Reviewerly/exHarmony huggingface dataset:
Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models

Complexity of evolving models in genetic programming (GP) can impact both the quality of the models and the evolutionary search. While previous studies have proposed several notions of GP model complexity, the size of a GP model is by far the most researched measure of model complexity. However, previous studies have also shown that controlling the size does not automatically improve the accuracy of GP models, especially the accuracy on out of sample (test) data. Furthermore, size does not represent the functional composition of a model, which is often related to its accuracy on test data. In this study, we explore the evaluation time of GP models as a measure of their complexity; we define the evaluation time as the time taken to evaluate a model over some data. We demonstrate that the evaluation time reflects both a model’s size and its composition; also, we show how to measure the evaluation time reliably. To validate our proposal, we leverage four well-known methods to size-control but to control evaluation times instead of the tree sizes; we thus compare size-control with time-control. The results show that time-control with a nuanced notion of complexity produces more accurate models on 17 out of 20 problem scenarios. Even when the models have slightly greater times and sizes, time-control counterbalances via superior accuracy on both training and test data. The paper also argues that time-control can differentiate functional complexity even better in an identically-sized population. To facilitate this, the paper proposes Fixed Length Initialisation (FLI) that creates an identically-sized but functionally-diverse population. The results show that while FLI particularly suits time-control, it also generally improves the performance of size-control. Overall, the paper poses evaluation-time as a viable alternative to tree sizes to measure complexity in GP.
Fuzzy logic in economic models

Models of economic behavior are based on the search for and establishing of relationships between the various economic variables included in the model. Generally speaking, those coefficients that appear in relationships between the model variables are specific values that are determined when the mo del is to be used to make a given prediction. In this article we propose incorporating fuzzy logic into the study of economic models via the incorporation of fuzzy numbers to express the coefficients relating the different variables. To develop this idea, we analyze a simplified model for determining national income in which it is assumed that, for the sake of equilibrium, said value is composed of consumption and investment. Also, by relating the consumption function to income, we analyze a relationship model between the variables. To obtain broader and more real information than that resulting from the application of classical models, we incorporate fuzzy logic by assuming the parameters that establish the degree of dependence between the variables to be fuzzy numbers with a known membership function. Depending on their form, we determine their respective membership function for national income.
Learning biases in opaque interactions

This study uses an artificial language learning experiment and computational modelling to test Kiparsky's claims about Maximal Utilisation and Transparency biases in phonological acquisition. A Maximal Utilisation bias would prefer phonological patterns in which all rules are maximally utilised, and a Transparency bias would prefer patterns that are not opaque. Results from the experiment suggest that these biases affect the learnability of specific parts of a language, with Maximal Utilisation affecting the acquisition of individual rules, and Transparency affecting the acquisition of rule orderings. Two models were used to simulate the experiment: an expectation-driven Harmonic Serialism learner and a sequence-to-sequence neural network. The results from these simulations show that both models’ learning is affected by these biases, suggesting that the biases emerge from the learning process rather than any explicit structure built into the model.
An Assessment of the Impact of Teachers’ Digital Competence on the Quality of Videos Developed for the Flipped Math Classroom

The aim of this research is to determine the impact of perceived Teachers’ Digital Competence (TDC) on how well math teachers prepare the educational videos needed to put the flipped classroom model into practice. In preparing the videos, the teachers had to select pre-existing audiovisual material and then edit the content to adapt it to the flipped classroom. Described here is a non-experimental study of a sample of 50 teachers pursuing a Master’s degree in Secondary School Math Education in Spain. This is a preliminary univariate descriptive study of the relationship between TDC and the quality of videos prepared. Possible correlations between these two variables and between the characteristics of the sample are also explored. In general, the teachers had an intermediate level of TDC and prepared satisfactory videos. Nevertheless, the videos were deficient in the sections related to their pedagogical and math instructional components. No correlation was observed between TDC and the quality of the videos prepared. These results indicate that the integration of technological, pedagogical, and math instructional components is more important for developing quality instructional videos than the technological component alone. Teacher training should incorporate elements which emphasize the application of technology to the pedagogical process of math instruction.
DVONet: Unsupervised Monocular Depth Estimation and Visual Odometry

