snu-aidas/HVMap-dataset huggingface dataset:
Research synthesis in software engineering: A tertiary study

0950-5849/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.infsof.2011.01.004 ⇑ Corresponding author. E-mail addresses: dcruzes@idi.ntnu.no (D.S. Cruzes Context: Comparing and contrasting evidence from multiple studies is necessary to build knowledge and reach conclusions about the empirical support for a phenomenon. Therefore, research synthesis is at the center of the scientific enterprise in the software engineering discipline. Objective: The objective of this article is to contribute to a better understanding of the challenges in synthesizing software engineering research and their implications for the progress of research and practice. Method: A tertiary study of journal articles and full proceedings papers from the inception of evidencebased software engineering was performed to assess the types and methods of research synthesis in systematic reviews in software engineering. Results: As many as half of the 49 reviews included in the study did not contain any synthesis. Of the studies that did contain synthesis, two thirds performed a narrative or a thematic synthesis. Only a few studies adequately demonstrated a robust, academic approach to research synthesis. Conclusion: We concluded that, despite the focus on systematic reviews, there is limited attention paid to research synthesis in software engineering. This trend needs to change and a repertoire of synthesis methods needs to be an integral part of systematic reviews to increase their significance and utility for research and practice. 2011 Elsevier B.V. All rights reserved.
Directional antennas in FANETs: A performance analysis of routing protocols

Flying Ad-hoc Networks (FANETs) [1] are mobile ad-hoc networks formed by small and medium-sized UAVs. Nowadays, most UAVs are equipped with omnidirectional antennas. In addition, most of the existing routing protocols were designed assuming the use of omnidirectional antennas. Directional antennas have the potential to increase spatial reuse, save the battery's energy, and substantially increase the transmission range. However, these benefits come with a few challenges. Existing directional MAC protocols deal with these challenges, mostly in static ad-hoc networks. We define DA-FANETs as FANETs where directional antennas are used. In this paper, we investigate the performance of existing routing protocols in DA-FANETs. First we implement an 802.11b-based directional MAC protocol that embodies common directional MAC protocol features. Then we evaluate the following routing protocols in DA-FANET scenarios: AODV, OLSR, RGR, and GRP. The results show that each antenna beamwidth has an optimal network size that gives the highest amount of routing. In addition, RGR is the best option for DA-FANETs while GRP is the worst. Although RGR shows the best performance of all, it still leaves a lot of room for improvement with a PDR sitting at just 85% and a relatively high amount of overhead in terms of the number of transmissions performed.
Accurate Singular Values of Bidiagonal Matrices

C Abstract om puting the singular valu es of a bidiagonal m atrix is the fin al phase of the stan dard algo-w rithm for the singular valu e decom position of a general m atrix. We present a ne w algorithm hich com putes all the singular valu es of a bidiagonal m atrix to high relative accuracy indepen-p dent of their m agn itudes. In contrast, the stan dard algorithm for bidiagonal m atrice s m ay com ute sm all singular valu es with no relative accuracy at all. Num erical experim ents sh ow th at K the new algorithm is com parable in speed to the stan dard algorithm , an d frequently faster. The stan dard algorithm for com puting the singular valu e decom position (SVD) of a gen-1 eral real m atrix A has two phases [7]:) Com pute orthogonal m atrices P an d Q su ch that B = P AQ is in bidiagonal form , i.e. 1 1 1 T 1. 2 has nonzero entries only on its diagonal an d first su perdiagonal) Com pute orthogonal m atrices P an d Q su ch that Σ = P BQ is diagonal an d nonnega-t i 2 2 2 T 2 ive. The diagonal entries σ of Σ are the singular values of A. We will take them to be-sorted in decreasing order: σ ≥ σ. The colum ns of Q = Q Q are the right singular vec i i + 1 1 2 t 1 2 ors, an d the colum ns of P= P P are the left singular vectors. n This process takes O (n) operations, where n is the dim ension of A. This is true eve 3 though Phase 2 is iterative, since it converges quickly in practice. The error an alysis o f this r u com bined procedure has a widely accepted conclusion [8], an d provided neither overflow no nderflow occurs m ay be su m m arized as follows: The com puted singular valu es σ differ from the true singular valu es of A by no m ore t 1 i han p (n). ε. A , where A = σ is the 2-norm of A, ε is the m ach ine precision, an d p (n) is a T slowly growing function of the dim ension n …
Ravenclaw: dialog management using hierarchical task decomposition and an expectation agenda

