A Knowledge Graph (KG) is a type of knowledge base that stores information in the form of semantic triples formed by a subject, a predicate, and an object. KGs represent both real and abstract entities internally as labelled and uniquely identifiable entities, such as The Moon or Happiness, and can amass information from a multitude of domains and sources by connecting such entities amongst themselves or to literals through relationships, coded via uniquely identified predicates. KGs serve as sources of both human and machine-readable semantically structured data for various crucial applications in the modern web landscape, such as Wikipedia infoboxes, search engines results, voice-activated assistants, and information gathering projects [30]. Developed and maintained by ontology experts, data curators, and even anonymous volunteers, KGs have massively grown in size and adoption in the last decade, mainly as secondary sources of information. This means not storing new information, but taking it from authoritative and reliable sources which are explicitly referenced. As such, KGs depend on well-documented and verifiable provenance to ensure they are regarded as trustworthy and usable [56]. Processes to assess and assure the quality of information provenance are thus crucial to KGs, especially measuring and maintaining verifiability, i.e. the degree to which consumers of KG triples can attest these are truly supported by their sources [56]. However, such processes are currently performed mostly manually, which does not scale with size. Manually ensuring high verifiability on vital KGs such as Wikidata and DBpedia is prohibitive due to their sheer size.

Abstract: The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision-making such as in climate change applications. We address both issues by successfully implementing a Bayesian Neural Network (BNN), where parameters are distributions rather than deterministic, and applying novel implementations of explainable AI (XAI) techniques. The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners' needs than predictions from a classical neural network. Using a BNN means we can calculate the entropy (i.e.

Capturing the similarities between human language units is crucial for explaining how humans associate different objects, and therefore its computation has received extensive attention, research, and applications. With the ever-increasing amount of information around us, calculating similarity becomes increasingly complex, especially in many cases, such as legal or medical affairs, measuring similarity requires extra care and precision, as small acts within a language unit can have significant real-world effects. My research goal in this thesis is to develop regression models that account for similarities between language units in a more refined way. Computation of similarity has come a long way, but approaches to debugging the measures are often based on continually fitting human judgment values. To this end, my goal is to develop an algorithm that precisely catches loopholes in a similarity calculation. Furthermore, most methods have vague definitions of the similarities they compute and are often difficult to interpret. The proposed framework addresses both shortcomings. It constantly improves the model through catching different loopholes. In addition, every refinement of the model provides a reasonable explanation. The regression model introduced in this thesis is called progressively refined similarity computation, which combines attack testing with adversarial training. The similarity regression model of this thesis achieves state-of-the-art performance in handling edge cases.

Dependency parsing is a crucial step towards deep language understanding and, therefore, widely demanded by numerous Natural Language Processing applications. In particular, left-to-right and top-down transition-based algorithms that rely on Pointer Networks are among the most accurate approaches for performing dependency parsing. Additionally, it has been observed for the top-down algorithm that Pointer Networks' sequential decoding can be improved by implementing a hierarchical variant, more adequate to model dependency structures. Considering all this, we develop a bottom-up-oriented Hierarchical Pointer Network for the left-to-right parser and propose two novel transition-based alternatives: an approach that parses a sentence in right-to-left order and a variant that does it from the outside in. We empirically test the proposed neural architecture with the different algorithms on a wide variety of languages, outperforming the original approach in practically all of them and setting new state-of-the-art results on the English and Chinese Penn Treebanks for non-contextualized and BERT-based embeddings.

The automatic scoring of summaries is important as it guides the development of summarizers. Scoring is also complex, as it involves multiple aspects such as fluency, grammar, and even textual entailment with the source text. However, summary scoring has not been considered a machine learning task to study its accuracy and robustness. In this study, we place automatic scoring in the context of regression machine learning tasks and perform evasion attacks to explore its robustness. Attack systems predict a non-summary string from each input, and these non-summary strings achieve competitive scores with good summarizers on the most popular metrics: ROUGE, METEOR, and BERTScore. Attack systems also "outperform" state-of-the-art summarization methods on ROUGE-1 and ROUGE-L, and score the second-highest on METEOR. Furthermore, a BERTScore backdoor is observed: a simple trigger can score higher than any automatic summarization method. The evasion attacks in this work indicate the low robustness of current scoring systems at the system level. We hope that our highlighting of these proposed attacks will facilitate the development of summary scores.

