Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Resource Estimation of Highly Skewed Gold Using Machine Learning Algorithms

With the complicated geology of vein deposits, their irregular and extremely skewed grade distribution, and the confined nature of gold, there is a propensity to overestimate or underestimate the ore grade. As a result, numerous estimation approaches for mineral resources have been developed. It was investigated in this study by using five machine learning algorithms to estimate highly skewed gold data in the vein-type at the Quartz Ridge region, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), Decision Tree Ensemble (DTE), Fully Connected Neural Network (FCNN), and K-Nearest Neighbors (K-NN). The accuracy of MLA is compared to that of geostatistical approaches, such as ordinary and indicator kriging. Significant improvements were made during data preprocessing and splitting, ensuring that MLA was estimated accurately. The data were preprocessed with two normalization methods (z-score and logarithmic) to enhance network training performance and minimize substantial differences in the dataset’s variable ranges on predictions. The samples were divided into two equal subsets using an integrated data segmentation approach based on the Marine Predators Algorithm (MPA). The ranking shows that the GPR with logarithmic normalization is the most efficient method for estimating gold grade, far outperforming kriging techniques. In this study, the key to producing a successful mineral estimate is more than just the technique. It also has to do with how the data are processed and split.
Understanding AutoML search spaces with local optima networks

AutoML tackles the problem of automatically configuring machine learning pipelines to specific data analysis problems. These pipelines may include methods for preprocessing and classifying data. There are many strategies to find the best pipeline configuration, and most of them address the problem from an optimization perspective, using Bayesian optimization and evolutionary algorithms. However, little is known about the shape of the search space these methods work upon. What is known for a fact is that these spaces include both categorical, continuous and conditional hyperparameters, and dealing with them may not be trivial. In this direction, this paper performs an analysis of AutoML search spaces using local optimal networks (LON) to better understand the global properties of the search space. Knowing these properties allow us to improve current optimization methods. By analyzing the metrics extracted from the LON we got many insights on the difficulty of the problem.
Multi-class classification algorithms for the diagnosis of anemia in an outpatient clinical setting

Anemia is one of the most pressing public health issues in the world with iron deficiency a major public health issue worldwide. The highest prevalence of anemia is in developing countries. The complete blood count is a blood test used to diagnose the prevalence of anemia. While earlier studies have framed the problem of diagnosis as a binary classification problem, this paper frames it as a multi class (three classes) classification problem with mild, moderate and severe classes. The three classes for the anemia classification (mild, moderate, severe) are so chosen as the world health organization (WHO) guidelines formalize this categorization based on the Haemoglobin (HGB) values of the chosen sample of patients in the Complete Blood Count (CBC) patient data set. Complete blood count test data was collected in an outpatient clinical setting in India. We used Feature selection with Majority voting to identify the key attributes in the input patient data set. In addition, since the original data set was imbalanced we used Synthetic Minority Oversampling Technique (SMOTE) to balance the data set. Four data sets including the original data set were used to perform the data experiments. Six standard machine learning algorithms were utilised to test our four data sets, performing multi class classification. Benchmarking these algorithms was performed and tabulated using both10 fold cross validation and hold out methods. The experimental results indicated that multilayer perceptron network was predominantly giving good recall values across mild and moderate class which are early and middle stages of the disease. With a good prediction model at early stages, medical intervention can provide preventive measure from further deterioration into severe stage or recommend the use of supplements to overcome this problem.
Sentiment Analysis in the Ecuadorian Presidential Election

The social network Twitter is characterized as one of the main means of communication used by politicians, governmental agencies, among others to communicate and engage with the people and prospective voters. This article proposes a methodology that might be employed in text extraction, acquisition, cleaning, and processing with the aim of analyzing the sentiments in the texts within the tweets of the 2021 Ecuadorian presidency candidates, Andrés Arauz and Guillermo Lasso. Many libraries that are specialized in text analysis were used to determine the sentiment analysis in the extracted tweets. The achieved results show a classification comparison among the employed libraries, highlighting that most of the tweets of both candidates were classified as positive and, following, neutral polarity.
Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach

