The Social Side of Software Platform Ecosystems
Software and Programming Tools
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de Souza, Cleidson R. B.
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Filho, Fernando Figueira
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Miranda, Müller
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Ferreira, Renato Pina
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Treude, Christoph
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Singer, Leif
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.3204-3214
© Copyright 2016 ACM
Summary: Software ecosystems as a paradigm for large-scale software development
encompass a complex mix of technical, business, and social aspects. While
significant research has been conducted to understand both the technical and
business aspects, the social aspects of software ecosystems are less well
understood. To close this gap, this paper presents the results of an empirical
study aimed at understanding the influence of social aspects on developers'
participation in software ecosystems. We conducted 25 interviews with mobile
software developers and an online survey with 83 respondents from the mobile
software development community. Our results point out a complex social system
based on continued interaction and mutual support between different actors,
including developers, friends, end users, developers from large companies, and
online communities. These findings highlight the importance of social aspects
in the sustainability of software ecosystems both during the initial adoption
phase as well as for long-term permanence of developers.
Experimenting on the cognitive walkthrough with users
Industrial case studies
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Lira, Wallace
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Ferreira, Renato
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de Souza, Cleidson
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Carvalho, Schubert
Proceedings of 2014 Conference on Human-Computer Interaction with Mobile
Devices and Services
2014-09-23
p.613-618
© Copyright 2014 ACM
Summary: This paper presents a case study aiming to investigate which variant of the
Think-Aloud Protocol (i.e., the Concurrent Think-Aloud and the Retrospective
Think-Aloud) better integrates with the Cognitive Walkthrough with Users. To
this end we performed a case study that involved twelve users and one usability
evaluator. Usability problems uncovered by each method were evaluated to help
us understand the strengths and weaknesses of the studied usability testing
methods. The results suggest that 1) the Cognitive Walkthrough with Users
integrates equally well with both the Think-Aloud Protocol variants; 2) the
Retrospective Think-Aloud find more usability problems and 3) the Concurrent
Think-Aloud is slightly faster to perform and was more cost effective. However,
this is only one case study, and further research is needed to verify if the
results are actually statistically significant.
A new sentence similarity assessment measure based on a three-layer sentence
representation
Document analysis I
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Ferreira, Rafael
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Lins, Rafael Dueire
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Freitas, Fred
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Simske, Steven J.
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Riss, Marcelo
Proceedings of the 2014 ACM Symposium on Document Engineering
2014-09-16
p.25-34
© Copyright 2014 ACM
Summary: Sentence similarity is used to measure the degree of likelihood between
sentences. It is used in many natural language applications, such as text
summarization, information retrieval, text categorization, and machine
translation. The current methods for assessing sentence similarity represent
sentences as vectors of bag of words or the syntactic information of the words
in the sentence. The degree of likelihood between phrases is calculated by
composing the similarity between the words in the sentences. Two important
concerns in the area, the meaning problem and the word order, are not handled,
however. This paper proposes a new sentence similarity assessment measure that
largely improves and refines a recently published method that takes into
account the lexical, syntactic and semantic components of sentences. The new
method proposed here was benchmarked using a publically available standard
dataset. The results obtained show that the new similarity assessment measure
proposed outperforms the state of the art systems and achieve results
comparable to the evaluation made by humans.
Transforming graph-based sentence representations to alleviate overfitting
in relation extraction
Document analysis II
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Lima, Rinaldo J.
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Batista, Jamilson
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Ferreira, Rafael
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Freitas, Fred
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Lins, Rafael Dueire
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Simske, Steven
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Riss, Marcelo
Proceedings of the 2014 ACM Symposium on Document Engineering
2014-09-16
p.53-62
© Copyright 2014 ACM
Summary: Relation extraction (RE) aims at finding the way entities, such as person,
location, organization, date, etc., depend upon each other in a text document.
Ontology Population, Automatic Summarization, and Question Answering are fields
in which relation extraction offers valuable solutions. A relation extraction
method based on inductive logic programming that induces extraction rules
suitable to identify semantic relations between entities was proposed by the
authors in a previous work. This paper proposes a method to simplify
graph-based representations of sentences that replaces dependency graphs of
sentences by simpler ones, keeping the target entities in it. The goal is to
speed up the learning phase in a RE framework, by applying several rules for
graph simplification that constrain the hypothesis space for generating
extraction rules. Moreover, the direct impact on the extraction performance
results is also investigated. The proposed techniques outperformed some other
state-of-the-art systems when assessed on two standard datasets for relation
extraction in the biomedical domain.
Economically-efficient sentiment stream analysis
Session 7a: sentiments
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Lourenco, Roberto, Jr.
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Veloso, Adriano
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Pereira, Adriano
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Meira, Wagner, Jr.
