Reviewerly/exHarmony huggingface dataset:
The Qualitative Analysis in Eye Tracking Studies: Including Subjective Data Collection in an Experimental Protocol

When conducting eye tracking studies, the benefits of collecting qualitative data are significant. In this paper, we propose to take qualitative data collection even further by exploring participants’ emotions and habitus during a semi-directive interview guided by the user himself. This method is part of a research on data visualization: how does the user make sense of data visualization and what is the influence of visualizations embellishment on this sense making? Since sense making can be stimulated as much by the human perceptual system as by personal experience and opinions, the methodological challenge of this research is to design an experimental protocol that considers these two dimensions. It is of great interest to combine an eye tracking study with qualitative data collection, which is intended to be both complementary and expanding. The challenge is to find the right method for collecting qualitative data when studying subjective elements. We then propose a semi-directive post-experimental interview method that will integrate all these aspects into the research.
Multiagent Technology for Parallel Implementation of Boolean Constraint Method for Qualitative Analysis of Binary Dynamic Systems

The high computational complexity of qualitative analysis problems requires the development of software and tools for its solution using parallel computations and service-oriented access to the resources of high-performance computing environments. We offer a new implementation technology of Boolean constraints method for the qualitative research of binary dynamic system functioning on a finite time interval. According to this method, the satisfiability verification of dynamic system properties is reduced to solving the Boolean satisfiability problem or quantified Boolean formula problem. Offered technology is based on a decentralized multi-agent approach to managing the construction and study of Boolean models of dynamic properties in a distributed computational network. Microservices-based software for models constructing and studying are installed on dedicated computational nodes. The agents associated with installation nodes, run these microservices. Local agent's knowledge base stores information about their neighborhoods. The discrete event-driven agent's behavior model provides decentralized control based on direct peer-to-peer interactions of agents. Using this approach, we developed a multiagent system automating the constructing a dynamic property model and parallel solving the Boolean satisfiability problem and quantified Boolean formula problem. The results of computational experiments confirmed the effectiveness of the developed software based on the proposed technology.
Job-Aware Optimization of File Placement in Hadoop

Hadoop is a popular data-analytics platform based on the MapReduce model. When analyzing extremely big data, hard disk drives are commonly used and Hadoop performance can be optimized by improving I/O performance. Hard disk drives have different performance depending on whether data are placed in the outer or inner disk zones. In this paper, we propose a method that uses knowledge of job characteristics to place data in hard disk drives so that Hadoop performance is improved. Files of a job that intensively and sequentially accesses the storage device are placed in outer disk tracks which have higher sequential access speed than inner tracks. Temporary and permanent files are placed in the outer and inner zones, respectively. This enables repeated usage of the faster zones by avoiding the use of the fastest zones by permanent files. Our evaluation demonstrates that the proposed method improves the performance of the Hadoop jobs by 15.0% over the normal case when file placement is not used. The proposed method also outperforms a previously proposed placement approach by 9.9%.
Exploration into Gray Area: Efficient Labeling for Malicious Domain Name Detection

This paper presents a method to reduce the labeling cost when acquiring training data for a system that detects malicious domain names by supervised machine learning. The conventional system requires large quantities of both benign and malicious domain names to be prepared as training data to obtain a classifier with high classification accuracy. In general, malicious domain names are observed less frequently than benign domain names. Therefore, it is difficult to acquire a large number of malicious domain names without a dedicated labeling method. We propose a method based on active learning that labels data around the decision boundary of classification, i.e., in the gray area, and we show that the classification accuracy can be improved by only using approximately 2.5% of the training data used by the conventional system. An additional disadvantage of the conventional system is that, if the classifier is trained with a small amount of training data, its generalization ability cannot be guaranteed. We propose a method based on ensemble learning that integrates multiple classifiers, and we show that the classification accuracy can be stabilized and improved.
Ontology as a Modeling Tool within Model Driven Architecture Abstraction

This paper is focused on automatic transformation process of top levels of Model Driven Architecture (MDA) within the information system development phase. System architects are always trying to find easier, complex and more united way of information system development. Although the Model Driven Architecture (MDA) provides a set of guidelines for the structuring of specifications it also comes with challenging tasks of transformations between the various levels of abstraction. The primary objective of this work is to design a universal automated approach within the Computer Independent Model (CIM) and Platform Independent Model (PIM) manual transformation. The manual process of the transformations within MDA could be automated using ontology model with the combination of mapping rules and Extensible Markup Process Definition Language (XPDL) and Extensible Markup Language Metadata interchange (XMI) conversion.
A General Performance and QoS Model for Distributed Software-Defined Environments