We describe RavenClaw, a new dialog management framework developed as a successor to the Agenda [1] architecture used in the CMU Communicator. RavenClaw introduces a clear separation between task and discourse behavior specification, and allows rapid development of dialog management components for spoken dialog systems operating in complex, goal-oriented domains. The system development effort is focused entirely on the specification of the dialog task, while a rich set of domain-independent conversational behaviors are transparently generated by the dialog engine. To date, RavenClaw has been applied to five different domains allowing us to draw some preliminary conclusions as to the generality of the approach. We briefly describe our experience in developing these systems.
IoT survey: An SDN and fog computing perspective

Recently, there has been an increasing interest in the Internet of Things (IoT). While some analysts disvalue the IoT hype, several technology leaders, governments, and researchers are putting serious efforts to develop solutions enabling wide IoT deployment. Thus, the huge amount of generated data, the high network scale, the security and privacy concerns, the new requirements in terms of QoS, and the heterogeneity in this ubiquitous network of networks make its implementation a very challenging task. SDN, a new networking paradigm, has revealed its usefulness in reducing the management complexities in today’s networks. Additionally, SDN, having a global view of the network, has presented effective security solutions. On the other hand, fog computing, a new data service platform, consists of pushing the data to the network edge reducing the cost (in terms of bandwidth consumption and high latency) of “big data” transportation through the core network. In this paper, we critically review the SDN and fog computingbased solutions to overcome the IoT main challenges, highlighting their advantages, and exposing their weaknesses. Thus, we make recommendations at the end of this paper for the upcoming research work. Keywords— IoT; Survey; SDN; Fog; Cloud; 5G.
Spatial and Spatio-Temporal Multidimensional Data Modelling: A Survey

Data warehouse store and provide access to large volume of historical data supporting the strategic decisions of organisations. Data warehouse is based on a multidimensional model which allow to express user’s needs for supporting the decision making process. Since it is estimated that 80% of data used for decision making has a spatial or location component, spatial data have been widely integrated in Data Warehouses and in OLAP systems. Extending a multidimensional data model by the inclusion of spatial data provides a concise and organised spatial data warehouse representation. This paper aims to provide a comprehensive review of literature on developed and suggested spatial and spatio-temporel multidimensional models. A benchmarking study of the proposed models is presented. Several evaluation criteria’s are used to identify the existence of trends as well as potential needs for further investigations. Keywords— Data warehouse, Spatial data, Multidimensional modelling, Temporal data
To queue or not to queue : equilibrium behavior in queueing systems

To the memory of my parents, Sara and Avraham Haviv To my mother and late father, Fela and Shimon Hassin Contents Preface xi 1. INTRODUCTION 1 1.1 Basic concepts 2 1.1.1 Strategies, payoffs, and equilibrium 2 1.1.2 Steady-state 4 1.1.3 Subgame perfect equilibrium 5 1.1.4 Evolutionarily stable strategies 5 1.1.5 The Braess paradox 5 1.1.6 Avoid the crowd or follow it? 6 1.2 Threshold strategies 7 1.3 Costs and objectives 9 1.4 Queueing theory preliminaries 11 1.5 A shuttle example 14 1.5.1 The unobservable model 14 1.5.2 The observable model 17 1.5.3 Social optimality 18 1.6 Non-stochastic models 19
Domain Adaptation with Adversarial Neural Networks and Auto-encoders

Background. Domain adaptation focuses on the situation where we have data generated from multiple domains, which are assumed to be different, but similar, in a certain sense. In this work we focus on the case where there are two domains, known as the source and the target domain. The source domain is assumed to have a large amount of labeled data while labeled data in the target domain is scarce. The goal of domain adaptation algorithms is to generalize better on the target domain by exploiting the large amount of labeled data in the related source domain.
NAViGaTOR: Network Analysis, Visualization and Graphing Toronto

SUMMARY
NAViGaTOR is a powerful graphing application for the 2D and 3D visualization of biological networks. NAViGaTOR includes a rich suite of visual mark-up tools for manual and automated annotation, fast and scalable layout algorithms and OpenGL hardware acceleration to facilitate the visualization of large graphs. Publication-quality images can be rendered through SVG graphics export. NAViGaTOR supports community-developed data formats (PSI-XML, BioPax and GML), is platform-independent and is extensible through a plug-in architecture.


