27,329 research outputs found
Smart Grid for the Smart City
Modern cities are embracing cutting-edge technologies to improve the services they offer to the citizens from traffic control to the reduction of greenhouse gases and energy provisioning. In this chapter, we look at the energy sector advocating how Information and Communication Technologies (ICT) and signal processing techniques can be integrated into next generation power grids for an increased effectiveness in terms of: electrical stability, distribution, improved communication security, energy production, and utilization. In particular, we deliberate about the use of these techniques within new demand response paradigms, where communities of prosumers (e.g., households, generating part of their electricity consumption) contribute to the satisfaction of the energy demand through load balancing and peak shaving. Our discussion also covers the use of big data analytics for demand response and serious games as a tool to promote energy-efficient behaviors from end users
Injecting equipment schemes for injecting drug users : qualitative evidence review
This review of the qualitative literature about needle and syringe programmes (NSPs) for injecting drug users (IDUs) complements the review of effectiveness and cost-effectiveness. It aims to provide a more situated narrative perspective on the overall guidance questions
Emotion Expression Extraction Method for Chinese Microblog Sentences
With the rapid spread of Chinese microblog, a large number of microblog topics are being generated in real-time. More and more users pay attention to emotion expressions of these opinionated sentences in different topics. It is challenging to label the emotion expressions of opinionated sentences manually. For this endeavor, an emotion expression extraction method is proposed to process millions of user-generated opinionated sentences automatically in this paper. Specifically, the proposed method mainly contains two tasks: emotion classification and opinion target extraction. We first use a lexicon-based emotion classification method to compute different emotion values in emotion label vectors of opinionated sentences. Then emotion label vectors of opinionated sentences are revised by an unsupervised emotion label propagation algorithm. After extracting candidate opinion targets of opinionated sentences, the opinion target extraction task is performed on a random walk-based ranking algorithm, which considers the connection between candidate opinion targets and the textual similarity between opinionated sentences, ranks candidate opinion targets of opinionated sentences. Experimental results demonstrate the effectiveness of algorithms in the proposed method
Stable Feature Selection for Biomarker Discovery
Feature selection techniques have been used as the workhorse in biomarker
discovery applications for a long time. Surprisingly, the stability of feature
selection with respect to sampling variations has long been under-considered.
It is only until recently that this issue has received more and more attention.
In this article, we review existing stable feature selection methods for
biomarker discovery using a generic hierarchal framework. We have two
objectives: (1) providing an overview on this new yet fast growing topic for a
convenient reference; (2) categorizing existing methods under an expandable
framework for future research and development
Understanding Psycholinguistic Behavior of predominant drunk texters in Social Media
In the last decade, social media has evolved as one of the leading platform
to create, share, or exchange information; it is commonly used as a way for
individuals to maintain social connections. In this online digital world,
people use to post texts or pictures to express their views socially and create
user-user engagement through discussions and conversations. Thus, social media
has established itself to bear signals relating to human behavior. One can
easily design user characteristic network by scraping through someone's social
media profiles. In this paper, we investigate the potential of social media in
characterizing and understanding predominant drunk texters from the perspective
of their social, psychological and linguistic behavior as evident from the
content generated by them. Our research aims to analyze the behavior of drunk
texters on social media and to contrast this with non-drunk texters. We use
Twitter social media to obtain the set of drunk texters and non-drunk texters
and show that we can classify users into these two respective sets using
various psycholinguistic features with an overall average accuracy of 96.78%
with very high precision and recall. Note that such an automatic classification
can have far-reaching impact - (i) on health research related to addiction
prevention and control, and (ii) in eliminating abusive and vulgar contents
from Twitter, borne by the tweets of drunk texters.Comment: 6 pages, 8 Figures, ISCC 2018 Workshops - ICTS4eHealth 201
Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis
Target-based sentiment analysis or aspect-based sentiment analysis (ABSA)
refers to addressing various sentiment analysis tasks at a fine-grained level,
which includes but is not limited to aspect extraction, aspect sentiment
classification, and opinion extraction. There exist many solvers of the above
individual subtasks or a combination of two subtasks, and they can work
together to tell a complete story, i.e. the discussed aspect, the sentiment on
it, and the cause of the sentiment. However, no previous ABSA research tried to
provide a complete solution in one shot. In this paper, we introduce a new
subtask under ABSA, named aspect sentiment triplet extraction (ASTE).
Particularly, a solver of this task needs to extract triplets (What, How, Why)
from the inputs, which show WHAT the targeted aspects are, HOW their sentiment
polarities are and WHY they have such polarities (i.e. opinion reasons). For
instance, one triplet from "Waiters are very friendly and the pasta is simply
average" could be ('Waiters', positive, 'friendly'). We propose a two-stage
framework to address this task. The first stage predicts what, how and why in a
unified model, and then the second stage pairs up the predicted what (how) and
why from the first stage to output triplets. In the experiments, our framework
has set a benchmark performance in this novel triplet extraction task.
Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art
related methods.Comment: This paper is accepted in AAAI 202
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