This paper proposes an unsupervised learning framework for monocular depth estimation and visual odometry (VO), referred to as DVONet. The framework is trained using stereo image sequences and is able to estimate absolute-scale scene depth and camera poses from monocular images. To mitigate the effect of stereo occlusions in training and improve the depth estimation, left-right occlusion mask is introduced. In addition, a novel VO network is proposed where the feature extraction network is shared between pose estimation and optical flow estimation. The proposed DVONet achieves state-of-the-art results for both depth estimation and VO tasks on the KITTI driving dataset, outperforming the existing unsupervised methods and being comparable to the traditional ones.
Information Privacy Protection Based on Verifiable (t, n)-Threshold Multi-Secret Sharing Scheme

General secret sharing schemes comprise a secret dealer and a number of participants. The dealer can randomly generate a secret and then distribute shares of the secret to the participants. Verifiable multi-secret sharing enables a dealer to share multiple secrets among a group of participants such that the deceptive behaviors of the dealer and the participants can be detected. However, in the absence of secure channels, few verifiable secret sharing schemes can simultaneously share multiple secrets at one time. In this paper, we present an information privacy protection and verifiable multi-secret sharing scheme with a simple structure and high security. Each participant can verify correctness of the share distribution process based on public information published by the dealer and guarantee validity of the received share to avoid offering fake information in the process of restoring the original secret. Our performance and security analysis indicate that the newly proposed scheme is more efficient and practical while maintaining the same level of security compared with similar protocols available.
The Ocean Best Practices System - Supporting a Transparent and Accessible Ocean

The development and deployment of best practices are playing increasingly important roles in supporting ocean observing. By their nature, well-adopted and reviewed best practices facilitate interoperability, reproducibility and enhance the quality of data and information products. To be effective, best practices must be easy to discover, access and adopt. Unfortunately, a wealth of best practices is undigitized, buried in local repositories or scattered on the web. To reduce this fragmentation and to help meet the urgent and existential global challenges rapidly approaching (see, for example, UN Decade of Ocean Science for Sustainable Development), the IOC-UNESCO Ocean Best Practices System (OBPS) has been created. The OBPS was recently described from an ocean research perspective [1]. In this paper, we describe the system's underlying technology and its core mission to enable content discovery and management through fine-scale indexing via text-mining and ontology-based semantic search tools. This relies on the reuse of well-adopted community thesauri and ontologies linking knowledge across the marine domain, through to the Sustainable Development Goals. We have implemented a constellation of software modules around the core OBPS repository (OBPS-R) to create a new, powerful, and extensible resource to accelerate best practice co-development, discovery, and access through intuitive user interfaces. While the system is operational, there are still many areas where further development can enhance the support of ocean observing. We address these in this paper and we invite the broader community to contribute to our common mission's open source codebase as well as creating and contributing ocean best practices to the OBPS.
Fuzzy model estimation of the risk factors impact on the target of promotion of the software product

This article studies the application of fuzzy logic to the risk analysis of a new software product development and marketing in specific case of a small size IT company. Identification and analysis of external and internal risk factors show that this type of business activity could be evaluated as high-risk enterprise. So, the purpose of the paper is to develop robust method to evaluate probability of occurrence of major risk events and their impact on the company financial health. The fuzzy logic is used to estimate degrees of threat of each relevant risk factor due to lack of reliable statistical data. The novelty of proposed approach is the inclusion into the model the risk event time.
Estimating Daily Global Solar Radiation with No Meteorological Data in Poland

The aim of the study was to calibrate coefficients and evaluate performance of simple, day-of-the-year, global solar radiation (H) models nominated from the literature. Day-of-the-year models enable estimation of global solar radiation when no meteorological data is available. The study used 16-year-long data series of daily H, taken at 15 actinometric stations located in various parts of Poland. The goodness-of-fit of the models to the actual long-term monthly average daily global solar radiation data expressed by determination coefficient (R2) ranges from 0.94 to 0.97. Depending on statistical indicators analysis (root mean square error—RMSE, mean absolute bias error—MABE, mean average percentage error—MAPE) the best model was selected. The averaged values of H computed by the recommended model deviate from those measured by 4.16% to 8.71%. Locally calibrated, day-of-the-year model provides satisfactory accuracy and—where meteorological data is unavailable—can be used to estimate mean monthly daily global solar radiation in Poland and similar climate conditions.
Learning actionable analytics from multiple software projects

