Reviewerly/exHarmony huggingface dataset:
Singular System Analysis with Application to Dynamical Systems

The chapter explains the theory of singular system analysis, a generalization of eigenfunction analysis, with particular reference to its use for extending the concepts of classical information theory. The classical theory of information is reviewed based upon an eigenvalue inversion in an L space of a first kind Fredholm equation. Broomhead and King have used the singular system analysis to construct an embedding of the global attractor. The measures of information content or number of degrees of freedom which the theory provides have been used to analyse time series data from nonlinear dynamical systems. The number of degrees of freedom that results from the singular system analysis identifies the dimension of a deterministic subspace. The singular system analysis offers a new direction in the treatment of data from that of conventional Fourier or correlation processing. A local analysis in this reduced space was then used to discover the underlying dimension or number of independent processes contributing to the motion.
Cascaded diffraction in optical systems. Part I: simulation model

In every real optical system, the light beam or ray bundle is laterally limited by diaphragms or lens boundaries. Therefore, from a rigorous point of view, a diffraction calculation of the point spread function by assuming a truncation at the exit pupil is a simplification. If several boundaries at different <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>z</mml:mi> </mml:math> positions inside a system truncate the rays, cascaded diffraction occurs and the field in the exit pupil is modified in amplitude and phase by the edge diffraction effects at all relevant boundaries. A simple model for a fast estimation of these effects and a rule of thumb for real optical systems are worked out in this work.
LRP-Based path relevances for global explanation of deep architectures

Understanding what Machine Learning models are doing is not always trivial. This is especially true for complex models such as Deep Neural Networks (DNN), which are the best-suited algorithms for modeling very complex and nonlinear relationships. But this need to understand has become a must since privacy regulations are hardening the industrial use of these models. There are different techniques to address the interpretability issues that Machine Learning models arises. This paper is focused on opening the so-called Deep Neural architectures black-box. This research extends the technique called Layer-wise Relevant Propagation (LRP) enhancing its properties to compute the most critical paths in different deep neural architectures using multicriteria analysis. We call this technique Ranked-LRP and it was tested on four different datasets and tasks, including classification and regression. The results show the worth of our proposal.
Thresholds of income inequality that mitigate the role of gender inclusive education in promoting gender economic inclusion in sub-Saharan Africa

Purpose This study aims to provide the thresholds of inequality that should not be exceeded if gender inclusive education is to enhance gender inclusive formal economic participation in sub-Saharan Africa. Design/methodology/approach The empirical evidence is based on the generalised method of moments and data from 42 countries during the period 2004-2014. Findings The following findings are established. First, inclusive tertiary education unconditionally promotes gender economic inclusion, while the interaction between tertiary education and inequality is unfavourable to gender economic inclusion. Second, a Gini coefficient that nullifies the positive incidence of inclusive tertiary education on female labour force participation is 0.562. Second, the Gini coefficient and Palma ratio that crowd-out the negative unconditional effects of inclusive tertiary education on female unemployment are 0.547 and 6.118, respectively. Third, a 0.578 Gini coefficient, a 0.680 Atkinson index and a 6.557 Palma ratio are critical masses that wipe out the positive unconditional effects of inclusive tertiary education on female employment. The findings associated with lower levels of education are not significant. Practical implications As the main policy implication, income inequality should not be tolerated above the established thresholds for gender inclusive education to promote gender inclusive formal economic participation. Other implications are discussed in the light of sustainable development goals. Originality/value This study complements the existing literature by providing inequality thresholds that should not be exceeded for gender inclusive education to promote the involvement of women in the formal economic sector.
Designing a Frame-Semantic Machine Translation Evaluation Metric

We propose a metric for machine translation evaluation based on frame semantics which does not require the use of reference translations or human corrections, but is aimed at comparing original and translated output directly.The metric is developed on the basis of an existing manual frame-semantic annotation of a parallel corpus with an English original and a Brazilian Portuguese and a German translation.We discuss implications of our metric design, including the potential of scaling it for multiple languages.
OpenReq Issue Link Map: A Tool to Visualize Issue Links in Jira