The landscape for cloud services and cyberinfrastructure offerings has increased drastically over the past few years. Initially, users moved their applications to the cloud to take advantage of a pay-per-usage model and on-demand access. However, as more cloud providers joined the market, users shifted their goals for using cloud computing from cost reduction to resilience, agility, and optimization. These goals can be achieved by dynamically combining services from multiple providers, for example, to avoid data center or cloud zone outages or to take advantage of extensive offerings with different price points. However, to efficiently support application deployment in this dynamic environment, new models and tools that can measure the application performance and the Quality of Service (QoS) of different configurations are required. The goal of this work is to evaluate the application performance and the QoS of a distributed Software-Defined Environment as well as calculate the QoS of alternative configurations from the underlying pool of services. In particular, we present a mathematical model and a tool for evaluating the performance and QoS of batch application workflows in a distributed environment. We experimentally evaluate the proposed model using a bioinformatics workflow running on infrastructure services from multiple cloud providers.
Cutting Edge and Cutting Corners: Evolving Technology, Expanding Usership, and Responsive Solutions in a Museum Database

ABSTRACT In the mid-1980s, the Anthropology Division of the American Museum of Natural History (AMNH) began the creation of digital resources as a means of collections access. Much of the database work was a secondary component of projects funded by outside grants and driven by new accountability mandates. The ongoing upgrading process was sporadic in its progress, but it still accomplished the primary goals of improved housing for collections and an exhaustive database. This paper discusses how the historical complications of the data, the scale of the database, its irregular schedule of funding, and deadline-driven projects resulted in inconsistency in data and difficulty in use. Although the examples provided will be specific to the AMNH Anthropology database, the circumstances and issues are common to many databases and the approaches presented broadly applicable. The discussion includes the practices used to mitigate the negative impact of these problems and the way the Division is positioning itself for the future, even as the database continues to provide unprecedented public and institutional access to and utility for the AMNH Anthropology collections.
Performance Comparison of New Fast Weighted Naïve Bayes Classifier with Other Bayes Classifiers

Rapid development of the technology, along with the increasing amount of data, makes data analysis inconvenient. Nowadays, it is important that many processes can be recorded, stored and accessed in an electronic environment. As long as the data is not processed, it does not make any sense. Data mining is used to make the data meaningful. Data mining enables useful information to be reached by separating information from large-scale data. At the same time, it is the process of searching for the data by using software to make predictions about the future. In this study, a new fast weighted Bayesian Classifier is proposed, and its performance is compared with Naïve Bayes Classifier and Weighted Naïve Bayes Classifier, which is one of the data mining classification methods. Various data sets are used to obtain the results of the comparison. It is observed that the accuracy rate of the Fast Weighted Bayes Algorithm is better than Naïve Bayes Classifier and it is faster than the Weighted Naïve Bayes Classifier.
Estimation of a physical activity energy expenditure with a patch‐type sensor module using artificial neural network

Summary Chronic diseases such as coronary artery diseases and diabetes are caused by lack of physical activities and are leading causes of high death and morbidity rates. In particular, the imbalance of consumption energy and intake energy has increased adult diseases such as obesity with high mortality. Until recently, direct calorimetry by production calorie and indirect calorimetry by energy expenditure have been regarded as the best methods for estimating physical activity and energy expenditure. These calorimetry methods are associated with limited practicality such as data acquisition in a limited time, high cost, and wearing an inconvenient mask for oxygen uptake measurement. In this study, we propose the most accurate method using a wireless patch‐type sensor to predict the energy expenditure of physical activities. Through the optimization of the prediction of energy expenditure of physical activities using the neural network algorithm, we achieved RMSE of 0.1893 and R 2 of 0.91 for the energy expenditures of aerobic and anaerobic exercises. These results indicate that the proposed system is useful and reliable for monitoring user's energy expenditure when using attached patch‐type sensors workouts.
Subtractive Clustering and Particle Swarm Optimization Based Fuzzy Classifier

