict the state space for synthesising the appropriate mediator. The synthesised mediator coordinates the behaviours of the capabilities to satisfy the behaviour specified by the security control. Our approach ensures that the implemented security controls are the optimal ones, given the capabilities available in the operating environment. We demonstrate the validity of our approach by implementing a feature-driven mediation for collaborative security tool and applying it to a collaborative robots case study.

Interoperability is a major concern for the software engineering field, given the increasing need to compose components dynamically and seamlessly. This dynamic composition is often hampered by differences in the interfaces and behaviours of independently-developed components. To address these differences without changing the components, mediators that systematically enforce interoperability between functionally-compatible components by mapping their interfaces and coordinating their behaviours are required. Existing approaches to mediator synthesis assume that an interface mapping is provided which specifies the correspondence between the operations and data of the components at hand. In this paper, we present an approach based on ontology reasoning and constraint programming in order to infer mappings between components' interfaces automatically. These mappings guarantee semantic compatibility between the operations and data of the interfaces. Then, we analyse the behaviours of components in order to synthesise, if possible, a mediator that coordinates the computed mappings so as to make the components interact properly. Our approach is formally-grounded to ensure the correctness of the synthesised mediator. We demonstrate the validity of our approach by implementing the MICS (Mediator synthesis to Connect Components) tool and experimenting it with various real-world case studies.