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Ferreira, Renato
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Parthasarathy, Srinivasan
Proceedings of the 2014 Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2014-07-06
p.637-646
© Copyright 2014 ACM
Summary: Text-based social media channels, such as Twitter, produce torrents of
opinionated data about the most diverse topics and entities. The analysis of
such data (aka. sentiment analysis) is quickly becoming a key feature in
recommender systems and search engines. A prominent approach to sentiment
analysis is based on the application of classification techniques, that is,
content is classified according to the attitude of the writer. A major
challenge, however, is that Twitter follows the data stream model, and thus
classifiers must operate with limited resources, including labeled data and
time for building classification models. Also challenging is the fact that
sentiment distribution may change as the stream evolves. In this paper we
address these challenges by proposing algorithms that select relevant training
instances at each time step, so that training sets are kept small while
providing to the classifier the capabilities to suit itself to, and to recover
itself from, different types of sentiment drifts. Simultaneously providing
capabilities to the classifier, however, is a conflicting-objective problem,
and our proposed algorithms employ basic notions of Economics in order to
balance both capabilities. We performed the analysis of events that
reverberated on Twitter, and the comparison against the state-of-the-art
reveals improvements both in terms of error reduction (up to 14%) and reduction
of training resources (by orders of magnitude).
Effective sentiment stream analysis with self-augmenting training and
demand-driven projection
Classification
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Silva, Ismael Santana
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Gomide, Janaína
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Veloso, Adriano
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Meira, Wagner, Jr.
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Ferreira, Renato
Proceedings of the 34th Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2011-07-25
p.475-484
© Copyright 2011 ACM
Summary: How do we analyze sentiments over a set of opinionated Twitter messages?
This issue has been widely studied in recent years, with a prominent approach
being based on the application of classification techniques. Basically,
messages are classified according to the implicit attitude of the writer with
respect to a query term. A major concern, however, is that Twitter (and other
media channels) follows the data stream model, and thus the classifier must
operate with limited resources, including labeled data for training
classification models. This imposes serious challenges for current
classification techniques, since they need to be constantly fed with fresh
training messages, in order to track sentiment drift and to provide up-to-date
sentiment analysis.
We propose solutions to this problem. The heart of our approach is a
training augmentation procedure which takes as input a small training seed, and
then it automatically incorporates new relevant messages to the training data.
Classification models are produced on-the-fly using association rules, which
are kept up-to-date in an incremental fashion, so that at any given time the
model properly reflects the sentiments in the event being analyzed. In order to
track sentiment drift, training messages are projected on a demand driven
basis, according to the content of the message being classified. Projecting the
training data offers a series of advantages, including the ability to quickly
detect trending information emerging in the stream. We performed the analysis
of major events in 2010, and we show that the prediction performance remains
about the same, or even increases, as the stream passes and new training
messages are acquired. This result holds for different languages, even in cases
where sentiment distribution changes over time, or in cases where the initial
training seed is rather small. We derive lower-bounds for prediction
performance, and we show that our approach is extremely effective under diverse
learning scenarios, providing gains that range from 7% to 58%.
Effects of Face-Threatening Acts in Human-Computer Dialogues
COMPUTER SYSTEMS: Computer Systems Posters
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Colon, Jaime X. Elias
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Perez-Quindeones, Manuel A.
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Ferreira, Raquel
Proceedings of the Human Factors and Ergonomics Society 45th Annual Meeting
2001-10-08
v.45
p.657-661
© Copyright 2001 HFES
Summary: This work explores the relationship between Face Threatening Acts (FTAs)
commonly performed by the computer (in the form of interruptions) while
interacting with users. It investigates the effects that these acts have upon
the user's satisfaction. An experiment proved a hypothesis: FTAs performed by a
computer interface have a detrimental effect upon the user perception of the
interaction with the computer. This effect is observed on the user perception
of the interaction as being less friendly, less motivating and less
cooperative. It was also found that politeness strategies had no effect on
minimizing the perception of a FTA.
Aging at Work: Survey among Health Care Shiftworkers of Sao Paulo, Brazil
4: AGING: Is Age a Key Human Factor To Be Considered as We Enter the New
Millennium? [Single-Session Symposium]
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Fischer, Frida Marina
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Bellusci, Silvia M.
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Borges, Flavio N. S.
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Teixeira, Liliane R.
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Christoffolete, Marcelo A.
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Martins, Samantha E.
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Ferreira, Regiane M.
Proceedings of the Joint IEA 14th Triennial Congress and Human Factors and
Ergonomics Society 44th Annual Meeting
2000-07-30
v.44
n.4
p.39-41
© Copyright 2000 HFES
Summary: A cross-sectional study was conducted among 176 nurses, mean age=36.9 (SD
8.5), working in a University Hospital in São Paulo, Brazil. The main
objective of this study was a self-evaluation of aging at work. Participants
volunteered to answer a health care workers survey, adapted from an English
version. Their main concerns about their exposure at the workplace (a) and
off-the job conditions (b) were: a) changes in equipment and technology,
transportation and changes in employer policies; b) personal safety, increases
in the cost of living, food safety, water and air quality. The majority of
workers considered themselves having adequate current work ability with respect
to physical, mental and social demands. Mean perceived ability to work on a
10-point scale was 8.3 (SD =1.18). Means of chronological age are higher than
the perception of the workers about how they look, act and feel. Traditional
approaches to improve some of the working conditions may be not be sufficient
to achieve a good quality of the working life.
