588 research outputs found

    Improving Cross-Lingual Transfer Learning for Event Detection

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    The widespread adoption of applications powered by Artificial Intelligence (AI) backbones has unquestionably changed the way we interact with the world around us. Applications such as automated personal assistants, automatic question answering, and machine-based translation systems have become mainstays of modern culture thanks to the recent considerable advances in Natural Language Processing (NLP) research. Nonetheless, with over 7000 spoken languages in the world, there still remain a considerable number of marginalized communities that are unable to benefit from these technological advancements largely due to the language they speak. Cross-Lingual Learning (CLL) looks to address this issue by transferring the knowledge acquired from a popular, high-resource source language (e.g., English, Chinese, or Spanish) to a less favored, lower-resourced target language (e.g., Urdu or Swahili). This dissertation leverages the Event Detection (ED) sub-task of Information Extraction (IE) as a testbed and presents three novel approaches that improve cross-lingual transfer learning from distinct perspectives: (1) direct knowledge transfer, (2) hybrid knowledge transfer, and (3) few-shot learning

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR

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    This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%. This is not via complicated design though, but by addressing two critical issues facing the community (i) the gold standard triplet loss does not enforce holistic latent space geometry, and (ii) there are never enough sketches to train a high accuracy model. For the former, we propose a simple modification to the standard triplet loss, that explicitly enforces separation amongst photos/sketch instances. For the latter, we put forward a novel knowledge distillation module can leverage photo data for model training. Both modules are then plugged into a novel plug-n-playable training paradigm that allows for more stable training. More specifically, for (i) we employ an intra-modal triplet loss amongst sketches to bring sketches of the same instance closer from others, and one more amongst photos to push away different photo instances while bringing closer a structurally augmented version of the same photo (offering a gain of ~4-6%). To tackle (ii), we first pre-train a teacher on the large set of unlabelled photos over the aforementioned intra-modal photo triplet loss. Then we distill the contextual similarity present amongst the instances in the teacher's embedding space to that in the student's embedding space, by matching the distribution over inter-feature distances of respective samples in both embedding spaces (delivering a further gain of ~4-5%). Apart from outperforming prior arts significantly, our model also yields satisfactory results on generalising to new classes. Project page: https://aneeshan95.github.io/Sketch_PVT/Comment: Accepted in CVPR 2023. Project page available at https://aneeshan95.github.io/Sketch_PVT

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    Information Retrieval: Recent Advances and Beyond

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    In this paper, we provide a detailed overview of the models used for information retrieval in the first and second stages of the typical processing chain. We discuss the current state-of-the-art models, including methods based on terms, semantic retrieval, and neural. Additionally, we delve into the key topics related to the learning process of these models. This way, this survey offers a comprehensive understanding of the field and is of interest for for researchers and practitioners entering/working in the information retrieval domain

    Radio frequency communication and fault detection for railway signalling

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    The continuous and swift progression of both wireless and wired communication technologies in today's world owes its success to the foundational systems established earlier. These systems serve as the building blocks that enable the enhancement of services to cater to evolving requirements. Studying the vulnerabilities of previously designed systems and their current usage leads to the development of new communication technologies replacing the old ones such as GSM-R in the railway field. The current industrial research has a specific focus on finding an appropriate telecommunication solution for railway communications that will replace the GSM-R standard which will be switched off in the next years. Various standardization organizations are currently exploring and designing a radiofrequency technology based standard solution to serve railway communications in the form of FRMCS (Future Railway Mobile Communication System) to substitute the current GSM-R. Bearing on this topic, the primary strategic objective of the research is to assess the feasibility to leverage on the current public network technologies such as LTE to cater to mission and safety critical communication for low density lines. The research aims to identify the constraints, define a service level agreement with telecom operators, and establish the necessary implementations to make the system as reliable as possible over an open and public network, while considering safety and cybersecurity aspects. The LTE infrastructure would be utilized to transmit the vital data for the communication of a railway system and to gather and transmit all the field measurements to the control room for maintenance purposes. Given the significance of maintenance activities in the railway sector, the ongoing research includes the implementation of a machine learning algorithm to detect railway equipment faults, reducing time and human analysis errors due to the large volume of measurements from the field