Intervening in and preventing diabetes distress requires an understanding of its causes and, in particular, from a patient's perspective. Social media data provide direct access to how patients see and understand their disease and consequently show the causes of diabetes distress.Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect relationships in patient-reported diabetes-related tweets and provide a methodology to better understand the opinions, feelings, and observations shared within the diabetes online community from a causality perspective.More than 30 million diabetes-related tweets in English were collected between April 2017 and January 2021. Deep learning and natural language processing methods were applied to focus on tweets with personal and emotional content. A cause-effect tweet data set was manually labeled and used to train (1) a fine-tuned BERTweet model to detect causal sentences containing a causal relation and (2) a conditional random field model with Bidirectional Encoder Representations from Transformers (BERT)-based features to extract possible cause-effect associations. Causes and effects were clustered in a semisupervised approach and visualized in an interactive cause-effect network.Causal sentences were detected with a recall of 68% in an imbalanced data set. A conditional random field model with BERT-based features outperformed a fine-tuned BERT model for cause-effect detection with a macro recall of 68%. This led to 96,676 sentences with cause-effect relationships. "Diabetes" was identified as the central cluster followed by "death" and "insulin." Insulin pricing-related causes were frequently associated with death.A novel methodology was developed to detect causal sentences and identify both explicit and implicit, single and multiword cause, and the corresponding effect, as expressed in diabetes-related tweets leveraging BERT-based architectures and visualized as cause-effect network. Extracting causal associations in real life, patient-reported outcomes in social media data provide a useful complementary source of information in diabetes research.
Participatory Design Going Digital: Challenges and Opportunities for Distributed Place-Making

Abstract COVID-19 has urged researchers to explore new options for distributed participatory design, as physical meetings and workshops became unfeasible. This situation posed new challenges but also opportunities, in particular with respect to engagement and inclusion. This paper focuses on distributed PD with Irish teenagers to support place-making during this period: to build relationships with each other and the community. In a two-week online summer school, teenagers explored a concern or highlighted a unique aspect of their local community and designed digital artworks in response. Activities and materials were designed to support reflection, empowerment, inclusiveness, emergence, and playfulness for participatory place-making. Analysis of the summer school provides insights and guidance on the design of online PD for engaging experiences, especially in the context of place-making.
Intellectual property in quantum computing and market power: a theoretical discussion and empirical analysis

One of the central goals of intellectual property rights (IPRs) and related rights is to incentivize and reward creative and innovative efforts that promote scientific and technical progress and stimulate fair competition through the distribution and commercialization of technologies.Yet, an excessive proliferation of exclusive rights can also result in fundamentally anticompetitive environments with potentially negative effects on scientific research, product development, fair distribution and equitable access to the technology.Hence, a reasonable balance must be found between the stimulation of sustainable innovation and competition, the promotion of scientific research and protection through IPRs.To reconcile these factors, each new technology has led to judicial responses and even modifications to the law.• We are on the verge of a technological revolution associated with quantum technologies, including quantum computing and quantum/artificial intelligence hybrids.Its complexity and global significance are creating challenges, which could not have been foreseen when the IP system was developed.• This article utilizes the insights gained from qualitative and quantitative studies to (a) inquire which IPRs and related rights are currently directed to quantum computing and (b) examine whether the strategic use of overlapping IPRs might lead to innovation distortions such as excessive anticompetitive effects and underuse associated with property fragmentation.Emphasis is laid on the question if, and if so to what degree, IP portfolio
Hydrothermal Alteration Mapping Using Landsat 8 and ASTER Data and Geochemical Characteristics of Precambrian Rocks in the Egyptian Shield: A Case Study from Abu Ghalaga, Southeastern Desert, Egypt

