Alessandra Mileo

• PhD
• Lecturer at Dublin City University

An increasing number of cities are confronted with challenges resulting from the rapid urbanisation and new demands that a rapidly growing digital economy imposes on current applications and information systems. Smart city applications enable city authorities to monitor, manage and provide plans for public resources and infrastructures in city envi...

The ability to enhance deep representations with prior knowledge is receiving a lot of attention from the AI community as a key enabler to improve the way modern Artificial Neural Networks (ANN) learn. In this paper we introduce our approach to this task, which comprises of a knowledge extraction algorithm, a knowledge injection algorithm and a com...

Due to its potential to solve complex tasks, deep learning is being used across many different areas. The complexity of neural networks however makes it difficult to explain the whole decision process used by the model, which makes understanding deep learning models an active research topic. In this work we address this issue by extracting the know...

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art expressive reasoners, limiting their applicability to process streaming information in a scalable way...

Data augmentation is essential for enhancing computer vision performance, with the KeepOriginalAugment method standing out for intelligently incorporating salient and less prominent regions. Despite its success in image classification, its potential in addressing biases is unexplored. We introduce FaceKeepOriginalAugment, extending KeepOriginalAugm...

Geographical, gender and stereotypical biases in computer vision models pose significant challenges to their performance and fairness. In this study, we present an approach named FaceSaliencyAug aimed at addressing the gender bias in Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Leveraging the salient regions of faces detecte...

Data augmentation has become a pivotal tool in enhancing the performance of computer vision tasks, with the KeepOriginalAugment method emerging as a standout technique for its intelligent incorporation of salient regions within less prominent areas, enabling augmentation in both regions. Despite its success in image classification, its potential in...

Geographical, gender and stereotypical biases in computer vision models pose significant challenges to their performance and fairness. {In this study, we present an approach named FaceSaliencyAug aimed at addressing the gender bias in} {Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Leveraging the salient regions} { of faces d...

Carcinoma is the prevailing type of cancer and can manifest in various body parts. It is widespread and can potentially develop in numerous locations within the body. In the medical domain, data for carcinoma cancer is often limited or unavailable due to privacy concerns. Moreover, when available, it is highly imbalanced, with a scarcity of positiv...

Deep learning is being very successful in supporting humans in the interpretation of complex data (such as images and text) for critical decision tasks. However, it still remains difficult for human experts to understand how such results are achieved, due to the “black box” nature of the deep models used. In high-stake decision making scenarios suc...

The global sports analytics industry has a market value of USD 3.78 billion in 2023. The increase of wearables such as GPS sensors has provided analysts with large fine-grained datasets detailing player performance. Traditional analysis of this data focuses on individual athletes with measures of internal and external loading such as distance cover...

Research in field sports often involves analysis of running performance profiles of players during competitive games with individual, per-position, and time-related descriptive statistics. Data are acquired through wearable technologies, which generally capture simple data points, which in the case of many team-based sports are times, latitudes, an...

Deep learning algorithms have exhibited impressive performance across various computer vision tasks; however, the challenge of overfitting persists, especially when dealing with limited labeled data. This survey explores the mitigation of the overfitting issue through a comprehensive examination of image data augmentation techniques, which aim to e...

Data augmentation has proven to be effective in training neural networks. Recently, a method called RandAug was proposed, randomly selecting data augmentation techniques from a predefined search space. RandAug has demonstrated significant performance improvements for image-related tasks while imposing minimal computational overhead. However, no pri...

This volume contains the Technical Communications presented at the 39th International Conference on Logic Programming (ICLP 2023), held at Imperial College London, UK from July 9 to July 15, 2023. Technical Communications included here concern the Main Track, the Doctoral Consortium, the Application and Systems/Demo track, the Recently Published Re...

