Agent-based autonomous systems and abstraction engines: Theory meets practice

We report on experiences in the development of hybrid autonomous systems where high-level decisions are made by a rational agent. This rational agent interacts with other sub-systems via an abstraction engine. We describe three systems we have developed using the EASS BDI agent programming language and framework which supports this architecture. As a result of these experiences we recommend changes to the theoretical operational semantics that underpins the EASS framework and present a fourth implementation using the new semantics

Policies to Regulate Distributed Data Exchange

This research is partially sponsored by the EPSRC grant EP/P011829/1, funded under the UK Engineering and Physical Sciences Council Human Dimensions of Cyber Security call (2016).Postprin

University–industry collaboration: using meta-rules to overcome barriers to knowledge transfer

This is the final version of the article. Available from Springer Verlag via the DOI in this record.University–industry knowledge transfer is an important source wealth of creation for all partners; however, the practical management of this activity within universities is often hampered by procedural rigidity either through the absence of decision-making protocols to reconcile conflicting priorities or through the inconsistent implementation of existing policies. This is problematic, since it can impede operational effectiveness, prevent inter-organisational knowledge-creation and hamper organisational learning. This paper addresses this issue by adopting a cross-discipline approach and presenting meta-rules as a solution to aid organisational decision making. It is proposed that meta-rules can help resolve tensions arising from conflicting priorities between academics, knowledge transfer offices and industry and help facilitate strategic alignment of processes and policies within and between organisations. This research contributes to the growing debate on the strategic challenges of managing knowledge transfer and presents meta-rules as a practical solution to facilitate strategic alignment of internal and external stakeholder tensions. Meta-rules has previously only been applied in a computer intelligence context however, this research proves the efficacy of meta rules in a university–industry knowledge transfer context. This research also has practical implications for knowledge transfer office managers who can use meta-rules to help overcome resource limitations, conflicting priorities and goals of diverse internal and external stakeholders

Intravenous alteplase for stroke with unknown time of onset guided by advanced imaging: systematic review and meta-analysis of individual patient data

Background: Patients who have had a stroke with unknown time of onset have been previously excluded from thrombolysis. We aimed to establish whether intravenous alteplase is safe and effective in such patients when salvageable tissue has been identified with imaging biomarkers. Methods: We did a systematic review and meta-analysis of individual patient data for trials published before Sept 21, 2020. Randomised trials of intravenous alteplase versus standard of care or placebo in adults with stroke with unknown time of onset with perfusion-diffusion MRI, perfusion CT, or MRI with diffusion weighted imaging-fluid attenuated inversion recovery (DWI-FLAIR) mismatch were eligible. The primary outcome was favourable functional outcome (score of 0–1 on the modified Rankin Scale [mRS]) at 90 days indicating no disability using an unconditional mixed-effect logistic-regression model fitted to estimate the treatment effect. Secondary outcomes were mRS shift towards a better functional outcome and independent outcome (mRS 0–2) at 90 days. Safety outcomes included death, severe disability or death (mRS score 4–6), and symptomatic intracranial haemorrhage. This study is registered with PROSPERO, CRD42020166903. Findings: Of 249 identified abstracts, four trials met our eligibility criteria for inclusion: WAKE-UP, EXTEND, THAWS, and ECASS-4. The four trials provided individual patient data for 843 individuals, of whom 429 (51%) were assigned to alteplase and 414 (49%) to placebo or standard care. A favourable outcome occurred in 199 (47%) of 420 patients with alteplase and in 160 (39%) of 409 patients among controls (adjusted odds ratio [OR] 1·49 [95% CI 1·10–2·03]; p=0·011), with low heterogeneity across studies (I2=27%). Alteplase was associated with a significant shift towards better functional outcome (adjusted common OR 1·38 [95% CI 1·05–1·80]; p=0·019), and a higher odds of independent outcome (adjusted OR 1·50 [1·06–2·12]; p=0·022). In the alteplase group, 90 (21%) patients were severely disabled or died (mRS score 4–6), compared with 102 (25%) patients in the control group (adjusted OR 0·76 [0·52–1·11]; p=0·15). 27 (6%) patients died in the alteplase group and 14 (3%) patients died among controls (adjusted OR 2·06 [1·03–4·09]; p=0·040). The prevalence of symptomatic intracranial haemorrhage was higher in the alteplase group than among controls (11 [3%] vs two [<1%], adjusted OR 5·58 [1·22–25·50]; p=0·024). Interpretation: In patients who have had a stroke with unknown time of onset with a DWI-FLAIR or perfusion mismatch, intravenous alteplase resulted in better functional outcome at 90 days than placebo or standard care. A net benefit was observed for all functional outcomes despite an increased risk of symptomatic intracranial haemorrhage. Although there were more deaths with alteplase than placebo, there were fewer cases of severe disability or death. Funding: None

Factors influencing user acceptance of public sector big open data

In recent years Government departments and public/private organizations are becoming increasingly transparent with their data to establish the whole new paradigm of big open data. Increasing research interest arises from the claimed usability of big open data in improving public sector reforms, facilitating innovation, improving supplier and distribution networks and creating resilient supply chains that help improve the efficiency of public services. Despite the advantages of big open data for supply chain and operations management, there is severe shortage of empirical analyses in this field, especially with regards to its acceptance. To address this gap, in this paper we use an extended Technology Acceptance Model (TAM) to empirically examine the factors affecting users’ behavioural intentions towards public sector big open data. We outline the importance of our model for operations and supply chain managers, the limitations of the study, and future research directions

A View-based Consistency Model based on Transparent Data Selection in Distributed Shared Memory

This paper proposes a novel View-based Consistency model for Distributed Shared Memory, in which a new concept, view, is coined. A view is a set of data objects that a processor has the right to access in the shared memory. The View-based Consistency model only requires that the data objects of a processor's view are updated before a processor accesses them. In this way, it can achieve the maximum relaxation of constraints on modification propagation and execution in data-race-free programs. This paper first briefly reviews a number of related consistency models in terms of their use of three techniques -- time, processor and data selection -- which each eliminate some unnecessary propagation of memory modifications while guaranteeing sequential consistency for data-race-free programs. Then, we present the View-based Consistency model and its implementation. In contrast with other models, the View-based Consistency model can achieve transparent data selection without programmer annotation and can offer the maximum performance advantage. Differences among related work are discussed through illustrative examples