We present QGen Studio: an adaptive question-answer generation, training, and evaluation platform. QGen Studio enables users to leverage large language models (LLMs) to create custom question-answer datasets and fine-tune models on this synthetic data. It features a dataset viewer and model explorer to streamline this process. The dataset viewer provides key metrics and visualizes the context from which the QA pairs are generated, offering insights into data quality. The model explorer supports model comparison, allowing users to contrast the performance of their trained LLMs against other models, supporting performance benchmarking and refinement. QGen Studio delivers an interactive, end-to-end solution for generating QA datasets and training scalable, domain-adaptable models. The studio will be open-sourced soon, allowing users to deploy it locally.

We propose a novel modeling framework that efficiently encodes seasonal climate predictions to provide robust and reliable time-series forecasting for supply chain functions. The encoding framework enables effective learning of latent representations—be it uncertain seasonal climate prediction or other time-series data (e.g., buyer patterns)—via a modular neural network architecture. Our extensive experiments indicate that learning such representations to model seasonal climate forecasts results in an error reduction of approximately 13% to 17% across multiple real-world data sets compared to existing demand forecasting methods.

Entity set expansion, the task of expanding a small set of similar entities into a much larger set, is a vital step for downstream tasks such as named entity recognition, knowledge base construction and information retrieval. Existing entity set expansion methods were developed by mainly considering entities at coarse-grained levels, which encounter difficulties for entity set expansion at fine-grained levels, due to the subtlety on fine-grained type inference and semantic drifting. In this study, we propose an automated (i.e. without human annotation), fine-grained set expansion framework, FGExpan, which utilizes a taxonomy structure and a pre-trained language model to achieve high performance. To facilitate our testing, a new fine-grained set expansion dataset is also constructed. Experiments on this dataset and those used in previous studies show that FGExpan achieves significantly better performance (MAP up by 0.176) on finegrained types and also the state-of-the-art expansion quality on coarse-grained entity sets.

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this article, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore, we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of88.26percent over the baselines of0.60in experiment 1 and96.1percent over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns.

Abstract Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and engineers who develop AI system components and scientists, researchers and other users grows. This could quickly become problematic, particularly where guidance or regulations change and once‐acceptable best practice becomes outdated, or where data sources are later discredited as biased or inaccurate. This paper presents a novel method for deriving a quantifiable metric capable of ranking the overall transparency of the process pipelines used to generate AI systems, such that users, auditors and other stakeholders can gain confidence that they will be able to validate and trust the data sources and contributors in the AI systems that they rely on. The methodology for calculating the metric, and the type of criteria that could be used to make judgements on the visibility of contributions to systems are evaluated through models published at ModelHub and PyTorch Hub, popular archives for sharing science resources, and is found to be helpful in driving consideration of the contributions made to generating AI systems and approaches toward effective documentation and improving transparency in machine learning assets shared within scientific communities.

We study the problem of in-network execution of data analytic services using multi-grade VNF chains. The nodes host VNFs offering different and possibly time-varying gains for each stage of the chain, and our goal is to maximize the analytics performance while minimizing the data transfer and processing costs. The VNFs' performance is revealed only after their execution, since it is data-dependent or controlled by third-parties, while the service requests and network costs might also vary with time. We devise an operation algorithm that learns, on the fly, the optimal routing policy and the composition and length of each chain. Our algorithm combines a lightweight sampling technique and a Lagrange-based primal-dual iteration, allowing it to be scalable and attain provable optimality guarantees. We demonstrate the performance of the proposed algorithm using a video analytics service, and explore how it is affected by different system parameters. Our model and optimization framework is readily extensible to different types of networks and services.

We identify optimal strategies for maximising influence within a social network in competitive settings under budget constraints. While existing work has focussed on simple threshold models, we consider more realistic settings, where (i) states are dynamic, i.e., nodes oscillate between influenced and uninfluenced states, and (ii) continuous amounts of resources (e.g., incentives or effort) can be expended on the nodes.

Cyber phyiscal systems are increasingly connected to the Internet for reasons of convenience and efficiency. However, such cyber-physical systems are exposed to new vulnerabilities since they may be compromised by an attack from the Internet. In addition to traditional network security mechanisms, such systems need additional mechanism which can prevent the exploitation of their physical vulnerabilities. We propose an architecture in which the behavior of the system can be controlled by having it generate the policies for its own protection automatically.