Explainable Artificial Intelligence (XAI) has recently become an active research field due to the need for transparency and accountability when deploying AI models for high-stake decision making. Despite state-of-the-art Convolutional Neural Networks (CNNs) have achieved great performance in computer vision, understanding their decision processes,...

The development of computer vision and in-situ monitoring using visual sensors allows the collection of large datasets from the additive manufacturing (AM) process. Such datasets could be used with machine learning techniques to improve the quality of AM. This paper examines two scenarios: first, using convolutional neural networks (CNNs) to accura...

Deep Learning models such as Convolutional Neural Networks (CNNs) are particularly successful in computer vision tasks. They have proven to be tremendously effective and popular in the last decade, reaching great accuracy in tasks such as image classification and object recognition. Despite their success, it is well known that conveying what the mo...

Advanced data augmentation techniques have demonstrated great success in deep learning algorithms. Among these techniques, single-image-based data augmentation (SIBDA), in which a single image’s regions are randomly erased in different ways, has shown promising results. However, randomly erasing image regions in SIBDA can cause a loss of the key di...

Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, where network performance is bad on unseen data as compared to training data. Consequently, it limits performance improvement. To cope with this problem, various techniques h...

Recent developments in in-situ monitoring and process control in Additive Manufacturing (AM), also known as 3D-printing, allows the collection of large amounts of emission data during the build process of the parts being manufactured. This data can be used as input into 3D and 2D representations of the 3D-printed parts. However the analysis and use...

Deep Learning models such as Convolutional Neural Networks (CNNs) are particularly successful in computer vision tasks. They have proven to be tremendously effective and popular in the last decade, reaching great accuracy in tasks such as image classification and object recognition. Despite their success, it is well known that conveying what the mo...

Over the past few years, Explainable Artificial Intelligence (XAI) has grown significantly. This is a result of the growing use of machine learning, especially deep learning, which has produced models that are very accurate but difficult to understand and interpret. The main goal of XAI is to provide an effective approach that is simpler for humans...

Explainable Artificial Intelligence (XAI) has recently become an active research field due to the need for transparency and accountability when deploying AI models for high-stake decision making. In Computer Vision, despite state-of-the-art Convolutional Neural Networks (CNNs) have achieved great performance, understanding their decision processes,...

This article explores the rapidly advancing innovation to endow robots with social intelligence capabilities in the form of multilingual and multimodal emotion recognition, and emotion-aware decision-making capabilities, for contextually appropriate robot behaviours and cooperative social human–robot interaction for the healthcare domain. The objec...

The popularity of deep learning has increased tremendously in recent years due to its ability to efficiently solve complex tasks in challenging areas such as computer vision and language processing. Despite this success, low-level neural activity reproduced by Deep Neural Networks (DNNs) generates extremely rich representations of the data. These r...

In recent years we have seen significant advances in the technology used to both publish and consume Linked Data. However, in order to support the next generation of ebusiness applications on top of interlinked machine readable data suitable forms of access control need to be put in place. Although a number of access control models and frameworks h...

Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Contributions are solicited in all areas of logic programming and related areas, including but not restricted to: - Foundations: Semantics, Formalisms, Answer-Set Programming, Non-monotonic Reasoning, Kno...

Enterprise Communication Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things (IoT) can play a crucial role in this process, but is far from being seamlessly integrated into modern online communications. In this paper, we present a...

Data stream applications are becoming increasingly popular on the web. In these applications, one query pattern is especially prominent: a join between a continuous data stream and some background data (BGD). Oftentimes, the target BGD is large, maintained externally, changing slowly, and costly to query (both in terms of time and money). Hence, pr...

The proliferation of sensor devices and services along with the advances in event processing brings many new opportunities as well as challenges. It is now possible to provide, analyze and react upon real-time, complex events in urban environments. When existing event services do not provide such complex events directly, an event service compositio...

In recent years we have seen significant advances in the technology used to both publish and consume structured data using the existing web infrastructure, commonly referred to as the Linked Data Web. However, in order to support the next generation of e-business applications on top of Linked Data suitable forms of access control need to be put in...

