94 research outputs found
Perturbations of rotating compact objects
No full textEThOS - Electronic Theses Online ServiceGBUnited Kingdo
Pushing the envelope of sentiment analysis beyond words and polarities
No full textIdioms are multi-word expressions which hold a literal and figurative meaning which is conventionally understood by native speakers. Their overall meaning, often, cannot be deduced from the literal meaning of their constituent words. Sentiment analysis, also referred to as opinion mining, aims to automatically extract and classify sentiments, opinions, and emotions expressed in text. The research in this thesis is motivated by the fact that idioms, which often express an affective stance towards an entity or an event, are not featured systematically in sentiment analysis. To estimate the degree to which the inclusion of idioms as features may improve the results of traditional sentiment analysis, we compared our results to two state-of-the-art sentiment analysis approaches. Firstly, we collected a set of idioms that are relevant to sentiment analysis, i.e. those that can be mapped to an emotion. These mappings were obtained using a crowdsourcing approach. Secondly, to evaluate the results of sentiment analysis, we assembled a corpus of sentences in which idioms are used in context. Each sentence was annotated with an emotion, which formed the basis for the gold standard used for the comparison against the baseline methods. The classification performance was improved by almost 20 percentage points.
Given the positive findings from our initial experiments, the main limitation was the significant knowledge-engineering overhead involved in hand-crafting lexico-semantic resources used to support idiom-based features. To minimise the bottleneck associated with the acquisition of such resources, we scaled up our original approach by automating their engineering. Subsequently, these resources were used to replace the manually engineered counterparts of such features in the originally proposed method. The fully automated approach outperformed the two baseline methods by 7 and 9 percentage points. These improvements, however, were poorer in comparison to those achieved in the initial study. Nevertheless, we have demonstrated, not only can idiom-based features be automatically engineered, but they too, improve sentiment classification results, when such features are present.
Taking a long-term view of the research in this thesis, we want to address the limitations of state-of-the-art sentiment analysis approaches by focusing on a full range of emotions, rather than sentiment polarity. However, there is no consensus among researchers on a standardised framework for classifying emotions. Proposing such a framework would be a major contribution to the field of sentiment analysis, as it would stimulate its evolution into fully-fledged emotion classification and allow for systematic comparison of independent studies. With this goal in mind, we investigated the utility of different classification frameworks for sentiment analysis. A comprehensive statistical analysis of our experimental results provided explicit evidence that, in relative terms, six basic emotions are best suited for sentiment analysis. However, we identified the major shortcoming of oversimplifying positive emotions
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Fox, hedgehog, human, machine: a cautious reappraisal of AI and copyright through rights and welfare
No full textThis chapter examines the challenges that generative AI (GenAI) poses for copyright law and argues for a carefully tailored regulatory approach. Placing the human author at the centre, it contends that fully autonomous AI-generated works, which lack meaningful human creative input, should fall outside the scope of copyright protection. At the same time, in support of a more participatory, socially inclusive, and responsive copyright framework, it argues that where human authors provide sufficiently detailed input and exercise creative choices that shape the final output, such contributions may qualify for protection. This analysis maintains that copyright’s adaptation in the age of GenAI is a cultural opportunity to redefine the future of creativity and advance human development. From a social welfare perspective, such an approach promotes democratic access to creativity and upholds the freedom to create, while highlighting essential questions about inclusivity and the evolving role of creativity in a GenAI-driven society
The emotional landscape of technological innovation: a data driven case study of ChatGPT's launch
No full textThe rapid development and deployment of Artificial Intelligence (AI) technologies have sparked intense public interest and debate. While these innovations promise to revolutionise various aspects of human life, it is crucial to understand the complex emotional responses they elicit from potential adopters and users. Such findings can offer crucial guidance for stakeholders involved in the development, implementation, and governance of AI technologies like OpenAI's ChatGPT, a Large Language Model (LLM) that garnered significant attention upon its release, enabling more informed decision-making regarding potential challenges and opportunities. While previous studies have employed data-driven approaches towards investigating public reactions to emerging technologies, they often relied on sentiment polarity analysis, which categorises responses as positive or negative. However, this binary approach fails to capture the nuanced emotional landscape surrounding technological adoption. This paper overcomes this limitation by presenting