Policy systems are critical for managing missions and collaborative activities carried out by coalitions involving different organizations. Conventional policy-based management approaches are not suitable for next-generation coalitions that will involve not only humans, but also autonomous computing devices and systems. It is critical that those parties be able to generate and customize policies based on contexts and activities. This paper introduces a novel approach for the autonomic generation of policies by autonomous parties. The framework combines context free grammars, answer set programs, and inductionbased learning. It allows a party to generate its own policies, based on a grammar and some semantic constraints, by learning from examples. The paper also outlines initial experiments in the use of such a symbolic approach and outlines relevant research challenges, ranging from explainability to quality assessment of policies.

Generative Policy Models (GPMs) have been proposed as a method for future autonomous decision making in a distributed, collaborative environment. To learn a GPM, previous policy examples that contain policy features and the corresponding policy decisions are used. Recently, GPMs have been constructed using both symbolic and statistical learning algorithms. In either case, the goal of the learning process is to create a model across a wide range of contexts from which specific policies may be generated in a given context. Empirically, we expect each learning approach to provide certain advantages over the other. This paper assesses the relative performance of each learning approach in order to examine these advantages and disadvantages. Several carefully prepared data sets are used to train a variety of models across different learning algorithms, where models for each learning algorithm are trained with varying amounts of labelled examples. The performance of each model is evaluated across a variety of metrics which indicates the strength of each learning algorithm for the different scenarios presented and the amount of training data provided. Finally, future research directions are outlined to fully realise GPMs in a distributed, collaborative environment.

Many enterprise solutions can greatly benefit from Machine Learning (ML) models that are created from cross-domain enterprise data. However, many enterprises cannot share data freely across different locations due to regulatory restrictions, performance issues in moving large data volumes, or requirements to maintain autonomy. In such situations, the enterprise can benefit from the concept of federated learning in which ML models are created at multiple different geographic sites. These are combined together at a federation server without the need to share data. Motivated by the fact that web-services based architectures provide a means for robust integration of cross-domain information, in this paper, we describe a solution to the federated learning problem using such an architecture. We specifically focus on the problems enterprises encounter in using distributed data and discuss how we solved those problems through the solution architecture.

In this paper, we study the problem of how to allocate bandwidth and computation resources to deliver data analytics services at the edge. The types of services we envision consist of a chain of tasks that must be carried out sequentially, and where the number of tasks executed in the chain determines the grade in which a service is delivered. An example of such type of service is video analytics where different deep-learning algorithms are combined to provide a more accurate description of a scene. The contributions of the paper are to formulate the static resource allocation problem as a linear program, to discuss the challenges of static formulations in dynamic settings, and to propose a control-type formulation that uses approximate system dynamics and time-varying cost functions. The work also highlights the need for policies that can operate the network and learn its characteristics simultaneously.

Policy based Security Management in an accepted practice in the industry, and required to simplify the administrative overhead associated with security management in complex systems. However, the growing dynamicity, complexity and scale of modern systems makes it difficult to write the security policies manually. Using AI, we can generate policies automatically. Security policies generated automatically can reduce the manual burden introduced in defining policies, but their impact on the overall security of a system is unclear. In this paper, we discuss the security metrics that can be associated with a system using generative policies, and provide a simple model to determine the conditions under which generating security policies will be beneficial to improve the security of the system. We also show that for some types of security metrics, a system using generative policies can be considered as equivalent to a system using manually defined policies, and the security metrics of the generative policy based system can be mapped to the security metrics of the manual system and vice-versa.

Given the rapid increase in urbanisation and a change in the mobility patterns of humans, traffic on our road networks is expanding at an exponential rate. Governments across the globe are investing a vast amount of money on expanding road networks to cater for this ever increasing demand. This, however adds to the cognitive burden of the driver as the moving parts on the road network are also increased. Motivated by this observation, in this paper, we hypothesise that advances in technology-be it connected cars or smart road infrastructures- could play a key role in effectively and efficiently utilising the road network, thus reducing the cognitive burden on drivers. In order to investigate our hypothesis, we have implemented a set of technologies that can seamlessly harness the power of driver specific information and correlate this information with road network features such as properties of the road itself (e.g., roads with high curvature) or traffic information (e.g., traffic flow) such that daily activities of road users can be satisfied. In this paper, we present our initial work to realise this end-to-end framework and present results on contextualising the driving environment by means of feature analysis on the road network.

