414 research outputs found
Enrichment of raw sensor data to enable high-level queries
Sensor networks are increasingly used across various application domains. Their usage has the advantage of automated, often continuous, monitoring of activities and events. Ubiquitous sensor networks detect location of people and objects and their movement. In our research,
we employ a ubiquitous sensor network to track the movement
of players in a tennis match. By doing so, our goal is to create a detailed analysis of how the match progressed, recording points scored, games and sets, and in doing so, greatly reduce the eort of coaches and players who are required to study matches afterwards. The sensor network
is highly efficient as it eliminates the need for manual recording of the match. However, it generates raw data that is unusable by domain experts as it contains no frame of reference or context and cannot be analyzed or queried. In this work, we present the UbiQuSE system of data transformers which bridges the gap between raw sensor data and the high-level requirements of domain specialists such as the tennis coach
Expanding sensor networks to automate knowledge acquisition
The availability of accurate, low-cost sensors to scientists has resulted in widespread deployment in a variety of sporting and health environments. The sensor data output is often in a raw, proprietary or unstructured format. As a result, it is often difficult to query multiple sensors for complex properties or actions. In our research, we deploy a heterogeneous sensor network to detect the various biological and physiological properties in athletes during training activities. The goal for exercise physiologists is to quickly identify key intervals in exercise such as moments of stress or fatigue. This is not currently possible because of low level sensors and a lack of query language support. Thus, our motivation is to expand the sensor network with a contextual layer that enriches raw sensor data, so that it can be exploited by a high level query language. To achieve this, the domain expert specifies events in a tradiational event-condition-action format to deliver the required contextual enrichment
Characterizing the Landscape of Musical Data on the Web: State of the Art and Challenges
Musical data can be analysed, combined, transformed and exploited for diverse purposes. However, despite the proliferation of digital libraries and repositories for music, infrastructures and tools, such uses of musical data remain scarce. As an initial step to help fill this gap, we present a survey of the landscape of musical data on the Web, available as a Linked Open Dataset: the musoW dataset of catalogued musical resources. We present the dataset and the methodology and criteria for its creation and assessment. We map the identified dimensions and parameters to existing Linked Data vocabularies, present insights gained from SPARQL queries, and identify significant relations between resource features. We present a thematic analysis of the original research questions associated with surveyed resources and identify the extent to which the collected resources are Linked Data-ready
Get Back Here: Robust Imitation by Return-to-Distribution Planning
We consider the Imitation Learning (IL) setup where expert data are not
collected on the actual deployment environment but on a different version. To
address the resulting distribution shift, we combine behavior cloning (BC) with
a planner that is tasked to bring the agent back to states visited by the
expert whenever the agent deviates from the demonstration distribution. The
resulting algorithm, POIR, can be trained offline, and leverages online
interactions to efficiently fine-tune its planner to improve performance over
time. We test POIR on a variety of human-generated manipulation demonstrations
in a realistic robotic manipulation simulator and show robustness of the
learned policy to different initial state distributions and noisy dynamics
BOWLL: A Deceptively Simple Open World Lifelong Learner
The quest to improve scalar performance numbers on predetermined benchmarks
seems to be deeply engraved in deep learning. However, the real world is seldom
carefully curated and applications are seldom limited to excelling on test
sets. A practical system is generally required to recognize novel concepts,
refrain from actively including uninformative data, and retain previously
acquired knowledge throughout its lifetime. Despite these key elements being
rigorously researched individually, the study of their conjunction, open world
lifelong learning, is only a recent trend. To accelerate this multifaceted
field's exploration, we introduce its first monolithic and much-needed
baseline. Leveraging the ubiquitous use of batch normalization across deep
neural networks, we propose a deceptively simple yet highly effective way to
repurpose standard models for open world lifelong learning. Through extensive
empirical evaluation, we highlight why our approach should serve as a future
standard for models that are able to effectively maintain their knowledge,
selectively focus on informative data, and accelerate future learning
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
Public transit route planning through lightweight linked data interfaces
While some public transit data publishers only provide a data dump – which only few reusers can afford to integrate within their applications – others provide a use case limiting origin-destination route planning api. The Linked Connections framework instead introduces a hypermedia api, over which the extendable base route planning algorithm “Connections Scan Algorithm” can be implemented. We compare the cpu usage and query execution time of a traditional server-side route planner with the cpu time and query execution time of a Linked Connections interface by evaluating query mixes with increasing load. We found that, at the expense of a higher bandwidth consumption, more queries can be answered using the same hardware with the Linked Connections server interface than with an origin-destination api, thanks to an average cache hit rate of 78%. The findings from this research show a cost-efficient way of publishing transport data that can bring federated public transit route planning at the fingertips of anyone
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