296 research outputs found
Dr. Yucel Yanikdag – Faculty Author Interview
No full textDr. Yucel Yanikdag, Associate Professor of History discusses his new book, Healing the Nation: Prisoners of War, Medicine and Nationalism in Turkey, 1914-1939, published recently by Edinburgh University Press. In this book, he explores how Ottoman prisoners of war and military doctors of the First World War discursively constructed their nation as a community, and at the same time attempted to exclude certain groups from that nation. Yanikdag aims to broaden the discussion of nationalism to explore how ideological and biological factors influenced each other
Extended Association Rule Mining with Correlation Functions
No full textThis paper proposes extended association rule mining that can deal with correlation functions. The extended association rule is expressed in the form of: A double right arrow Correl(X; Y) where Correl(X; Y) is a correlation function with two variables X and Y. By this extension, data analysts can discover the condition A that lead to low (or high) correlation between two given variables from a large dataset. In order to show the efficacy of the proposed method, a case study is performed on an industry dataset of software developments, assuming the scenario of discovering a condition, where software development effort is predictable (or unpredictable) from the size of the project, i.e. there exists a significantly high (or low) correlation between size and effort. Since such a condition cannot be obtained by conventional association rule mining, we confirm the efficiency of the proposed extended association rule mining
A Simulation Model of Software Quality Assurance in the Software Lifecycle
No full textSoftware quality assurance (SQA) is a series of activities within the software development lifecycle that repetitively verify or test the software deliverables to ensure their quality. In this paper, we propose a simulation model of SQA to quantitatively demonstrate the positive effect of adding quality assurance (QA) effort especially in early phases of software development. The proposed model can represent the relationship among the number of bugs in each phase, the amount of QA effort, the expected number of detectable bugs and the amount of bug fixing effort. The model can simulate the different QA strategies in a given software development context; thus, it is useful to identify the best or better strategies to improve software quality with smaller QA and bug fixing effort
Data Smoothing for Software Effort Estimation
No full textThe goal of this paper is to improve the estimation performance of software development effort by mitigating the problem caused by outliers in a historical software project data set, which is used to construct an effort estimation model. To date, outlier removal methods have been proposed to solve this problem; however, they are not always effective because removing outliers reduces the number of data points (= software projects in our case) in a data set, and a model built from a small data set often suffers from lack of generality. In such a case, estimation performance can become even worse. In this paper we propose a method called data smoothing to mitigate the problem of outliers without reducing the number of data points. We consider that data points are outliers if they do not meet the assumption of Analogy-Based Estimation (ABE) such that "projects with similar features require similar development efforts." The proposed method changes the effort values (person-months or person-hours) in a data set so as to satisfy this assumption; and by this way, all outliers become non-outliers without decreasing the data points. As a result of experimental evaluation using 8 software development data sets, we found that the proposed data smoothing showed the same or higher effort estimation accuracy than the non-smoothing case, while conventional outlier removal method showed worse accuracy in some data set
Association Metrics Between Two Continuous Variables for Software Project Data
No full textThe correlation coefficient is commonly used in analyses of software project data sets for the purpose of quantifying the relationship between two variables. However, while there are various types of relationships between two variables, the correlation coefficient cannot distinguish between these types. This study proposes new metrics between two continuous variables that havethe potential to characterize the relationship types
Prediction of Software Defects Using Automated Machine Learning
No full textThe effectiveness of defect prediction depends on modeling techniques as well as their parameter optimization, data preprocessing and ensemble development. This paper focuses on auto-sklearn, which is a recently-developed software library for automated machine learning, that can automatically select appropriate prediction models, hyperparameters and data preprocessing techniques for a given data set and develop their ensemble with optimized weights. In this paper we empirically evaluate the effectiveness of auto-sklearn in predicting the number of defects in software modules. In the experiment, we used software metrics of 20 OSS projects for cross-release defect prediction and compared auto-sklearn with random forest, decision tree and linear discriminant analysis by using Norm(Popt) as a performance measure. As a result, auto-sklearn showed similar prediction performance as random forest, which is one of the best prediction models for defect prediction in past studies. This indicates that auto-sklearn can obtain good prediction performance for defect prediction without any knowledge of machine learning techniques and models
Proceedings of Pedestrian and Evacuation Dynamics 2016
No full textn a recent series of papers, we proposed a mathematical model for the dynamics of a group
of interacting pedestrians. The model is based on a non-Newtonian potential, that accounts for the need of