Setting a compact and accurate rule base constitutes the principal objective in designing fuzzy rule-based classifiers. In this regard, the authors propose a designing scheme based on the combination of the subtractive clustering (SC) and the particle swarm optimization (PSO). The main idea relies on the application of the SC on each class separately and with a different radius in order to generate regions that are more accurate, and to represent each region by a fuzzy rule. However, the number of rules is then affected by the radiuses, which are the main preset parameters of the SC. The PSO is therefore used to define the optimal radiuses. To get good compromise accuracy-compactness, the authors propose using a multi-objective function for the PSO. The performances of the proposed method are tested on well-known data sets and compared with several state-of-the-art methods.
Enhancing secure business process design with security process patterns

Business process definition and analysis are an important activity for any organisation. As research has demonstrated, well-defined business processes can reduce cost, improve productivity and provide organisations with competitive advantages. In the last few years, the need to ensure the security of business processes has been identified as a major research challenge. Limited security expertise of business process developers together with a clear lack of appropriate methods and techniques to support the security analysis of business processes is important prohibitors to providing answers to that research challenge. This paper introduces the first attempt in the literature to produce a novel pattern-based approach to support the design and analysis of secure business processes. Our work draws on elements from the security requirements engineering area and the security patterns area, combined with business process modelling, and it produces a set of process-level security patterns which are used to implement security in a given business process model. Such an approach advances the existing literature by providing a structured way of operationalising security at the business process level of abstraction. The applicability of the work is illustrated through an application to a real-life information system, and the effectiveness and usability of the work are evaluated via a workshop-based experiment. The evaluation clearly indicates that non-experts are able to comprehend and utilise the developed patterns to construct secure business process designs.
Pragmatic Text Mining Method to Find the Topics of Citation Network

Here is a large amount of data being generated every minute via websites, which are usually stored in corpus, and these data are either structured or unstructured or semi-structured. Each document in corpus often composed of a large number of words that are frequently repeated and which are insignificant. The traditional method to text preprocessing is following the sequence that is Tokenization, Stopwords Removal, Punctuation Removal and Stemming and finally computes TF-IDF to extract the score based on their frequency for each keyword. This paper focuses on building a pragmatic method to handle the documents of corpus and determined the topics for each document. This method involves (i) preprocessing the text documents using Rake that prove it extremely more efficient in term of time consuming (ii) Cleaning the document from some impurities (iii) building dictionary to keywords (iv) Apply LDA model to find the topics for each document. Two case study achieve in this paper, the first apply traditional method step on corpus while the second apply pragmatic method on the same corpus then compare the result of both methods. As result, we found pragmatic method given result based than traditional method in terms of accuracy and efficiency.
Many-view clustering

Multi-view problems generalize standard machine learning problems to situations in which data entities are described from multiple different perspectives, a situation that arises in many applications due to the consideration of multiple data sources or multiple metrics of dissimilarity between entities. Multi-view algorithms for data clustering offer the opportunity to fully consider and integrate this information during the clustering process, but current algorithms are often limited to the use of two views.
Identification of User Application by an External Eavesdropper using Machine Learning Analysis on Network Traffic

An eavesdropper may infer the computer applications a person uses by collecting and analyzing the network traffic they generate. Such inference may be performed despite applying encryption on the generated packets. In this paper, we investigate the extent of the ability of several machine learning algorithms to perform this privacy breach on the network traffic generated by a user. We measure their accuracy in identifying different applications by analyzing several statistical properties of the generated traffic rather than looking into the encrypted content. We compare the performance of these algorithms and select the one with higher precision; random forest. We also evaluate the application of packet padding to modify the packet length to avoid identification by machine learning algorithms. We test the effect of packet padding on the identification ability of the various machine-learning algorithms. We investigate the performance of the random forest algorithm in detail when applied to intact and padded traffic. We show that padding may decrease the efficacy of a machine-learning algorithm when used for application classification.
The SAMBA Approach for Self-Adaptive Model-Based Online Testing of Services Orchestrations

Service Oriented Architecture (SOA) is a popular design pattern that allows building applications composed of loosely-coupled and autonomous services. Such services may evolve and change at runtime, often outside the control of the owner of the application. Consequently, typical validation approaches, like offline testing performed before services deployment, are necessary but not sufficient: offline testing cannot assure the correct behavior of the SOA during its execution. To cope with the evolution of services and their orchestrations, in this paper we present a Self-Adaptive Model-BAsed online testing framework called SAMBA. SAMBA aims to assess the proper behavior of a SOA during its lifecycle executing model-based online testing at runtime, under the coordination of a MAPE-K control loop. SAMBA is assessed in a case study, where its detection capability are proved through functional, mutation and fault injection tests.
A User-Centric Design Framework for Smart Built Environments

