Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Visual Inspection in Industrial Problems

This work presents an automatic visual inspection system for detection of defects in ceramic plates, using image processing techniques and automatic decision. Three different types of defects are used, type printing, bubble and decal, they are detected, located and identified. T ype printing defects are characterized by the lack of i nk on the plate, bubble defects are characterized by concave ircles with small cracks and decal defects are the mixture between the last two. This work suggests a system for recog niti n of patterns, where the input are the images of ceramic plates with or without defects and the output are the resu lts of the identification. This sytem is based on three main p oints, pre-processing, feature extraction and finally trai ning and identification. For the feature extraction, descrip tors SIFT and SURF were used and for automatic decision, arti ficial neural networks multi layer perceptron (MLP) were u sed. Also a comparative study between two different imag e acquisition systems and descriptors were conducted. The system was validated successfully with an accuracy te of identification of 85 %, with SURF descriptors being more effective than SIFT and the second image acquisitio n system to prevail over the first.
Measurements of black carbon aerosols and their associated absorption characteristics at Anantapur, Andhra Pradesh, India

In situ measurements of black carbon (BC) mass concentration were made at Anantapur, a semi-arid station in southern India during January–December 2013. The daily BC mass concentration varied between 0.9 and 2.92 μg/m3 during this period. Diurnal variation of BC showed two peaks, one in the morning and another in the evening. These peaks are largely related to daily changes in the local boundary layer. The seasonal mean values of BC concentrations were found to be 4.76 ± 0.97 μg/m3 during the winter followed by summer and post-monsoon season. The lowest values were recorded during the monsoon season. The highest absorption coefficient was observed during winter and the lowest value observed during the monsoon season. Absorption Angstrom Exponents (AAE) were analysed in the 370 – 950 nm wavelength region in order to provide information on the origin of BC. A significant negative correlation was observed between BC and wind speed i.e., An increase in wind speed results in a decrease in BC mass concentration.
Objectives Towards Stable Adversarial Training Without Gradient Penalties

Recent advances in adversarial learning observe that stabilization with gradient penalties trades off generated sample quality. We try to address this limitation by proposing novel objectives inspired by the logical XOR operation, which should not depend on gradient penalty regularization in order to be locally stable. In the sections that follow, we will present a theoretical study on this type of objective functions. We will prove global optimality conditions with similar assumptions as those made in the original GAN paper by Goodfellow et al., we will notice connections between the XOR-type objectives and the original GAN, and we will define non zero-sum parametric objectives based on that connection. Finally, we will attempt to study the local stability of the continuous-time training dynamical system around desirable equilibria.
Iterative Learning Control for Precise Aircraft Trajectory Tracking in Continuous Climb Operations

In this paper, an iterative learning control method is used to improve precision in aircraft trajectory tracking in which, given a departure procedure, the dynamical model of an aircraft and a trajectory to be followed, the problem consists in defining an iterative learning control scheme which is able to improve the precision of the aircraft in following the trajectory taking into account the deviations suffered by previous flights. It is assumed that all the flights are operated with the same aircraft model and that they successively follow the same trajectory with short timebased separation and therefore are subject to similar recurrent disturbances. In the iterative learning control scheme used in this paper, the control action consists in generating at each iteration a new reference trajectory for the aircraft which compensates for recurrent disturbances. Thus, it can be applied to systems with underlying controllers for trajectory tracking, such as aircraft. In this case, the feedback trajectory tracking control is intended to reduce non-repetitive disturbances while the iterative learning control is intended to reject repetitive disturbances. The iterative learning control problem is solved in two steps: disturbance estimation and aircraft reference trajectory update. Both steps rely on a nominal model of the aircraft in which input and state constraints are explicitly taken into account. Continuous climb operations, defined within a standard instrumental departure, are considered in the simulations. The result show the effectiveness of the method which is able to reduce the trajectory tracking error due to recurrent disturbances in a few iterations, thus improving their predictability. Higher predictability of aircraft trajectories would simplify both management and control of air traffic, would improve the capacity of the air traffic management system and would allow a better exploitation of the infrastructures. Greater predictability of aircraft trajectories would also allow airlines to define and follow trajectories with a smaller number of alterations. This would result in a reduction of costs and emissions.
A Neural Network Based Fault Management Scheme for Reliable Image Processing

Traditional reliability approaches introduce relevant costs to achieve unconditional correctness during data processing. However, many application environments are inherently tolerant to a certain degree of inexactness or inaccuracy. In this article, we focus on the practical scenario of image processing in space, a domain where faults are a threat, while the applications are inherently tolerant to a certain degree of errors. We first introduce the concept of usability of the processed image to relax the traditional requirement of unconditional correctness, and to limit the computational overheads related to reliability. We then introduce our new flexible and lightweight fault management methodology for inaccurate application environments. A key novelty of our scheme is the utilization of neural networks to reduce the costs associated with the occurrence and the detection of faults. Experiments on two aerospace image processing case studies show overall time savings of 14.89 and 34.72 percent for the two applications, respectively, as compared with the baseline classical Duplication with Comparison scheme.
Absolute three-dimensional micro surface profile measurement based on a Greenough-type stereomicroscope