pedestrians to keep both their interacting partner and their walking goal in their vision field, and to keep a
comfortable distance between them. These two behaviours account respectively for the angular and radial
part of the potential from which the force providing the pedestrian acceleration is derived. The angular term
is asymmetric, i.e. does not follow the third law of dynamics, with observable effects of group formation
and velocity. We first assumed the group to move in a scarcely dense environment, whose effect could be
modelled through a “white noise thermic bath”, and successfully compared the predictions of the model with
observations of real world pedestrian behaviour. We then studied, both from an empirical and a theoretical
standpoint, the effect of crowd density on group dynamics. We verified that the average effect of crowd density
may be modelled by adding a harmonic term to the group potential. The model predictions, which include
“phase transitions” in the group configuration (e.g. in 3 people groups transition from a “V” formation to
a “Λ” one, and eventually to pedestrians walking in a line), are again confirmed, at least in the observed
density range, by a comparison with real world data. Until now we had averaged all pedestrian data collected
in a given environmental setting (i.e. in corridors of similar width and at similar crowd densities) without
differentiating on group composition and social roles. In this work, we present preliminary results on these
features, namely we study how the group configuration and velocity is affected by inter-pedestrian relation
(family, couples, colleagues, friends), purpose (work, leisure) and gender. We also show results related to
the effect of asymmetric interactions, that confirm further the non-Newtonian nature of gaze-based angular
interaction in our model
Gender Profiling of Pedestrian Dyads
No full textIn traffic safety community, behavioral differences between genders
have been attracting considerable attention in recent decades. Various empirical
studies have proven that gender has a significant relation to drivers’, cyclists’ or
pedestrians’ decision making, route choice, rule compliance, as well as risk taking/
perception. However, most studies examine behavior of individuals, and only
very few consider (pedestrian) groups with different gender profiles. Therefore,
this study investigates effect of gender composition of pedestrian dyads on the
tangible dynamics, which may potentially help in automatically understanding
and interpreting higher level behaviors such as decision making.We first propose
a set of variables to represent dyads’s physical/dynamical state. Observing empirical
distributions, we comment on the effect of gender interplay on locomotion
preferences. In order to verify our inferences quantitatively, we propose a gender
profile recognition algorithm. Removing one variable at a time, contribution of
each variable to recognition is evaluated. Our findings indicate that height related
variables have a more strict relation to gender, followed by group velocity and
inter-personal distance. Moreover, the “male” effect on dyad motion is found to
somehow diminish when the male is paired with a female
Social group behaviour of triads. Dependence on purpose and gender
No full textWe analysed a set of uninstructed pedestrian trajectories automatically tracked in a public area, and we
asked a human coder to assess their group relationships. For those pedestrians who belong to the groups, we asked
the coder to identify their apparent purpose of visit to the tracking area and apparent gender. We studied the quantitative
dependence of the group dynamics on such properties in the case of triads (three people groups) and compared
them to the two pedestrian group case (dyads), studied in a previous work. We found that the group velocity strongly
depends on relation and gender for both triads and dyads, while the influence of these properties on spatial structure
of groups is less clear in the triadic case. We discussed the relevance of these results to the modelling of pedestrian
and crowd dynamics, and examined the possibility of the future works on this subject
Effect of Tool Specificity on the Performance of DNN-Based Saliency Prediction Methods
No full textThis study focuses on the performance of saliency
models concerning a specific type of image, namely hand tools.
These objects are characterized by functionally distinct segments
with various manipulative roles (i.e. end-effectors) and parts
that are used to grasp and operate (i.e. handles). A highlighted
by various studies in behavioral science, en-effectors of tools
inherently draw humans’ attention. However, it remains unclear
whether saliency models effectively address this intrinsic
aspect. To shed light on this, we selected four recent notable
saliency models, i.e. EML-NET, SalGAN, DeepGaze IIE, and
DeepGaze III, known for their reliance on transfer learning. Our
aim is to evaluate their performance in capturing the influence
of semantic segments within tools. To conduct the assessment,
we carefully chose a set of images featuring tool and nontool
objects from a large standardized dataset and applied each
saliency model to these images. Subsequently, these images were
presented to a group of human participants, and empirical gaze
data was recorded. Finally, we evaluated the correspondence
(or discrepancy) between the saliency maps and the empirical
data using six different evaluation criteria (i.e. CC, NSS, LL,
IG, KL, SIM). Our findings reveal that, across all four models,
the accuracy in predicting saliency concerning tool images often
lags behind that of non-tool images. Moreover, two out of the
four models exhibited consistently lower accuracy in predicting
saliency on tool images compared to non-tool images across
all six evaluation criteria. This indicates a lack of adequate
consideration for the specificity of tools in these recent saliency
models, highlighting the necessity to propose solutions to rectify
this limitation
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