Crowd anomaly detection is one of the most popular topics in computer vision in the context of smart cities. A plethora of deep learning methods have been proposed that generally outperform other machine learning solutions. Our review primarily discusses algorithms that were published in mainstream conferences and journals between 2020 and 2022. We present datasets that are typically used for benchmarking, produce a taxonomy of the developed algorithms, and discuss and compare their performances. Our main findings are that the heterogeneities of pre-trained convolutional models have a negligible impact on crowd video anomaly detection performance. We conclude our discussion with fruitful directions for future research.

In a fair world, people have equitable opportunities to education, to conduct scientific research, to publish, and to get credit for their work, regardless of where they live. However, it is common knowledge among researchers that a vast number of papers accepted at top NLP venues come from a handful of western countries and (lately) China; whereas, very few papers from Africa and South America get published. Similar disparities are also believed to exist for paper citation counts. In the spirit of "what we do not measure, we cannot improve", this work asks a series of questions on the relationship between geographical location and publication success (acceptance in top NLP venues and citation impact). We first created a dataset of 70,000 papers from the ACL Anthology, extracted their meta-information, and generated their citation network. We then show that not only are there substantial geographical disparities in paper acceptance and citation but also that these disparities persist even when controlling for a number of variables such as venue of publication and sub-field of NLP. Further, despite some steps taken by the NLP community to improve geographical diversity, we show that the disparity in publication metrics across locations is still on an increasing trend since the early 2000s. We release our code and dataset here: https://github.com/iamjanvijay/acl-cite-net

Abstract: Having the Fifth Generation (5G) mobile communication system recently rolled out in many countries, the wireless community is now setting its eyes on the next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial use cases, 6G is envisaged to become the infrastructural backbone of future intelligent industry. Especially, a combination of 6G and the emerging technologies of Digital Twins (DT) will give impetus to the next evolution of Industry 4.0 (I4.0) systems. Here we provide a vision for the future 6G industrial DT ecosystem, which shall bridge the gaps between humans, machines, and the data infrastructure, and therewith enable numerous novel application scenarios. Subsequently, we explore the technical challenges that are brought by such ambitions, and identify the key enabling technologies that may help tackle down these issues.

The European Space Agency is well known as a powerful force for scientific discovery in numerous areas related to Space. The amount and depth of the knowledge produced throughout the different missions carried out by ESA and their contribution to scientific progress is enormous, involving large collections of documents like scientific publications, feasibility studies, technical reports, and quality management procedures, among many others. Through initiatives like the Open Space Innovation Platform, ESA also acts as a hub for new ideas coming from the wider community across different challenges, contributing to a virtuous circle of scientific discovery and innovation. Handling such wealth of information, of which large part is unstructured text, is a colossal task that goes beyond human capabilities, hence requiring automation. In this paper, we present a methodological framework based on artificial intelligence and natural language processing and understanding to automatically extract information from Space documents, generating value from it, and illustrate such framework through several case studies implemented across different functional areas of ESA, including Mission Design, Quality Assurance, Long-Term Data Preservation, and the Open Space Innovation Platform. In doing so, we demonstrate the value of these technologies in several tasks ranging from effortlessly searching and recommending Space information to automatically determining how innovative an idea can be, answering questions about Space, and generating quizzes regarding quality procedures. Each of these accomplishments represents a step forward in the application of increasingly intelligent AI systems in Space, from structuring and facilitating information access to intelligent systems capable to understand and reason with such information.

We introduce the first automated models for classifying natural language descriptions provided in cost documents called "Bills of Quantities" (BoQs) popular in the infrastructure construction industry, into the International Construction Measurement Standard (ICMS). The models we deployed and systematically evaluated for multi-class text classification are learnt from a dataset of more than 50 thousand descriptions of items retrieved from 24 large infrastructure construction projects across the United Kingdom. We describe our approach to language representation and subsequent modelling to examine the strength of contextual semantics and temporal dependency of language used in construction project documentation. To do that we evaluate two experimental pipelines to inferring ICMS codes from text, on the basis of two different language representation models and a range of state-of-the-art sequence-based classification methods, including recurrent and convolutional neural network architectures. The findings indicate a highly effective and accurate ICMS automation model is within reach, with reported accuracy results above 90% F1 score on average, on 32 ICMS categories. Furthermore, due to the specific nature of language use in the BoQs text; short, largely descriptive and technical, we find that simpler models compare favourably to achieving higher accuracy results. Our analysis suggest that information is more likely embedded in local key features in the descriptive text, which explains why a simpler generic temporal convolutional network (TCN) exhibits comparable memory to recurrent architectures with the same capacity, and subsequently outperforms these at this task.