Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey

Machine learning-based wearable human activity recognition (WHAR) models enable the development of various smart and connected community applications such as sleep pattern monitoring, medication reminders, cognitive health assessment, sports analytics, etc. However, the widespread adoption of these WHAR models is impeded by their degraded performance in the presence of data distribution heterogeneities caused by the sensor placement at different body positions, inherent biases and heterogeneities across devices, and personal and environmental diversities. Various traditional machine learning algorithms and transfer learning techniques have been proposed in the literature to address the underpinning challenges of handling such data heterogeneities. Domain adaptation is one such transfer learning techniques that has gained significant popularity in recent literature. In this paper, we survey the recent progress of domain adaptation techniques in the Inertial Measurement Unit (IMU)-based human activity recognition area, discuss potential future directions

Using ICT tools to manage knowledge: a student perspective in determining the quality of education

Within the e-learning context of a university, technology has the potential to facilitate the knowledge interaction between the source (instructor) and the recipient (students). From a literature review, it can be concluded that prior studies have not explored the types of channels that encourage knowledge transfer in this environment. For example, how explicit knowledge travels through the e-learning environment and goes through interaction processes and is received and acquired is largely unknown. According to Alavi & Leidner (2001), Information and Communication Technology (ICT) can help speed up the processes of transferring knowledge from those who have knowledge to those seeking knowledge. Within the university context, technologies such as email, Internet, IRC chat, bulletin boards and tools such as WebCT and BlackBoard have the potential to facilitate the transfer of knowledge and act as a link between source and recipient. Effective knowledge transfer has to consider effective knowledge acquisition, which are therefore inexplicably linked. Nonaka's spiral model addresses knowledge acquisition through spiraling processes in which an individual would be able to convert tacit knowledge to explicit knowledge and vice versa. According to Nonaka & Takeuchi (1995) there are four types of interaction, which give way to the conversion of one form of knowledge into another, namely tacit-to-tacit, tacit-to-explicit, explicit-to-tacit and explicit-to-explicit. In an academic environment, this can be studied as the source, either transferring tacit or explicit knowledge, and similarly as the recipient, receiving knowledge either in tacit or explicit form. Nonaka & Takeuchi (1995) also refer to this as the SECI model, where SECI stands for Socialisation, Externalisation, Combination and Internalisation. This 'Research in Progress' reports the outcomes of a study undertaken to understand how and to what extent knowledge spiraling processes and accompanying characteristics of SECI can be ICT-enabled to contribute towards the studying and learning processes for university education. A survey instrument was developed for this purpose and it is currently undergoing peer-review and other customary validity and reliability tests. Once the instrument is validated, it will be administered on about 50 tertiary students. It is hoped that the results obtained from this survey will be reported in the QIK 2005 conference

Progressive Transfer Learning for Dexterous In-Hand Manipulation with Multi-Fingered Anthropomorphic Hand

Dexterous in-hand manipulation for a multi-fingered anthropomorphic hand is extremely difficult because of the high-dimensional state and action spaces, rich contact patterns between the fingers and objects. Even though deep reinforcement learning has made moderate progress and demonstrated its strong potential for manipulation, it is still faced with certain challenges, such as large-scale data collection and high sample complexity. Especially, for some slight change scenes, it always needs to re-collect vast amounts of data and carry out numerous iterations of fine-tuning. Remarkably, humans can quickly transfer learned manipulation skills to different scenarios with little supervision. Inspired by human flexible transfer learning capability, we propose a novel dexterous in-hand manipulation progressive transfer learning framework (PTL) based on efficiently utilizing the collected trajectories and the source-trained dynamics model. This framework adopts progressive neural networks for dynamics model transfer learning on samples selected by a new samples selection method based on dynamics properties, rewards and scores of the trajectories. Experimental results on contact-rich anthropomorphic hand manipulation tasks show that our method can efficiently and effectively learn in-hand manipulation skills with a few online attempts and adjustment learning under the new scene. Compared to learning from scratch, our method can reduce training time costs by 95%.Comment: 12 pages, 7 figures, submitted to TNNL

Effective and Efficient Transfer Learning in the Era of Large Language Models

Substantial progress has been made in the field of natural language processing (NLP) due to the advent of large language models (LLMs)—deep neural networks with millions or billions of parameters pre-trained on large amounts of unlabeled data. However, these models have common weaknesses, including degenerate performance in data-scarce scenarios, and substantial computational resource requirements. This thesis aims to develop methods to address these limitations for improved applicability and performance of LLMs in resource-constrained settings with limited data and/or computational resources. To address the need for labeled data in data-scarce scenarios, I present two methods, in Chapter 2 and Chapter 3, respectively. The first method leverages beneficial relationships between NLP tasks for transfer learning, while the second method combines data augmentation and self-training to boost few-shot learning performance—the ability to perform novel tasks from only a few labeled examples. Additionally, in Chapter 4, I introduce a novel parameter-efficient transfer learning approach that reuses a single frozen model for all tasks while only learning minimal task-specific parameters (soft/continuous prompts) to represent tasks and transfer knowledge. Our method can match or outperform fine-tuning task-specific models (training the whole model on each task). In Chapter 5, I demonstrate the benefits of parameter-efficient transfer learning in a cross-lingual transfer setting. Finally, I conclude the thesis in Chapter 6 by outlining potential avenues for future research that aim to advance NLP through large-scale multi-task learning using multilingual and multimodal data

Self-Supervised Pretraining and Transfer Learning on fMRI Data with Transformers

Transfer learning is a machine learning technique founded on the idea that knowledge acquired by a model during “pretraining” on a source task can be transferred to the learning of a target task. Successful transfer learning can result in improved performance, faster convergence, and reduced demand for data. This technique is particularly desirable for the task of brain decoding in the domain of functional magnetic resonance imaging (fMRI), wherein even the most modern machine learning methods can struggle to decode labelled features of brain images. This challenge is due to the highly complex underlying signal, physical and neurological differences between brains, low data collection throughput, and other factors. Transfer learning is exciting in its potential to mitigate these challenges, but with this application still in its infancy, we must begin on the ground floor. The goals of this thesis were to design, implement, and evaluate a framework for pretraining and transfer learning on arbitrary fMRI datasets, then demonstrate its performance with respect to the literature, and achieve substantive progress toward generalized pretrained models of the brain. The primary contribution is our novel framework which achieves these goals, called BEAT, which stands for Bi-directional Encoders for Auditory Tasks. The design and implementation of BEAT include adapting state-of-the-art deep learning architectures to sequences of fMRI data, as well as a novel self-supervised pretraining task called Next Thought Prediction and several novel supervised brain decoding tasks. To evaluate BEAT, we pretrained ii on Next Thought Prediction and performed transfer learning to the brain decoding tasks, which are specific to one of three fMRI datasets. To demonstrate significant benefits of transfer learning, BEAT decoded instrumental timbre from one of the fMRI datasets which standard methods failed to decode in addition to improved downstream performance. Toward generalized pretrained models of the brain, BEAT learned Next Thought Prediction on one fMRI dataset, and then successfully transferred that learning to a supervised brain decoding task on an entirely distinct dataset, with different participants and stimuli. To our knowledge this is the first instance of transfer learning across participants and stimuli–a necessity for whole-brain pretrained models