19,115 research outputs found
Rapid-Rate: A Framework for Semi-supervised Real-time Sentiment Trend Detection in Unstructured Big Data
Commercial establishments like restaurants, service centres and retailers
have several sources of customer feedback about products and services, most of
which need not be as structured as rated reviews provided by services like
Yelp, or Amazon, in terms of sentiment conveyed. For instance, Amazon provides
a fine-grained score on a numeric scale for product reviews. Some sources,
however, like social media (Twitter, Facebook), mailing lists (Google Groups)
and forums (Quora) contain text data that is much more voluminous, but
unstructured and unlabelled. It might be in the best interests of a business
establishment to assess the general sentiment towards their brand on these
platforms as well. This text could be pipelined into a system with a built-in
prediction model, with the objective of generating real-time graphs on opinion
and sentiment trends. Although such tasks like the one described about have
been explored with respect to document classification problems in the past, the
implementation described in this paper, by virtue of learning a continuous
function rather than a discrete one, offers a lot more depth of insight as
compared to document classification approaches. This study aims to explore the
validity of such a continuous function predicting model to quantify sentiment
about an entity, without the additional overhead of manual labelling, and
computational preprocessing & feature extraction. This research project also
aims to design and implement a re-usable document regression pipeline as a
framework, Rapid-Rate, that can be used to predict document scores in
real-time.Comment: 11 pages, 6 figure
Using Context Information to Enhance Simple Question Answering
With the rapid development of knowledge bases(KBs),question
answering(QA)based on KBs has become a hot research issue. In this paper,we
propose two frameworks(i.e.,pipeline framework,an end-to-end framework)to focus
answering single-relation factoid question. In both of two frameworks,we study
the effect of context information on the quality of QA,such as the entity's
notable type,out-degree. In the end-to-end framework,we combine char-level
encoding and self-attention mechanisms,using weight sharing and multi-task
strategies to enhance the accuracy of QA. Experimental results show that
context information can get better results of simple QA whether it is the
pipeline framework or the end-to-end framework. In addition,we find that the
end-to-end framework achieves results competitive with state-of-the-art
approaches in terms of accuracy and take much shorter time than them.Comment: under review World Wide Web Journa
Comparative study of the Pros and Cons of Programming languages Java, Scala, C++, Haskell, VB .NET, AspectJ, Perl, Ruby, PHP & Scheme - a Team 11 COMP6411-S10 Term Report
With the advent of numerous languages it is difficult to realize the edge of
one language in a particular scope over another one. We are making an effort,
realizing these few issues and comparing some main stream languages like Java,
Scala, C++, Haskell, VB .NET, AspectJ, Perl, Ruby, PHP and Scheme keeping in
mind some core issues in program development.Comment: 28 pages, 2 table
IBM Deep Learning Service
Deep learning driven by large neural network models is overtaking traditional
machine learning methods for understanding unstructured and perceptual data
domains such as speech, text, and vision. At the same time, the
"as-a-Service"-based business model on the cloud is fundamentally transforming
the information technology industry. These two trends: deep learning, and
"as-a-service" are colliding to give rise to a new business model for cognitive
application delivery: deep learning as a service in the cloud. In this paper,
we will discuss the details of the software architecture behind IBM's deep
learning as a service (DLaaS). DLaaS provides developers the flexibility to use
popular deep learning libraries such as Caffe, Torch and TensorFlow, in the
cloud in a scalable and resilient manner with minimal effort. The platform uses
a distribution and orchestration layer that facilitates learning from a large
amount of data in a reasonable amount of time across compute nodes. A resource
provisioning layer enables flexible job management on heterogeneous resources,
such as graphics processing units (GPUs) and central processing units (CPUs),
in an infrastructure as a service (IaaS) cloud
Reconfigurable Hardware Accelerators: Opportunities, Trends, and Challenges
With the emerging big data applications of Machine Learning, Speech
Recognition, Artificial Intelligence, and DNA Sequencing in recent years,
computer architecture research communities are facing the explosive scale of
various data explosion. To achieve high efficiency of data-intensive computing,
studies of heterogeneous accelerators which focus on latest applications, have
become a hot issue in computer architecture domain. At present, the
implementation of heterogeneous accelerators mainly relies on heterogeneous
computing units such as Application-specific Integrated Circuit (ASIC),
Graphics Processing Unit (GPU), and Field Programmable Gate Array (FPGA). Among
the typical heterogeneous architectures above, FPGA-based reconfigurable
accelerators have two merits as follows: First, FPGA architecture contains a
large number of reconfigurable circuits, which satisfy requirements of high
performance and low power consumption when specific applications are running.
Second, the reconfigurable architectures of employing FPGA performs prototype
systems rapidly and features excellent customizability and reconfigurability.
