Air pollution poses significant health, social, and economic risks, necessitating effective communication to promote awareness and behavioural adaptation. This study explores how combining behavioural and science communication strategies shapes stakeholder participation in co-designing air quality (AQ) communication technologies and how stakeholders engage with, represent and negotiate environmental data and issues. Data was collected across 11 workshops involving 169 participants of different ages, combining education, storytelling, user testing and participatory design to foster learning and dialogue. Findings highlight that AQ communication is entangled with questions of data usability, trust and environmental literacy. Participants emphasised the importance of audience-specific AQ messaging, integrating AQ technical data with comprehensible visualisations, and combining persuasive communication with evidence-based information. Tensions emerged between individual adaptive behaviours and structural barriers, reinforcing the socio-ecological complexity of AQ communication. Challenges included involving underrepresented groups and assessing long-term impacts of interventions. The findings offer insights into how public engagement can be utilised to inform the design of an AQ communication technology to support protective and sustainable behaviours. Future research should explore ways to enhance inclusivity, refine message design for different user groups, and assess the impact of participatory AQ communication initiatives.

Abstract A novel explainable AI method called CLEAR Image is introduced in this paper. CLEAR Image is based on the view that a satisfactory explanation should be contrastive, counterfactual and measurable. CLEAR Image seeks to explain an image’s classification probability by contrasting the image with a representative contrast image, such as an auto-generated image obtained via adversarial learning. This produces a salient segmentation and a way of using image perturbations to calculate each segment’s importance. CLEAR Image then uses regression to determine a causal equation describing a classifier’s local input–output behaviour. Counterfactuals are also identified that are supported by the causal equation. Finally, CLEAR Image measures the fidelity of its explanation against the classifier. CLEAR Image was successfully applied to a medical imaging case study where it outperformed methods such as Grad-CAM and LIME by an average of 27% using a novel pointing game metric. CLEAR Image also identifies cases of causal overdetermination, where there are multiple segments in an image that are sufficient individually to cause the classification probability to be close to one.

Scenario decomposition algorithms for stochastic programs compute bounds by dualizing all nonanticipativity constraints and solving individual scenario problems independently. We develop an approach that improves on these bounds by reinforcing a carefully chosen subset of nonanticipativity constraints, effectively placing scenarios into groups. Specifically, we formulate an optimization problem for grouping scenarios that aims to improve the bound by optimizing a proxy metric based on information obtained from evaluating a subset of candidate feasible solutions. We show that the proposed grouping problem is NP-hard in general, identify a polynomially solvable case, and present two formulations for solving the problem: a matching formulation for a special case and a mixed-integer programming formulation for the general case. We use the proposed grouping scheme as a preprocessing step for a particular scenario decomposition algorithm and demonstrate its effectiveness in solving standard test instances of two-stage 0–1 stochastic programs. Using this approach, we are able to prove optimality for all previously unsolved instances of a standard test set. Additionally, we implement this scheme as a preprocessing step for PySP, a publicly available and widely used implementation of progressive hedging, and compare this grouping approach with standard grouping approaches on large-scale stochastic unit commitment instances. Finally, the idea is extended to propose a finitely convergent algorithm for two-stage stochastic programs with a finite feasible region.

For the first time at an RE conference we propose a formal debate on a topic of critical importance to the RE community. The debate format is based on that of the Economist newspaper, in turn derived from the long-standing Oxford Union practices.

The Republic of Ireland has been a surprising success story in the global software market for reasons which are neither agreed nor obvious. However outsourcing towards lower cost countries is a threat to this success. One potential counter move is to invest heavily in software engineering research so as to stay ahead of the curve. To meet the national strategic need this research needs to be internationally credible but locally applicable. Experience to date has shown that this can be done, but only if the research resources are carefully organised and directed. The lessons learned could be applicable to other small and medium sized countries.

The new paradigm of dynamic spectrum access (DSA) networks aims to provide opportunistic access to large parts of the underutilized spectrum. The majority of research in this area has focused on free-for-all, uncoordinated access methods common in ad-hoc military applications (Horne, W. 2003; Leaves, P. et al., 2002; Lehr, W. et al., 2002; Schafer, D.J.; To/spl uml/njes, R., 2002). We argue that a simpler pragmatic approach that offers coordinated, spatially aggregated spectrum access via a regional spectrum broker is more attractive in the immediate future. We first introduce two new concepts, coordinated access band (CAB) and statistically multiplexed access (SMA), to the spectrum. We describe their implementation in the new DIMSUMnet (dynamic intelligent management of spectrum for ubiquitous mobile-access network) architecture consisting of four elements: base stations; clients; a radio access network manager (RAN-MAN) that obtains spectrum leases; a per-domain spectrum broker that controls spectrum access. We also discuss in detail various issues in the design of spectrum brokers and spectrum allocation policies and algorithms.

No abstract available.

The potential role of natural language processing in the requirements engineering process has been overstated in the past, possibly due to fundamental misunderstandings of the requirements engineering process itself. Since more realistic ambitions are likely to lead to less disappointment in the future, an effort is made to identify some phases and tasks where natural language processing may usefully be applied. It is suggested that the validation of requirements must remain an informal, social process.>

The types of knowledge used during requirements acquisition are identified and a tool to aid in this process, the ReqColl (requirements collector), is introduced. The tool uses conceptual graphs to represent domain concepts, and attempts to recognize new concepts through the use of a matching facility. The overall approach to requirements capture is described and the approach to matching is illustrated informally. The detailed procedure for matching conceptual graphs is given. ReqColl is compared to similar work elsewhere, and some future research directions are indicated.>