Managing software projects gets more and more complicated with an increasing project and product size. To cope with this complexity, many organizations use issue tracking systems, where tasks, bugs, and requirements are stored as issues. Unfortunately, managing software projects might remain chaotic even when using issue trackers. Particularly for long lasting projects with a large number of issues and links between them, it is often hard to maintain an overview of the dependencies, especially when dozens of new issues get reported every day. We present a Jira plug-in that supports developers, project managers, and product owners in managing and overviewing issues and their dependencies. Our tool visualizes the issue links, helps to find missing or unknown links between issues, and detects inconsistencies.
Situated interaction

research-article Situated interaction Share on Authors: Dan Bohus View Profile , Eric Horvitz View Profile Authors Info & Claims The Handbook of Multimodal-Multisensor Interfaces: Language Processing, Software, Commercialization, and Emerging DirectionsJuly 2019 Pages 105–143https://doi.org/10.1145/3233795.3233800Published:01 July 2019 1citation115DownloadsMetricsTotal Citations1Total Downloads115Last 12 Months10Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An Intelligent Controller based on LAMDA

In this paper, a new intelligent controller, which is based on the machine learning algorithm called LAMDA (Learning Algorithm for Multivariable Data Analysis) is presented. This algorithm can be used for supervised and unsupervised learning, and it has been applied in the detection of functional states of systems. LAMDA is based on the calculation of the Global Adequacy Degree (GAD) of an individual to a class in order to determine its degree of similarity. In the training phase is established the centers of the classes, which correspond to the functional states of the system. However, being a fuzzy classification algorithm, it does not have an inference method; which is proposed in this work, assigning a numerical value at the output of the controller when the system is in one of the classes. The idea is that the controller can take the system from a current functional state to a desired one. Our proposal has been tested in two case studies, where it is demonstrated its versatility in the implementation of PD and PI controllers. Finally, its performance is compared to other controllers.
An Approach for Distributing Sensitive Values in k-Anonymity

k-anonymity is a popular model in privacy preserving data publishing. It provides privacy guarantee when a microdata table is released. In microdata, sensitive attributes contain high-sensitive and low sensitive values. Unfortunately, study in anonymity for distributing sensitive value is still rare. This study aims to distribute evenly high-sensitive value to quasi identifier group. We proposed an approach called Simple Distribution of Sensitive Value. We compared our method with systematic clustering which is considered as very effective method to group quasi identifier. Information entropy is used to measure the diversity in each quasi identifier group and in a microdata table. Experiment result show our method outperformed systematic clustering when high-sensitive value is distributed.
Creative thinking development value in the bachelor-designers training

Abstract The relationship of learning and development in a creative environment, the process of formation of creative subjectivity are considered in the article. The creativity development, the creative type creative activity methodological conditions, the creative type artistic activity principles, the reality sensory-perceptual reflection in the creative development thinking context are analyzed.
Cryptanalysis of a rank-based signature with short public keys

Following Schnorr framework for obtaining digital signatures, Song et al. recently proposed a new instantiation of a signature scheme featuring small public keys from coding assumptions in rank metric, which was accepted at PKC’19. Their proposal makes use of rank quasi-cyclic (RQC) codes to reduce the public key size. We show that it is possible to turn a valid, legitimate signature into an efficiently solvable decoding problem, which allows to recover the randomness used for signing and hence the secret key, from a single signature, in about the same amount of time as required for signing.
Combining Visual Cleansing and Exploration for Clinical Data

Clinical data have their own peculiarities, as they evolve over time, may be incomplete, and are highly heterogeneous. These characteristics turn a thorough analysis into a challenging task, especially since domain experts are aware of the data flaws, which may impact their trust in the data. As we obtained anonymized clinical data from more than 3,500 patients with retinal diseases, we have to address these challenges. We define a workflow that integrates data cleansing and exploration in an iterative process, so that users are able to easily find anomalies and patterns in the data at any point in their analysis. We implement our workflow in a user-centered visual analytics tool with dedicated visualization and interaction techniques. In collaboration with experts, we apply our tool to examine the interdependency between patients' visual acuity developments and treatment patterns. We find, that real-life data often have unforeseen incidents which can strongly influence the overall visual acuity development. This differs to study results, which are usually conducted under restrictive conditions and have shown visual acuity improvement with on-schedule treatment.
[An overview of multiple linear regression model and its application].