With the advancement in the technology, deploying connected assets - especially intelligent autonomous assets - to obtain the evolving picture of dynamic environments are fast becoming a reality - and a need - for effective and efficient decision making. In such environments, these assets need to function in unison with each other to achieve the goals, and especially in a collaborative environments (e.g., coalition environments) they need to respect the constraints placed on them by the collective as well as by the owner parties. Typically, policies are used to govern such constraints and interactions, but the existing state-of-the-art relies on predefined user policies to achieve the effect, which is not scalable nor practical in collaborative and dynamic environments. Motivated by this observation and the recent uptake in learning technologies, in this paper, we present our vision on a framework that can (a) employ multiple techniques to create domain knowledge that can help assets to determine which policies are critical for which context, how to solve conflicts among policies, and how to autonomously generate and refine existing policies; (b) represent knowledge in a localized wiki-like approach so that fault tolerant knowledge discovery is supported; (c) provide efficient query interface for assets to discover needed knowledge in a secure manner; and (d) contextualize knowledge so as to enable other similar assets to quickly bootstrap or initialize themselves in unknown contexts when new events occur.

Monitoring and following health and safety regulations are especially important - but made difficult - in hazardous work environments such as underground mines to prevent work place accidents and illnesses. Even though there are IoT solutions for health and safety, every work place has different characteristics and monitoring is typically done by humans in control rooms. During emergencies, conflicts may arise among prohibitions and obligations, and humans may not be better placed to make decision without any assistance as they do not have a bird's-eye-view of the environment. Motivated by this observations, in this paper, we discuss how health and safety regulations can be implemented using a semantic policy framework. We then show how this framework can be integrated into an in-use smart underground mine solution. We also evaluate the performance of our framework to show that it can cope with the complexity and the amount of data generated by the system.

Edge Computing is a scheme to improve the performance, latency and security guidelines for IoT applications. However, edge deployment of an application also comes with additional complexity in management, an increased attack surface for security vulnerability, and could potentially result in a more expensive solution. As a result, the conditions under which an edge deployment of IoT applications delivers a better solution is not always obvious. Metrics which would be able to predict whether or not an IoT application is suitable for edge deployment can provide useful insights to address this question. In this paper, we examine the key performance indicators for IoT applications, namely the responsiveness, scalability and cost models for different types of IoT applications. Our analysis identifies that network centrality of an IoT application is a key characteristic which determines whether or not an IoT application is a good candidate for edge deployment. We discuss the different measures of network centrality that can be used to characterize applications, and the relative performance of edge deployment compared to centralized deployment for various IoT applications.

Due to data intensive and sophisticated tasks in scientific experiments, workflows have been widely used to enable repetitive task automation and data reproducibility. This yields to the need for effective and efficient search mechanisms for scientific workflows discovery as workflow retrieval systems require a model which fulfills several requirements: unification, accuracy, and rich representations. Motivated by the recent uptake in provenance based models for scientific workflow discovery, in this paper, we propose a provenance-based architecture for retrieving workflows. Specifically, the paper presents an architecture which transforms data provenance into workflows and then organizes data into a set of indexes to support efficient querying mechanisms. The architecture enables composite queries supporting three types of search criteria: keywords of workflow tasks, workflow structure patterns, and metadata about workflows-e.g., how often a workflow was used.

With the proliferation of technology, connected and interconnected devices (henceforth referred to as IoT) are fast becoming a viable option to automate the day-to-day interactions of users with their environment—be it manufacturing or home-care automation. However, with the explosion of IoT deployments we have observed in recent years, manually governing the interactions between humans-to-devices—and especially devices-to- devices—is an impractical task, if not an impossible task. This is because devices have their own obligations and prohibitions in context, and humans are not equip to maintain a bird’s-eye-view of the interaction space. Motivated by this observation, in this paper, we propose an end-to-end framework that (a) automatically dis- covers devices, and their associated services and capabilities w.r.t. an ontology; (b) supports representation of high-level—and expressive—user policies to govern the devices and services in the environment; (c) pro- vides efficient procedur es to refine and reason about policies to automate the management of interactions; and (d) delegates similar capable devices to fulfill the interactions, when conflicts occur. We then present our initial work in instrumenting the framework and discuss its details.

The US Army Research Laboratory (ARL) and the Air Force Research Laboratory (AFRL) have established a collaborative research enterprise referred to as the Situational Understanding Research Institute (SURI). The goal is to develop an information processing framework to help the military obtain real-time situational awareness of physical events by harnessing the combined power of multiple sensing sources to obtain insights about events and their evolution. It is envisioned that one could use such information to predict behaviors of groups, be they local transient groups (e.g., Protests) or widespread, networked groups, and thus enable proactive prevention of nefarious activities. This paper presents a vision of how social media sources can be exploited in the above context to obtain insights about events, groups, and their evolution.