The current generation of software analytics tools are mostly prediction algorithms (e.g. support vector machines, naive bayes, logistic regression, etc). While prediction is useful, after prediction comes planning about what actions to take in order to improve quality. This research seeks methods that generate demonstrably useful guidance on “what to do” within the context of a specific software project. Specifically, we propose XTREE (for within-project planning) and BELLTREE (for cross-project planning) to generating plans that can improve software quality. Each such plan has the property that, if followed, it reduces the expected number of future defect reports. To find this expected number, planning was first applied to data from release x. Next, we looked for change in release x + 1 that conformed to our plans. This procedure was applied using a range of planners from the literature, as well as XTREE. In 10 open-source JAVA systems, several hundreds of defects were reduced in sections of the code that conformed to XTREE’s plans. Further, when compared to other planners, XTREE’s plans were found to be easier to implement (since they were shorter) and more effective at reducing the expected number of defects.
Stereoscopic Video Quality Assessment Based on The Two-step-training Binocular Fusion Network

In this paper, we propose a novel binocular fusion network for stereoscopic video quality assessment (SVQA). In this network, we construct a long-term fusion, competition, and processing process by simulating the long-term complex process of the whole visual pathway. And we employ a two-step-training strategy for this network, which solves the problem that the network is difficult to fit caused by using the same value to label the different quality regions and views of the same stereoscopic video. In the first step, we use the computed quality scores of different patches to train the local network, namely local regression. And then the global regression is performed by using MOS value based on the first step trained model. Besides, considering temporal information, we take spatiotemporal saliency feature flows as the inputs of the proposed network. The proposed method is tested on public stereoscopic video databases, and results show that our method outperforms any other methods.
Improved Classification of Malaria Parasite Stages with Support Vector Machine Using Combined Color and Texture Features

Malarial is a mosquito born deadly disease that quickly grows from person to person because of the infectious mosquito bite. Knowing accurately the developing stages of a parasite is critical for accurate drag selection for early recovery. However, limited study were found that dealt with the automated classification of malaria parasite stages. In this study, a supervised method for classifying malaria parasite stages from microscopy images has been proposed. To achieve the target, this method combines color and texture features with the support vector machine (SVM) classifier. Three texture features, namely histogram of oriented pattern (HOG), local binary pattern (LBP), Grey-level Co-occurrence Matrix (GLCM), and four color features, namely local color moments (StatMom) and color histograms (HSV, LAB, and YCrCb), have been considered. An experimental analysis with an unbalanced dataset of 46,978 single-cell thin blood smear images showed promising performances of the color features compared to the texture features. Using SVM classifier, the proposed color-texture feature (YCrCb_HOG) showed the highest classification accuracy (96.9%) on average, which exceeds the performance of a recently published method using HOG_LBP feature with the SVM (87.1%).
Multi-target identity management with decentralized optimal sensor scheduling

This paper proposes a multi-target identity management algorithm with two types of sensors: a primary sensor which has a large detection range to provide the targets’ state estimates, and multiple secondary sensors which are capable of recognizing the targets’ identities. Each of the secondary sensors is assigned to a sector of the operation area. For the secondary sensors, we develop a sensor scheduling strategy composed of two parts: deciding which target to be identified and controlling the secondary sensor to identify the selected target, by formulating an optimization problem to minimize the uncertainty of the targets’ identities subject to the sensor dynamic constraints. In addition, a feedback term is included in the secondary sensor control to compensate for the modeling/measurement error of the targets’ states. The proposed algorithm is decentralized in that the secondary sensors only communicate with the primary sensor for the target information, and need not to synchronize with each other. By integrating the above with the existing multi-target tracking algorithms, we develop a robust closed-loop multi-target identity management algorithm with guaranteed performance. The effectiveness of the proposed algorithm is demonstrated with illustrative numerical examples.
Gradient-based point tracking method and its application in the modal test of a solar array model

Structural dynamic testing is of great importance for a built-up aerospace structure. The response acquisition is commonly time and labor-consuming when the size of the test structure is large. The recently developed image-based measurement techniques, such as point tracking (PT) method, are well-suited to obtain the responses of lightweight and flexible membrane structures in structural dynamic testing. This paper develops two new PT methods: the improved PT method and the gradient-based PT method. In order to access the accuracy of the two methods and the original PT method, a comparison experiment of a cantilever beam is conducted, where the laser Doppler vibrometer signal is set as the reference. For the testing of a solar array model, the gradient-based PT method is applied to extract the responses of the 254 points on the surface. The first five modes of the solar array are estimated based on the extracted responses from the gradient-based PT method, which show agreement with the results from the traditional measurement technique. The gradient-based PT method shows advantages in reducing testing time, saving labor, and providing high spatial resolution in mode shapes.
Training Assistant for Industrial Processes through Augmented Reality