Nowadays, in top-tier conferences of computer architecture, emerging a batch of
accelerating works based on FPGA or other reconfigurable architectures. To
better review the related work of reconfigurable computing accelerators
recently, this survey reserves latest high-level research products of
reconfigurable accelerator architectures and algorithm applications as the
basis. In this survey, we compare hot research issues and concern domains,
furthermore, analyze and illuminate advantages, disadvantages, and challenges
of reconfigurable accelerators. In the end, we prospect the development
tendency of accelerator architectures in the future, hoping to provide a
reference for computer architecture researchers
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages
Measuring the semantic similarity between two sentences (or Semantic Textual
Similarity - STS) is fundamental in many NLP applications. Despite the
remarkable results in supervised settings with adequate labeling, little
attention has been paid to this task in low-resource languages with
insufficient labeling. Existing approaches mostly leverage machine translation
techniques to translate sentences into rich-resource language. These approaches
either beget language biases, or be impractical in industrial applications
where spoken language scenario is more often and rigorous efficiency is
required. In this work, we propose a multilingual framework to tackle the STS
task in a low-resource language e.g. Spanish, Arabic , Indonesian and Thai, by
utilizing the rich annotation data in a rich resource language, e.g. English.
Our approach is extended from a basic monolingual STS framework to a shared
multilingual encoder pretrained with translation task to incorporate
rich-resource language data. By exploiting the nature of a shared multilingual
encoder, one sentence can have multiple representations for different target
translation language, which are used in an ensemble model to improve similarity
evaluation. We demonstrate the superiority of our method over other state of
the art approaches on SemEval STS task by its significant improvement on non-MT
method, as well as an online industrial product where MT method fails to beat
baseline while our approach still has consistently improvements
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a
booming of artificial intelligence (AI) applications and services, spanning
from personal assistant to recommendation systems to video/audio surveillance.
More recently, with the proliferation of mobile computing and
Internet-of-Things (IoT), billions of mobile and IoT devices are connected to
the Internet, generating zillions Bytes of data at the network edge. Driving by
this trend, there is an urgent need to push the AI frontiers to the network
edge so as to fully unleash the potential of the edge big data. To meet this
demand, edge computing, an emerging paradigm that pushes computing tasks and
services from the network core to the network edge, has been widely recognized
as a promising solution. The resulted new inter-discipline, edge AI or edge
intelligence, is beginning to receive a tremendous amount of interest. However,
research on edge intelligence is still in its infancy stage, and a dedicated
venue for exchanging the recent advances of edge intelligence is highly desired
by both the computer system and artificial intelligence communities. To this
end, we conduct a comprehensive survey of the recent research efforts on edge
intelligence. Specifically, we first review the background and motivation for
artificial intelligence running at the network edge. We then provide an
overview of the overarching architectures, frameworks and emerging key
technologies for deep learning model towards training/inference at the network
edge. Finally, we discuss future research opportunities on edge intelligence.
We believe that this survey will elicit escalating attentions, stimulate
fruitful discussions and inspire further research ideas on edge intelligence.Comment: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang,
"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge
Computing," Proceedings of the IEE
On whether and how D-RISC and Microgrids can be kept relevant (self-assessment report)
This report lays flat my personal views on D-RISC and Microgrids as of March
2013. It reflects the opinions and insights that I have gained from working on
this project during the period 2008-2013. This report is structed in two parts:
deconstruction and reconstruction. In the deconstruction phase, I review what I
believe are the fundamental motivation and goals of the D-RISC/Microgrids
enterprise, and identify what I judge are shortcomings: that the project did
not deliver on its expectations, that fundamental questions are left
unanswered, and that its original motivation may not even be relevant in
scientific research any more in this day and age. In the reconstruction phase,
I start by identifying the merits of the current D-RISC/Microgrids technology
and know-how taken at face value, re-motivate its existence from a different
angle, and suggest new, relevant research questions that could justify
continued scientific investment.Comment: 45 pages, 5 figures, 2 table
MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an
ecosystem of enhancements that expand the Apache Spark distributed computing
library to tackle problems in Deep Learning, Micro-Service Orchestration,
Gradient Boosting, Model Interpretability, and other areas of modern
computation. Furthermore, we present a novel system called Spark Serving that
allows users to run any Apache Spark program as a distributed, sub-millisecond
latency web service backed by their existing Spark Cluster. All MMLSpark
contributions have the same API to enable simple composition across frameworks
and usage across batch, streaming, and RESTful web serving scenarios on static,
elastic, or serverless clusters. We showcase MMLSpark by creating a method for
deep object detection capable of learning without human labeled data and
demonstrate its effectiveness for Snow Leopard conservation
Ray: A Distributed Framework for Emerging AI Applications
The next generation of AI applications will continuously interact with the
environment and learn from these interactions. These applications impose new
and demanding systems requirements, both in terms of performance and
flexibility. In this paper, we consider these requirements and present Ray---a
distributed system to address them. Ray implements a unified interface that can
express both task-parallel and actor-based computations, supported by a single
dynamic execution engine. To meet the performance requirements, Ray employs a
distributed scheduler and a distributed and fault-tolerant store to manage the
system's control state. In our experiments, we demonstrate scaling beyond 1.8
million tasks per second and better performance than existing specialized
systems for several challenging reinforcement learning applications.Comment: 17 pages, 14 figures, 13th USENIX Symposium on Operating Systems
Design and Implementation, 201
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