Abstract The transformation of cities into smarter and more efficient environments relies on proactive and timely detection and maintenance of city-wide infrastructure, including roadside infrastructure such as road signs and the cleaning of illegally dumped rubbish. Currently, these maintenance tasks rely predominantly on citizen reports or on-site checks by council staff. However, this approach has been shown to be time-consuming and highly costly, resulting in significant delays that negatively impact communities. This paper presents AIoT-CitySense, an AI and IoT-driven city-scale sensing framework, developed and piloted in collaboration with a local government in Australia. AIoT-CitySense has been designed to address the unique requirements of roadside infrastructure maintenance within the local government municipality. A tailored solution of AIoT-CitySense has been deployed on existing waste service trucks that cover a road network of approximately 100 kms in the municipality. Our analysis shows that proactive detection for roadside infrastructure maintenance using our solution reached an impressive 85%, surpassing the timeframes associated with manual reporting processes. AIoT-CitySense can potentially transform various domains, such as efficient detection of potholes and precise line marking for pedestrians. This paper exemplifies the power of leveraging city-wide data using AI and IoT technologies to drive tangible changes and improve the quality of city life.

Abhik Bhattacharjee, Tahmid Hasan, Wasi Uddin Ahmad, Yuan-Fang Li, Yong-Bin Kang, Rifat Shahriyar. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Timely detection of roadside assets that require maintenance is essential for improving citizen satisfaction. Currently, the process of identifying such maintenance issues is typically performed manually, which is time consuming, expensive, and slow to respond. In this paper, we present Mobile IoT-RoadBot, a mobile 5G-based Internet of Things (IoT) solution, powered by Artificial Intelligence (AI) techniques to enable opportunistic real-time identification and detection of maintenance issues with roadside assets. The Mobile IoT-RoadBot solution has been deployed on 11 bin service (waste collection) trucks in the western suburbs of Melbourne, Australia, performing real-time assessments of road-side assets as they service areas within the local government. We present the architecture of Mobile IoT-RoadBot and demonstrate its capability via an online 'points of maintenance' (PoMs) map.

A chatbot is a software that interacts with humans by conducting conversations via textual or auditory methods. Chatbots have recently been used for plethora of applications including travel, medical, education, retail etc. Several cloud-based platforms (e.g. IBM, Amazon, Google, Microsoft) are available for developing and deploying chatbots. However, there is a lack of an evaluation methodology and a tool for evaluating chatbots comprehensively. Current approaches for comparing cloud-based chatbots are manual and rely on expert's judgement. In this short paper, we propose, devise, implement and demonstrate a tool namely ECHO for empirical evaluation of cloud-based chatbots. ECHO is capable of automatically evaluating multiple cloud-based chatbots and report the outcomes of the comparative evaluation. We validate the efficacy of ECHO by conducting comparative evaluation of 3 popular cloud-based chatbots in 2 different question-answering application scenarios with 3 levels of complexities.

Ontologies are widely used to formally represent abstract domain knowledge.Logic reasoning ensures the logical consistency of ontologies, and infers knowledge implicitly encoded in ontologies.It has been shown both theoretically and empirically that for large and complex ontologies, reasoning is still time-consuming and resource-intensive.Meta-reasoning exploits machine learning techniques to tackle the important problems of understanding the source of reasoning hardness and to predict reasoning efficiency, with the overall goal of improving reasoning efficiency.In this paper, we highlight recent advances in meta-reasoning for Semantic Web ontologies, briefly present technical innovations and results, and discuss important problems for future research.

Smart buildings equipped with various building management systems and digital control systems produce enormous amounts of sensor data that can be used to investigate and diagnose operational issues such as unsatisfactory thermal comfort outcomes, excessive energy consumption and/or predicting failures before they occur. However, current building management systems often face the issues with incomplete or unstructured metadata associated with sensor data which prevent such pro-active, predictive and prescriptive analysis. Currently, building service engineers manually map the sensor data streams to aid their diagnostic process. This process is expensive, ineffective and is also prone to human errors. This paper proposes a novel semi-automated approach that annotates incoming sensor data streams. We also propose extensions to Project Haystack, a well-known ontology used for naming conventions and taxonomies for building equipment and operational data. We have developed a tool that is currently used by our industry partner and incorporates the proposed automatic annotation approach and maps the data streams to our Haystack-extended ontology. The tool includes an easy to use interface for engineers to easily diagnose issues in mechanical building services. The proposed approach has been validated via both usability and technical evaluation.

