Abstract Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer recommendations for overcoming them, ultimately expediting the pace of contact centre automation.

The continuous operation of an industrial process, such as water treatment or power generation, is governed by an Industrial Control System (ICS). Cyber-attacks on industrial networks are of growing concern because of the disruption they can cause, leading to loss of revenue, and the possibility of harm to workers, plant and surroundings. Operators therefore need a Network Intrusion Detection System (NIDS) to analyse industrial network traffic in real time for adversarial behaviour. Machine Learning (ML) is applicable to the problem of network intrusion detection. This paper investigates the possibility of training an ML-based NIDS for an ICS (specifically, the well-known Secure Water Treatment testbed) by combining network traffic data and physical process data. In the supplied dataset, data had already been labelled “according to normal and abnormal behaviours”; the labelling of data collected around the start and end of each attack was scrutinized and, where found to be problematic, labelled data were excluded in order to improve the effectiveness of supervised learning. The ML technique of “Learning using Privileged Information” was evaluated and found to be superior to six baseline ML algorithms trained on network traffic data alone.

In this paper we present an ontological perspective on ongoing work in histological and histopathological imaging involving the quantitative and algorithmic analysis of digitised images of cells and tissues. We present the derivation of consistent histological models from initially captured images of prepared tissue samples as a progression through a number of ontological levels, each populated by its distinctive classes of entities related in systematic ways to entities at other levels. We see this work as contributing to ongoing efforts to provide a consistent and widely accepted suite of ontological resources such as those currently constituting the OBO Foundry, and where possible we draw links between our work and existing ontologies within that suite.

Learning Using privileged Information (LUPI), originally proposed in [1], is an advanced learning paradigm that aims to improve the supervised learning in the presence of additional (privileged) information, available during training, but not in the test phase. We present a novel metric learning methodology that is specially designed for incorporating privileged information in ordinal classification tasks, where there is a natural order on the set of classes. This is done by changing the global metric in the input space, based on distance relations revealed by the privileged information. The proposed model is formulated in the context of ordinal prototype based classification with metric adaptation. Unlike the existing nominal version of LUPI in prototype models [8], [9], in ordinal classifications the proposed LUPI model takes explicitly into account the class order information during the input space metric learning. Experiments demonstrate that incorporating privileged information via the proposed ordinal-based metric learning can improve the ordinal classification performance.

In some pattern analysis problems, there exists expert knowledge, in addition to the original data involved in the classification process. The vast majority of existing approaches simply ignore such auxiliary (privileged) knowledge. Recently a new paradigm-learning using privileged information-was introduced in the framework of SVM+. This approach is formulated for binary classification and, as typical for many kernel-based methods, can scale unfavorably with the number of training examples. While speeding up training methods and extensions of SVM+ to multiclass problems are possible, in this paper we present a more direct novel methodology for incorporating valuable privileged knowledge in the model construction phase, primarily formulated in the framework of generalized matrix learning vector quantization. This is done by changing the global metric in the input space, based on distance relations revealed by the privileged information. Hence, unlike in SVM+, any convenient classifier can be used after such metric modification, bringing more flexibility to the problem of incorporating privileged information during the training. Experiments demonstrate that the manipulation of an input space metric based on privileged data improves classification accuracy. Moreover, our methods can achieve competitive performance against the SVM+ formulations.

Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases, nominal classification schemes will ignore the class order relationships, which can have a detrimental effect on classification accuracy. This article introduces two novel ordinal learning vector quantization (LVQ) schemes, with metric learning, specifically designed for classifying data items into ordered classes. In ordinal LVQ, unlike in nominal LVQ, the class order information is used during training in selecting the class prototypes to be adapted, as well as in determining the exact manner in which the prototypes get updated. Prototype-based models in general are more amenable to interpretations and can often be constructed at a smaller computational cost than alternative nonlinear classification models. Experiments demonstrate that the proposed ordinal LVQ formulations compare favorably with their nominal counterparts. Moreover, our methods achieve competitive performance against existing benchmark ordinal regression models.