Reviewerly/exHarmony huggingface dataset:
Metallogenic Regularity and Prospecting Predictions of Gold Deposits in China

In this paper, based on the analysis of 1572 small and above-scale gold deposits in China, the gold deposits are divided into four types for prediction purposes: sedimentary, volcanic rock, magmatic hydrothermal and shear zone types. The spatial and temporal distribution of gold deposits in China is non–uniform, as 60% of the deposits are distributed in the land block and Mesozoic deposits account for 71.5% of the national total. It is found that intrusive and sub-volcanic rocks are the main ore–forming geological bodies. Here, the method for accurately identifying the spatial positions of concealed rocks is summarized. Through the geological profile study of 450 representative gold deposits, this paper suggests that there are mainly two kinds of gold deposits in China. One is characterized as an association of Au and Ag, with formation temperatures of approximately 250°C; it has very low concentrations of As and Sb. The fineness of the gold is generally below 900. Unequal proportions of K–feldsparization and albitization formed in the early stage. The other is an association of Au, As and Sb with a formation temperature below 250°C; the fineness of the gold is generally higher than 900; it has low Ag and high As and Sb concentrations. Alterations were mainly in the form of silicon with some secondary quartz formed in the early mineralization stage. Taking into account the elemental geochemical characteristics, the mechanism of gold migration and precipitation in the transient transition process from fluid to solid minerals in the ore-forming structure is discussed. The discussion provides a reference for future gold prospecting.
Reinforcement-Based Simultaneous Algorithm and Its Hyperparameters Selection

There exist many algorithms for data analysis, especially for classification problems. To solve data analysis problem, a proper algorithm should be chosen, and also its hyperparameters should be selected. In this paper we present a new method for the simultaneous selection of an algorithm and its hyperparameters. In order to do so, we reduced this problem to the multi-armed bandit problem. We consider an algorithm as an arm and algorithm hyperparameters search during a fixed time as the corresponding arm play. We also suggest a problem-specific reward function. We performed the experiments on 10 real datasets and compare the suggested method with the existing one implemented in Auto-WEKA. The results show that our method is significantly better in most cases and never worse than the Auto-WEKA.
A Comparison of Radial Basis Function and Multilayer Perceptron Network as tool for classification of Medical Data

Abstract Artificial Neural Network has become a popular tool in developing systems that encircles human proficiency. The importance of exact detection is exceptionally important for proper treatment and preserve of disease. Clinical cytology has improved tremendously in disease diagnosis. In this paper, two Artificial Neural Network (ANN) methods, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) network are compared. The RBF network predicts comparatively better accuracy in compared to MLP methods. Also it was detected that the RBF method requires a lesser amount of time for the development of the model, this is because there is no repetition to reach the favourable parameters in the model.
Adaptation of an authentication protocol based on asymmetric keys for use in UAV C2 link security systems

The paper describes one of possible ways of mutual authentication in unmanned aircraft systems supporting exterior hardware cryptographic keystorage devices. The suggested method is based on the elliptic curve Diffie-Hellman (ECDH) protocol. In addition to authentication procedure, it provides the key distribution scheme among all the entities and the generation of session keys. Consequent message encryption procedure includes the protection against replay attacks.
Fast Filtering for Nearest Neighbor Search by Sketch Enumeration Without Using Matching

A sketch is a lossy compression of high-dimensional data into compact bit strings such as locality sensitive hash. In general, k nearest neighbor search using sketch consists of the following two stages. The first stage narrows down the top K candidates, for some $$K \ge k$$, using a priority measure of sketch as a filter. The second stage selects the k nearest objects from K candidates. In this paper, we discuss the search algorithms using fast filtering by sketch enumeration without using matching. Surprisingly, the search performance is rather improved by the proposed method when narrow sketches with smaller number of bits such as 16-bits than the conventional ones are used. Furthermore, we compare the search efficiency by sketches of various widths for several databases, which have different numbers of objects and dimensionalities. Then, we can observe that wider sketches are appropriate for larger databases, while narrower sketches are appropriate for higher dimension.
Research on Anomaly Detection Algorithms for Financial Data Based on Angle

Abstract With the emergence of more and more massive data, under the background of large data, the existing angle-based anomaly detection algorithms have the problem of too much computation. Based on this, this paper improves the angle-based anomaly detection method, and proposes an angle-based anomaly detection method based on Data Center for anomaly detection of large amount of network financial transaction data. By establishing data update mechanism for different data sets, real-time data detection can be carried out.
Blind image quality assessment based on statistics features and perceptual features

