22 research outputs found
No-Sense: Sense with Dormant Sensors
Wireless sensor networks (WSNs) have enabled continuous monitoring of an area
of interest (body, room, region, etc.) while eliminating expensive wired
infrastructure. Typically in such applications, wireless sensor nodes report
the sensed values to a sink node, where the information is required for the
end-user. WSNs also provide the flexibility to the end-user for choosing
several parameters for the monitoring application. For example, placement of
sensors, frequency of sensing and transmission of those sensed data. Over the
years, the advancement in embedded technology has led to increased processing
power and memory capacity of these battery powered devices. However, batteries
can only supply limited energy, thus limiting the lifetime of the network. In
order to prolong the lifetime of the deployment, various efforts have been made
to improve the battery technologies and also reduce the energy consumption of
the sensor node at various layers in the networking stack. Of all the
operations in the network stack, wireless data transmission and reception have
found to consume most of the energy. Hence many proposals found in the
literature target reducing them through intelligent schemes like power control,
reducing retransmissions, etc. In this article we propose a new framework
called Virtual Sensing Framework (VSF), which aims to sufficiently satisfy
application requirements while conserving energy at the sensor nodes.Comment: Accepted for publication in IEEE Twentieth National Conference on
Communications (NCC-2014
tagE: Enabling an Embodied Agent to Understand Human Instructions
Natural language serves as the primary mode of communication when an
intelligent agent with a physical presence engages with human beings. While a
plethora of research focuses on natural language understanding (NLU),
encompassing endeavors such as sentiment analysis, intent prediction, question
answering, and summarization, the scope of NLU directed at situations
necessitating tangible actions by an embodied agent remains limited. The
inherent ambiguity and incompleteness inherent in natural language present
challenges for intelligent agents striving to decipher human intention. To
tackle this predicament head-on, we introduce a novel system known as task and
argument grounding for Embodied agents (tagE). At its core, our system employs
an inventive neural network model designed to extract a series of tasks from
complex task instructions expressed in natural language. Our proposed model
adopts an encoder-decoder framework enriched with nested decoding to
effectively extract tasks and their corresponding arguments from these
intricate instructions. These extracted tasks are then mapped (or grounded) to
the robot's established collection of skills, while the arguments find
grounding in objects present within the environment. To facilitate the training
and evaluation of our system, we have curated a dataset featuring complex
instructions. The results of our experiments underscore the prowess of our
approach, as it outperforms robust baseline models.Comment: Accepted in EMNLP Findings 202
The Uniqueness of Achatina fulica in its Evolutionary Success
The increasing load of environmental pollutants poses a serious threat over the globe. In this vulnerable situation, it is essential to have alternative sources of medicines, may be from invertebrates. Among invertebrates, although molluscs are known for their consumption as food and ethno‐medicinal use, the importance of these animals is still overlooked. Presently attention has been geared toward molluscs including Achatina fulica which are now considered as one of the most evolutionary successful animals. During the last few decades, researchers are trying to decipher their complex immune system to harvest valuable molecules to treat human diseases. In the present review, the existence of important immunological factors in Achatina is discussed addressing the coagulation system, innate immune molecules, bioactive proteins and lastly the enigmatic C‐reactive proteins