186,347 research outputs found
Self-Supervised Monocular Depth Underwater
Depth estimation is critical for any robotic system. In the past years
estimation of depth from monocular images have shown great improvement,
however, in the underwater environment results are still lagging behind due to
appearance changes caused by the medium. So far little effort has been invested
on overcoming this. Moreover, underwater, there are more limitations for using
high resolution depth sensors, this makes generating ground truth for learning
methods another enormous obstacle. So far unsupervised methods that tried to
solve this have achieved very limited success as they relied on domain transfer
from dataset in air. We suggest training using subsequent frames
self-supervised by a reprojection loss, as was demonstrated successfully above
water. We suggest several additions to the self-supervised framework to cope
with the underwater environment and achieve state-of-the-art results on a
challenging forward-looking underwater dataset
Preliminary Results in a Multi-site Empirical Study on Cross-organizational ERP Size and Effort Estimation
This paper reports on initial findings in an empirical study carried out with representatives of two ERP vendors, six ERP adopting organizations, four ERP implementation consulting companies, and two ERP research and advisory services firms. Our study’s goal was to gain understanding of the state-of-the practice in size and effort estimation of cross-organizational ERP projects. Based on key size and effort estimation challenges identified in a previously published literature survey, we explored some difficulties, fallacies and pitfalls these organizations face. We focused on collecting empirical evidence from the participating ERP market players to assess specific facts about the state-of-the-art ERP size and effort estimation practices. Our study adopted a qualitative research method based on an asynchronous online focus group
A Scalable and Generalizable Pathloss Map Prediction
Large-scale channel prediction, i.e., estimation of the pathloss from
geographical/morphological/building maps, is an essential component of wireless
network planning. Ray tracing (RT)-based methods have been widely used for many
years, but they require significant computational effort that may become
prohibitive with the increased network densification and/or use of higher
frequencies in B5G/6G systems. In this paper, we propose a data-driven,
model-free pathloss map prediction (PMP) method, called PMNet. PMNet uses a
supervised learning approach: it is trained on a limited amount of RT (or
channel measurement) data and map data. Once trained, PMNet can predict
pathloss over location with high accuracy (an RMSE level of ) in a few
milliseconds. We further extend PMNet by employing transfer learning (TL). TL
allows PMNet to learn a new network scenario quickly (x5.6 faster training) and
efficiently (using x4.5 less data) by transferring knowledge from a pre-trained
model, while retaining accuracy. Our results demonstrate that PMNet is a
scalable and generalizable ML-based PMP method, showing its potential to be
used in several network optimization applications
Learning Effective Changes for Software Projects
The primary motivation of much of software analytics is decision making. How
to make these decisions? Should one make decisions based on lessons that arise
from within a particular project? Or should one generate these decisions from
across multiple projects? This work is an attempt to answer these questions.
Our work was motivated by a realization that much of the current generation
software analytics tools focus primarily on prediction. Indeed prediction is a
useful task, but it is usually followed by "planning" about what actions need
to be taken. This research seeks to address the planning task by seeking
methods that support actionable analytics that offer clear guidance on what to
do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set
of actionable plans within and across projects. Each of these plans, if
followed will improve the quality of the software project.Comment: 4 pages, 2 figures. This a submission for ASE 2017 Doctoral Symposiu
R&D and Technology Transfer: Firm-Level Evidence from Chinese Industry
The capacity of developing economies to narrow the gap in living standards with the OECD nations depends critically on their ability to imitate and innovate new technologies. Toward this end, developing economies have access to three avenues of technological advance: technology transfer, domestic R&D, and foreign direct investment. This paper examines the contributions of each of these avenues, as well as their interactions, to productivity and knowledge production within Chinese industry. Based on a large data set for China’s large and medium-size enterprises, the estimation results show that technology transfer – whether domestic or foreign – affects productivity only through its interactions with in-house R&D. Foreign direct investment does not appear to facilitate the adoption of market-mediated foreign technology transfer. Firms wishing to produce patentable knowledge do not benefit from technology transfer; patentable knowledge is created exclusively through in-house R&D operations.http://deepblue.lib.umich.edu/bitstream/2027.42/39968/3/wp582.pd
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