Fine grained process modelling: An experiment at British Airways

We report on the experimental application of process technology at British Airways (BA). We used SLANG to model BA's C++ class library management process, and we constructed an experimental process centred software engineering environment (PSEE) based on SPADE. BA required processes to be automated at a finer degree of granularity than tool invocation. We have demonstrated that SLANG and SPADE offer the basic mechanisms for modelling these fine grained processes. We have also shown that it is feasible to generate tools for dedicated processes and integrate them with a SLANG model so as to facilitate fine grained process automation. However, our experience highlighted some open problems. For instance, SLANG process models are tuned to efficient enactment, thus containing very detailed process fragments. These are not the most appropriate representation for humans trying to understand the process model. A more comprehensible notation is needed for design and documentation purposes. Although the airline did not deploy the PSEE in its production environment, the experiment proved beneficial for BA. The modelling uncovered serious flaws in the existing process, and the BA engineers improved their knowledge of process technology

Verbs in action: improving verb understanding in video-language models

Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time. Recently, state-of-the-art video-language models based on CLIP have been shown to have limited verb understanding and to rely extensively on nouns, restricting their performance in real-world video applications that require action and temporal understanding. In this work, we improve verb understanding for CLIP-based video-language models by proposing a new Verb-Focused Contrastive (VFC) framework. This consists of two main components: (1) leveraging pretrained large language models (LLMs) to create hard negatives for cross-modal contrastive learning, together with a calibration strategy to balance the occurrence of concepts in positive and negative pairs; and (2) enforcing a fine-grained, verb phrase alignment loss. Our method achieves state-of-the-art results for zero-shot performance on three downstream tasks that focus on verb understanding, including video-text matching, video question-answering and video classification; while maintaining performance on noun-focused settings. To the best of our knowledge, this is the first work which proposes a method to alleviate the verb understanding problem, and does not simply highlight it. Our code is publicly available at [16] : scenic/projects/verbs_in_action

Verbs in Action: Improving verb understanding in video-language models

Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time. Recently, state-of-the-art video-language models based on CLIP have been shown to have limited verb understanding and to rely extensively on nouns, restricting their performance in real-world video applications that require action and temporal understanding. In this work, we improve verb understanding for CLIP-based video-language models by proposing a new Verb-Focused Contrastive (VFC) framework. This consists of two main components: (1) leveraging pretrained large language models (LLMs) to create hard negatives for cross-modal contrastive learning, together with a calibration strategy to balance the occurrence of concepts in positive and negative pairs; and (2) enforcing a fine-grained, verb phrase alignment loss. Our method achieves state-of-the-art results for zero-shot performance on three downstream tasks that focus on verb understanding: video-text matching, video question-answering and video classification. To the best of our knowledge, this is the first work which proposes a method to alleviate the verb understanding problem, and does not simply highlight it

Environmental and anthropogenic factors affecting the respiratory toxicity of volcanic ash

The potential adverse health outcomes of exposure to inhalable volcanic ash have been a long-standing concern, especially as it is known that respirable-sized particles can potentially contribute towards the onset or exacerbation of respiratory and cardiovascular diseases. In recent years, substantial knowledge of the posed respiratory hazard, alongside extensive characterisation of the physicochemical properties of volcanic ash that may influence its biological reactivity, has been obtained. However, knowledge of how external factors, including the volcanic plume, itself, and anthropogenic pollutants, may alter potential ash toxicity and contribute to any adverse respiratory health effects is limited. Using novel, multidisciplinary approaches and methods, across geochemistry and particle toxicology, this thesis is the first to assess whether ash particle coatings, which originate from in-plume reactions with volcanic gases, can contribute to or alter ash toxicity, as well as if concomitant exposure to volcanic and anthropogenic pollutants poses a greater respiratory hazard than the individual respiratory toxicities of either anthropogenic pollution or volcanic ash alone. Combined exposure to respirable-sized volcanic and diesel exhaust particles was shown to induce (pro-)inflammatory response in a multicellular human lung model in vitro, implying a potentially-greater hazard of simultaneously inhaling both particle types. However, no significant toxicological effects of in-plume processing or co-exposures with complete (gasoline) exhaust were found. The fact that sulphate salts dissolve rapidly, likely prior to cellular uptake, is a finding which helps explain why the salt-laden samples had no toxicological impact. Although further work is required to derive a more comprehensive understanding of the interactions of volcanic ash and urban pollutants in the ambient air and potential impacts of co-exposures, the findings of this thesis provide the first evidence which can be used towards the assessment of respiratory health hazard following the onset of new volcanic activity where exposed communities live in heavily polluted urban areas

NOW: Orchestrating services in a nomadic network using a dedicated workflow language

AbstractOrchestrating services in nomadic or mobile ad hoc networks is not without a challenge, since these environments are built upon volatile connections. Services residing on mobile devices are exposed to (temporary) network failures, which must be considered the rule rather than the exception. This paper proposes a dedicated workflow language built on top of an ambient-oriented programming language that supports dynamic service discovery and communication primitives resilient to network failures. The proposed workflow language, NOW, has support for high level workflow abstractions for control flow, rich network and service failure detection, and failure handling through compensating actions, and dynamic data flow between the services in the environment. By adding this extra layer of abstraction, the application programmer is offered a flexible way to develop applications for nomadic networks