Policy Conflict Resolution in IoT via Planning

With the explosion of connected devices to automate tasks, manually governing interactions among such devices—and associated services—has become an impossible task. This is because devices have their own obligations and prohibitions in context, and humans are not equipped to maintain a bird’s-eye-view of the environment. Motivated by this observation, in this paper, we present an ontology-based policy framework which can efficiently detect policy conflicts and automatically resolve such using an AI planner

Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

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 paper, 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 of 88.26% over the baselines of 0.60 in experiment 1 and 96.1% 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

Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network

Current time-series forecasting problems use short-term weather attributes as exogenous inputs. However, in specific time-series forecasting solutions (e.g., demand prediction in the supply chain), seasonal climate predictions are crucial to improve its resilience. Representing mid to long-term seasonal climate forecasts is challenging as seasonal climate predictions are uncertain, and encoding spatio-temporal relationship of climate forecasts with demand is complex. 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 forecast results in an error reduction of approximately 13\% to 17\% across multiple real-world data sets compared to existing demand forecasting methods.Comment: 15 page

Optimizing the efficiency of collective decision making in groups

The complexity of modern military operations create a demand for efficient collaborative decision making and problem solving. Additionally, as military units operate in increasingly dynamic environments, the ability to respond to changing circumstances becomes paramount for mission success. An effective response rests on correct dissemination and transfer of information across the command and control structure, and thus is critically linked to the network of human interactions. In this paper, we take an agent-based modeling approach to collective problem solving. We investigate three key factors affecting the performance in collaborative environments: (1) the structure of network used to share information between agents, (2) the search strategies adopted by agents, and (3) the complexity of problems facing the group. In particular we study how the trade-off between exploitation of known solutions and exploration for novel ones is related to the efficiency of collective search. Additionally we consider the role of agent behavior: propensity for risk-taking and trustworthiness, as well as the dynamic nature of social connections. Finally, we outline the directions for future work regarding the efficiency of problem solving on military-like command and control structures

Constructing distributed time-critical applications using cognitive enabled services

Time-critical analytics applications are increasingly making use of distributed service interfaces (e.g., micro-services) that support the rapid construction of new applications by dynamically linking the services into different workflow configurations. Traditional service-based applications, in fixed networks, are typically constructed and managed centrally and assume stable service endpoints and adequate network connectivity. Constructing and maintaining such applications in dynamic heterogeneous wireless networked environments, where limited bandwidth and transient connectivity are commonplace, presents significant challenges and makes centralized application construction and management impossible. In this paper we present an architecture which is capable of providing an adaptable and resilient method for on-demand decentralized construction and management of complex time-critical applications in such environments. The approach uses a Vector Symbolic Architecture (VSA) to compactly represent an application as a single semantic vector that encodes the service interfaces, workflow, and the time-critical constraints required. By extending existing services interfaces, with a simple cognitive layer that can interpret and exchange the vectors, we show how the required services can be dynamically discovered and interconnected in a completely decentralized manner. We demonstrate the viability of this approach by using a VSA to encode various time-critical data analytics workflows. We show that these vectors can be used to dynamically construct and run applications using services that are distributed across an emulated Mobile Ad-Hoc Wireless Network (MANET). Scalability is demonstrated via an empirical evaluation

Flexible Resource Assignment in Sensor Networks : A Hybrid Reasoning Approach

Today, sensing resources are the most valuable assets of critical tasks (e.g., border monitoring). Although, there are various types of assets available, each with different capabilities, only a subset of these assets is useful for a specific task. This is due to the varying information needs of tasks. This gives rise to assigning useful assets to tasks such that the assets fully cover the information requirements of the individual tasks. The importance of this is amplified in the intelligence,surveillance, and reconnaissance (ISR) domain, especially in a coalition context. This is due to a variety of reasons such as the dynamic nature of the environment, scarcity of assets, high demand placed on available assets, sharing of assets among coalition parties, and so on. A significant amount of research been done by different communities to effciently assign assets to tasks and deliver information to the end user. However, there is little work done to infer sound alternative means to satisfy the information requirements of tasks so that the satisfiable tasks are increased. In this paper, we propose a hybrid reasoning approach (viz., a combination of rule-based and ontology-based reasoning) based on current Semantic Web technologies to infer assets types that are necessary and sufficient to satisfy the requirements of tasks in a flexible manner

Social Signal Processing for Real-time Situational Understanding: A Vision and Approach

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

A Scalable Vector Symbolic Architecture Approach for Decentralized Workflows

Vectors Symbolic Architectures (VSAs) are distributed representations that combine random patterns, representing atomic symbols across a hyper-dimensional vector space, into new symbolic vector representations that semantically represent the component vectors and their relationships. In this paper, we extend the VSA approach and apply it to decentralized workflows, capable of executing distributed compute nodes and their interdependencies. To achieve this goal, services must be discovered and orchestrated in a decentralized way with the minimum communication overhead whilst providing detailed information about the workflow - tasks, dependencies, location, metadata, and so on. To this end, we extended VSAs using a hierarchical vector chunking scheme that enables semantic matching at each level and provides scaling up to tens of thousands of services. We then show how VSAs can be used to encode complex workflows by building primitives that represent sequences (pipelines) and then extend this to support full Directed Acyclic Graphs (DAGs) and apply this to five well-known Pegasus scientific workflows to demonstrate the approac

A computational framework for modelling inter-group behaviour using psychological theory

Psychological theories of inter-group behaviour offer justified representations for interaction, influence, and motivation for coalescence. Agent-based modelling of this behaviour, using evolutionary approaches, further provides a powerful tool to examine the implications of these theories in a dynamic context. In particular, this can enhance our understanding of the escalation of hostility and warfare, and its mitigation, contributing to policy and interventions. In this paper we propose a framework through which social psychology can be embedded in computation for the examination of inter-group behaviour. We examine how various social-psychological theories can be embedded in evolutionary models, and identify ways in which visualisation can support the objective assessment of emergent behaviour. We also discuss how real-world data can be used to parameterise scenarios on which modelling is conducted