89 research outputs found
Smart City Development with Urban Transfer Learning
Nowadays, the smart city development levels of different cities are still
unbalanced. For a large number of cities which just started development, the
governments will face a critical cold-start problem: 'how to develop a new
smart city service with limited data?'. To address this problem, transfer
learning can be leveraged to accelerate the smart city development, which we
term the urban transfer learning paradigm. This article investigates the common
process of urban transfer learning, aiming to provide city planners and
relevant practitioners with guidelines on how to apply this novel learning
paradigm. Our guidelines include common transfer strategies to take, general
steps to follow, and case studies in public safety, transportation management,
etc. We also summarize a few research opportunities and expect this article can
attract more researchers to study urban transfer learning
Grand challenges for the spatial information community
The spatial information (SI) community has an opportunity to address major societal and scientific problems including public health, climate change, air pollution, transportation, and others. Beyond the significant contributions made by the SI community, more can be done by focusing the efforts of the community, and generalizing them. Focus can be achieved by an IMAGENET-like spatial information database and competition. Generalization can be achieved by solving spatio-temporal information problems in disciplines such as neuroscience, chemistry, biology, astronomy, and engineering
Exploring Context Generalizability in Citywide Crowd Mobility Prediction: An Analytic Framework and Benchmark
Contextual features are important data sources for building citywide crowd
mobility prediction models. However, the difficulty of applying context lies in
the unknown generalizability of contextual features (e.g., weather, holiday,
and points of interests) and context modeling techniques across different
scenarios. In this paper, we present a unified analytic framework and a
large-scale benchmark for evaluating context generalizability. The benchmark
includes crowd mobility data, contextual data, and advanced prediction models.
We conduct comprehensive experiments in several crowd mobility prediction tasks
such as bike flow, metro passenger flow, and electric vehicle charging demand.
Our results reveal several important observations: (1) Using more contextual
features may not always result in better prediction with existing context
modeling techniques; in particular, the combination of holiday and temporal
position can provide more generalizable beneficial information than other
contextual feature combinations. (2) In context modeling techniques, using a
gated unit to incorporate raw contextual features into the deep prediction
model has good generalizability. Besides, we offer several suggestions about
incorporating contextual factors for building crowd mobility prediction
applications. From our findings, we call for future research efforts devoted to
developing new context modeling solutions
A Unified Knowledge Graph Service for Developing Domain Language Models in AI Software
Natural Language Processing (NLP) is one of the core techniques in AI
software. As AI is being applied to more and more domains, how to efficiently
develop high-quality domain-specific language models becomes a critical
question in AI software engineering. Existing domain-specific language model
development processes mostly focus on learning a domain-specific pre-trained
language model (PLM); when training the domain task-specific language model
based on PLM, only a direct (and often unsatisfactory) fine-tuning strategy is
adopted commonly. By enhancing the task-specific training procedure with domain
knowledge graphs, we propose KnowledgeDA, a unified and low-code domain
language model development service. Given domain-specific task texts input by a
user, KnowledgeDA can automatically generate a domain-specific language model
following three steps: (i) localize domain knowledge entities in texts via an
embedding-similarity approach; (ii) generate augmented samples by retrieving
replaceable domain entity pairs from two views of both knowledge graph and
training data; (iii) select high-quality augmented samples for fine-tuning via
confidence-based assessment. We implement a prototype of KnowledgeDA to learn
language models for two domains, healthcare and software development.
Experiments on five domain-specific NLP tasks verify the effectiveness and
generalizability of KnowledgeDA. (Code is publicly available at
https://github.com/RuiqingDing/KnowledgeDA.)Comment: 12 page
- …