Reviewerly/exHarmony huggingface dataset:
Infrastructuring Bodies: Choreographies of Power in the Computational City

The aim of this chapter is to shed light on the power-related infrastructural dynamic that actualises in the interrelations of big data collection and the bodily movement of urbanites in contemporary cities. By drawing from Husserl’s and Merleau-Ponty’s phenomenologies of the body and combining them with recent theorisations on choreography, material media theory and critical technology studies, the authors address city dwellers’ embodied relations with mobile devices and ambient technologies as integral to the micro-, meso- and macro-level (re)production of urban infrastructures. By way of discussing the technologically mediated kinaesthesia and movement trajectories of lived bodies, the chapter develops a novel conceptualisation of urban choreography for exploring the mechanisms through which dwelling-in-the-city today functions in a globally extensive cybernetic feedback loop with profit-motivated and surveillant big data operations.
Car handle character recognition algorithm based on circular projection and SVM

Character recognition is vital to character quality inspection on the surface of car handles. In order to solve the problem of characters with unfixed scales, rotation, and gray changes, we propose a car handle character recognition algorithm based on support vector machine and circular projection. Firstly, the algorithm binarizes car handle character image to eliminate the influence of grayscale changes. Secondly, according to the principle of the circular projection matching algorithm, equidistant circles are applied to extract the feature about the number of pixels within the minimum circumscribed circle of the character. Finally, a new character feature similarity measure function is introduced and combined with support vector machines to recognize the car handle characters. After being tested, the handle recognition characters with arbitrary rotation angles and scale changes in the range between 1 to 1.5 times, the algorithm recognition accuracy reached 98%.
MaskLayer: Enabling scalable deep learning solutions by training embedded feature sets

Deep learning-based methods have shown to achieve excellent results in a variety of domains, however, some important assets are absent. Quality scalability is one of them. In this work, we introduce a novel and generic neural network layer, named MaskLayer. It can be integrated in any feedforward network, allowing quality scalability by design by creating embedded feature sets. These are obtained by imposing a specific structure of the feature vector during training. To further improve the performance, a masked optimizer and a balancing gradient rescaling approach are proposed. Our experiments show that the cost of introducing scalability using MaskLayer remains limited. In order to prove its generality and applicability, we integrated the proposed techniques in existing, non-scalable networks for point cloud compression and semantic hashing with excellent results. To the best of our knowledge, this is the first work presenting a generic solution able to achieve quality scalable results within the deep learning framework.
Application Algorithms of Nonlinear Dimensionality Reduction to Material Database Visualization

In our world there is a huge quantity of different materials, alloys, compositions, substances. They have different mechanical properties, chemical compositions, different colors, smells, and, most importantly, applications in human life, etc. Because of so many properties of various compounds, it poses the problem of storing information about them and also creates a space for their visualization in different ways.This work deals with the creation of an online application and database that makes it possible to use information about metals, their alloys, and the search for the closest analogs. The web application gives the user a wide selection of devices to use and also uninstalls additional software products. During development, we used the general-purpose programming language Python with Flask library and web technologies HTML, CSS, and JavaScript. For the correct search for similar materials, a reasonable search is developed and implemented in an online application. The database is created based on SQL technologies the database management system - MySQL. To obtain information about metals on the Internet, a parser program is developed that selectively received data from open sources and recorded them in the created database.Humans can effectively use visual information in a maximum of three projections. Therefore, the task of reducing the dimension of information, and its visualization is one of the main directions when working with multidimensional data. The solution to this problem allows the researcher to correctly build assumptions and manipulate data in the course of reasoning or evidence.The task of the correct visualization of multidimensional data on the plane and three-dimensional space is solved by several well-known algorithms, namely: Isomap, t-SNE, MDS. As a result of the visualization of all the collected information about metals, clusters of materials is identified that correspond to the already known classes of metals. The study confirmed the correctness of the algorithms for reducing the dimension, the correctness of the information collected, the usability of the web application.
Fundamental Structure of Orthogonal Variable Commutative Quaternion Neurons

In this paper, we formulate a commutative quaternion neuron model with parameters represented by an orthogonal coordinate, and makes clear the differences among the commutative quaternion neuron, a real-valued neuron, and a complex-valued neuron.
A Hybrid DE-ACO Algorithm for the Global Optimization