Blind image quality assessment (BIQA) aims to evaluate the quality of an image without information regarding its reference image. In this paper, we proposed a novel BIQA method, which combines thirty six natural scene statistics (NSS) features, two color statistics features and four perceptual feat ures to construct an image quality assessment model. Support Vector Regression (SVR) is adopted to build the relationship between these features and image quality scores, yielding a measure of image quality. Experimental results in LIVE, TID2013 databases and their cross validations show that the proposed method records a higher correlations with human subjective judgments of visual quality and delivers highly competitive performance with state-of-the-art BIQA models.
Modeling and Simulation of three Control Techniques for UAV Quadrotor

This paper presents the modeling of a four rotor type unmanned aerial vehicle (UAV) called the quadrotor and three techniques to control its altitude, attitude, heading and position. The dynamic model of the quadrotor is nonlinear and has been derived using Newton's and Euler's laws. The first approach is designed using a linear Proportional-Integral-Derivative (PID) control technique. The second approach is based on a nonlinear Back-stepping controller while the third one is a gain Gain-Scheduling based PID controller. The Genetic Algorithm technique has been used to get an optimal tuning for the gains and parameters of the three mentioned controllers. MATLAB/Simulink software was used to evaluate and compare the three designed control approaches in terms of the stability, the effect of possible disturbances and the dynamic performance.
Shadow detection on color images

Abstract The shadow areas cause significant problems in objects recognition and classification applications. The shadows have uniform properties in some colour spaces. Thus, the shadow detection can be effectively done by applying the threshold processing. This paper proposes a simple method to detect shadows in images using the combination of two components from different colour spaces. The B component of LAB colour space is used to detect homogenous areas using contour segmentation algorithm. The method uses the V component of HSV colour space to decide whether the obtained areas (the result from the segmentation stage) are shadowed or not. This stage is done using the mean value of the V component for these areas and subsequent thresholding based on Otsu’s method. The experimental results show that the proposed approach can get accurate detection results like the state-of-art-methods, while it is more stable for the difference of characteristics of initial images.
Mobile Client-Server Approach for Handwriting Digit Recognition

In the era of an Internet of Things, pattern recognition technology is growing rapidly, especially by the massive implementations of artificial intelligence (AI). Selecting the right implementation for an AI algorithm for an application could be quite challenging and time-consuming. In this paper, we propose a client-server system implementation for handwriting digit recognition. A client-server is set based on TensorFlow with multiple models for classifications. The client is set based on a mobile application that the user inputs the digit by touch panel of the mobile. In this paper, the models at the server are trained and tested by using MNIST database of handwritten. In addition, we use convolutional neural network (CNN) to improve the performance of the neural network. The client-server allows many users to be accessed by AI model from the same time. The advantage of using client-server approach is reducing power and time for processing handwriting recognition in client device. It also speeds up the time of development and implementation of algorithm on the server.
Teaching Mobile Robotics Using the Autonomous Driving Simulator of the Portuguese Robotics Open

Teaching mobile robotics adequately is a complex task. Within the strategies found in the literature, the one used in this work includes the use of a simulator. This simulator represents the Autonomous Driving Competition of the Portuguese Robotics Open. Currently, the simulator supports two different robots and all challenges of the autonomous driving competition. This simulator was used at a Robotics course of the Integrated Master Degree in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto. In order to study the influence of the simulator in the college students learning process, a survey was conducted. The results and its corresponding analysis indicate that the simulator is suited to teach some of the mobile robotics challenges crossing several fields of study, including image processing, computer vision and control.
A demonstration of cARe

Cognitive impairment such as memory loss, an impaired executive function and decreasing motivation can gradually undermine instrumental activities of daily living (IADL). With an older growing population, previous works have explored assistive technologies (ATs) to automate repetitive components of therapy and thereby increase patients' autonomy and reduce dependence on carers. While most ATs were built around screens and projection-based augmented reality (AR), the potential of head-mounted displays (HMDs) for therapeutic assistance is still under-explored. In this interactive demonstration, we present cARe, an HMD-based AR framework that uses in-situ instructions and a guidance mechanism to assist patients with manual tasks. In a case study with six geriatric patients, we investigated the prototype's feasibility during a cooking task in comparison to a regular paper-based recipe. Qualitative and quantitative results indicate that cARe has potential to offer assistance to older individuals with declining cognitive function in their day-to-day tasks and increase their independence in an enjoyable way.
Representation and simulating of extended Markov chains over a finite field with a predefined precision