Fringe projection profilometry has become a widely used method in 3D shape measurement and 3D data acquisition for the features of flexibility, noncontactness, and high accuracy. By combining fringe projection setup with microscopic optics, the fringe pattern can be projected and imaged within a small area, making it possible for measuring 3D surfaces of micro-components. In this paper, a Greenough-type stereomicroscope arrangement is firstly applied for this situation by using the two totally separated and coaxial optical paths of the stereomicroscope. The calibration framework of the stereomicroscope-based system is proposed, which enables high-accuracy calibration of the optical setup for quantitative measurement with the effect of lens distortion eliminated. In the process of 3D reconstruction, depth information is firstly retrieved through the phase-height relation calibrated by a nonlinear fitting algorithm, and the transverse position can be subsequently obtained by solving the equations derived from the calibrated model of the camera. Experiments of both calibration and measurements are conducted and the results reveal that our system is capable of conducting fully automated 3D measurements with a depth accuracy of approximately 4 μm in a volume of approximately 8(L) mm × 6(W) mm × 3(H) mm.
Evaluation of the Early Years Programme of the Childhood Development Initiative: Full Report

The Early Childhood Care and Education Programme of the Childhood Development Initiative is a 2-year programme targeted at children and their families in Tallaght West. The final report of the evaluation team reports on the baseline, mid-phase and end phase findings for the whole sample of children, parents and childcare services.
Data Reconstruction Based on Temporal Expressions in Clinical Notes

Learning representations of clinical notes poses challenges in handling complex content that necessitates preprocessing steps to make the data more suitable for data mining. An important issue, addressed here, is that of temporal expressions, where cues indicate the time when clinical events occur. We present a three-step data reconstruction algorithm for transforming similar clinical entities (e.g., symptoms, complications) into sequential data through unsupervised annotation of temporal expressions. First, the data reconstruction algorithm detects if an expression has temporal intent. Second, it decomposes and rewrites the expression into non-temporal sub-expression and temporal constraints. Finally, it clusters similar non-temporal sub-expressions by using unsupervised sentence embedding under the modified $K$-medoids paradigm. We experimented with our proposed algorithm on clinical notes associated with chronic obstructive pulmonary disease (COPD). Visualizing reconstruction results of cardiology reports for a longitudinal cohort of patients with COPD demonstrated that this algorithm is feasible.
A Machine Learning based Yelp Recommendation System

The Yelp Data Challenge has produced hundreds of academic papers using modern techniques to extract insights from user, business, and review data. A variety of binary classification models are used to predict if a user will rate a business highly, with the downstream goal of generating business recommendations for users. Linear models, tree based models, and neural networks were used, with an XGBoost classifier achieving the best performance and generalizing well to future data.
Effects of an Online Public Health Service Training among Grass-Root Health Care Workers: A Pilot Study in Rural China

Objective: To assess the effectiveness of online training platform in grass-root health care workers. Methods: A pre-test/post-test study was conducted among 200 grass-root health workers from primary health institutions in ZhiJiang county, HuBei Province in China. We evaluated the training effects by comparing the trainees’ test scores before and after training and the results of a self-administrated questionnaire survey among the trainees. Results: The average post-test score improved significantly in the two courses. The results of questionnaire survey among the trainees showed the majority of respondents agreed that the online training platform meet their training needs and easy to use. Conclusion: The pilot study suggested that the online training platform was an effective approach to enhance the knowledge of public health services among grass-root health care workers.
CNN Image Recognition Architecture Simplification using Patch-Based Data Reduction Techniques

Convolutional Neural Networks (CNN) have risen in popularity as the forefront technique of image recognition technology. CNNs are so successful because they adapt neural networks’ non-linear interpolation abilities to image datasets, providing versatility to learn a hierarchy of filters useful for image classification tasks. However, as the race to set new benchmark heights continues, one significant issue is often overlooked. Neural networks are not run for free. By design, they require massive datasets in order to effectively train and yield generalizable results, and for CNNs even more so. Likewise, processing through an image requires runtime storage of an amount of weights that increases with image size. Massive amounts of energy and physical storage is needed to run a state-of-the-art CNN. While there exist many research efforts in the direction of reducing computational intensity for neural networks, most of the time a lack of emphasis on this issue is perhaps due to “a lack of familiarity with the approaches to estimate energy consumption” (Martin et al, 2018).
An optimization algorithm for the calculation of the electrostatic discharge current equations’ parameters

