Reviewerly/exHarmony huggingface dataset:
Exploring Abstractive vs. Extractive Summarisation Techniques for Sports News

Abstract The high demand generated by the information age has led to recent breakthroughs in both extractive and abstractive text summarisation. This work explores the algorithms that were the product of these advances, focusing on the domain of sports news summarisation. By creating a new hybrid evaluation system that incorporates automatic evaluation metrics, such as ROUGE and BLEU scores, with human evaluation, we observe that abstractive techniques return the best results in the sports domain. This also generalises to the domain of political articles. However, here the metrics report lower scores across most algorithms. Another finding is that the algorithms considered perform independently of the dialect of English used.
Intelligent Image Compression Using Traffic Scene Analysis

Abstract The quantity of images generated at the edge of the Cloud is growing year-on-year, which puts an increasing strain on existing telecommunications infrastructure. There is also an associated increased cost for transmission bandwidth and storage of video images in the Cloud. In our modern society we tend to accumulate data, and are reluctant to throw it away, without asking “what is the value of this data?” and “do we need it?”. One of the major sources of video streams are the increasing number of traffic cameras, used to maintain the efficient flow of vehicles on our roads. In this work we focus on images taken from road traffic cameras, and show how their transmission bandwidth and storage requirements can be reduced. By analysing video feeds on a simulated edge device, we have shown that it is possible to extract objects of interest from the image, and discard or dramatically reduce irrelevant information in the content. Our technique also generates associated metadata, in the form of JSON-LD, which annotates the original image and maintains its semantic fidelity and provenance after compression. Our technique is compatible with conventional compression techniques, and thus the potential bandwidth savings would be incremental. We present the potential savings that can be made in the transmission and storage of unstructured data, as well as some of the challenges still to be overcome.
Privacy-Enhanced ZKP-Inspired Framework for Balanced Federated Learning

Abstract Federated learning (FL) is a distributed machine learning approach that enables remote devices i.e. workers to collaborate to compute the fitting of a neural network model without sharing their data. While this method is favorable to ensure data privacy, an imbalanced data distribution can introduce unfairness in the model training, causing discriminatory bias towards certain under-represented groups. In this paper, we show that imbalance federated data decreases indexes of equity i.e. differences in treatment for underrepresented classes. To address the problem, we propose a federated learning framework called Z-Fed that 1) balances the training without exchange of privacy protected data using a zero knowledge proof (ZKP) technique, and 2) allows for the collection of information on data distributions based on one or more categorical features to produce metadata about population proportions. The proposed framework infers the precise data distribution without exchanging knowledge of the data categories and uses it to coordinate a balanced training set. Z-Fed aims to mitigate the effect of imbalanced data in FL while respecting privacy and without using mediators or probabilistic approaches. Compared to a non-balanced framework, Z-Fed improves fairness and equality measured in equal opportunities (EPD) by 53.54%, equal odds (EOD) by 56.41%, and statistical parity (SPD) by 46.1% on imbalanced UTK datasets, reducing biased predictions among subgroups. EPD, EOD, and SPD measure the disparity of treatment between privileged e.g. over-represented and non-privileged groups. Given the results obtained, Z-Fed can reduce discriminatory behaviors and enhance trustworthiness of federated learning.
Unimodal and Multimodal Representation Training for Relation Extraction

Abstract Multimodal integration of text, layout and visual information has achieved SOTA results in visually rich document understanding (VrDU) tasks, including relation extraction (RE). However, despite its importance, evaluation of the relative predictive capacity of these modalities is less prevalent. Here, we demonstrate the value of shared representations for RE tasks by conducting experiments in which each data type is iteratively excluded during training. In addition, text and layout data are evaluated in isolation. While a bimodal text and layout approach performs best (F1 = 0.684), we show that text is the most important single predictor of entity relations. Additionally, layout geometry is highly predictive and may even be a feasible unimodal approach. Despite being less effective, we highlight circumstances where visual information can bolster performance. In total, our results demonstrate the efficacy of training joint representations for RE.
Meme Sentiment Analysis Enhanced with Multimodal Spatial Encoding and Face Embedding

Internet memes are characterised by the interspersing of text amongst visual elements. State-of-the-art multimodal meme classifiers do not account for the relative positions of these elements across the two modalities, despite the latent meaning associated with where text and visual elements are placed. Against two meme sentiment classification datasets, we systematically show performance gains from incorporating the spatial position of visual objects, faces, and text clusters extracted from memes. In addition, we also present facial embedding as an impactful enhancement to image representation in a multimodal meme classifier. Finally, we show that incorporating this spatial information allows our fully automated approaches to outperform their corresponding baselines that rely on additional human validation of OCR-extracted text.
Recommendation Uncertainty in Implicit Feedback Recommender Systems

