246 research outputs found
Improving Natural Language Interaction with Robots Using Advice
Over the last few years, there has been growing interest in learning models
for physically grounded language understanding tasks, such as the popular
blocks world domain. These works typically view this problem as a single-step
process, in which a human operator gives an instruction and an automated agent
is evaluated on its ability to execute it. In this paper we take the first step
towards increasing the bandwidth of this interaction, and suggest a protocol
for including advice, high-level observations about the task, which can help
constrain the agent's prediction. We evaluate our approach on the blocks world
task, and show that even simple advice can help lead to significant performance
improvements. To help reduce the effort involved in supplying the advice, we
also explore model self-generated advice which can still improve results.Comment: Accepted as a short paper at NAACL 2019 (8 pages
Robust and Fast 3D Scan Alignment using Mutual Information
This paper presents a mutual information (MI) based algorithm for the
estimation of full 6-degree-of-freedom (DOF) rigid body transformation between
two overlapping point clouds. We first divide the scene into a 3D voxel grid
and define simple to compute features for each voxel in the scan. The two scans
that need to be aligned are considered as a collection of these features and
the MI between these voxelized features is maximized to obtain the correct
alignment of scans. We have implemented our method with various simple point
cloud features (such as number of points in voxel, variance of z-height in
voxel) and compared the performance of the proposed method with existing
point-to-point and point-to- distribution registration methods. We show that
our approach has an efficient and fast parallel implementation on GPU, and
evaluate the robustness and speed of the proposed algorithm on two real-world
datasets which have variety of dynamic scenes from different environments
Decision blocks: A tool for automating decision making in CLIPS
The human capability of making complex decision is one of the most fascinating facets of human intelligence, especially if vague, judgemental, default or uncertain knowledge is involved. Unfortunately, most existing rule based forward chaining languages are not very suitable to simulate this aspect of human intelligence, because of their lack of support for approximate reasoning techniques needed for this task, and due to the lack of specific constructs to facilitate the coding of frequently reoccurring decision block to provide better support for the design and implementation of rule based decision support systems. A language called BIRBAL, which is defined on the top of CLIPS, for the specification of decision blocks, is introduced. Empirical experiments involving the comparison of the length of CLIPS program with the corresponding BIRBAL program for three different applications are surveyed. The results of these experiments suggest that for decision making intensive applications, a CLIPS program tends to be about three times longer than the corresponding BIRBAL program
Interactively Learning Social Media Representations Improves News Source Factuality Detection
The rise of social media has enabled the widespread propagation of fake news,
text that is published with an intent to spread misinformation and sway
beliefs. Rapidly detecting fake news, especially as new events arise, is
important to prevent misinformation.
While prior works have tackled this problem using supervised learning
systems, automatedly modeling the complexities of the social media landscape
that enables the spread of fake news is challenging. On the contrary, having
humans fact check all news is not scalable. Thus, in this paper, we propose to
approach this problem interactively, where humans can interact to help an
automated system learn a better social media representation quality. On real
world events, our experiments show performance improvements in detecting
factuality of news sources, even after few human interactions.Comment: Accepted at Findings of IJCNLP-AACL 202
An Interactive Framework for Profiling News Media Sources
The recent rise of social media has led to the spread of large amounts of
fake and biased news, content published with the intent to sway beliefs. While
detecting and profiling the sources that spread this news is important to
maintain a healthy society, it is challenging for automated systems.
In this paper, we propose an interactive framework for news media profiling.
It combines the strengths of graph based news media profiling models,
Pre-trained Large Language Models, and human insight to characterize the social
context on social media. Experimental results show that with as little as 5
human interactions, our framework can rapidly detect fake and biased news
media, even in the most challenging settings of emerging news events, where
test data is unseen
The Cross Cultural Study Concerning Gender Stereotyping in Computing: Comparison between the US and India
Computing has long been considered a male domain in the US. If this perception is true, then this situation can be detrimental to the success of women in the workplace because computing is integral to success in most jobs. Recently however, women in the global workforce are using computers and the Internet at an increasing rate, which brings into question whether computing is globally perceived as “male.” In addition, this particular phenomenon gains a more interesting aspect when the computing workforce has been outsourced off shore to countries such India, where the service work force also consists of a significant number of female computing professionals. The cultural differences between the US and India bring to light insightful aspects toward the gender perception in computing, which may benefit the interaction between business personnel when gender stereotyping is a concern. The empirical result showed that gender gaps in usage and attitudes between America and India exist in some degree
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