Abstract A method is proposed for representing and simulating the extended Markov chains of a defined type by minimal polynomials over finite field GF( q ). Simulation problem is being solved as a problem of constructing the minimal polynomial over field GF( q ) using the Berlekamp-Massey algorithm. The polynomial produces a sequence of the length of N . A stochastic matrix relevant to that sequence approximates the given stochastic matrix of an extended Markov chain with a predefined precision proportional to the 1/ N value. The polynomial constructed defines unambiguously the structure of a q -ary linear shift register for simulating extended Markov chains. The method allows constructing the implementations of this class of Markov sequences on linear q -ary shift registers with a pre-defined “linear complexity” defined by the value of N .
Ultrasound Image Denoising Using Orthogonal Decomposition in Frequency Domain

The ultrasound images contain significant concentration of speckle noise. Speckles denoising is of prime importance in order to make the correct diagnosis. This paper provides an orthognalization based technique with projection in the frequency domain that has proved to be effective in speckles denoising. The performance of proposed scheme has been verified on both simulated and real ultrasound images. A comparative analysis between proposed scheme and benchmark schemes such as Orthogonal regular, Frost and Total Variation Filtering (TVF) is presented in this research. The proposed algorithm starts with segmenting a noise corrupted ultrasound image into small sized blocks in an overlapping manner. These blocks contribute in creation of an overall covariance matrix. Fourier transform of the overall covariance matrix is taken to project the image into frequency domain. A subset of the orthonormal vectors are secured by applying the orthogonalization scheme, and a features matrix is formed that is used to clean the speckle noise. The outcomes show that proposed approach is more effective in speckle denoising as compared to benchmark schemes with reference to Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR) and Peak Signal to Noise Ratio (PSNR). The aforementioned parameters are of significant importance in assessing performance of any despeckling scheme.
Knowledge Base Intelligent System of Optimal Locations for Safe Water Wells

The union of expert systems with the advanced technology elements is constantly giving us more developed and improved values or results, and indeed exact results with some human decision-making practices. As a consequence, this area of study is continuing to be the center of interest. In this paper an expert system using VP-Expert was designed to help improve the siting of water wells. The paper explains major factors to consider before siting water wells in Africa. According to World Health Organization about 900 million of the world population do not have access to sustainable safe drinking water, 84% of this estimated population dwell in rural areas. In fact, Africa has the lowest potable water coverage when compared to other continents of the world. In this paper a system was developed to identify optimal locations to site water wells. The procedure of the knowledge acquisition in the design of this system was done through interviewing geology experts, text books, journals and various related sources, and the knowledge base intelligent was represented in the rule-based procedure. These rules identify the quantity and quality of water that a targeted location is capable of producing. VP-Expert software was used for the design of the system and the system was validated by geologists from the Kaduna State Government of Nigeria. The developed system can be used effectively by governments, non- governmental organizations and individuals to improve the supply of safe drinking water.
Interactive Deep Metric Learning for Healthcare Cohort Discovery

Given the continuous growth of large-scale complex electronic healthcare data, a data-driven healthcare cohort discovery facilitated by machine learning tools with domain expert knowledge is required to gain further insights of the healthcare system. Specifically, clustering plays a crucial role in healthcare cohort discovery, and metric learning is able to incorporate expert feedback to generate more fit-for-purpose clustering outputs. However, most of the existing metric learning methods assume all labelled instances already pre-exists, which is not always true in real-world applications. In addition, big data in healthcare also brings new challenges to metric learning on handling complex structured data. In this paper, we propose a novel systematic method, namely Interactive Deep Metric Learning (IDML), which uses an interactive process to iteratively incorporate feedback from domain experts to identify cohorts that are more relevant to a particular pre-defined purpose. Moreover, the proposed method leverages powerful deep learning-based embedding techniques to incrementally gain effective representations for the complex structures inherit in patient journey data. We experimentally evaluate the effectiveness of the proposed IDML using two public healthcare datasets. The proposed method has also been implemented into an interactive cohort discovery tool for a real-world application in healthcare.
Integration as Didactic Prerequisite for Creating Situations of Axiological Choice to be Made by Students

