52,604 research outputs found
Learning and Transferring IDs Representation in E-commerce
Many machine intelligence techniques are developed in E-commerce and one of
the most essential components is the representation of IDs, including user ID,
item ID, product ID, store ID, brand ID, category ID etc. The classical
encoding based methods (like one-hot encoding) are inefficient in that it
suffers sparsity problems due to its high dimension, and it cannot reflect the
relationships among IDs, either homogeneous or heterogeneous ones. In this
paper, we propose an embedding based framework to learn and transfer the
representation of IDs. As the implicit feedbacks of users, a tremendous amount
of item ID sequences can be easily collected from the interactive sessions. By
jointly using these informative sequences and the structural connections among
IDs, all types of IDs can be embedded into one low-dimensional semantic space.
Subsequently, the learned representations are utilized and transferred in four
scenarios: (i) measuring the similarity between items, (ii) transferring from
seen items to unseen items, (iii) transferring across different domains, (iv)
transferring across different tasks. We deploy and evaluate the proposed
approach in Hema App and the results validate its effectiveness.Comment: KDD'18, 9 page
Towards on-chip generation, routing and detection of non-classical light
We fabricate an integrated photonic circuit with emitter, waveguide and
detector on one chip, based on a hybrid superconductor-semiconductor system. We
detect photoluminescence from self-assembled InGaAs quantum dots on-chip using
NbN superconducting nanowire single photon detectors. Using the fast temporal
response of these detectors we perform time-resolved studies of non-resonantly
excited quantum dots. By introducing a temporal filtering to the signal, we are
able to resonantly excite the quantum dot and detect its resonance uorescence
on-chip with the integrated superconducting single photon detector.Comment: 9 pages, 5 figure
BlogForever D2.6: Data Extraction Methodology
This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform
A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures
This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverableâs authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes
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Stacking-based visualization of trajectory attribute data
Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well
An initial performance review of software components for a heterogeneous computing platform
The design of embedded systems is a complex activity that involves a lot of
decisions. With high performance demands of present day usage scenarios and
software, they often involve energy hungry state-of-the-art computing units.
While focusing on power consumption of computing units, the physical properties
of software are often ignored. Recently, there has been a growing interest to
quantify and model the physical footprint of software (e.g. consumed power,
generated heat, execution time, etc.), and a component based approach
facilitates methods for describing such properties. Based on these, software
architects can make energy-efficient software design solutions. This paper
presents power consumption and execution time profiling of a component software
that can be allocated on heterogeneous computing units (CPU, GPU, FPGA) of a
tracked robot
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