AVAILABILITY
NAViGaTOR is freely available to the research community from http://ophid.utoronto.ca/navigator/. Installers and documentation are provided for 32- and 64-bit Windows, Mac, Linux and Unix.


CONTACT
juris@ai.utoronto.ca


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Improving feature selection techniques for machine learning

As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been discovered that no single criterion is best for all applications. We proposed a hybrid feature selection framework called based on genetic algorithms (GAs) that employs a target learning algorithm to evaluate features, a wrapper method. We call it hybrid genetic feature selection (HGFS) framework. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for the target algorithm. The experiments on genomic data demonstrate that ours is a robust and effective approach that can find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. A common characteristic of text categorization tasks is multi-label classification with a great number of features, which makes wrapper methods time-consuming and impractical. We proposed a simple filter (non-wrapper) approach called Relation Strength and Frequency Variance (RSFV) measure. The basic idea is that informative features are those that are highly correlated with the class and distribute most differently among all classes. The approach is compared with two well-known feature selection methods in the experiments on two standard text corpora. The experiments show that RSFV generate equal or better performance than the others in many cases. INDEX WORDS: Feature selection, Gene selection, Term selection, Dimension Reduction, Genetic algorithm, Text categorization, Text classification IMPROVING FEATURE SELECTION TECHNIQUES FOR
Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM

Chronic kidney disease (CKD), also known as chronic renal disease. Chronic kidney disease involves conditions that damage your kidneys and decrease their ability to keep you healthy. You may develop complications like high blood pressure, anemia (low blood count), weak bones, poor nutritional health and nerve damage. . Early detection and treatment can often keep chronic kidney disease from getting worse. Data Mining is the term used for knowledge discovery from large databases. The task of data mining is to make use of historical data, to discover regular patterns and improve future decisions, follows from the convergence of several recent trends: the lessening cost of large data storage devices and the everincreasing ease of collecting data over networks; the expansion of robust and efficient machine learning algorithms to process this data; and the lessening cost of computational power, enabling use of computationally intensive methods for data analysis. Machine learning, has already created practical applications in such areas as analyzing medical science outcomes, detecting fraud, detecting fake users etc. Various data mining classification approaches and machine learning algorithms are applied for prediction of chronic diseases. The objective of this research work is to introduce a new decision support system to predict chronic kidney disease. The aim of this work is to compare the performance of Support vector machine (SVM) and K-Nearest Neighbour (KNN) classifier on the basis of its accuracy, precision and execution time for CKD prediction. From the experimental results it is observed that the performance of KNN classifier is better than SVM. Keywords—Data Mining, Machine learning, Chronic kidney disease, Classification, K-Nearest Neighbour, Support vector machine.
Prader-Willi syndrome: a review of clinical, genetic, and endocrine findings

INTRODUCTION
Prader-Willi syndrome (PWS) is a multisystemic complex genetic disorder caused by lack of expression of genes on the paternally inherited chromosome 15q11.2-q13 region. There are three main genetic subtypes in PWS: paternal 15q11-q13 deletion (65-75 % of cases), maternal uniparental disomy 15 (20-30 % of cases), and imprinting defect (1-3 %). DNA methylation analysis is the only technique that will diagnose PWS in all three molecular genetic classes and differentiate PWS from Angelman syndrome. Clinical manifestations change with age with hypotonia and a poor suck resulting in failure to thrive during infancy. As the individual ages, other features such as short stature, food seeking with excessive weight gain, developmental delay, cognitive disability and behavioral problems become evident. The phenotype is likely due to hypothalamic dysfunction, which is responsible for hyperphagia, temperature instability, high pain threshold, hypersomnia and multiple endocrine abnormalities including growth hormone and thyroid-stimulating hormone deficiencies, hypogonadism and central adrenal insufficiency. Obesity and its complications are the major causes of morbidity and mortality in PWS.