Taxonomy learning is an important task for knowledge acquisition, sharing, and classification as well as application development and utilization in various domains. To reduce human effort to build a taxonomy from scratch and improve the quality of the learned taxonomy, we propose a new taxonomy learning approach, namedTaxoFinder. TaxoFinder takes three steps to automatically build a taxonomy. First, it identifies domain-specific concepts from a domain text corpus. Second, it builds a graph representing how such concepts are associated together based on their co-occurrences. As the key method in TaxoFinder, we propose a method for measuring associative strengths among the concepts, which quantify how strongly they are associated in the graph, using similarities between sentences and spatial distances between sentences. Lastly, TaxoFinder induces a taxonomy from the graph using a graph analytic algorithm. TaxoFinder aims to build a taxonomy in such a way that it maximizes the overall associative strengths among the concepts in the graph to build a taxonomy. We evaluate TaxoFinder using gold-standard evaluation on three different domains:emergency management for mass gatherings,autism research, anddiseasedomains. In our evaluation, we compare TaxoFinder with a state-of-the-art subsumption method and show that TaxoFinder is an effective approach significantly outperforming the subsumption method.

For a large research institution and a broad research discipline such as the life sciences, it is a highly important and very challenging task to capture each researcher's expertise, and to match researchers by expertise to assist in identifying inter-disciplinary collaboration opportunities and in making informed policy decisions. The challenges are multi-dimensional, stemming from the needs to (a) provide thorough coverage of the breadth and depth of the disciplinary areas, (b) develop accurate representation of researcher's expertise, and (c) process large volumes of data efficiently. Medical Subject Headings (MeSH), a comprehensive taxonomy for the life sciences, has been widely used for indexing MEDLINE publications. In this paper, we present a novel framework for capturing and matching research expertise based on knowledge encoded in MeSH. Specifically, (1) we design a novel and effective hybrid MeSH classification algorithm by combining state-of-the-art methods, and (2) using MeSH terms aggregated from a researcher's publications, we design a researcher matching algorithm based on semantic similarity that takes into consideration the structure of the MeSH taxonomy.

For expressive ontology languages such as OWL 2 DL, classification is a computationally expensive task—2NEXPTIME-complete in the worst case. Hence, it is highly desirable to be able to accurately estimate classification time, especially for large and complex ontologies. Recently, machine learning techniques have been successfully applied to predicting the reasoning hardness category for a given (ontology, reasoner) pair. In this paper, we further develop predictive models to estimate actual classification time using regression techniques, with ontology metrics as features. Our large-scale experiments on 6 state-of-the-art OWL 2 DL reasoners and more than 450 significantly diverse ontologies demonstrate that the prediction models achieve high accuracy, good generalizability and statistical significance. Such prediction models have a wide range of applications. We demonstrate how they can be used to efficiently and accurately identify performance hotspots in a large and complex ontology, an otherwise very time-consuming and resource-intensive task.

It has been shown, both theoretically and empirically, that reasoning about large and expressive ontologies is computationally hard. Moreover, due to the different reasoning algorithms and optimisation techniques employed, each reasoner may be efficient for ontologies with different characteristics. Based on recently-developed prediction models for various reasoners for reasoning performance, we present our work in developing a meta-reasoner that automatically selects from a number of state-of-the-art OWL reasoners to achieve optimal efficiency. Our preliminary evaluation shows that the meta-reasoner significantly and consistently outperforms 6 state-of-the-art reasoners and it achieves a performance close to the hypothetical gold standard reasoner.

Retrieval is a key phase in case-based reasoning (CBR), since it lays the foundation for the overall effectiveness of CBR systems. Its aim is to retrieve useful cases that can be used to solve the target problem. To perform the retrieval process, CBR systems typically exploit similarity knowledge and is called similarity-based retrieval (SBR). However, SBR tends to rely strongly on similarity knowledge, ignoring other forms of knowledge that can be further leveraged to improve the retrieval performance. This paper argues and motivates that association analysis of stored cases can significantly strengthen SBR. We propose a novel retrieval strategy USIMSCAR that substantially outperforms SBR by leveraging association knowledge, encoded via a certain form of association rules, in conjunction with similarity knowledge. We also propose a novel approach for extracting association knowledge from a given case base using various association rule mining techniques. We evaluate the significance of USIMSCAR in three application domains-medical diagnosis, IT service management, and product recommendation.

The main problem in this paper is how to find the character which is placed on a line or curves of raster map. We give one novel algorithm to group each separated characters in a map. This word grouping is difficult especially in a map, since a map has many different types of character and each word has its own slanting line. For this, we propose the 3D neighborhood graph G from a given set of characters. In this graph, each vertex of G represents the separated characters and it is placed in 3D space according to the size of the character. This makes the bigger characters being located in the upper position, the smaller characters being placed in the bottom. We give an edge if two vertices are nearly placed in that 3D space. By this edge connection strategy we can easily find the words of various different size in a map.