Modelling Road Congestion using a Fuzzy System and Real-World Data for Connected and Autonomous Vehicles

Road congestion is estimated to cost the United Kingdom £307 billion by 2030. Furthermore, congestion contributes enormously to damaging the environment and people’s health. In an attempt to combat the damage congestion is causing, new technologies are being developed, such as intelligent infrastructures and smart vehicles. The aim of this study is to develop a fuzzy system that can classify congestion using a real-world dataset referred to as Manchester Urban Congestion Dataset, which contains data similar to that collected by connected and autonomous vehicles. A set of fuzzy membership functions and rules were developed using a road congestion ontology and in conjunction with domain experts. Experiments are conducted to evaluate the fuzzy system in terms of its precision and recall in classifying congestion. Comparisons are made in terms of performance with traditional classification algorithms decision trees and Naïve Bayes using the Red, Amber, and Green classification methods currently implemented by Transport for Greater Manchester to label the dataset. The results have shown the fuzzy system has the ability to predict road congestion using volume and journey time, outperforming both decision trees and Naïve Bayes

Trust in Computational Intelligence Systems: A Case Study in Public Perceptions

The public debate and discussion about trust in Computational Intelligence (CI) systems is not new, but a topic that has seen a recent rise. This is mainly due to the explosion of technological innovations that have been brought to the attention of the public, from lab to reality usually through media reporting. This growth in the public attention was further compounded by the 2018 GDPR legislation and new laws regarding the right to explainable systems, such as the use of “accurate data”, “clear logic” and the “use of appropriate mathematical and statistical procedures for profiling”. Therefore, trust is not just a topic for debate – it must be addressed from the onset, through the selection of fundamental machine learning processes that are used to create models embedded within autonomous decision-making systems, to the selection of training, validation and testing data. This paper presents current work on trust in the field of Computational Intelligence systems and discusses the legal framework we should ascribe to trust in CI systems. A case study examining current public perceptions of recent CI inspired technologies which took part at a national science festival is presented with some surprising results. Finally, we look at current research underway that is aiming to increase trust in Computational Intelligent systems and we identify a clear educational gap

A Heuristic Based Pre-processing Methodology for Short Text Similarity Measures in Microblogs

Short text similarity measures have lots of applications in online social networks (OSN), as they are being integrated in machine learning algorithms. However, the data quality is a major challenge in most OSNs, particularly Twitter. The sparse, ambiguous, informal, and unstructured nature of the medium impose difficulties to capture the underlying semantics of the text. Therefore, text pre-processing is a crucial phase in similarity identification applications, such as clustering and classification. This is because selecting the appropriate data processing methods contributes to the increase in correlations of the similarity measure. This research proposes a novel heuristicdriven pre-processing methodology for enhancing the performance of similarity measures in the context of Twitter tweets. The components of the proposed pre-processing methodology are discussed and evaluated on an annotated dataset that was published as part of SemEval-2014 shared task. An experimental analysis was conducted using the cosine angle as a similarity measure to assess the effect of our method against a baseline (C-Method). Experimental results indicate that our approach outperforms the baseline in terms of correlations and error rates

An Empirical Performance Evaluation of Semantic-Based Similarity Measures in Microblogging Social Media

Measuring textual semantic similarity has been a subject of intense discussion in NLP and AI for many years. A new area of research has emerged that applies semantic similarity measures within Twitter. However, the development of these measures for the semantic analysis of tweets imposes fundamental challenges. The sparsity, ambiguity, and informality present in social media are hampering the performance of traditional textual similarity measures as “tweets”, have special syntactic and semantic characteristics. This paper reviews and evaluates the performance of topological, statistical, and hybrid similarity measures, in the context of Twitter analysis. Furthermore, the performance of each measure is compared against a naïve keyword-based similarity computation method to assess the significance of semantic computation in capturing the meaning in tweets. An experiment is designed and conducted to evaluate the different measures through examining various metrics, including correlation, error rates, and statistical tests on a benchmark dataset. The potential weaknesses of semantic similarity measures in relation to Twitter applications of textual similarity assessment and the research contributions are discussed. This research highlights challenges and potential improvement areas for the semantic similarity of tweets, a resource for researchers and practitioners

Interpreting Human Responses in Dialogue Systems using Fuzzy Semantic Similarity Measures

Dialogue systems are automated systems that interact with humans using natural language. Much work has been done on dialogue management and learning using a range of computational intelligence based approaches, however the complexity of human dialogue in different contexts still presents many challenges. The key impact of work presented in this paper is to use fuzzy semantic similarity measures embedded within a dialogue system to allow a machine to semantically comprehend human utterances in a given context and thus communicate more effectively with a human in a specific domain using natural language. To achieve this, perception based words should be understood by a machine in context of the dialogue. In this work, a simple question and answer dialogue system is implemented for a café customer satisfaction feedback survey. Both fuzzy and crisp semantic similarity measures are used within the dialogue engine to assess the accuracy and robustness of rule firing. Results from a 32 participant study, show that the fuzzy measure improves rule matching within the dialogue system by 21.88% compared with the crisp measure known as STASIS, thus providing a more natural and fluid dialogue exchange

Building Trustworthy AI Solutions: A Case for Practical Solutions for Small Businesses

Building trustworthy AI solutions, whether in academia or industry, must take into consideration a number of dimensions including legal, social, ethical, public opinion and environmental aspects. A plethora of guidelines, principles and toolkits have been published globally, but have seen limited grassroots implementation, especially among small and medium sized enterprises (SME), mainly due to lack of knowledge, skills, and resources. In this paper, we report on qualitative SME consultations over two events to establish their understanding of both data and AI ethical principles and to identify the key barriers SMEs face in their adoption of ethical AI approaches. We then use independent experts to review and code 77 published toolkits designed to build and support ethical and responsible AI practices, based on 33 evaluation criteria. The toolkits were evaluated considering their scope to address the identified SME barriers to adoption, human-centric AI principles, AI lifecycle stages, and key themes around responsible AI and practical usability. Toolkits were ranked based on criteria coverage and expert inter-coder agreement. Results show that there is not a one-size-fits-all toolkit that addresses all criteria suitable for SMEs. Our findings show few exemplars of practical application, little guidance on how to use/apply the toolkits and very low uptake by SMEs. Our analysis provides a mechanism for SMEs to select their own toolkits based on their current capacity, resources, and ethical awareness levels focusing initially at the conceptualization stage of the AI lifecycle and then extending throughout