Hybrid metaheuristic algorithms have become popular for solving complex problems that are very challenging to overcome with conventional methods in recent years. This paper presents a hybrid algorithm called DE-ACO that combines Differential Evolution (DE) and Ant Colony Optimization (ACO) algorithms. The main idea is to use the Differential Evolution (DE) algorithm for a good starting point for Ant Colony Optimization (ACO) algorithm. A benchmark of six well-known test functions is employed to check the performances of the proposed approach in terms of convergence rate, quality of optimum solutions and computing time. The results obtained show that the performances of the hybrid algorithm outperform significantly the DE and ACO algorithms.
A Face Detection Method Based on Skin Color Features and AdaBoost Algorithm

Abstract The face detection based on skin color features is greatly affected by illumination and the face detection based on AdaBoost algorithm is greatly affected by multi-pose of face, and this means that the large face deflection will make the detection less accurate. So we investigated a face detection method that combines the skin color model and the improved AdaBoost algorithm in this paper. This method first judges whether the collected face image needs illumination compensation. The image is converted from RGB color space to YCbCr color space with higher pixel clustering characteristics and distribution rules by whether it is needed or not. Meanwhile, the candidate regions containing face images are carried out through morphological process and shape select after establishing the Gaussian model. Finally, the face detection results are obtained through detecting the candidate regions with face images by improved AdaBoost algorithm. A large number of experimental results show that this new method can effectively reduce the false detection rate and improve the efficiency of face detection.
Communication of Changes in Continuous Software Development

The industry competition has changed the way software is managed, developed, and delivered over the years. Some of the approaches that emerged to continuously deliver software to users are Continuous Delivery (CDE) and Continuous Deployment (CD). CDE is the ability to get software functionalities, of any kind, into the hands of users, in small batches, and short cycles. In CD, every change that passes all stages of the production pipeline is released to customers without human intervention. The agility proposed by the Continuous Delivery and Continuous Deployment approaches may introduce some challenges to the software development life-cycle. Some of these challenges are related to the Software Configuration Management process and the communication of software changes to relevant stakeholders such as operations teams. In order to better understand which communication practices are used to communicate software changes in environments where Continuous Delivery or Continuous Deployment were adopted, a systematic literature review and an empirical study with global companies were performed, which allowed us to consolidate a collection of communication practices for communicating software changes that could benefit companies that already adopted or are planning to implement continuous software delivery practices.
Predicting Consumer Default: A Deep Learning Approach

We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model provides favorable credit risk assessment to young borrowers relative to standard credit scoring models, while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation.
Combining Tensor Decomposition and Word Embedding for Asymmetrical Relationship Prediction in Knowledge Graphs

Completing knowledge graphs can effectively organize irregular heterogeneous data and mine information hidden in it. However, most of the existing knowledge graph completion models mainly focus on symmetrical relationships, and there is an absence of excellent solutions to the prediction of asymmetrical relationships. This paper proposes an asymmetrical relationship prediction method based on tensor decomposition and word embedding to complete knowledge graphs, which gives different weights to the two entities of the asymmetrical relationship. The structural characteristics of tensors and the adaptability of word embedding to sparse data are combined well to find out the hidden information in knowledge graphs to automatically complete knowledge graphs. The experiments on the FB15k and WN18 data sets show that the model proposed in this paper has an up to 10% improvement over the linear state-of-the-art methods.
Selection of Co-Belonging Ceramic Fragments from Archaeological Excavations and Their Location in Vase Bodies from Thermoremanent Magnetization

: Selection of co-belonging fragments from the numerous ceramic findings of an archaeological excavation based exclusively on the experience and patience of the conservators remains a difficult process of questionable effectiveness. While the screening of the fragments is a central prerequisite and the most important stage of the process of vase reconstruction, established methods based on scientific criteria and guaranteed efficiency for the detection of co-belonging ceramic fragments suggested in the bibliography do not exist. On the contrary, for methods dealing with the assembly of vases from co-belonging fragments, which is a secondary process that can be done more easily and effectively in an empirical way, there exist numerous studies based on fragment morphology. However, even these are also not implemented because of the time requirements, sheer volume and complexity of the proposed methods, in order for them to be applicable in practice. The proposed methods in this paper are based on thermoremanent magnetization (A/m), which is calculated from the weak magnetic field measurements by a fluxgate-sensor/magnet apparatus forming a three-dimensional orthogonal system. Experimental measurements from fragments of six vases show that the magnetization magnitude of co-belonging fragments display similar values, despite the magnetic anisotropy of the ceramic material, since these belong to vases made of the same clay and fired under the same conditions. This is the criterion for finding ceramic fragments of the same vase from archaeological excavations. The thermoremanent magnetism directionality of fragments, which is aligned along the geomagnetic field at the same place and time during the vase firing process, as it is configured by their rotational symmetry, defines the position of the fragments on the body of the six vases. The shape of the original vase can be reconstructed when only a few non adjacent fragments are available. The proposed measurement apparatus can be used for the construction of a useable portable magnetometer specialized for ceramic surface measurements to achieve the above objectives.
Development of a High-Precision Multifunctional Probe for Measuring Microstructures

