375 research outputs found
Towards Integration of IndoorGML and GDF for Robot Navigation in Warehouses
With the development of the navigation technology, the outdoor navigation has made great progress, whereas the indoor navigation has some areas which is underdeveloped, insufficient to meet the rapidly increasing demands of people as well as the robotics. Even though, the advance in indoor navigation technology still has really brought a wide range of applications and a broad market, for instance, the flourishing intelligent warehouse system utilizes multi-robot operation which have the certain requirement for an accurate indoor navigation system. As for the indoor navigation, the OGC standard IndoorGML has been released and undergoing revision constantly. While the document really provides more advantageous support for the applications of Indoor Location-Based Services (LBS), in some aspects, especially the door-to-door navigation and the warehouse environment, it is not sufficiently adaptable, with still some room for improvement. IndoorGML is powerful for the common indoor scenarios like malls and offices, while as for carefully-arranged warehouse environment and other large-scale operation scenarios with multi-robots that is more similar to an ordered system, it is obviously insufficient. In this paper, we discuss about the potential to combination of IndoorGML and ITS standard ISO 20524 (GDF5.1), and extend the OGC standard indoorGML. We analyze the definition as well as function of related concepts, making some comparisons between these two standards. We conclude that these two standards are well-matched with vital potential to merge and unify the indoor and outdoor systems for spatial information
Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge
Query-Focused Meeting Summarization (QFMS) aims to generate a summary of a
given meeting transcript conditioned upon a query. The main challenges for QFMS
are the long input text length and sparse query-relevant information in the
meeting transcript. In this paper, we propose a knowledge-enhanced two-stage
framework called Knowledge-Aware Summarizer (KAS) to tackle the challenges. In
the first stage, we introduce knowledge-aware scores to improve the
query-relevant segment extraction. In the second stage, we incorporate
query-relevant knowledge in the summary generation. Experimental results on the
QMSum dataset show that our approach achieves state-of-the-art performance.
Further analysis proves the competency of our methods in generating relevant
and faithful summaries.Comment: AACL 2023 Finding
A meta learning scheme for fast accent domain expansion in Mandarin speech recognition
Spoken languages show significant variation across mandarin and accent.
Despite the high performance of mandarin automatic speech recognition (ASR),
accent ASR is still a challenge task. In this paper, we introduce meta-learning
techniques for fast accent domain expansion in mandarin speech recognition,
which expands the field of accents without deteriorating the performance of
mandarin ASR. Meta-learning or learn-to-learn can learn general relation in
multi domains not only for over-fitting a specific domain. So we select
meta-learning in the domain expansion task. This more essential learning will
cause improved performance on accent domain extension tasks. We combine the
methods of meta learning and freeze of model parameters, which makes the
recognition performance more stable in different cases and the training faster
about 20%. Our approach significantly outperforms other methods about 3%
relatively in the accent domain expansion task. Compared to the baseline model,
it improves relatively 37% under the condition that the mandarin test set
remains unchanged. In addition, it also proved this method to be effective on a
large amount of data with a relative performance improvement of 4% on the
accent test set
Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation
Self-supervised sequential recommendation significantly improves
recommendation performance by maximizing mutual information with well-designed
data augmentations. However, the mutual information estimation is based on the
calculation of Kullback Leibler divergence with several limitations, including
asymmetrical estimation, the exponential need of the sample size, and training
instability. Also, existing data augmentations are mostly stochastic and can
potentially break sequential correlations with random modifications. These two
issues motivate us to investigate an alternative robust mutual information
measurement capable of modeling uncertainty and alleviating KL divergence
limitations. To this end, we propose a novel self-supervised learning framework
based on Mutual WasserStein discrepancy minimization MStein for the sequential
recommendation. We propose the Wasserstein Discrepancy Measurement to measure
the mutual information between augmented sequences. Wasserstein Discrepancy
Measurement builds upon the 2-Wasserstein distance, which is more robust, more
efficient in small batch sizes, and able to model the uncertainty of stochastic
augmentation processes. We also propose a novel contrastive learning loss based
on Wasserstein Discrepancy Measurement. Extensive experiments on four benchmark
datasets demonstrate the effectiveness of MStein over baselines. More
quantitative analyses show the robustness against perturbations and training
efficiency in batch size. Finally, improvements analysis indicates better
representations of popular users or items with significant uncertainty. The
source code is at https://github.com/zfan20/MStein.Comment: Updated with the correction of the asymmetric mistake on the mutual
information connectio
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