a comprehensive analysis for investigating the emotional landscape surrounding technology adoption by using the launch of ChatGPT as a case study. In particular, a large corpus of social media texts containing references to ChatGPT was compiled. Text mining techniques were applied to extract emotions, capturing a more nuanced and multifaceted representation of public reactions. This approach allows the identification of specific emotions such as excitement, fear, surprise, and frustration, providing deeper insights into user acceptance, integration, and potential adoption of the technology. By analysing this emotional landscape, we aim to provide a more comprehensive understanding of the factors influencing ChatGPT's reception and potential long-term impact. Furthermore, we employ topic modelling to identify and extract the common themes discussed across the dataset. This additional layer of analysis allows us to understand the specific aspects of ChatGPT driving different emotional responses. By linking emotions to particular topics, we gain a more contextual understanding of public reaction, which can inform decision-making processes in the development, deployment, and regulation of AI technologies
Reconstitution of ion channels from intracellular membranes and bacteria not amenable to conventional electrophysiological techniques
No full textA preliminary description of mood in Welsh
No full textIn this paper we propose a functional account of the Welsh mood system, focussing on responsives in particular. The discourse functions of responsives are interpreted through the concept of negotiation within the systemic functional linguistic framework, which offers a rich model for accounting for both initiations and responses, including possible tracking and challenging moves. By examining the interaction of mood together with specific features of Welsh, e.g. a dominant VSO clause ordering, mood particles, Subject ellipsis and a complex system of negation, we are able to show that Welsh tends to highlight interpersonal meanings in clause initial position. As the first functional description of Welsh, we also set out important directions for future research, based on the findings presented in this paper
Pfam: The protein families database in 2021
No full textThe Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/
Comparing hierarchical approaches to enhance supervised emotive text classification
No full textThe performance of emotive text classification using affective hierarchical schemes (e.g. WordNet-Affect) is often evaluated using the same traditional measures used to evaluate the performance of when a finite set of isolated classes are used. However, applying such measures means the full characteristics and structure of the emotive hierarchical scheme are not considered. Thus, the overall performance of emotive text classification using emotion hierarchical schemes is often inaccurately reported and may lead to ineffective information retrieval and decision making. This paper provides a comparative investigation into how methods used in hierarchical classification problems in other domains, which extend traditional evaluation metrics to consider the characteristics of the hierarchical classification scheme can be applied and subsequently improve the classification of emotive texts. This study investigates the classification performance of three widely used classifiers, Naive Bayes, J48 Decision Tree, and SVM, following the application of the aforementioned methods. The results demonstrated that all methods improved the emotion classification. However, the most notable improvement was recorded when a depth-based method was applied to both the testing and validation data, where the precision, recall, and F1-score were significantly improved by around 70 percentage points for each classifier
Investigating radio frequency vulnerabilities in the Internet of Things (IoT)
No full textWith the increase in the adoption of Internet of Things (IoT) devices, the security threat they face has become more pervasive. Recent research has demonstrated that most IoT devices are insecure and vulnerable to a range of cyber attacks. The impact of such attacks can vary significantly, from affecting the service of the device itself to putting their owners and their personal information at risk. As a response to improving their security, the focus has been on attacks, specifically on the network layer. However, the importance and impact of other vulnerabilities, such as low-level Radio Frequency (RF) attacks, have been neglected. Such attacks are challenging to detect, and they can be deployed using non-expensive equipment and can cause significant damage. This paper explores security vulnerabilities that target RF communications on popular commercial IoT devices such as Wi-Fi, Zigbee, and 433 Mz. Using software-defined radio, a range of attacks were deployed against the devices, including jamming, replay attacks, packet manipulation, protocol reverse engineering, and harmonic frequency attacks. The results demonstrated that all devices used were susceptible to jamming attacks, and in some cases, they were rendered inoperable and required a hard reset to function correctly again. This finding highlights the lack of protection against both intentional and unintentional jamming. In addition, all devices demonstrated that they were susceptible to replay attacks, which highlights the need for more hardened security measures. Finally, this paper discusses proposals for defence mechanisms for enhancing the security of IoT devices against the aforementioned attacks
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