Smart Built Environments (SBEs) empowered by the Internet of Things (IoT) dramatically augment the capabilities of traditional built environments by imbuing everyday objects with computational and communication capabilities. SBEs primarily consist of three types of components: architectural elements, embedded technology (smart objects) and enhanced interaction modalities. As smart objects hold the ability to change the state of the environment, inefficient design of smart configurations can lead to potentially harmful conditions affecting the safety and security of the inhabitants. The interaction scenarios and space use pattern of SBEs are also notably different from traditional built environments. But, to the best of our knowledge, there has been limited work on developing a consolidated design framework addressing the three interdependent SBE elements and evaluating the safety and security of the IoT application environment. We propose an SBE design framework based on the traditional architectural design process. The framework combines the technological aspects of SBEs with the traditional architectural design process while leveraging Building Information Modeling (BIM) and participatory design. We describe a Mixed Reality(MR)-based reference framework implementation that is particularly helpful for representing, visualizing and modeling the vast amount of data, digital components and novel SBE interaction scenarios.
Featured Event Sequence Graphs for Model-Based Incremental Testing of Software Product Lines

The goal of software product lines (SPLs) is rapid development of high-quality software products in a specific domain with cost minimization. To assure quality of software products from SPLs, products need to be tested systematically. However, testing every product variant in isolation is generally not feasible for large number of product variants. An approach to deal with this issue is to use incremental testing, where test artifacts that are developed for one product are reused for another product which can be obtained by incrementally adding features to the prior product. We propose a novel model-based test generation approach for products developed using SPL that follows incremental testing paradigm. First, we introduce Featured Event Sequence Graphs (FESGs), an extension of ESGs, that provide necessary definitions and operations to support commonalities and variabilities in SPLs with respect to test models. Then we propose a test generation technique for the product variants of an SPL, which starts from any product. The proposed technique with FESGs avoids redundant test generation for each product from SPL. We compare our technique with in-isolation testing approach by a case study.
Expected Label Value Computation for Atlas-Based Image Segmentation

The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible transformations and compute the expected label value (ELV), thereby not relying merely on the transformation resulting from the registration. Moreover, we do so without actually performing deformable registration, thus avoiding the associated computational costs. We evaluate our ELV computation approach by applying it to liver segmentation on a dataset of computed tomography (CT) images.
Discovering Identity Specific Activation Patterns in Deep Descriptors for Template Based Face Recognition

The majority of recent face recognition systems are based on Deep Convolutional Neural Networks (DCNNs). These networks are trained on massive amounts of face images so as to learn a compact representation (deep descriptor) aimed at capturing the identity information. Recognition is then performed by computing some similarity (or distance) measure between descriptors. However, in practice, descriptors encode also other intra-class variabilities such as pose and expressions. This well-known problem is usually addressed by designing specific loss-functions or metric learning modules such that the learned descriptors maximize the inter-class (identity) distances and minimize the intra-class differences in the feature space. We tackle this problem from a different perspective by observing that descriptors associated with images of the same subject, on average, share similar patterns in the highest activation units. We demonstrate this assumption by showing that improved accuracy can be obtained in a template-based recognition scenario by retaining the descriptor bins with the average highest activation, and dropping all the others to zero. These activation patterns are also employed to build identity-representative binary masks that are effectively used in place of the descriptors to match templates. We investigate this strategy by performing experiments on the IJB-A dataset, and show that it can significantly boost the recognition accuracy.
Supervised and Unsupervised-Based Analytics of Intensive Care Unit Data

Resources and personnel availability in Intensive Care Units (ICUs) of hospitals are scarce and challenging to manage, particularly certain group of patients are more likely to be dead than alive after released from ICUs. There has been availability of ICU data, opening the door for performing analytical approach to uncover the trends and patterns for better policy and resource allocation decision towards improved outcome of the patients. In this paper, we explored MIMIC III dataset and applied supervised and unsupervised learning approaches to shed some lights on the complex underlying relationships between the patient's Length of Stay (LOS) and a number of attributes available from data. Our results indicate that neural network-based approaches perform the best for predicting the mortality outcome compared to other supervised and unsupervised approaches.