The international standard IEC 61000-4-2 for the electrostatic discharge does not provide an equation for the electrostatic discharge current, but it just defines a waveform for it. In the current paper a new optimization algorithm is presented that has been developed in order to calculate the parameters of equations proposed in the technical literature to describe the current during an electrostatic discharge. Electrostatic discharge current data obtained from real measurements are also used in this work in order to compare the results produced using the optimization algorithm in an effort to prove the efficiency and accuracy of the proposed algorithm and to identify the equation that describes better the electrostatic discharge current. Key-Words: Discharge current; Electrostatic discharge; Electrostatic discharge generators; Optimization algorithm.
The "Philosophy of Liberation," the Postmodern Debate and Latin American Studies

The operative theoretical framework that was constructed in the late 90s, as much within Latin America as by Latin American scholars in the United States (philosophers, literary critics and anthropologists, as well as historians, sociologists, etc.), has diversified and acquired such complexity that it has become necessary to map a topography of these positions in order to deepen the debate. In other words, the perspectives, the categories, the planes of "localization" of subjects within theoretical and interpretative discourse have changed so much that it has become difficult to continue the Latin American debate without a preliminary understanding of its theoretical and conceptual basis. The old Latin Americanism ("Latin Americanism 1") seems to have become a museum-object rather than an obligatory point of reference in any discussion. Let us, then, briefly look at said topography on the debate, knowing that it is only "one" possible interpretation of the field. This is just a point of departure to illustrate the terms of the debate.
Humpback whales extend their stay in a breeding ground in the Tropical Eastern Pacific

the Tropical Eastern Pacific Isabel Cristina Avila *, Carsten F. Dormann, Carolina Garcı́a, Luis Fernando Payán, and Marı́a Ximena Zorrilla Department of Biometry and Environmental System Analysis, Faculty of Environment and Natural Resources, University of Freiburg, Breisgau, Germany Grupo de Ecologı́a Animal, Departamento de Biologı́a, Universidad del Valle, Cali, Colombia Geography and Environmental Studies, University of Tasmania, Hobart, Australia Parques Nacionales Naturales de Colombia, Dirección Territorial Pacifico, Cali, Colombia *Corresponding author: tel: þ57 3188548269; e-mail: isabel_c_avila@yahoo.com.
Using anthropogenic geomorphological change associated with historic maritime infrastructure to predict the location of coastal archaeological sites in Queenscliff, Victoria

This paper explores the role environmental determinants play on cultural landscape evolution and vice versa. Despite a long history of shipwreck investigation, Australian archaeology has only recently begun to explore the field of inter-tidal and coastal infrastructure sites associated with shipping and the general use of the foreshore environment. This field is evolving as new characterisations of largely unexplored sites types are observed. The potential scope for archaeological research is enormous, as the location and types of marine infrastructure offer observations into the diverse range of activities being undertaken on the coastal fringe, providing valuable insights into the interconnectivity between the terrestrial and maritime spheres.
How Does Visualization Help People Learn Deep Learning? Evaluation of GAN Lab

While a rapidly growing number of people want to learn artiﬁcial intelligence (AI) and deep learning, the increasing complexity of such models poses signiﬁcant learning barriers. Recently, interactive visualizations, such as TensorFlow Playground and GAN Lab, have demonstrated success in lowering these barriers. However, there has been little work in formally evaluating these tools. This paper presents our ongoing efforts in evaluating GAN Lab, an interactive tool designed to help people learn how Generated Adversarial Networks (GANs) works. Through an observational study, we investigate how the tool was used and what users had learned from their usage. Based on the study and our experience in developing and successfully deploying the tool, we discuss future research challenges in the evaluation of interactive educational tools for AI.
Effects of exposure to literary narrative fiction

Literary narrative fiction may be particularly effective in enhancing Theory of Mind (ToM), as it requires readers to contemplate author and character intentions in filling the literary ‘gaps’ that have been suggested to characterise this fiction type. The current study investigates direct and cumulative effects of reading literature on ToM using confirmatory Bayesian analyses. Direct effects were assessed by comparing the ToM skills of participants who read texts that were manipulated to differ in the amount of gap filling they required. Cumulative effects were assessed by considering the relationship between lifetime literary fiction exposure and ToM. Results showed no evidence for direct effects of reading literature on ToM. However, lifetime literary fiction exposure was associated with higher ToM, particularly cognitive ToM. Although reading a specific piece of literary fiction may thus not have immediately measurable effects on ToM, lifetime exposure to this fiction type is associated with more advanced ToM.
Augmented Reality Indoor Environment Detection: Proof-of-Concept