Multiple Linear Regression (MLR) is a generalization of simple linear regression and is one of the commonly used models in multivariate statistical analysis. This article introduces the MLR model from the perspective of practical application. Four parts, including basic principle, application examples, the application condition and diagnosis, and the extension of the model, are sequentially illustrated in this article. Particularly, in the last part, alternative methods of the model are introduced when the application condition of the model is not met. We sincerely hope that this article could make our audiences have a better understanding of the MLR model in order to improve the efficiency of data utilization and statistical analysis by correctly performing this model in their research.多重线性回归（MLR）是简单线性回归的推广，是多变量统计分析中的常用方法之一。本文从实际应用的角度出发，介绍多重线性回归分析方法。内容重点包括多重线性回归基本原理、应用实例拟合及结果解释、模型应用条件及模型诊断、MLR的扩展应用四大部分。特别是在第四部分方法扩展应用中，简要介绍了对于多重现性回归条件不满足的时候可以用哪些方法进行替代。以期读者对多重线性回归分析有所了解，在科研工作中能正确使用多变量回归模型分析，提高数据使用效率和统计分析水平。.
Research on medical image segmentation based on fuzzy clustering algorithm

Objectives: The aim of the study is to apply the fuzzy clustering algorithm to medical image segmentation technology and analyze the application effect of the algorithm. Methods: In this study, the application of bacterial fuzzy clustering algorithm and bacterial foraging optimization algorithm in tooth image segmentation is analyzed. Among them, bacteria fuzzy clustering algorithm is a research group, whereas bacteria foraging optimization algorithm is a conventional group. Relevant researchers need to compare the separation index, partition coefficient, and partition index of the two algorithms. Results: Compared with the conventional group, the separation index and the partition coefficient of the experimental group were relatively high, and the two groups in the separation index and partition coefficients have a statistically significant difference (P < 0.05); compared with the experimental group, the index value was higher in the conventional group, and there was significant difference between the two groups in the zoning index (P < 0.05). Conclusions: Compared with the traditional bacterial optimization algorithm, the application of the bacterial fuzzy clustering algorithm in tooth image segmentation is more remarkable.
Accident detection from dashboard camera video

Intelligent transport system plays a key role in the current digital era of smart cities. Due to increased population and number of vehicles, there is a proportionate increase in the number of accidents and their mortality rate. The video-based traffic monitoring system is one of the main approaches used to address this challenge but is limited to the region of surveillance. To overcome this limitation, we propose an approach for detecting accidents from dashboard camera video using computer vision-based techniques. The variation in visual information during accidents is analyzed to design a computationally less expensive accident detection model for practical use. As a result, accidents are identified from the change in visual information during an accident. It is evaluated on the new Dashboard Video Accident Detection (DVAD) dataset, proposed in this work. The experimental study suggests the effectiveness of the proposed approach for accident detection, that can be further extended to notify the concerned in case of an accident.
An Efficient Algorithm of Facial Expression Recognition by TSG-RNN Network

Facial expression recognition remains a challenging problem and the small datasets further exacerbate the task. Most previous works realize facial expression by fine-tuning the network pre-trained on a related domain. They have limitations inevitably. In this paper, we propose an optimal CNN model by transfer learning and fusing three characteristics: spatial, temporal and geometric information. Also, the proposed CNN module is composed of two-fold structures and it can implement a fast training. Evaluation experiments show that the proposed method is comparable to or better than most of the state-of-the-art approaches in both recognition accuracy and training speed.
A New Approach to JPEG Tampering Detection Using Convolutional Neural Networks

The digital world is overloaded with information nowadays. It is quite hard to distinguish between original and fake news. That is why digital image forgery detection algorithms became very popular and develop actively. One of the ways to hide important area of an image is to replace it with another image part. If the original image was not compressed and the new image part was JPEG compressed, then we need some algorithm to distinguish between compressed and non-compressed blocks. That is why in this paper we propose a new algorithm for JPEG compressed local tampering detection using deep convolutional neural networks. The algorithm description and comparison of the proposed approach with existing algorithms are presented in the paper. Our approach shows better quality results in comparison to existing methods and can be used for detection of JPEG compressed areas with different quality factors.
Vehicle Direction Detection Based on YOLOv3

