Intelligent Computing for Sustainable Energy and Environment (ICSEE 2012)

No Abstract available

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

AgentSpeak+: AgentSpeak with Probabilistic Planning

AgentSpeak is a logic-based programming language, based on the Belief-Desire-Intention (BDI) paradigm, suitable for building complex agent-based systems. To limit the computational complexity, agents in AgentSpeak rely on a plan library to reduce the planning problem to the much simpler problem of plan selection. However, such a plan library is often inadequate when an agent is situated in an uncertain environment. In this paper, we propose the AgentSpeak+ framework, which extends AgentSpeak with a mechanism for probabilistic planning. The beliefs of an AgentSpeak+ agent are represented using epistemic states to allow an agent to reason about its uncertain observations and the uncertain effects of its actions. Each epistemic state consists of a POMDP, used to encode the agent’s knowledge of the environment, and its associated probability distribution (or belief state). In addition, the POMDP is used to select the optimal actions for achieving a given goal, even when facing uncertainty.

A simple inhibition coefficient for quantifying potency of biocontrol agents against plant-pathogenic fungi

Microbial interactions depend on a range of biotic and environmental variables, and are both dynamic and unpredictable. For some purposes, and under defined conditions, it is nevertheless imperative to evaluate the inhibitory efficacy of microbes, such as those with potential as biocontrol agents. We selected six, phylogenetically diverse microbes to determine their ability to inhibit the ascomycete Fusarium
coeruleum, a soil-dwelling pathogen of potato tubers that causes the storage disease dry rot. Interaction assays, where colony development was quantified (for both fungal pathogen and potential control agents), were therefore carried out on solid media. The key parameters that contributed to, and were indicative of, inhibitory efficacy were identified as: fungal growth-rates (i) prior to contact with the biocontrol
agent and (ii) if/once contact with the biocontrol agent was established (i.e. in the zone of mixed
culture), and (iii) the ultimate distance traveled by the fungal mycelium. It was clear that there was no correlation between zones of fungal inhibition and the overall reduction in the extent of fungal colony development. An inhibition coefficient was devised which incorporated the potential contributions of distal inhibition of fungal growth-rate; prevention of mycelium development in the vicinity of the biocontrol
agent; and ability to inhibit plant-pathogen growth-rate in the zone of mixed culture (in a ratio of 2:2:1). The values derived were 84.2 for Bacillus subtilis (QST 713), 74.0 for Bacillus sp. (JC12GB42), 30.7 for Pichia anomala (J121), 19.3 for Pantoea agglomerans (JC12GB34), 13.9 for Pantoea sp. (S09:T:12), and
21.9 (indicating a promotion of fungal growth) for bacterial strain (JC12GB54). This inhibition coefficient, with a theoretical maximum of 100, was consistent with the extent of F. coeruleum-colony development (i.e. area, in cm2) and assays of these biocontrol agents carried out previously against Fusarium
spp., and other fungi. These findings are discussed in relation to the dynamics and inherent complexity of natural ecosystems, and the need to adapt models for use under specific sets of conditions.

A critical ethnography of communication processes involving the management of oral chemotherapeutic agents by patients with a primary diagnosis of colorectal cancer: study protocol

Aim
To describe the protocol used to examine the processes of communication between health professionals, patients and informal carers during the management of oral chemotherapeutic medicines to identify factors that promote or inhibit medicine concordance.

Background
Ideally communication practices about oral medicines should incorporate shared decision-making, two-way dialogue and an equality of role between practitioner and patient. While there is evidence that healthcare professionals are adopting these concordant elements in general practice there are still some patients who have a passive role during consultations. Considering oral chemotherapeutic medications, there is a paucity of research about communication practices which is surprising given the high risk of toxicity associated with chemotherapy.

Design
A critical ethnographic design will be used, incorporating non-participant observations, individual semi-structured and focus-group interviews as several collecting methods.

Methods
Observations will be carried out on the interactions between healthcare professionals (physicians, nurses and pharmacists) and patients in the outpatient departments where prescriptions are explained and supplied and on follow-up consultations where treatment regimens are monitored. Interviews will be conducted with patients and their informal carers. Focus-groups will be carried out with healthcare professionals at the conclusion of the study. These several will be analysed using thematic analysis. This research is funded by the Department for Employment and Learning in Northern Ireland (Awarded February 2012).

