813 research outputs found
Application of Learning Strategies to Culture-Based Language Instruction
Learning strategy is one of the most important factors that determine the learning result. So, teaching learners to grasp certain kinds of strategies is a key factor which can promote the learning efficiency. This thesis discusses the learning strategies in the theoretical and pedagogical aspects, illustrates the significance of culture-based language instruction in second language teaching, and elaborates three ways to help students use appropriate strategies in their culture-based language learning
Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition
Self-supervised learning (SSL) based speech pre-training has attracted much
attention for its capability of extracting rich representations learned from
massive unlabeled data. On the other hand, the use of weakly-supervised data is
less explored for speech pre-training. To fill this gap, we propose a
weakly-supervised speech pre-training method based on speaker-aware speech
data. It adopts a similar training procedure to the widely-used masked speech
prediction based SSL framework, while incorporating additional target-speaker
enrollment information as an auxiliary input. In this way, the learned
representation is steered towards the target speaker even in the presence of
highly overlapping interference, allowing potential applications to tasks such
as target speech recognition. Our experiments on Libri2Mix and WSJ0-2mix
datasets show that the proposed model achieves significantly better ASR
performance compared to WavLM, the state-of-the-art SSL model with denoising
capability.Comment: Accepted by Interspeech; 5 pages, 1 figure, 3 table
Chindia: Collaboration, Compromising, or Competition?
As two fast-growing economies with huge populations, China and India have become formidable forces in the world today. How do these two Asian giants see each other? Are they economic partners, political equals, and friendly neighbors? Or are they economic competitors, political rivals, and territorial enemies when it comes to border conflicts? This research attempts to answer some of the above questions
Language-Assisted 3D Scene Understanding
The scale and quality of point cloud datasets constrain the advancement of
point cloud learning. Recently, with the development of multi-modal learning,
the incorporation of domain-agnostic prior knowledge from other modalities,
such as images and text, to assist in point cloud feature learning has been
considered a promising avenue. Existing methods have demonstrated the
effectiveness of multi-modal contrastive training and feature distillation on
point clouds. However, challenges remain, including the requirement for paired
triplet data, redundancy and ambiguity in supervised features, and the
disruption of the original priors. In this paper, we propose a
language-assisted approach to point cloud feature learning (LAST-PCL),
enriching semantic concepts through LLMs-based text enrichment. We achieve
de-redundancy and feature dimensionality reduction without compromising textual
priors by statistical-based and training-free significant feature selection.
Furthermore, we also delve into an in-depth analysis of the impact of text
contrastive training on the point cloud. Extensive experiments validate that
the proposed method learns semantically meaningful point cloud features and
achieves state-of-the-art or comparable performance in 3D semantic
segmentation, 3D object detection, and 3D scene classification tasks.Comment: Technical report, unpublished, 16 page
Chinese Perspectives on India and Indian People
As the two most populous countries with a growing economy, China and India have become formidable forces on the world stage. The relationship between the two countries are more than complex. How Chinese people see India and Indian people has become a significant topic to study. This poster is designed to study the Sino-Indian relations with a focus on Chinese public opinions on India and Indian people
CoLight: Learning Network-level Cooperation for Traffic Signal Control
Cooperation among the traffic signals enables vehicles to move through
intersections more quickly. Conventional transportation approaches implement
cooperation by pre-calculating the offsets between two intersections. Such
pre-calculated offsets are not suitable for dynamic traffic environments. To
enable cooperation of traffic signals, in this paper, we propose a model,
CoLight, which uses graph attentional networks to facilitate communication.
Specifically, for a target intersection in a network, CoLight can not only
incorporate the temporal and spatial influences of neighboring intersections to
the target intersection, but also build up index-free modeling of neighboring
intersections. To the best of our knowledge, we are the first to use graph
attentional networks in the setting of reinforcement learning for traffic
signal control and to conduct experiments on the large-scale road network with
hundreds of traffic signals. In experiments, we demonstrate that by learning
the communication, the proposed model can achieve superior performance against
the state-of-the-art methods.Comment: 10 pages. Proceedings of the 28th ACM International on Conference on
Information and Knowledge Management. ACM, 201
EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding
3D visual grounding aims to find the object within point clouds mentioned by
free-form natural language descriptions with rich semantic cues. However,
existing methods either extract the sentence-level features coupling all words
or focus more on object names, which would lose the word-level information or
neglect other attributes. To alleviate these issues, we present EDA that
Explicitly Decouples the textual attributes in a sentence and conducts Dense
Alignment between such fine-grained language and point cloud objects.
Specifically, we first propose a text decoupling module to produce textual
features for every semantic component. Then, we design two losses to supervise
the dense matching between two modalities: position alignment loss and semantic
alignment loss. On top of that, we further introduce a new visual grounding
task, locating objects without object names, which can thoroughly evaluate the
model's dense alignment capacity. Through experiments, we achieve
state-of-the-art performance on two widely-adopted 3D visual grounding
datasets, ScanRefer and SR3D/NR3D, and obtain absolute leadership on our
newly-proposed task. The source code will be available at
https://github.com/yanmin-wu/EDA.Comment: 16 pages with 5 pages of supplementary materia
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking
Federated learning (FL) enables collaborative model training without
centralizing data. However, the traditional FL framework is cloud-based and
suffers from high communication latency. On the other hand, the edge-based FL
framework that relies on an edge server co-located with mobile base station for
model aggregation has low communication latency but suffers from degraded model
accuracy due to the limited coverage of edge server. In light of high accuracy
but high-latency cloud-based FL and low-latency but low-accuracy edge-based FL,
this paper proposes a new FL framework based on cooperative mobile edge
networking called cooperative federated edge learning (CFEL) to enable both
high-accuracy and low-latency distributed intelligence at mobile edge networks.
Considering the unique two-tier network architecture of CFEL, a novel federated
optimization method dubbed cooperative edge-based federated averaging
(CE-FedAvg) is further developed, wherein each edge server both coordinates
collaborative model training among the devices within its own coverage and
cooperates with other edge servers to learn a shared global model through
decentralized consensus. Experimental results based on benchmark datasets show
that CFEL can largely reduce the training time to achieve a target model
accuracy compared with prior FL frameworks.Comment: accepted for publication in IEEE Transactions on Mobile Computin
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