The conventional museum experience offers the visitors glimpses of the past with the narrative limited to the static art that garnishes it. Through technology we already can mix the past with the future, immersing the visitors in a true dynamic journey across the same walls that guard our history. One of these technologies is the Augmented Reality, which aims to enhance our surroundings into a new era of creativity and discovery. This paper presents the proof-of-concept of an indoor portable environment pose estimation module (PEPE) present inside M5SAR, a project that aims to develop a five senses augmented reality system for museums. The current state of development of this module shows that is already achievable real-world wall(s) detection and a new environment superimposition over the detection, i.e., it is now possible to have a dynamic museum experience with the ability of transforming rooms into historic live stages.
On Solving Local Minimax Optimization: A Follow-the-Ridge Approach

Many tasks in modern machine learning can be formulated as finding equilibria in sequential games. In particular, two-player zero-sum sequential games, also known as minimax optimization, have received growing interest. It is tempting to apply gradient descent to solve minimax optimization given its popularity in supervised learning. However, we note that naive application of gradient descent fails to find local minimax – the analogy of local minima in minimax optimization, since the fixed points of gradient dynamics might not be local minimax. In this paper, we propose Follow-the-Ridge (FR), an algorithm that locally converges to and only converges to local minimax. We show theoretically that the algorithm addresses the limit cycling problem around fixed points, and is compatible with preconditioning and positive momentum. Empirically, FR solves quadratic minimax problems and improves GAN training on simple tasks.
Predicting the Response of Triple-Negative Breast Cancer Patients to Neoadjuvant Chemotherapy using Unstructured Text

Triple-negative breast cancer is a comparatively rare form of breast cancer where the cancer is not fueled by the hormones estrogen and progesterone or the HER2 protein [20]. Many treatments for breast cancer typically target these growth factors, so triple-negative breast cancer is significantly more difficult to treat than other varieties. Often, triple-negative breast cancer patients are given neoadjuvant chemotherapy as a preliminary form of treatment before surgery [20]. A successful response to NAC is a good indicator of long-term survival, so the ability to predict early in a patient’s treatment process how well they will respond to this therapy could provide a significant prediction of their long-term health and help doctors who are trying to decide on a patient’s treatment [20]. Our goal was to predict how well patients with triple-negative breast cancer would respond to neoadjuvant chemotherapy (NAC) based on medical reports written before they underwent treatment. We implemented Naive Bayes, logistic regression, and RNN models which took pathology and radiology reports in the form of unstructured text as inputs and attempted to predict whether the patient later had a successful or unsuccessful response to NAC.
The effects of Cr and Si additions and deposition conditions on the structure and properties of the (Zr-Ti-Nb)N coatings

In this study, (Zr-Ti-Nb)N, (Zr-Ti-Cr-Nb)N and (Zr-Ti-Cr-Nb-Si)N nitride coatings were obtained using a welldeveloped vacuum arc deposition. The systematical investigations demonstrate that the chemical composition, microstructure, and properties of the coatings intimately rely on the deposition parameters (pressure of working gas and substrate bias). Effects of Cr and Si additions on microstructure and mechanical properties of the (ZrTi-Nb)N coatings have been investigated using scanning electron microscopy (SEM) equipped with energy dispersive spectrum (EDS), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), transmission electron microscope (TEM), hardness measurements and adhesion testing. First-principles band-structure calculations and Gibbs-Rosenbaum triangle representation have been used to investigate the elemental and phase compositions in nitride coatings. The multi-component (Zr-Ti-Cr-Nb-Si)N and (Zr-Ti-Nb)N coatings are found to be a simple face-centered cubic (FCC) solid solution. For the coatings without Si, the structure is mainly composed of TiN fcc phase and Cr2N trigonal modification. The hardness values were in the ranges (24– 42 GPa). The (Zr-Ti-Nb)N, (Zr-Ti-Cr-Nb)N coatings provided the best adhesive strength in different conditions. The (Zr-Ti-Cr-Nb-Si)N coatings exhibited the worst adhesive strength, which may be attributed to the relative
Compositional Calculus of Regret Minimizers∗

Regret minimization is a powerful tool for solving large-scale problems; it was recently used in breakthrough results for large-scale extensive-form game solving. This was achieved by composing simplex regret minimizers into an overall regretminimization framework for extensive-form game strategy spaces. In this paper we study the general composability of regret minimizers. We derive a calculus for constructing regret minimizers for composite convex sets that are obtained from convexity-preserving operations on simpler convex sets. We show that local regret minimizers for the simpler sets can be combined with additional regret minimizers into an aggregate regret minimizer for the composite set. As one application, one can show that the CFR framework can be constructed easily from our framework. One can also show ways to include curtailing (constraining) operations into our framework. For one, they enable the construction of CFR generalization for extensive-form games with general convex strategy constraints that can cut across decision points.