This article presents an application of augmented reality, as a contribution to the industrial and education sector, through a technological tool for the assistance of operators in industrial processes. It facilitates the identification of equipment and instruments, granting the management and visualization of the parts constitutive of the elements of the process. Additionally, it guides in the calibration of instruments and the simulation of a closed loop control algorithm that allows control by regulation. Its development focuses on the recognition of equipment through a Smartphone for detect characteristic points of objects and image recognition, 3D modeling through CAD software and integration in a multiplatform, incorporation of animations and mathematical modeling of industrial processes that allows of the development of closed loop control algorithms. The tests performed on the augmented reality application demonstrated an easy handling and high interactivity with the user. The results provide greater knowledge in the operation and structure of the equipment, instrument and process. Finally, the results support the skills development for the tuning of PID controllers in industrial processes.
Trusted systems of records based on Blockchain technology ‐ a prototype for mileage storing in the automotive industry

Summary Blockchain technology has the potential to bring transparency and trust to a multitude of use cases. Our research demonstrates that the technology can reduce asymmetric information in markets by bridging trust gaps. The combination of blockchain and Internet of Things technology that automatically collects sensor data, provides a feasible, decentralized technological solution for such an inefficient “Market of Lemons” coined by nobel laureate Georg Akerlof. In this paper, we develop a system prototype to reduce mileage fraud on the used car markets. Our work demonstrates the feasibility of a trusted system of records for (vehicle) data such as mileage data using a distributed database based on the public Ethereum network and smart contracts. We have identified eight requirements that are fulfilled by the prototype and the functional logic and design of thesolution can be reproduced to any other application area characterized by a lack of trust between actors or by the absence of a trusted central authority. However, the developed prototype suffers from similar limitations and challenges as the technology itself. Low throughput causes limitations in scalability and transaction costs are unpredictable. Further development of the blockchain technology and considering more cost‐efficient consensus mechanisms will address these issues.
Image Segmentation Based Graph-Cut Approach to Fast Color Image Coding via Graph Fourier Transform

Colorization-based image coding is a technique to compress chrominance information of an image using a colorization technique. The conventional algorithm applies graph Fourier transform to the colorization-based coding. In this algorithm, several pixels on the image are defined as vertices of the graph, and the chrominance values of that pixels are set as graph signals. Then, the graph signal corresponding to the several chrominance values on the image is transformed to the graph spectrum based on the graph Fourier transform, and the graph spectrum is compressed and stored. Because the stored graph spectrum gives the graph signal on the image based on the inverse graph Fourier transform in decoding phase, the color image is recovered from the luminance image and the several chrominance values corresponding to the graph signal. However, high calculation time is required to perform graph Fourier transform, and therefore, this paper proposes a fast graph Fourier transform to improve the conventional colorization-based image coding algorithm. In numerical examples, although the PSNR value is decreased 0.3 dB, the proposed algorithm is 16.8 times faster than the conventional method.
Unmanned aerial vehicle-aided stereo camera calibration for outdoor applications

Monitoring the static and dynamic displacements of large engineering structures, such as buildings and bridges, can provide quantitative information for evaluating structural safety and maintenance purposes. Camera calibration is a key process in vision-based sensor systems for remote displacement measurement. Due to the large field of view of engineering structures, conventional camera calibration methods using precise calibration boards are difficult to apply. A modified calibration method for a binocular stereo vision system based on the epipolar constraint relationship is proposed to simplify the calibration process. Due to the absence of reference points in outdoor applications, an unmanned aerial vehicle that carries a reference marker is adopted. During its flight in the field, sequential images are captured simultaneously with the left and right imaging stations. An alternative to determine the scale factor is also proposed, which provides adequate precision for camera calibration. Two important issues are discussed, including the number of reference points and their selection with regards to the depth of view. The experimental results show that the proposed method is convenient to apply in outdoor situations and can achieve high accuracy in displacement measurement.