An increasing number of micro/nano-components with complex structures have emerged in recent years. Different kinds of feature sizes usually exist in a single component and require the use of instruments with nanometer-level accuracy. The traditional types of equipment cannot meet the current demands because of their lack of function and effectiveness. In this study, a multifunctional probe is designed by combining the interference and focusing principles. The Linnik principle is adopted in the interferometry module for its high vertical resolution and large horizontal range and its ability to measure surface topography. The focusing module modified from a Blue-ray DVD pickup head has a high horizontal resolution and a large vertical range suitable for measuring step height and line/groove width. The two modules share a polarizing beam splitter and an objective lens to reduce probe size. Then, the probe is manufactured, calibrated, and verified by measuring the surface topography with a reference height of 100 nm, a step height of approximately $4~\mu \text{m}$ , and a diameter of approximately $25~\mu \text{m}$ . The proposed probe has a high resolution and large range in 3D and can measure high-precision components with multiple features.
Monocular Depth Estimation with a Multi-task and Multiple-input Architecture Using Depth Gradient

In this paper we address the monocular depth estimation task with a multi-task and multiple-input network architecture. The integrated network that we develop use depth gradient information and can be applied to supervised and unsupervised learning. We confirmed that our architecture improve depth estimation on the supervised learning and improve structure-from-motion on the unsupervised learning.
Conversational game to improve digital health literacy

Digital literacy in health is a current challenge for citizens. Having this skill is a fundamental element of e-health services. However, people frequently do not have the essential skills to find, understand, and appraise online health information and apply their knowledge to make health decisions. For this reason, we have designed a serious game entitled "Conversational Adventure for IC-Health" for training children and adolescents in digital health skills. In particular, the principal elements of the game are described. Also, the narrative structure and the educational contents worked through the conversational game are presented.
Mapping Low-Dimensional Dynamics to High-Dimensional Neural Activity: A Derivation of the Ring Model From the Neural Engineering Framework

Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity-the neural engineering framework. We analytically solve the framework for the classic ring model-a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.
Classification of Binary Class HIV/AIDS Test Results Using Ensemble Learning Models

In HIV/AIDS datasets, there is lack of adequate and imbalanced samples includes repetitive and unnecessary features that cause high dimensionality spaces. To address this problem, selections of features are examined. In this research, we propose an ensemble learning method for the classification of a binary class of HIV/AIDS test results. The backward feature selection (BFS) of the wrapper method is used for feature selection. Five established classifiers are used, namely Gradient Boosting (GB), Multilayer Perceptron (MLP), Random Forest (RF), Extra Tree (ET), and K-nearest neighbor (KNN). For preparation and testing of the model, 10-fold cross-validation is applied. Experiments are carried out on the EDHS-HIV/AIDS dataset. Various performance measurements are used to assess the model's performance. The confusion matrix is used to demonstrate whether the samples are labeled correctly or not. Based on performance evaluation parameters, a review of the results of each classifier is provided. Significant performance enhancements are seen in the results when feature selection is considered to be better on the original dataset comparison to the selected features for all classifier outputs.
Personalized Implicit Negative Feedback Enhancements for Fuzzy D'Hondt's Recommendation Aggregations