Schooling helps to take on the image of “Self” by integrating the meanings and narratives of our culture, identifying oneself with its values, including social ones. Integration is viewed by the authors as a process bringing multifarious meanings together, into a single interface between the meanings of various subjects and objects of the pedagogical process. It is proved that such approach encourages students to make a certain axiological choice, the first manifestation of meaning. Selected elements of pedagogical process - problems, problems-antinomies, events, concrete facts, personalization - combine various contents, make them into a single whole, creating prerequisites for axiological choices. It was found that the greater is the meaningful potential of integrated knowledge in the choice situations, the higher is the personal relevance of the problem for the student; events, specific facts and “acting out the characters” should affect the emotional and aesthetic sphere of the student. Due to this the axiological choice will be based on inner emotion, experiencing which is an indispensable prerequisite of personality development
Unimodal Medical Image Registration Based on Genetic Algorithm Optimization

This research work proposes unimodal image registration based on genetic algorithm. The intensity-based image registration is employed here, and normalized cross-correlation is used as the similarity index, and for choosing the optimal values of image registration parameters, genetic algorithm was employed. The performance of the image registration was validated by the performance metrics and tested on MR brain images of BrainWeb database. The performance metrics peak-to-signal noise ratio (PSNR), mean squared error (MSE), normalized cross-correlation (NCC), and mutual information (MI) reveals the superiority of the image registration algorithm.
Morph Animation and Facial Rigging

In the previous chapters, we learned how to create custom character rigs using bones, bipeds, and CAT tools. In this chapter, we will look at creating facial animation for our characters. We will also learn about the techniques that are primarily used for facial animations:
Improving Signature Schemes with Tight Security Reductions

In 2003, Katz and Wang proposed the claw-free trapdoor full domain hash (CFT-FDH) which achieves a tight security for FDH signature schemes using the bit selector technique. However, it is noted that the CFT-FDH is not backward compatible with its original FDH counterpart, since the selected bit is hashed with the message, modifying the structure of the original signature. In this paper, we take a step further to propose a general framework that is able to achieve backward compatibility while maintaining the tight reduction of FDH signatures using the properties of trapdoor samplable relations and also Katz-Wang’s bit selector technique.
Proving Data-Poisoning Robustness in Decision Trees

Machine learning models are brittle, and small changes in the training data can result in different predictions. We study the problem of proving that a prediction is robust to data poisoning , where an attacker can inject a number of malicious elements into the training set to influence the learned model. We target decision tree models, a popular and simple class of machine learning models that underlies many complex learning techniques. We present a sound verification technique based on abstract interpretation and implement it in a tool called Antidote. Antidote abstractly trains decision trees for an intractably large space of possible poisoned datasets. Due to the soundness of our abstraction, Antidote can produce proofs that, for a given input, the corresponding prediction would not have changed had the training set been tampered with or not. We demonstrate the effectiveness of Antidote on a number of popular datasets.
Information Flow Tracking in RISC-V

In the process of globalization, heterogeneous SoCs play an important role in an embedded application, security aspects of such systems are crucial. The system is susceptible to many attacks out of which we focus on the run-time software attacks which causes memory corruption. In this scheme we propose a software-based solution to secure the system integrity by providing Information Flow Tracking (IFT) technique to track the spurious data during run-time and preventing buffer overflow attacks. This scheme is implemented on RISC-V and protects bare-metal applications against memory corruption by adding tag bits to the untrusted data inputs.
Can the One-Parameter Logistic Model Be a Spurious Finding for a Heterogeneous Population?

Utilizing the perspective of finite mixture modeling, this note considers whether a finding of a plausible one-parameter logistic model could be spurious for a population with substantial unobserved heterogeneity. A theoretically and empirically important setting is discussed involving the mixture of two latent classes, with the less restrictive two-parameter logistic model holding within each of the classes while the more restrictive one-parameter logistic model is plausible overall. A numerical example is presented then of obtaining the one-parameter logistic model as a spurious result in a finite mixture study. Implications for behavioral and social research are discussed in light of these findings.
Recursive Background Modeling with Place-specific Autoencoders for Large-scale Image Change Detection

Image change detection (ICD) to detect changed objects in front of a vehicle with respect to a place-specific background model using an on-board monocular vision system is a fundamental problem in intelligent vehicle (IV). From the perspective of recent large-scale IV applications, it can be impractical in terms of space/time efficiency to train place-specific background models for every possible place. To address these issues, we introduce a new autoencoder (AE) based lightweight ICD framework that combines the advantages of AE-based anomaly detection (AD) and AE-based image compression (IC). We propose a method that uses AE reconstruction errors as a single unified measure for training a minimal set of place-specific AEs while maintaining detection accuracy. We further present an efficient incremental recursive AE (rAE) training framework that recursively summarizes a large collection of background images into the AE set. The results of experiments on challenging cross-season ICD tasks validate the efficacy of the proposed approach.