167 research outputs found
Data Mining to Understand Customer Behaviour in Usage of Mobile Applications Services
Customer usage data produced by mobile applications and mobile phones contain valuable knowledge about users and market. Such knowledge can help companies to conduct customer acquisition. Using customer personal data exacted from both mobile applications and telecoms operator, this study tries to investigate the features of the valuable potential customer by using the integrated data mining methods. The findings will help the developer to identify the value of different groups and target worthy potential customers. Such knowledge will also enable developers to offer personalized promotions and marketing information to potential customers. The mobile application using behaviour and mobile-service performance data also helps the developer of the mobile application and telecoms operator to utilize each other’s resource to extend their customer base. The approach balances complexity with ease of use and thus facilitates the developer to make use of user usage behaviour data to improve marketing decisions
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems
recently achieve promising progress to take over tedious examinations by
cardiologists. However, their vulnerability to adversarial attacks still lack
comprehensive investigation. The existing attacks in image domain could not be
directly applicable due to the distinct properties of ECGs in visualization and
dynamic properties. Thus, this paper takes a step to thoroughly explore
adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the
properties of ECGs to design effective attacks schemes under two attacks models
respectively. Our results demonstrate the blind spots of DNN-powered diagnosis
systems under adversarial attacks, which calls attention to adequate
countermeasures.Comment: Accepted by AAAI 202
Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels
This paper investigates ways to obtain consumer preferences for technology products to help designers identify the key attributes that contribute to a product's market success. A case study of residential photovoltaic panels is performed in the context of the California, USA, market within the 2007–2011 time span. First, interviews are conducted with solar panel installers to gain a better understanding of the solar industry. Second, a revealed preference method is implemented using actual market data and technical specifications to extract preferences. The approach is explored with three machine learning methods: Artificial neural networks (ANN), Random Forest decision trees, and Gradient Boosted regression. Finally, a stated preference self-explicated survey is conducted, and the results using the two methods compared. Three common critical attributes are identified from a pool of 34 technical attributes: power warranty, panel efficiency, and time on market. From the survey, additional nontechnical attributes are identified: panel manufacturer's reputation, name recognition, and aesthetics. The work shows that a combination of revealed and stated preference methods may be valuable for identifying both technical and nontechnical attributes to guide design priorities.Center for Scalable and Integrated Nanomanufacturin
Approaches for identifying consumer preferences for the design of technology products : a case study of residential solar panels
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 91-94).This thesis investigates ways to obtain consumer preferences for technology products to help designers identify the key attributes that contribute to a product's market success. A case study of residential solar PV panels is conducted in the context of the California, USA market within the 2007-2011 time span. First, interviews are conducted with solar panel installers to gain a better understanding of the solar industry. Second, a revealed preference method is implemented using actual market data and technical specifications to extract preferences. The approach is explored with three machine learning methods: Artificial Neural Networks, Random Forest decision trees, and Gradient Boosted regression. Finally, a stated preference self-explicated survey is conducted, and the results using the two methods compared. Three common critical attributes are identified from a pool of 34 technical attributes: power warranty, panel efficiency, and time on market. From the survey, additional non-technical attributes are identified: panel manufacturer's reputation, name recognition, and aesthetics. The work shows that a combination of revealed and stated preference methods may be valuable for identifying both technical and non-technical attributes to guide design priorities.by Heidi Qianyi Chen.S.M
Propagating Uncertainty in Solar Panel Performance for Life Cycle Modeling in Early Stage Design
One of the challenges in accurately applying metrics for life
cycle assessment lies in accounting for both irreducible and
inherent uncertainties in how a design will perform under
real world conditions. This paper presents a preliminary
study that compares two strategies, one simulation-based
and one set-based, for propagating uncertainty in a system.
