588 research outputs found
Improving Cross-Lingual Transfer Learning for Event Detection
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
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR
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
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
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
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
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
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
184 p.La visión por computador es una tecnologÃa 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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