Enterprise Communication Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things (IoT) can play a crucial role in this process, but is far from being seamlessly integrated into modern online communications. In this paper, we present a...

Smart City applications often use event processing techniques to detect coarse-grained events and situations from fine-grained events of the physical and social world. They operate in dynamic environments in which the properties of underlying resources and streams need to be constantly updated according to changes and events in the real world (e.g....

Location based services and Geospatial web applications have become popular in recent years due to wide adoption of mobile devices. Search and recommendation of places or Points of Interests (PoIs) are prominent services available on them. The effectiveness of these services crucially depends on the availability of tags that are descriptive of plac...

Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they...

Enterprise Collaboration Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things can play a crucial role in this process, but is far from being seamlessly integrated in modern online communications. In this paper, we showcase the use o...

With the growing popularity of Internet of Things (IoT) and IoT-enabled smart city applications, RDF stream processing (RSP) is gaining increasing attention in the Semantic Web community. As a result, several RSP engines have emerged, which are capable of processing semantically annotated data streams on the fly. Performance, correctness and techni...

We describe a prototypical software framework for probabilistic inductive logic programming which supports the seamless combination of nonmonotonic reasoning, probabilistic inference and parameter learning. While building upon existing as well as new approaches to probabilistic Answer Set Programming, our framework distinguishes itself from related...

We present a probabilistic inductive logic programming framework which integrates non-monotonic reasoning, probabilistic inference and param-eter learning. In contrast to traditional approaches to probabilistic Answer Set Programming (ASP), our framework imposes only comparatively little restric-tions on probabilistic logic programs - in particular...

Advances in the Internet of Things and the Web of Data created huge opportunities for developing applications that can generate actionable knowledge out of streaming data. The trade-off between scalability and expressivity is a key challenge in this setting, and more investigation is required to identify what are the relevant features in optimizing...

A fast growing torrent of data is being created by companies, social networks, mobile phones, smart homes, public transport vehicles, healthcare devices, and other modern infrastructures. Being able to unlock the potential hidden in this torrent of data would open unprecedented opportunities to improve our daily lives that were not possible before....

To perform complex tasks, RDF Stream Processing Web applications evaluate continuous queries over streams and quasi-static (background) data. While the former are pushed in the application, the latter are continuously retrieved from the sources. As soon as the background data increase the volume and become distributed over the Web, the cost to retr...

In Web stream processing, there are queries that integrate Web data of various velocity, categorized broadly as streaming (i.e., fast changing) and background (i.e., slow changing) data. The introduction of local views on the background data speeds up the query answering process, but requires maintenance processes to keep the replicated data up-to-...

In Web stream processing, there are queries that integrate Web data of various velocity, categorized broadly as streaming (i.e., fast changing) and background (i.e., slow changing) data. The introduction of local views on the background data speeds up the query answering process, but requires maintenance processes to keep the replicated data up-to-...

In recent years, research in contextual knowledge representation and reasoning became more relevant in the areas of Semantic Web, Linked Open Data, and Ambient Intelligence, where knowledge is not considered a monolithic and static asset, but it is distributed in a network of interconnected heterogeneous and evolving knowledge resources. The challe...

ARCOE-Logic 2014, the 6th International Workshop on Acquisition, Representation and Reasoning about Context with Logic, was held in co-location with the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014) on November 25, 2014 in Link\"oping, Sweden. These notes contain the five papers which were accepted and...

Lifelogging is the digital recording of our everyday behaviour in order to identify human activities and build applications that support daily life. Lifelogs represent a unique form of personal multimedia content in that they are temporal, synchronised, multi-modal and composed of multiple media. Analysing lifelogs with a view to supporting content...

The proliferation of sensor devices and services along with the advances in event processing brings many new opportunities as well as challenges. It is now possible to provide, analyze and react upon real- time, complex events about physical or social environments. When exist- ing event services do not provide such complex events directly, an event...