These strategies for uncertainty propagation are then
aggregated. This work is conducted in the context of an
amorphous photovoltaic (PV) panel, using data gathered
from the National Solar Radiation Database, as well as
realistic data collected from an experimental hardware setup
specifically for this study. Results show that the influence of
various sources of uncertainty can vary widely, and in
particular that solar radiation intensity is a more significant
source of uncertainty than the efficiency of a PV panel. This
work also shows both set-based and simulation-based
approaches have limitations and must be applied
thoughtfully to prevent unrealistic results. Finally, it was
found that aggregation of the two uncertainty propagation
methods provided faster results than either method alone.Center for Scalable and Integrated NanomanufacturingNational Science Foundation (U.S.) (Nanoscale Science and Engineering Center
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
Federated Learning (FL) requires frequent exchange of model parameters, which
leads to long communication delay, especially when the network environments of
clients vary greatly. Moreover, the parameter server needs to wait for the
slowest client (i.e., straggler, which may have the largest model size, lowest
computing capability or worst network condition) to upload parameters, which
may significantly degrade the communication efficiency. Commonly-used client
selection methods such as partial client selection would lead to the waste of
computing resources and weaken the generalization of the global model. To
tackle this problem, along a different line, in this paper, we advocate the
approach of model parameter dropout instead of client selection, and
accordingly propose a novel framework of Federated learning scheme with
Differential parameter Dropout (FedDD). FedDD consists of two key modules:
dropout rate allocation and uploaded parameter selection, which will optimize
the model parameter uploading ratios tailored to different clients'
heterogeneous conditions and also select the proper set of important model
parameters for uploading subject to clients' dropout rate constraints.
Specifically, the dropout rate allocation is formulated as a convex
optimization problem, taking system heterogeneity, data heterogeneity, and
model heterogeneity among clients into consideration. The uploaded parameter
selection strategy prioritizes on eliciting important parameters for uploading
to speedup convergence. Furthermore, we theoretically analyze the convergence
of the proposed FedDD scheme. Extensive performance evaluations demonstrate
that the proposed FedDD scheme can achieve outstanding performances in both
communication efficiency and model convergence, and also possesses a strong
generalization capability to data of rare classes
StreamFunnel: Facilitating Communication Between a VR Streamer and Many Spectators
The increasing adoption of Virtual Reality (VR) systems in different domains
have led to a need to support interaction between many spectators and a VR
user. This is common in game streaming, live performances, and webinars. Prior
CSCW systems for VR environments are limited to small groups of users. In this
work, we identify problems associated with interaction carried out with large
groups of users. To address this, we introduce an additional user role: the
co-host. They mediate communications between the VR user and many spectators.
To facilitate this mediation, we present StreamFunnel, which allows the co-host
to be part of the VR application's space and interact with it. The design of
StreamFunnel was informed by formative interviews with six experts.
StreamFunnel uses a cloud-based streaming solution to enable remote co-host and
many spectators to view and interact through standard web browsers, without
requiring any custom software. We present results of informal user testing
which provides insights into StreamFunnel's ability to facilitate these
scalable interactions. Our participants, who took the role of a co-host, found
that StreamFunnel enables them to add value in presenting the VR experience to
the spectators and relaying useful information from the live chat to the VR
user.Comment: 12 pages, 7 figure
Explicit Correspondence Matching for Generalizable Neural Radiance Fields
We present a new generalizable NeRF method that is able to directly
generalize to new unseen scenarios and perform novel view synthesis with as few
as two source views. The key to our approach lies in the explicitly modeled
correspondence matching information, so as to provide the geometry prior to the
prediction of NeRF color and density for volume rendering. The explicit
correspondence matching is quantified with the cosine similarity between image
features sampled at the 2D projections of a 3D point on different views, which
is able to provide reliable cues about the surface geometry. Unlike previous
methods where image features are extracted independently for each view, we
consider modeling the cross-view interactions via Transformer cross-attention,
which greatly improves the feature matching quality. Our method achieves
state-of-the-art results on different evaluation settings, with the experiments
showing a strong correlation between our learned cosine feature similarity and
volume density, demonstrating the effectiveness and superiority of our proposed
method. Code is at https://github.com/donydchen/matchnerfComment: Code and pre-trained models: https://github.com/donydchen/matchnerf
Project Page: https://donydchen.github.io/matchnerf
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