Abstract A Recommender System’s recommendations will each carry a certain level of uncertainty. The quantification of this uncertainty can be useful in a variety of ways. Estimates of uncertainty might be used externally; for example, showing them to the user to increase user trust in the abilities of the system. They may also be used internally; for example, deciding the balance of ‘safe’ and less safe recommendations. In this work, we explore several methods for estimating uncertainty. The novelty comes from proposing methods that work in the implicit feedback setting. We use experiments on two datasets to compare a number of recommendation algorithms that are modified to perform uncertainty estimation. In our experiments, we show that some of these modified algorithms are less accurate than their unmodified counterparts, but others are actually more accurate. We also show which of these methods are best at enabling the recommender to be ‘aware’ of which of its recommendations are likely to be correct and which are likely to be wrong.
Identity Term Sampling for Measuring Gender Bias in Training Data

Abstract Predictions from machine learning models can reflect biases in the data on which they are trained. Gender bias has been identified in natural language processing systems such as those used for recruitment. The development of approaches to mitigate gender bias in training data typically need to be able to isolate the effect of gender on the output to see the impact of gender. While it is possible to isolate and identify gender for some types of training data, e.g. CVs in recruitment, for most textual corpora there is no obvious gender label. This paper proposes a general approach to measure bias in textual training data for NLP prediction systems by providing a gender label identified from the textual content of the training data. The approach is compared with the identity term template approach currently in use, also known as Gender Bias Evaluation Datasets (GBETs), which involves the design of synthetic test datasets which isolate gender and are used to probe for gender bias in a dataset. We show that our Identity Term Sampling (ITS) approach is capable of identifying gender bias at least as well as identity term templates and can be used on training data that has no obvious gender label.
Author Gender Identification Considering Gender Bias

Abstract Writing style and choice of words used in textual content can vary between men and women both in terms of who the text is talking about and who is writing the text. The focus of this paper is on author gender prediction, identifying the gender of who is writing the text. We compare closed and open vocabulary approaches on different types of textual content including more traditional writing styles such as in books, and more recent writing styles used in user generated content on digital platforms such as blogs and social media messaging. As supervised machine learning approaches can reflect human biases in the data they are trained on, we also consider the gender bias of the different approaches across the different types of dataset. We show that open vocabulary approaches perform better both in terms of prediction performance and with less gender bias.
Analyzing the Rainfall Pattern in Honduras Through Non-Homogeneous Hidden Markov Models

One of the major climatic interests of the last decades has been to understand and describe the rainfall patterns of specific areas of the world as functions of other climate covariates. We do it for the historical climate monitoring data from Tegucigalpa, Honduras, using non-homogeneous hidden Markov models (NHMMs), which are dynamic models usually used to identify and predict heterogeneous regimes. For estimating the NHMM in an efficient and scalable way, we propose the stochastic Expectation-Maximization (EM) algorithm and a Bayesian method, and compare their performance in synthetic data. Although these methodologies have already been used for estimating several other statistical models, it is not the case of NHMMs which are still widely fitted by the traditional EM algorithm. We observe that, under tested conditions, the performance of the Bayesian and stochastic EM algorithms is similar and discuss their slight differences. Analyzing the Honduras rainfall data set, we identify three heterogeneous rainfall periods and select temperature and humidity as relevant covariates for explaining the dynamic relation among these periods.
Automatic Linking of Podcast Segments to Topically Related Webpages

Abstract Podcasts are becoming an increasingly popular source of information. However, they often rely on the topical knowledge of the listener in order for them to be fully understood. We describe an investigation into methods to augment the contents of podcasts with related information from the Web. We seek to identify webpages related to segments within a podcast. NLP techniques are used to analyze audio podcast transcripts and link these to related content. We propose and examine 10 methods for automatically generating search queries from transcript segments, which are then used to search for related content on the web. The relevance of retrieved webpages to retrieved content is evaluated using crowdsourcing via Amazon Mechanical Turk. Extracting key phrases directly from the podcasts using YAKE was the most successful approach with more than 90% returned pages assessed as relevant, with precision at rank 1 and rank 3 above 0.9.
CouRGe: Counterfactual Reviews Generator for Sentiment Analysis

