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Discovering knowledge from social media becomes a trend in many domains such as tourism, where users' feedback and rating are the basis of recommendation systems. In this context, cluster analysis has been a major tool to disclose user groups by which the process of collaborative filtering can better determine a personalised suggestion. Matching this to the curse of big data is a challenge with previous studies either implementing conventional techniques on a distributed system or making use of data sampling. Specific to ensemble clustering, only a few aim to obtain both scalability and privacy preserving that are significant to handling social data. This paper presents a new bi-level framework of ensemble clustering in which an instance-segment based analysis is adopted to ensure data privacy and reduce the complexity of clustering the whole dataset. Unlike existing studies, instead of drawing a single clustering from each segment, multiple clusterings are selected to better represent instances therein. Based on published tourism datasets and different experimental settings, the new approach usually outperforms its baselines whilst being competitive to related methods found in the literature. Additional case studies on simulated big datasets and noisy variations are reported and discussed in addition to the analysis of algorithmic parameters.

As robots begin to coexist with humans, the need for efficient and safe social robot navigation becomes increasingly pressing. In this paper we investigate how world models can enhance the effectiveness of reinforcement learning in social navigation tasks. We introduce three approaches that leverage predictive world models, which are then benchmarked against state-of-the-art algorithms. For a comprehensive and reliable evaluation, we employed multiple metrics during the training and testing phases. The key novelty of our approach consists in the integration and evaluation of predictive world models within the context of social navigation, as well as in the models themselves. Based on a diverse set of performance metrics, the experimental results provide evidence that predictive world models help improve reinforcement learning techniques for social navigation.

A chatbot is one of the most innovative tools for interaction between humans and machines. It simulates human behaviour and interacts with a user in natural language to provide required information, services, thus enhancing the user experience. An increasing number of organisations are utilising chatbots to offer better services to users in real time to improve user satisfaction, streamline the user journey, and provide user-centric support. However, this requires building specific chatbots for specific services, which leads to variational designs of chatbots based on their application areas.This paper aims to demystify fundamentals of a chatbot including its working, types, benefits and limitations. It will mainly focus on two main categories of chatbots rule-based and AI-based chatbots, and explain all the aforementioned facts as well their comparative analysis to simplify the chatbot topic in general.

Machine learning has been constantly evolving and revolutionizing every aspect of our lives. There is ongoing research to enhance and modify machine learning models where scientists and researchers are finding ways to improve the effectiveness and adaptability of models with the changing technology moulding to user requirements for real life application. The main challenges in this endeavour of enhancing machine learning models are obtaining quality data, selecting an appropriate model, and ensuring the data privacy. Federated learning has been developed to address the aforementioned challenges, which is an effective way to train machine learning models in a collaborative manner by using the local data from a large number of devices without directly exchanging their raw data whilst simultaneously delivering on model performance. Federated learning is not just a type of machine learning, it is an amalgamation of several technologies and techniques. To fully understand its concepts a comprehensive study is required. This paper aims to simplify the fundamentals of federated learning in order to provide a better understanding of it. It explains federated learning in a step-by-step manner covering its comprehensive definition, detailed working, different types, benefits and limitations.

The landscape of machine learning is changing rapidly due to the ever-evolving nature of data and devices. The large centralized data is replaced by the distributed data and a central server is replaced with a large number of geographically distributed, loosely connected devices, such as smartphones, laptops, and other IoT devices. Therefore, the centralized machine learning (CML) which involves centralized data training on a central server is no longer an effective solution when the data is inherently distributed or too big to process on a central server, or data privacy is paramount; and the quest for a suitable machine learning to resolve these issues led to the evolution of distributed machine learning (DML). For large-scale learning tasks, DML has evolved to effectively handle enormous data within big data and distributed computing environment. Resolving most limitations faced by CML with the implementation of parallel learning on a large number of nodes to optimise time, learning resources and performance. However, DML may not necessarily ensure strict data privacy leading to further development and innovation of federated machine learning (FML) which is a type of DML that further decentralizes learning operations using local data on each participating node incorporating data privacy adherence. This paper analyses the transformation journey of machine learning whilst explaining its evolution from centralized, distributed to federated machine learning. Examining these three variants of machine learning exemplifies their coherent and comparative analysis. Which helps grasp a better understanding of each machine learning type as well as presenting the reason for the changing landscape. Additionally, the paper will address each type of machine learning alongside their different types, strengths and limitations.

The production of chemicals is a complex task due to the highly nonlinear behaviour of chemical processes; therefore, traditional approaches may not be very effective in developing or predicting such processes and their outcomes at optimal level. Consequently, it has always been challenging to find ways to improve efficiency and productivity while reducing the time and cost. Artificial Intelligence (AI) techniques are becoming valuable in chemistry due to several reasons such as easy to learn and use, simple implementation, easy designing, effectiveness, generality, robustness, and flexibility. AI is comprised of several techniques within it, such as artificial neural networks, evolutionary algorithms and fuzzy logic. AI techniques have been widely used in various areas of chemistry including molecule design, molecular property prediction, retrosynthesis, reaction outcome prediction and reaction conditions prediction. Therefore, this paper investigates AI applications in the aforementioned areas, wherein it explains about each aforementioned area with a suitable example, limitations of traditional techniques, and types of AI techniques which are utilised within those areas.

In this paper, we consider observations from a series of smart meters that are either completely or partially aggregated, and our aim is to estimate the metering hierarchy. We propose to estimate this important metadata through a novel adaptation of the Chow–Liu tree learning procedure. Our approach takes into account prior knowledge from a set of dominance conditions that are easily elicited from the consumption data. In addition to more traditional correlation-based approaches we also introduce a distance-correlation-based method for detecting edges. Synthetic experiments show the benefits of distance correlation and the dominance conditions in recovering tree structure. The paper concludes with a real-world application of the method to infer energy metering hierarchies in a library building.