With the popularity of traffic monitoring systems and traffic intelligence management, a large number of fixed and mobile cameras are installed in the city. However, manual monitoring of the camera is not efficient. The target detection method can monitor the screen and analyze the direction in which the vehicle drives and parks, helping to reduce the labor burden and improve efficiency. In this paper, we add a fine classification network after YOLOv3 to detect and discriminate the vehicle's driving and parking directions. YOLOv3 detects and extracts vehicle location information, and from which the latter network is responsible for finely classifying directions. We achieved a 91% classification accuracy on a test database of 400 images. This modified system can increase the practicality of the YOLOv3 target detection in the traffic scene.
Image-based Kinship Verification using Fusion Convolutional Neural Network

In this paper, we investigate the performance of fusion convolutional neural network (CNN) classifier for image-based kinship verification problem. Two fusion configurations were used for the experiments, early fusion CNN classifier and late fusion CNN classifier. The early fusion configuration of the CNN classifier takes combined two face images as input for verification. The advantages of early fusion configuration are no heavy changes in the classifier architecture and only the first layer that have a different filter size. The late fusion configuration of the CNN classifier formed by creating dual CNN network for extracting the deep features of each face image and classify the kinship relationship using two fully-connected layers. The softmax and angular softmax (a-softmax) loss are used for evaluating the network in the training process with fine-tuning strategy. The classifier then evaluated using large-scale FIW (Family in the Wild) kinship verification dataset consists of 1,000 family and 11 different kinship relationship. Experiments using the 5-fold configuration on FIW dataset show that the ensemble of fusion CNN classifier produces comparable performance with several different state-of-the-art methods.
Honing emergent literacy via food

This study explores the honing of children’s emerging literacy skills through the use of food that is inspired by children’s books. Besides digital and printed books, edible texts have the potential to aid language acquisition and literary appreciation. When edible materials and children’s books are synthesised into a new form to facilitate edible readings, the combination may inspire more families to engage in everyday literacy activities with their children. Using historical examples of edible reading that support emergent literacy, this work investigates how children have fed on edible materialities that appeal to their senses on multiple levels. As well as traditional methods, this study looks at innovative methods of food printing and production such as 2D and 3D printing technologies and how these may be integrated into edible texts through prototypes presented by the author.
Impact of the WampServer application in Blended learning considering data science, machine learning, and neural networks

This quantitative research aims to analyze the impact of the WampServer application in Blended learning during the educational process of computing through data science, machine learning, and neural networks. WampServer is a free application that allows the creation of websites considering the use of the database. This research proposes the use of Blended learning in the Development of applications subject in order to facilitate the teaching–learning process in the Database unit. The students discuss and reflect the concepts on the database in the classroom and carry out various school activities on the construction of websites at home through the use of the WampServer application. The sample consists of 28 students who took the Development of applications subject during the 2016 school year. The results of machine learning (linear regression) with 50, 60, and 70% of training indicate that the use of PHP, HTML, and SQL languages in the WampServer application positively influences the assimilation of knowledge and development of skills on web programming. Data science identifies six predictive models about the use of WampServer in the educational process of computing. On the other hand, neural networks determine the factors that influence the assimilation of knowledge and development of skills on web programming. This research recommends the incorporation of the WampServer application in the school activities related to the computer field to create new educational spaces and facilitate the teaching–learning process. Teachers can transform the educational context through the organization and realization of creative activities inside and outside the classroom. Finally, the use of WampServer in Blended learning allows creating new spaces for teaching and learning of computer science because this application favors the assimilation of knowledge and development of skills on web programming.
Evaluating Random Walk-Based Network Embeddings for Web Service Applications

Network embedding models automatically learn low-dimensional and neighborhood graph representation in vector space. Even-though these models have shown improved performances in various applications such as link prediction and classification compare to traditional graph mining approaches, they are still difficult to interpret. Most works rely on visualization for the interpretation. Moreover, it is challenging to quantify how well these models can preserve the topological properties of real networks such as clustering, degree centrality and betweenness. In this paper, we study the performance of recent unsupervised network embedding models in Web service application. Specifically, we investigate and analyze the performance of recent random walk-based embedding approaches including node2vec, DeepWalk, LINE and HARP in capturing the properties of Web service networks and compare the performances of the models for basic web service prediction tasks. We based the study on the Web service networks constructed in our previous works. We evaluate the models with respect to the precision with which they unpack specific topological properties of the networks. We investigate the influence of each topological property on the accuracy of the prediction task. We conduct our experiment using the popular ProgrammableWeb dataset. The results present in this work are expected to provide insight into application of network embedding in service computing domain especially for applications that aim at exploiting machine learning models.