Due to the decentralised and autonomous architecture of the Web of Data, data replication and local deployment of SPARQL endpoints is inevitable. Nowadays, it is common to have multiple copies of the same dataset accessible by various SPARQL endpoints, thus leading to the problem of selecting optimal data source for a user query based on data prope...

With the growing popularity of Internet of Things (IoT) technologies and sensors deployment, more and more cities are leaning towards the initiative of smart cities. Smart city applications are mostly developed with aims to solve domain-specific problems. Hence, lacking the ability to automatically discover and integrate heterogeneous sensor data s...

We propose a framework for reasoning about dynamic Web data, based on probabilistic Answer Set Programming (ASP). Our approach, which is proto-typically implemented, allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities, and for learning of such weights from ex-amples (parameter estimation). Knowledge a...

We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in term...

The tables embedded in Wikipedia articles contain rich, semi-structured encyclopaedic content. However, the cumulative content of these tables cannot be queried against. We thus propose methods to recover the semantics of Wikipedia tables and, in particular, to extract facts from them in the form of RDF triples. Our core method uses an existing Lin...

When it comes to publishing data on the web, the level of access control required (if any) is highly dependent on the type of content exposed. Up until now RDF data publishers have focused on exposing and linking public data. With the advent of SPARQL 1.1, the linked data infrastructure can be used, not only as a means of publishing open data but a...

Presence based notification systems play a pivotal role in any collaborative working environment by providing near real time information about the status, locality and presence of the collaborators. Instant Messaging (IM) tools provide a simple low cost solution to support communication and collaboration in the working environment. With the wide ad...

Stream reasoning is an emerging research field focused on dynamic processing and continuous reasoning over huge volumes of streaming data. Finding the right trade-off between scalability and expressivity is a key challenge in this area. In this paper, we want to provide a baseline for exploring the applicability of complex reasoning to the Web of D...

In this paper we examine how Discretionary Access Control principles, that have been successfully applied to relational and XML data, can be applied to the Resource Description Framework (RDF) graph data model. The objective being to provide a baseline for the specification of a general authorisation framework for the RDF data model. Towards this e...

XSPARQL is a query language which facilitates query, integration and transformation between XML and RDF data formats. Although XSPARQL supports semantic data integration by providing uniform access over XML and RDF, but it requires users to be familiar with both of its underlying query languages (e.g XQuery and SPARQL). In this system demo, we show...

XSPARQL is a transformation and querying language that provides an integrated access over heterogeneous data sources on the fly. It is an extension of XQuery which supports a subset of SPARQL and SQL to provide unified access over XML, RDF and RDB formats. In practical applications, data integration does not only require the integrated access over...

Tables are widely used in Wikipedia articles to display relational information --- they are inherently concise and information rich. However, aside from info-boxe s, there are no automatic methods to exploit the integrated content of these tables. We thus present DRETa: a tool that uses DBpedia as a reference knowledge-base to extract RDF triples f...

We are currently investigating methods to triplify the content of Wikipedia's tables. We propose that existing knowledge-bases can be leveraged to semi-automatically extract high-quality facts (in the form of RDF triples) from tables embedded in Wikipedia articles (henceforth called \Wikitables"). We present a survey of Wikitables and their content...

When it comes to publishing data on the web, the level of access control required (if any) is highly dependent on the type of content exposed. Up until now RDF data publishers have focused on exposing and linking public data. With the advent of SPARQL 1.1, the linked data infrastructure can be used, not only as a means of publishing open data but a...

The explosion of digital content and the heterogeneity of enterprise content sources have pushed existing data integration solutions to their boundaries. Although RDF can be used as a representation format for integrated data, enterprises have been slow to adopt this technology. One of the primary inhibitors to its widespread adoption in industry i...