Human activity recognition based on generated sensor data plays a major role in a large number of applications such as healthcare monitoring and surveillance system. Yet, accurately recognizing human activities is still challenging and active research due to people’s tendency to perform daily activities in a different and multitasking way. Existing approaches based on the recurrent setting for human activity recognition have some issues, such as the inability to process data parallelly, the requirement for more memory and high computational cost albeit they achieved reasonable results. Convolutional Neural Network processes data parallelly, but, it breaks the ordering of input data, which is significant to build an effective model for human activity recognition. To overcome these challenges, this study proposes causal convolution based on performers-attention and supervised contrastive learning to entirely forego recurrent architectures, efficiently maintain the ordering of human daily activities and focus more on important timesteps of the sensors’ data. Supervised contrastive learning is integrated to learn a discriminative representation of human activities and enhance predictive performance. The proposed network is extensively evaluated for human activities using multiple datasets including wearable sensor data and smart home environments data. The experiments on three wearable sensor datasets and five smart home public datasets of human activities reveal that our proposed network achieves better results and reduces the training time compared with the existing state-of-the-art methods and basic temporal models.

Electronic Government (e-Government) systems constantly provide greater services to people, businesses, organisations, and societies by offering more information, opportunities, and platforms with the support of advances in information and communications technologies. This usually results in increased system complexity and sensitivity, necessitating stricter security and privacy-protection measures. The majority of the existing e-Government systems are centralised, making them vulnerable to privacy and security threats, in addition to suffering from a single point of failure. This study proposes a decentralised e-Government framework with integrated threat detection features to address the aforementioned challenges. In particular, the privacy and security of the proposed e-Government system are realised by the encryption, validation, and immutable mechanisms provided by Blockchain. The insider and external threats associated with blockchain transactions are minimised by the employment of an artificial immune system, which effectively protects the integrity of the Blockchain. The proposed e-Government system was validated and evaluated by using the framework of Ethereum Visualisations of Interactive, Blockchain, Extended Simulations (i.e. eVIBES simulator) with two publicly available datasets. The experimental results show the efficacy of the proposed framework in that it can mitigate insider and external threats in e-Government systems whilst simultaneously preserving the privacy of information.

As more business transactions and information services have been implemented via communication networks, both personal and organization assets encounter a higher risk of attacks. To safeguard these, a perimeter defence like NIDS (network-based intrusion detection system) can be effective for known intrusions. There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks, where obfuscation techniques are applied to disguise patterns of intrusive traffics. The current research focuses on non-payload connections at the TCP (transmission control protocol) stack level that is applicable to different network applications. In contrary to the wrapper method introduced with the benchmark dataset, three new filter models are proposed to transform the feature space without knowledge of class labels. These ECT (ensemble clustering based transformation) techniques, i.e., ECT-Subspace, ECT-Noise and ECT-Combined, are developed using the concept of ensemble clustering and three different ensemble generation strategies, i.e., random feature subspace, feature noise injection and their combinations. Based on the empirical study with published dataset and four classification algorithms, new models usually outperform that original wrapper and other filter alternatives found in the literature. This is similarly summarized from the first experiment with basic classification of legitimate and direct attacks, and the second that focuses on recognizing obfuscated intrusions. In addition, analysis of algorithmic parameters, i.e., ensemble size and level of noise, is provided as a guideline for a practical use.

Human activity recognition based on generated sensor data plays a major role in a large number of applications such as healthcare monitoring and surveillance system. Yet, accurately recognizing human activities is still challenging and active research due to people’s tendency to perform daily activities in a different and multitasking way. Existing approaches based on the recurrent setting for human activity recognition have some issues, such as the inability to process data parallelly, the requirement for more memory and high computational cost albeit they achieved reasonable results. Convolutional Neural Network processes data parallelly, but, it breaks the ordering of input data, which is significant to build an effective model for human activity recognition. To overcome these challenges, this study proposes causal convolution based on performers-attention and supervised contrastive learning to entirely forego recurrent architectures, efficiently maintain the ordering of human daily activities and focus more on important timesteps of the sensors’ data. Supervised contrastive learning is integrated to learn a discriminative representation of human activities and enhance predictive performance. The proposed network is extensively evaluated for human activities using multiple datasets including wearable sensor data and smart home environments data. The experiments on three wearable sensor datasets and five smart home public datasets of human activities reveal that our proposed network achieves better results and reduces the training time compared with the existing state-of-the-art methods and basic temporal models. © 2022, The Author(s).

Existing models based on sensor data for human activity recognition are reporting state-of-the-art performances. Most of these models are conducted based on single-domain learning in which for each domain a model is required to be trained. However, the generation of adequate labelled data and a learning model for each domain separately is often time-consuming and computationally expensive. Moreover, the deployment of multiple domain-wise models is not scalable as it obscures domain distinctions, introduces extra computational costs, and limits the usefulness of training data. To mitigate this, we propose a multi-domain learning network to transfer knowledge across different but related domains and alleviate isolated learning paradigms using a shared representation. The proposed network consists of two identical causal convolutional sub-networks that are projected to a shared representation followed by a linear attention mechanism. The proposed network can be trained using the full training dataset of the source domain and a dataset of restricted size of the target training domain to reduce the need of large labelled training datasets. The network processes the source and target domains jointly to learn powerful and mutually complementary features to boost the performance in both domains. The proposed multi-domain learning network on six real-world sensor activity datasets outperforms the existing methods by applying only 50% of the labelled data. This confirms the efficacy of the proposed approach as a generic model to learn human activities from different but related domains in a joint effort, to reduce the number of required models and thus improve system efficiency.