In this paper, we focus on the problems of fair aggregation of recommender systems (RS) and over-exposure of users with insignificant recommendations. While fair aggregation of diverse RS may contribute to both calibration and diversity challenges, some recently proposed methods suffer from repeating the same set of recommendations to the user over and over again. However, it may be difficult to distinguish between situations when users ignore recommendations because they are irrelevant or because they did not notice them. In order to cope with these challenges, we propose an innovative off-line RS evaluation methodology based on the noticeability of recommended items. We further propose a Fuzzy D'Hondt's algorithm with personalized implicit negative feedback attribution (FDHondtINF). The algorithm is designed to provide a fair ordering of candidate items coming from multiple individual RS, while considering also the objects previously ignored by the current user. FDHondtINF was evaluated off-line along with other aggregation methods and individual RS on MovieLens 1M dataset. The algorithm performs especially well in situations when the recommended items are less noticeable, or when a sequence of multiple recommendations for the same user model is given.
Machine Learning Techniques to Determine the Polarity of Messages on Social Networks

With the origin of Web 2.0, the Internet contains large amounts of user-generated information on an unlimited number of topics. Many entities such as corporations or political groups seek to gain knowledge through the opinions expressed by users. Social platforms such as Facebook or Twitter have proven to be successful for these tasks, due to the high volume of real-time messages generated and the large number of users that use them every day (Leis et al., J Med Internet Res 21(6):e14199, 2019, []). This paper focuses on the problem of Global Sentiment Analysis. Using the texts that compose the corpus in Spanish built by Sixto et al. (International conference on applications of natural language to information systems. Springer, Cham, 2016 []), and selecting the three most used classifiers by the state of the art of Naive Bayes, MSV and J48 through the Weka software.
Confidence-Based Global Attention Guided Network for Image Inpainting

Most of recent generative image inpainting methods have shown promising performance by adopting attention mechanisms to fill hole regions with known-region features. However, these methods tend to neglect the impact of reliable hole-region information, which leads to discontinuities in structure and texture of final results. Besides, they always fail to predict plausible contents with realistic details in hole regions due to the ineffectiveness of vanilla decoder in capturing long-range information at each level. To handle these problems, we propose a confidence-based global attention guided network (CGAG-Net) consisting of coarse and fine steps, where each step is built upon the encoder-decoder architecture. CGAG-Net utilizes reliable global information to missing contents through an attention mechanism, and uses attention scores learned from high-level features to guide the reconstruction of low-level features. Specifically, we propose a confidence-based global attention layer (CGA) embedded in the encoder to fill hole regions with reliable global features weighted by learned attention scores, where reliability of features is measured by automatically generated confidence values. Meanwhile, the attention scores learned by CGA are repeatedly used to guide the feature prediction at each level of the attention guided decoder (AG Decoder) we proposed. Thus, AG Decoder can obtain semantically-coherent and texture-coherent features from global regions to predict missing contents. Extensive experiments on Paris StreetView and CelebA datasets validate the superiority of our proposed approach through quantitative and qualitative comparisons with existing methods.
Radar Signal Separation and Recognition based on Semantic Segmentation

Signal recognition is a key technology in the current information and communication field, and it plays an important role in the civilian and military fields. With the diversification of signal modulation forms, the electronic reconnaissance environment becomes more and more complex, and the signal density continues to increase. Time and frequency domain aliasing will become a serious problem in the field of signal recognition. Consequently, the separation and recognition of multi-component signals is an aspect to be studied in the radar system. This paper extracted the time-frequency images (TFIs) of received signals by Choi-Williams distribution (CWD). Second, the semantic segmentation network DeepLab V3+ is used to segment the TFIs of the signal. Finally, we obtained the recognition result and visualized the result. The simulation results show that the method proposed in this paper can effectively solve the multi-signal separation and recognition of time-frequency domain aliasing.
A Machine Learning Approach to Human Activity Recognition

Human Activity Recognition (HAR) is the problem of classifying an individual's activity into well-defined moments, utilizing responsive sensors that are influenced by human movement. Sensor-enabled smartphones make Human Activity Recognition progressively significant and well known. The physical sensors, gyroscope and accelerometer combinedly allow the devices to provide motion measuring capabilities in a more accurate manner. The present research work adopts a machine learning based approach for recognizing activity on the basis of data collected through the smartphone sensors (accelerometer and gyroscope). Various state-of-the-art machine learning based techniques have been employed and compared on the basis of the performance metrics, accuracy, recall, precision, and the F1-score. Of all the selected different machine learning classifiers, the best result is given by the Support Vector Machine (SVM) with `RBF' kernel, which achieved an accuracy of 96.61 % in classifying the activities into the six different classes.