    Symmetric Contrastive Learning On Programming Languages

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    Contrastive pre-training has been shown to learn good features by finding the inner difference and similar latent traits among the samples. The pairwise data programming languages and natural language also have strong inner-relationship that can be used on the downstream tasks. Pre-trained models for Natural Languages have been recently shown to transfer well to Programming Languages (PL) and primarily benefit different intelligence code-related tasks, such as code search, clone detection, programming translation and code document generation. However, existing pre-trained methods for programming languages are mainly conducted by masked language modelling. This restricted form limits their performance and transferability since PL and NL have different syntax rules. Here we introduce C3P, a Contrastive Code-Comment Pre-training approach, to solve various downstream tasks by pre-training the multi-representation features on both programming and natural syntax. The model encodes the code syntax and natural language description (comment) by two encoders and the encoded embeddings are projected into a multi-modal space for learning the latent representation. In the latent space, C3P jointly trains the code and comment encoders by the symmetric loss function, which aims to maximize the cosine similarity of the correct code-comment pairs while minimizing the similarity of unrelated pairs. We verify the empirical performance of the proposed pre-trained models on multiple downstream code-related tasks. The comprehensive experiments demonstrate that C3P outperforms previous work on the understanding tasks of code search and code clone, as well as the generation tasks of programming translation and document generation. Furthermore, we validate the transferability of C3P to the new programming language. The results show our model surpasses all supervised methods and in some programming language cases even outperforms prior pre-trained approaches

    Towards a Peaceful Development of Cyberspace - Challenges and Technical Measures for the De-escalation of State-led Cyberconflicts and Arms Control of Cyberweapons

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    Cyberspace, already a few decades old, has become a matter of course for most of us, part of our everyday life. At the same time, this space and the global infrastructure behind it are essential for our civilizations, the economy and administration, and thus an essential expression and lifeline of a globalized world. However, these developments also create vulnerabilities and thus, cyberspace is increasingly developing into an intelligence and military operational area – for the defense and security of states but also as a component of offensive military planning, visible in the creation of military cyber-departments and the integration of cyberspace into states' security and defense strategies. In order to contain and regulate the conflict and escalation potential of technology used by military forces, over the last decades, a complex tool set of transparency, de-escalation and arms control measures has been developed and proof-tested. Unfortunately, many of these established measures do not work for cyberspace due to its specific technical characteristics. Even more, the concept of what constitutes a weapon – an essential requirement for regulation – starts to blur for this domain. Against this background, this thesis aims to answer how measures for the de-escalation of state-led conflicts in cyberspace and arms control of cyberweapons can be developed. In order to answer this question, the dissertation takes a specifically technical perspective on these problems and the underlying political challenges of state behavior and international humanitarian law in cyberspace to identify starting points for technical measures of transparency, arms control and verification. Based on this approach of adopting already existing technical measures from other fields of computer science, the thesis will provide proof of concepts approaches for some mentioned challenges like a classification system for cyberweapons that is based on technical measurable features, an approach for the mutual reduction of vulnerability stockpiles and an approach to plausibly assure the non-involvement in a cyberconflict as a measure for de-escalation. All these initial approaches and the questions of how and by which measures arms control and conflict reduction can work for cyberspace are still quite new and subject to not too many debates. Indeed, the approach of deliberately self-restricting the capabilities of technology in order to serve a bigger goal, like the reduction of its destructive usage, is yet not very common for the engineering thinking of computer science. Therefore, this dissertation also aims to provide some impulses regarding the responsibility and creative options of computer science with a view to the peaceful development and use of cyberspace

    Estrategias de visión por computador para la estimación de pose en el contexto de aplicaciones robóticas industriales: avances en el uso de modelos tanto clásicos como de Deep Learning en imágenes 2D

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    184 p.La visión por computador es una tecnología habilitadora que permite a los robots y sistemas autónomos percibir su entorno. Dentro del contexto de la industria 4.0 y 5.0, la visión por ordenador es esencial para la automatización de procesos industriales. Entre las técnicas de visión por computador, la detección de objetos y la estimación de la pose 6D son dos de las más importantes para la automatización de procesos industriales. Para dar respuesta a estos retos, existen dos enfoques principales: los métodos clásicos y los métodos de aprendizaje profundo. Los métodos clásicos son robustos y precisos, pero requieren de una gran cantidad de conocimiento experto para su desarrollo. Por otro lado, los métodos de aprendizaje profundo son fáciles de desarrollar, pero requieren de una gran cantidad de datos para su entrenamiento.En la presente memoria de tesis se presenta una revisión de la literatura sobre técnicas de visión por computador para la detección de objetos y la estimación de la pose 6D. Además se ha dado respuesta a los siguientes retos: (1) estimación de pose mediante técnicas de visión clásicas, (2) transferencia de aprendizaje de modelos 2D a 3D, (3) la utilización de datos sintéticos para entrenar modelos de aprendizaje profundo y (4) la combinación de técnicas clásicas y de aprendizaje profundo. Para ello, se han realizado contribuciones en revistas de alto impacto que dan respuesta a los anteriores retos
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