The Resource Description Framework (RDF) is an interoperable data representation format suitable for interchange and integration of data, especially in Open Data contexts. However, RDF is also becoming increasingly attractive in scenarios involving sensitive data, where data protection is a major concern. At its core, RDF does not support any form...

In recent years there has been growing interest in solutions for the delivery of clinical care for the elderly, due to the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden fo...

The Resource Description Framework (RDF) is an interoperable data representation format suitable for interchange and integration of data, especially in Open Data contexts. However, RDF is also becoming increasingly attractive in scenarios involving sensitive data, where data protection is a major concern. At its core, RDF does not support any form...

The explosion of digital content and the heterogeneity of enterprise content sources have pushed existing data integration solutions to their boundaries. Although RDF can be used as a representation format for integrated data, enterprises have been slow to adopt this technology. One of the primary inhibitors to its widespread adoption in industry i...

In pervasive environments, presence-based application development via Presence Management Systems (PMSs) is a key factor to optimise the management of communication channels, driving productivity increase. Solutions for presence management should satisfy the interoperability requirements, in turn providing context-centric presence analysis and priv...

Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in...

In this paper we propose an innovative approach to the problem of indoor position estimation that aims at extending tracking to a new level of “awareness” bringing to bear new ambient data and opening the possibility of “reasoning” not only on simple positioning but also on the situation at hand. In order to validate the approach, we implemented a...

This paper describes an intelligent home healthcare system characterized by a wireless sensor network (WSN) and a reasoning component. The aim of the system is to allow constant and unobtrusive monitoring of a patient in order to enhance autonomy and increase quality of life. Data collected by the sensor network are used to support a reasoning comp...

Movement recognition constitutes a central task in home-based assisted living environments and in many application domains where activity recognition is crucial. Solutions in these application areas often rely on an heterogeneous collection of body-sensors whose diver-sity and lack of precision has to be compensated by advanced techniques for featu...

In recent years there has been growing interest in solutions for the delivery of clinical care for the elderly, due to the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden fo...

This paper tackles the problem of supporting independent living and well-being for people that live in their homes and have no critical chronic condition. The paper assumes the presence of a monitoring system equipped with a pervasive sensor network and a non-monotonic reasoning engine. The rich set of sensors that can be used for monitoring in hom...

In recent years there has been growing interest in solutions for the delivery of clinical care for the elderly, due to the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but this activity must not be too invasive and a burden for clinicians. We prototyped...

This paper deals with the problems of using pervasive wireless sensor networks to monitor people that live in their homes and have no critical chronic condition. The rich set of sensors that can be used and their sheer number make it quite complex to interpret the data: in the paper we argue that it is possible and very useful to add a reasoning co...

This paper tackles the problem of supporting independent living and well-being for people that live in their homes and have no critical chronic condition. The paper assumes the presence of a monitoring system equipped with a pervasive sensor network and a nonmonotonic reasoning engine. The rich set of sensors that can be used for monitoring in home...

Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but this activity must not be too much invasive and a burden for clinicians. For this reason we prototyped a system called SINDI (Secure and INDependent lIving), focused on i) collecting a limited amount of data about the person and the environm...

The large increase in aging population implies that automatic home monitoring will represent a major challenge for the near future. In this paper we describe an "intelligent" home environment in which modern Wireless Sensor Networks (WSN) technologies allow constant monitoring of a patient in a context-aware setting. Data collected and manipulated...

We consider advanced policy description specifications in the context of Answer Set Programming (ASP). Motivated by our applica- tion scenario, we further extend an existing policy description language, so that it allows for expressing preferences among sets of objects. This is done by extending the concept of ordered disjunctions to cardinality co...

This paper proposes a framework based on modern tools and technologies to enable home healthcare through the observation of i) patient's clinical details by means of a Wearable Acquisition Device, ii) movements detected by sensors networks and iii) habits/actions inferred by an ASP logic program.