Discussion
Dissemination of these findings will contribute to the understanding of issues involved when communicating with people about oral chemotherapy. It is anticipated that findings will inform education, practice and policy.

Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

Despite its importance in social interactions, laughter remains little studied in affective computing. Intelligent virtual agents are often blind to users’ laughter and unable to produce convincing laughter themselves. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received less attention. The aim of this study is threefold. First, to probe human laughter perception by analyzing patterns of categorisations of natural laughter animated on a minimal avatar. Results reveal that a low dimensional space can describe perception of laughter “types”. Second, to investigate observers’ perception of laughter (hilarious, social, awkward, fake, and non-laughter) based on animated avatars generated from natural and acted motion-capture data. Significant differences in torso and limb movements are found between animations perceived as laughter and those perceived as non-laughter. Hilarious laughter also differs from social laughter. Different body movement features were indicative of laughter in sitting and standing avatar postures. Third, to investigate automatic recognition of laughter to the same level of certainty as observers’ perceptions. Results show recognition rates of the Random Forest model approach human rating levels. Classification comparisons and feature importance analyses indicate an improvement in recognition of social laughter when localized features and nonlinear models are used.

An Intelligent Threat Prevention Framework with Heterogeneous Information

Three issues usually are associated with threat prevention intelligent surveillance systems. First, the fusion and interpretation of large scale incomplete heterogeneous information; second, the demand of effectively predicting suspects’ intention and ranking the potential threats posed by each suspect; third, strategies of allocating limited security resources (e.g., the dispatch of security team) to prevent a suspect’s further actions towards critical assets. However, in the literature, these three issues are seldomly considered together in a sensor network based intelligent surveillance framework. To address
this problem, in this paper, we propose a multi-level decision support framework for in-time reaction in intelligent surveillance. More specifically, based on a multi-criteria event modeling framework, we design a method to predict the most plausible intention of a suspect. Following this, a decision support model is proposed to rank each suspect based on their threat severity and to determine resource allocation strategies. Finally, formal properties are discussed to justify our framework.

Intelligent potentiostat for identification of heavy metals in situ

This article presents a low-cost portable electrochemical instrument capable of on-site identification of heavy metals. The instrument acquires metal-specific voltage and current signals by the application of differential pulse anodic stripping voltammetry. This technique enhances the analytical current and rejects the background current, resulting in a higher signal-to-noise ratio for a better detection limit. The identification of heavy metals is based on an intelligent machine-based method using a multilayer perceptron neural network consisting of three layers of neurons. The neural network is implemented using a 16 bit microcontroller. The system is developed for use in the field in order to avoid expensive and time-consuming procedures and can be used in a variety of situations to help environmental assessment and control. 

Plan Selection for Probabilistic BDI Agents

When an agent wants to fulfill its desires about the world, the agent usually has multiple plans to choose from and these plans have different pre-conditions and additional effects in addition to achieving its goals. Therefore, for further reasoning and interaction with the world, a plan selection strategy (usually based on plan cost estimation) is mandatory for an autonomous agent. This demand becomes even more critical when uncertainty on the observation of the world is taken into account, since in this case, we consider not only the costs of different plans, but also their chances of success estimated according to the agent's beliefs. In addition, when multiple goals are considered together, different plans achieving the goals can be conflicting on their preconditions (contexts) or the required resources. Hence a plan selection strategy should be able to choose a subset of plans that fulfills the maximum number of goals while maintaining context consistency and resource-tolerance among the chosen plans. To address the above two issues, in this paper we first propose several principles that a plan selection strategy should satisfy, and then we present selection strategies that stem from the principles, depending on whether a plan cost is taken into account. In addition, we also show that our selection strategy can partially recover intention revision.

A Syntactic Approach to Revising Epistemic States with Uncertain Inputs

Revising its beliefs when receiving new information is an important ability of any intelligent system. However, in realistic settings the new input is not always certain. A compelling way of dealing with uncertain input in an agent-based setting is to treat it as unreliable input, which may strengthen or weaken the beliefs of the agent. Recent work focused on the postulates associated with this form of belief change and on finding semantical operators that satisfy these postulates. In this paper we propose a new syntactic approach for this form of belief change and show that it agrees with the semantical definition. This makes it feasible to develop complex agent systems capable of efficiently dealing with unreliable input in a semantically meaningful way. Additionally, we show that imposing restrictions on the input and the beliefs that are entailed allows us to devise a tractable approach suitable for resource-bounded agents or agents where reactiveness is of paramount importance.