METHODS
An extensive review of the literature was performed and interpreted within the context of clinical practice and frequently asked questions from referring physicians and families to include the current status of the cause and diagnosis of the clinical, genetics and endocrine findings in PWS.


CONCLUSIONS
Updated information regarding the early diagnosis and management of individuals with Prader-Willi syndrome is important for all physicians and will be helpful in anticipating and managing or modifying complications associated with this rare obesity-related disorder.
Biomedical ontology alignment: an approach based on representation learning

BACKGROUND
While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance.


RESULTS
An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results.


CONCLUSIONS
Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.
Dual Policy Iteration

A novel class of Approximate Policy Iteration (API) algorithms have recently demonstrated impressive practical performance (e.g., ExIt [1], AlphaGo-Zero [2]). This new family of algorithms maintains, and alternately optimizes, two policies: a fast, reactive policy (e.g., a deep neural network) deployed at test time, and a slow, non-reactive policy (e.g., Tree Search), that can plan multiple steps ahead. The reactive policy is updated under supervision from the non-reactive policy, while the non-reactive policy is improved via guidance from the reactive policy. In this work we study this class of Dual Policy Iteration (DPI) strategy in an alternating optimization framework and provide a convergence analysis that extends existing API theory. We also develop a special instance of this framework which reduces the update of non-reactive policies to model-based optimal control using learned local models, and provides a theoretically sound way of unifying model-free and model-based RL approaches with unknown dynamics. We demonstrate the efficacy of our approach on various continuous control Markov Decision Processes.
Neonatal Abstinence Syndrome and High School Performance.

BACKGROUND AND OBJECTIVES
Little is known of the long-term, including school, outcomes of children diagnosed with Neonatal abstinence syndrome (NAS) (International Statistical Classification of Disease and Related Problems [10th Edition], Australian Modification, P96.1).


METHODS
Linked analysis of health and curriculum-based test data for all children born in the state of New South Wales (NSW), Australia, between 2000 and 2006. Children with NAS (n = 2234) were compared with a control group matched for gestation, socioeconomic status, and gender (n = 4330, control) and with other NSW children (n = 598 265, population) for results on the National Assessment Program: Literacy and Numeracy, in grades 3, 5, and 7.


RESULTS
Mean test scores (range 0-1000) for children with NAS were significantly lower in grade 3 (359 vs control: 410 vs population: 421). The deficit was progressive. By grade 7, children with NAS scored lower than other children in grade 5. The risk of not meeting minimum standards was independently associated with NAS (adjusted odds ratio [aOR], 2.5; 95% confidence interval [CI], 2.2-2.7), indigenous status (aOR, 2.2; 95% CI, 2.2-2.3), male gender (aOR, 1.3; 95% CI, 1.3-1.4), and low parental education (aOR, 1.5; 95% CI, 1.1-1.6), with all Ps < .001.


CONCLUSIONS
A neonatal diagnostic code of NAS is strongly associated with poor and deteriorating school performance. Parental education may decrease the risk of failure. Children with NAS and their families must be identified early and provided with support to minimize the consequences of poor educational outcomes.
Vaginal Labiaplasty: Defense of the Simple “Clip and Snip” and a New Classification System

Vaginal labiaplasty has become a more frequently performed procedure as a result of the publicity and education possible with the internet. Some of our patients have suffered in silence for years with large, protruding labia minora and the tissue above the clitoris that is disfiguring and uncomfortable and makes intercourse very difficult and painful. We propose four classes of labia protrusion based on size and location: Class 1 is normal, where the labia majora and minora are about equal. Class 2 is the protrusion of the minora beyond the majora. Class 3 includes a clitoral hood. Class 4 is where the large labia minora extends to the perineum. There are two principal means of reconstructing this area. Simple amputation may be possible for Class 2 and Class 4. Class 2 and Class 3 may be treated with a wedge resection and flap advancement that preserves the delicate free edge of the labia minora (Alter, Ann Plast Surg 40:287, 1988). Class 4 may require a combination of both amputation of the clitoral hood and/or perineal extensions and rotation flap advancement over the labia minora. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .