57,124 research outputs found
Time-Efficient Hybrid Approach for Facial Expression Recognition
Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
This paper presents a Lisp architecture for a portable NLP system, termed
LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard,
customized and in-house developed NLP tools. Our system facilitates portability
across different institutions and data systems by incorporating an enriched
Common Data Model (CDM) to standardize necessary data elements. It utilizes
UMLS to perform domain adaptation when integrating generic domain NLP tools. It
also features stand-off annotations that are specified by positional reference
to the original document. We built an interval tree based search engine to
efficiently query and retrieve the stand-off annotations by specifying
positional requirements. We also developed a utility to convert an inline
annotation format to stand-off annotations to enable the reuse of clinical text
datasets with inline annotations. We experimented with our system on several
NLP facilitated tasks including computational phenotyping for lymphoma patients
and semantic relation extraction for clinical notes. These experiments
showcased the broader applicability and utility of LAPNLP.Comment: 6 pages, accepted by IEEE BIBM 2018 as regular pape
Graph-based Features for Automatic Online Abuse Detection
While online communities have become increasingly important over the years,
the moderation of user-generated content is still performed mostly manually.
Automating this task is an important step in reducing the financial cost
associated with moderation, but the majority of automated approaches strictly
based on message content are highly vulnerable to intentional obfuscation. In
this paper, we discuss methods for extracting conversational networks based on
raw multi-participant chat logs, and we study the contribution of graph
features to a classification system that aims to determine if a given message
is abusive. The conversational graph-based system yields unexpectedly high
performance , with results comparable to those previously obtained with a
content-based approach
Relational Data Mining Through Extraction of Representative Exemplars
With the growing interest on Network Analysis, Relational Data Mining is
becoming an emphasized domain of Data Mining. This paper addresses the problem
of extracting representative elements from a relational dataset. After defining
the notion of degree of representativeness, computed using the Borda
aggregation procedure, we present the extraction of exemplars which are the
representative elements of the dataset. We use these concepts to build a
network on the dataset. We expose the main properties of these notions and we
propose two typical applications of our framework. The first application
consists in resuming and structuring a set of binary images and the second in
mining co-authoring relation in a research team
Community Detection and Growth Potential Prediction from Patent Citation Networks
The scoring of patents is useful for technology management analysis.
Therefore, a necessity of developing citation network clustering and prediction
of future citations for practical patent scoring arises. In this paper, we
propose a community detection method using the Node2vec. And in order to
analyze growth potential we compare three ''time series analysis methods'', the
Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of
our experiments, we could find common technical points from those clusters by
Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model
was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author
A Socio-ecological Approach to Measure Progress for Ontario’s Transition to a Green Economy: The Use of the Happy Planet Index
Government tends to look at economic growth and GDP as the primary measure of wellbeing in society. However, GDP does not consider many environmental impacts which have critical short and long-term economic effects. Due to this miscalculation about the concept of wellbeing, governments may downplay the ecological implications of growth and its contribution to inequality and poverty. Alternative measures to GDP exist to address the social and environmental aspects needed for a sustainable society. Alternative means are usually evaluated at the national level, but due to the Canadian political separation of powers and responsibilities, provincial governments have more responsibilities for environmental and social policy. This research paper explores the Happy Planet Index (HPI) in Ontario for over ten years, evaluating to what extent the Happy Planet Index addresses flaws in a GDP-based policy framework in Ontario. HPI is an eco-efficiency indicator which measures sustainable well-being, enabling policymakers to create effective policies towards the achievement of long, happy, and sustainable lives. HPI incorporates social and environmental variables which can be used by the provincial government in policy evaluation. The index includes three indicators: life satisfaction, a subjective measure of wellbeing that looks from the individual’s perspective on how people rank their happiness and life satisfaction; health-adjusted life expectancy, the average number of years that an individual is expected to live in a healthy state, or the average lifetime someone is expected to live; and ecological footprint, which measures a person’s consumption of nature
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