60 research outputs found
Data-driven Job Search Engine Using Skills and Company Attribute Filters
According to a report online, more than 200 million unique users search for
jobs online every month. This incredibly large and fast growing demand has
enticed software giants such as Google and Facebook to enter this space, which
was previously dominated by companies such as LinkedIn, Indeed and
CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine",
"Google For Jobs" while Facebook released "Facebook Jobs" within their
platform. These current job search engines and platforms allow users to search
for jobs based on general narrow filters such as job title, date posted,
experience level, company and salary. However, they have severely limited
filters relating to skill sets such as C++, Python, and Java and company
related attributes such as employee size, revenue, technographics and
micro-industries. These specialized filters can help applicants and companies
connect at a very personalized, relevant and deeper level. In this paper we
present a framework that provides an end-to-end "Data-driven Jobs Search
Engine". In addition, users can also receive potential contacts of recruiters
and senior positions for connection and networking opportunities. The high
level implementation of the framework is described as follows: 1) Collect job
postings data in the United States, 2) Extract meaningful tokens from the
postings data using ETL pipelines, 3) Normalize the data set to link company
names to their specific company websites, 4) Extract and ranking the skill
sets, 5) Link the company names and websites to their respective company level
attributes with the EVERSTRING Company API, 6) Run user-specific search queries
on the database to identify relevant job postings and 7) Rank the job search
results. This framework offers a highly customizable and highly targeted search
experience for end users.Comment: 8 pages, 10 figures, ICDM 201
DiffPose:SpatioTemporal Diffusion Model for Video-Based Human Pose Estimation
Denoising diffusion probabilistic models that were initially proposed for realistic image generation have recently shown success in various perception tasks (e.g., object detection and image segmentation) and are increasingly gaining attention in computer vision. However, extending such models to multi-frame human pose estimation is non-trivial due to the presence of the additional temporal dimension in videos. More importantly, learning representations that focus on keypoint regions is crucial for accurate localization of human joints. Nevertheless, the adaptation of the diffusion-based methods remains unclear on how to achieve such objective. In this paper, we present DiffPose, a novel diffusion architecture that formulates video-based human pose estimation as a conditional heatmap generation problem. First, to better leverage temporal information, we propose SpatioTemporal Representation Learner which aggregates visual evidences across frames and uses the resulting features in each denoising step as a condition. In addition, we present a mechanism called Lookup-based MultiScale Feature Interaction that determines the correlations between local joints and global contexts across multiple scales. This mechanism generates delicate representations that focus on keypoint regions. Altogether, by extending diffusion models, we show two unique characteristics from DiffPose on pose estimation task: (i) the ability to combine multiple sets of pose estimates to improve prediction accuracy, particularly for challenging joints, and (ii) the ability to adjust the number of iterative steps for feature refinement without retraining the model. DiffPose sets new state-of-the-art results on three benchmarks: PoseTrack2017, PoseTrack2018, and PoseTrack21
Learning the Network of Graphs for Graph Neural Networks
Graph neural networks (GNNs) have achieved great success in many scenarios
with graph-structured data. However, in many real applications, there are three
issues when applying GNNs: graphs are unknown, nodes have noisy features, and
graphs contain noisy connections. Aiming at solving these problems, we propose
a new graph neural network named as GL-GNN. Our model includes multiple
sub-modules, each sub-module selects important data features and learn the
corresponding key relation graph of data samples when graphs are unknown.
GL-GNN further obtains the network of graphs by learning the network of
sub-modules. The learned graphs are further fused using an aggregation method
over the network of graphs. Our model solves the first issue by simultaneously
learning multiple relation graphs of data samples as well as a relation network
of graphs, and solves the second and the third issue by selecting important
data features as well as important data sample relations. We compare our method
with 14 baseline methods on seven datasets when the graph is unknown and 11
baseline methods on two datasets when the graph is known. The results show that
our method achieves better accuracies than the baseline methods and is capable
of selecting important features and graph edges from the dataset. Our code will
be publicly available at \url{https://github.com/Looomo/GL-GNN}
Employing the Houseless as Corporate Social Responsibility
Purpose
Many hospitality organizations see the benefits of engaging in corporate social responsibility (CSR), which can take many forms. This study aims to examine one relatively unique form of CSR: hiring individuals experiencing houselessness. This research aimed to investigate the impact of hiring individuals experiencing houselessness on customers’ behavioral intentions, attitudes toward an organization and perceptions of CSR actions. Design/methodology/approach
Across two experiments, this study investigated the impact of employing individuals experiencing houselessness on customers’ perceptions of the employee and organization using organizational legitimacy theory. Findings
Results demonstrate that employees known to be houseless elicited more positive employee and organizational perceptions from the customers, mediated by CSR perceptions. In addition, the gender of the employees or the quality of the organization did not impact these findings. Practical implications
Hospitality and tourism organizations should consider using available resources or tax benefits to make a deliberate effort to employ those experiencing houselessness. Originality/value
Using organizational legitimacy theory, this study examines CSR perceptions as a potential explanatory mechanism between houselessness and customers’ reactions
LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields
We introduce a new task, novel view synthesis for LiDAR sensors. While
traditional model-based LiDAR simulators with style-transfer neural networks
can be applied to render novel views, they fall short of producing accurate and
realistic LiDAR patterns because the renderers rely on explicit 3D
reconstruction and exploit game engines, that ignore important attributes of
LiDAR points. We address this challenge by formulating, to the best of our
knowledge, the first differentiable end-to-end LiDAR rendering framework,
LiDAR-NeRF, leveraging a neural radiance field (NeRF) to facilitate the joint
learning of geometry and the attributes of 3D points. However, simply employing
NeRF cannot achieve satisfactory results, as it only focuses on learning
individual pixels while ignoring local information, especially at low texture
areas, resulting in poor geometry. To this end, we have taken steps to address
this issue by introducing a structural regularization method to preserve local
structural details. To evaluate the effectiveness of our approach, we establish
an object-centric multi-view LiDAR dataset, dubbed NeRF-MVL. It contains
observations of objects from 9 categories seen from 360-degree viewpoints
captured with multiple LiDAR sensors. Our extensive experiments on the
scene-level KITTI-360 dataset, and on our object-level NeRF-MVL show that our
LiDAR-NeRF surpasses the model-based algorithms significantly.Comment: This paper introduces a new task of novel LiDAR view synthesis, and
proposes a differentiable framework called LiDAR-NeRF with a structural
regularization, as well as an object-centric multi-view LiDAR dataset called
NeRF-MV
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