Abstract Past literature in Natural Language Processing (NLP) has demonstrated that counterfactual data points are useful, for example, for increasing model generalisation, enhancing model interpretability, and as a data augmentation approach. However, obtaining counterfactual examples often requires human annotation effort, which is an expensive and highly skilled process. For these reasons, solutions that resort to transformer-based language models have been recently proposed to generate counterfactuals automatically, but such solutions show limitations. In this paper, we present CouRGe, a language model that, given a movie review (i.e. a seed review) and its sentiment label, generates a counterfactual review that is close (similar) to the seed review but of the opposite sentiment. CouRGe is trained by supervised fine-tuning of GPT-2 on a task-specific dataset of paired movie reviews, and its generation is prompt-based. The model does not require any modification to the network’s architecture or the design of a specific new task for fine-tuning. Experiments show that CouRGe’s generation is effective at flipping the seed sentiment and produces counterfactuals reasonably close to the seed review. This proves once again the great flexibility of language models towards downstream tasks as hard as counterfactual reasoning and opens up the use of CouRGe’s generated counterfactuals for the applications mentioned above.
Unsupervised Machine Learning for the Quadratic Assignment Problem

An unsupervised machine learning method based on association rule is studied for the Quadratic Assignment Problem. Parallel extraction of itemsets and local search algorithms are proposed. The extraction of frequent itemsets in the context of local search is shown to produce good results for a few problem instances. Negative results of the proposed learning mechanism are reported for other instances. This result contrasts with other hard optimization problems for which efficient learning processes are known in the context of local search.
Personalised Filter Bias with Google and DuckDuckGo: An Exploratory Study

Abstract Personalisation in search has improved performance, focus, and user experience to a great extent, however, it also arguably polarises informational perspectives. This paper seeks to illustrate an experimental methodology to quantify how three situational user variables affect personalisation across two search engines: Google and DuckDuckGo. We find that the presence of cookies and prior search history markedly affect the first page of search results on both platforms, but that prior (shallow) browsing history has no observable effect. We also find that there is very little in common between the results of both search engines. We argue that these results advocate more consideration of how personalisation fosters filter biases.
Analysis of Attention Mechanisms in Box-Embedding Systems

Abstract Large-scale Knowledge Graphs (KGs) have recently gained considerable research attention for their ability to model the inter- and intra- relationships of data. However, the huge scale of KGs has necessitated the use of querying methods to facilitate human use. Question Answering (QA) systems have shown much promise in breaking down this human-machine barrier. A recent QA model that achieved state-of-the-art performance, Query2box, modelled queries on a KG using box embeddings with an attention mechanism backend to compute the intersections of boxes for query resolution. In this paper, we introduce a new model, Query2Geom, which replaces the Query2box attention mechanism with a novel, exact geometric calculation. Our findings show that Query2Geom generally matches the performance of Query2box while having many fewer parameters. Our analysis of the two models leads us to formally describe the interaction between knowledge graph data and box embeddings with the concepts of semantic-geometric alignment and mismatch. We create the Attention Deviation Metric as a measure of how well the geometry of box embeddings captures the semantics of a knowledge graph, and apply it to explain the difference in performance between Query2box and Query2Geom. We conclude that Query2box’s attention mechanism operates using “latent intersections” that attend to the semantic properties in embeddings not expressed in box geometry, acting as a limit on model interpretability. Finally, we generalise our results and propose that semantic-geometric mismatch is a more general property of attention mechanisms, and provide future directions on how to formally model the interaction between attention and latent semantics.
Run-Time Norms Synthesis in Dynamic Environments with Changing Objectives

Abstract Normative Multi-Agent Systems (NorMAS) can model real-world applications as multi-agent systems and facilitate the coordination of the social behaviour of various entities (agents) interacting in an environment using norms. Aligning such norms with the objectives of the agents is crucially important to ensure that applying the norms would not affect the achievement of their objectives. However, when the environment is dynamic, agents can face unseen situations and might need to change their objectives accordingly. Therefore, it becomes more challenging to understand the change, synthesise norms, and align them with such dynamic objectives. This paper introduces a Dynamic Objectives and Norms Synthesizer and Reasoner (DONSR) model to align objectives and norms using a utility-based approach. An ontology-based schema, forward reasoning, and backward reasoning are used to identify the change in the environment and synthesise new objectives. Case-based reasoning enables the dynamic changing and reasoning of previously created objectives and synthesising norms. DONSR is evaluated using multiple simulated traffic scenarios, including different unseen situations (emergency events). Results show that norms can be synthesised and maintained efficiently while the objectives are being created and changed. Further, DONSR showed its efficacy in handling unseen situations, creating new objectives, and aligning them with the created norms.
Graph-Based Diffusion Method for Top-N Recommendation

