212 research outputs found
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
Room geometry blind inference based on the localization of real sound source and first order reflections
The conventional room geometry blind inference techniques with acoustic
signals are conducted based on the prior knowledge of the environment, such as
the room impulse response (RIR) or the sound source position, which will limit
its application under unknown scenarios. To solve this problem, we have
proposed a room geometry reconstruction method in this paper by using the
geometric relation between the direct signal and first-order reflections. In
addition to the information of the compact microphone array itself, this method
does not need any precognition of the environmental parameters. Besides, the
learning-based DNN models are designed and used to improve the accuracy and
integrity of the localization results of the direct source and first-order
reflections. The direction of arrival (DOA) and time difference of arrival
(TDOA) information of the direct and reflected signals are firstly estimated
using the proposed DCNN and TD-CNN models, which have higher sensitivity and
accuracy than the conventional methods. Then the position of the sound source
is inferred by integrating the DOA, TDOA and array height using the proposed
DNN model. After that, the positions of image sources and corresponding
boundaries are derived based on the geometric relation. Experimental results of
both simulations and real measurements verify the effectiveness and accuracy of
the proposed techniques compared with the conventional methods under different
reverberant environments
An efficient graph partition method for fault section estimation inlarge-scale power network
In order to make fault section estimation (FSE) in large scale power networks using a distributed artificial intelligence approach, we have to develop an efficient way to partition the large-scale power network into the desired number of connected sub-networks such that each sub-network should have balanced working burden in performing FSE. In this paper, a new efficient multiple-way graph partition method is suggested for the partition task. The method consists of three basic steps. The first step is to form the weighted depth-first-search tree of the power network. The second step is to further partition the network into connected balanced sub-networks. The last step is an iterative process, which tries to minimize the number of the frontier nodes of the sub-networks in order to reduce the required interaction of the adjacent sub-networks. The proposed graph partition approach has been implemented with applications of sparse storage technique. It is further tested in the IEEE 14-bus, 30-bus and 118-bus systems respectively. Computer simulation results show that the proposed multiple-way graph partition approach is suitable for FSE in large-scale power networks and is compared favorably with other graph partition methods suggested in references.published_or_final_versio
Gravitational Lensing by Transparent Janis-Newman-Winicour Naked Singularities
The Janis-Newman-Winicour (JNW) spacetime can describe a naked singularity
with a photon sphere that smoothly transforms into a Schwarzschild black hole.
Our analysis reveals that photons, upon entering the photon sphere, converge to
the singularity in a finite coordinate time. Furthermore, if the singularity is
subjected to some regularization, these photons can traverse the regularized
singularity. Subsequently, we investigate the gravitational lensing of distant
sources and show that new images emerge within the critical curve formed by
light rays escaping from the photon sphere. These newfound images offer a
powerful tool for the detection and study of JNW naked singularities.Comment: 28 pages, 5 figure
HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding
There are several opportunities for automation in healthcare that can improve
clinician throughput. One such example is assistive tools to document diagnosis
codes when clinicians write notes. We study the automation of medical code
prediction using curriculum learning, which is a training strategy for machine
learning models that gradually increases the hardness of the learning tasks
from easy to difficult. One of the challenges in curriculum learning is the
design of curricula -- i.e., in the sequential design of tasks that gradually
increase in difficulty. We propose Hierarchical Curriculum Learning (HiCu), an
algorithm that uses graph structure in the space of outputs to design curricula
for multi-label classification. We create curricula for multi-label
classification models that predict ICD diagnosis and procedure codes from
natural language descriptions of patients. By leveraging the hierarchy of ICD
codes, which groups diagnosis codes based on various organ systems in the human
body, we find that our proposed curricula improve the generalization of neural
network-based predictive models across recurrent, convolutional, and
transformer-based architectures. Our code is available at
https://github.com/wren93/HiCu-ICD.Comment: To appear at Machine Learning for Healthcare Conference (MLHC2022
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
Intrusion Detection for Mobile Ad Hoc Networks Based on Node Reputation
The mobile ad hoc network (MANET) is more vulnerable to attacks than traditional networks, due to the high mobility of nodes, the weakness of transmission media and the absence of central node. To overcome the vulnerability, this paper mainly studies the way to detect selfish nodes in the MANET, and thus prevent network intrusion. Specifically, a data-driven reputation evaluation model was proposed to detect selfish nodes using a new reputation mechanism. The mechanism consists of a monitoring module, a reputation evaluation module, penalty module and a response module. The MANET integrated with our reputation mechanism was compared with the traditional MANET through simulation. The results show that the addition of reputation mechanism can suppress the selfish behavior of network nodes and enhance network security
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