Abstract Data that may be used for personalised recommendation purposes can intuitively be modelled as a graph. Users can be linked to item data; item data may be linked to item data. With such a model, the task of recommending new items to users or making new connections between items can be undertaken by algorithms designed to establish the relatedness between vertices in a graph. One such class of algorithm is based on the random walk, whereby a sequence of connected vertices are visited based on an underlying probability distribution and a determination of vertex relatedness established. A diffusion kernel encodes such a process. This paper demonstrates several diffusion kernel approaches on a graph composed of user-item and item-item relationships. The approach presented in this paper, RecWalk* , consists of a user-item bipartite combined with an item-item graph on which several diffusion kernels are applied and evaluated in terms of top-n recommendation . We conduct experiments on several datasets of the RecWalk* model using combinations of different item-item graph models and personalised diffusion kernels. We compare accuracy with some non-item recommender methods. We show that diffusion kernel approaches match or outperform state-of-the-art recommender approaches.
Variable Neighborhood Descent for Software Quality Optimization

In the Software Development Life-Cycle, the maintenance phase is often the most costly stage due to the efforts devoted to understanding the system. The Software Module Clustering Problem (SMCP) is an optimization problem which objective is to find the most modular organization of software systems to ease their comprehension. In this problem, software projects are frequently modeled as graphs. Then, the objective of the SMCP, which is proved to be $$\mathcal{N}\mathcal{P}$$ -hard, is to group the vertices in modules such that the modularity of the graph is maximized. In this work, we present an algorithm based on Variable Neighborhood Descent for the SMCP and study a novel quality metric, the Function of Complexity Balance, which was recently proposed for the problem. Our proposal has been favorably evaluated over a dataset of 34 real software projects, outperforming the previous state-of-the-art method, a Hybrid Genetic Algorithm, in terms of both quality and computing time. Furthermore, the results are statistically significant according to the Wilcoxon’s signed rank test.
Explaining Deep Neural Networks for Point Clouds Using Gradient-Based Visualisations

Explaining decisions made by deep neural networks is a rapidly advancing research topic. In recent years, several approaches have attempted to provide visual explanations of decisions made by neural networks designed for structured 2D image input data. In this paper, we propose a novel approach to generate coarse visual explanations of networks designed to classify unstructured 3D data, namely point clouds. Our method uses gradients flowing back to the final feature map layers and maps these values as contributions of the corresponding points in the input point cloud. Due to dimensionality disagreement and lack of spatial consistency between input points and final feature maps, our approach combines gradients with points dropping to compute explanations of different parts of the point cloud iteratively. The generality of our approach is tested on various point cloud classification networks, including 'single object' networks PointNet, PointNet++, DGCNN, and a 'scene' network VoteNet. Our method generates symmetric explanation maps that highlight important regions and provide insight into the decision-making process of network architectures. We perform an exhaustive evaluation of trust and interpretability of our explanation method against comparative approaches using quantitative, quantitative and human studies. All our code is implemented in PyTorch and will be made publicly available.
A Learning Metaheuristic Algorithm for a Scheduling Application

Tabu Search is among one of the metaheuristic algorithms that are widely recognized as efficient approaches to solve many combinatorial problems. Studies to improve the performance of metaheuristics have increasingly relied on the use of various methods, either combining different metaheuristics or originating outside of the metaheuristic field. This paper presents a learning algorithm to improve the performance of tabu search by reducing its search space and the evaluation effort. We study its performance using classification methods in an attempt to select moves through the search space more intelligently. The experimental results demonstrate the benefit of using a learning mechanism under deterministic environment and with uncertainty conditions.
Selecting the Parameters of an Evolutionary Algorithm for the Generation of Phenotypically Accurate Fractal Patterns

This paper describes the selection of parameters of an Evolutionary Algorithm (EA) suitable for optimising the genotype of a fractal model of phenotypically realistic structures. To achieve the proposed goal an EA is implemented as a metaheuristic search tool to find the coefficients of the transformation matrices of an Iterated Function System (IFS) which then generates regular fractal patterns. Fractal patterns occur throughout nature, a striking example being the fern patterns modelled by Barnsley. Thus the algorithm is evaluated using the IFS for the fern fractal using the EA-evolved parameters.