This study evaluates the geological attributes of rocks within the Abu Ghalaga area using spatial, geochemical, and petrographic approaches. ASTER and Landsat imagery processed using band ratio and principal component analysis were used to map hydrothermal alterations, while a regional tectonic evaluation was based on automated extraction of lineaments from a digital elevation model. Geochemical and petrographic analyses were then employed for discrete scale evaluation of alteration patterns of rocks across the study location. Based on satellite image processing, alteration patterns across the study area are widespread, while evidence from lineament analysis suggests a dominant NW–SE tectonic trend accompanied by a less dominant ENE–WSW direction. The different rock units exposed in the studied district are arranged chronologically from oldest to youngest as arc metavolcanic group (basalt and rhyolite), arc metagabbro–diorite, gneissose granite (granodiorite and tonalite), and dykes (aplite and felsite). Various types of igneous and metamorphic rocks have propylitic, phyllic, and argillic zones. Geochemical data indicate that the studied rocks are classified into granite, granodiorite, gabbroic diorite, and gabbro. Geochemically, the rocks have a sub-alkaline magma type. The granodiorite–tonalite is derived from the calc–alkaline magma nature, while gabbro and diorite samples exhibit tholeiitic to calc–alkaline affinity. The tectonic setting of the studied rocks trends toward volcanic arc granite (VAG). Based on petrographic, geochemical, and remote analyses, sericitization, chloritization, epidotization, kaolinitization, carbonatization, and silicification are the main alteration types present in the study area. As a result of lineaments analysis, the existing fractures and structural planes form valid flow paths for mineral-bearing hydrothermal solutions.
On a Blockchain-Based Security Scheme for Defense against Malicious Nodes in Vehicular Ad-Hoc Networks

Vehicular ad-hoc networks (VANETs) aim to provide a comfortable driving experience. Sharing messages in VANETs can help with traffic management, congestion mitigation, and driving safety. However, forged or false messages may undermine the efficiency of VANETs. In this paper, we propose a security scheme based on blockchain technology, where two types of blockchain are constructed based on roadside units (RSUs) and Certificate Authorities (CAs), respectively. The proposed security scheme has multifold goals to identify malicious nodes and detect forged messages based on multiple factors, such as reputation of sender nodes, and time and distance effectiveness of messages. In addition, an incentive mechanism is introduced on the RSU blockchain to encourage RSUs to adopt active behaviors. Extensive simulations show that the proposed scheme exhibits superior performances to existing methods in detecting forged messages and identifying malicious nodes. Meanwhile, it provides privacy protection and improves the efficiency of vehicular networks.
Towards explainable metaheuristic

Metaheuristic search algorithms look for solutions that either maximise or minimise a set of objectives, such as cost or performance. However most real-world optimisation problems consist of nonlinear problems with complex constraints and conflicting objectives.
Multi-target evolutionary latent space search of a generative adversarial network for human face generation

This article presents an evolutionary approach for multi-target synthesized human face image generation based on exploring the latent space of generative adversarial networks. The proposed approach seeks to generate different human face images those share similarities to two given target images. The optimization applies generative adversarial networks for face generation, facial recognition for similarity evaluation, and an ad-hoc evolutionary algorithm for exploring the search space. The main results show that realistic images are generated, properly blending the main features of the two given target images, and deceiving a well-known facial recognition system.
Research on Question Answering System Based on BERT Model

The core of natural language processing is the science of how computers understand and respond to the influence of human language. This is also the main research direction in the field of machine intelligence development. Question answering system is one of the development directions. How to make the answer of question answering system more intelligent is the focus of research in recent years. Based on this, this thesis proposes a question answering system based on the BERT model that combines the co-attention mechanism and the self-attention mechanism. In order to better show the representation of questions and articles at their respective semantic levels, this article will choose BERT language to encode them. Attention and fusion are then performed horizontally and vertically at different levels of granularity between questions and paragraphs. Experiments show that the model has an average EM of 46.56 and an average F1 of 58.90 on all datasets. The average F1 for the development dataset is 54.85, and the average F1 for the test dataset is 62.95. The content answered by the question answering system based on the BERT model is basically reasonable, and the intelligence of the question answering system is effectively improved.
Research on the Method of Color Compensation and Underwater Image Restoration Based on Polarization Characteristics

The attenuation degree of light in different bands is different when transmitting underwater, which leads to the problems of color distortion and blurred target details in the turbid underwater image. Combined with Monte Carlo simulation and measured experiments, this paper studied the polarization characteristics of light transmission in different wavelength bands underwater. The results show that the longer the wavelength of light, the stronger the ability of polarization retention underwater. Based on this feature, a new method of color compensation of underwater images is proposed. By fusing the polarization image with the intensity image, the exposure of the low-brightness areas in the image is improved, so as to compensate for the areas with serious color distortion in the underwater image. And then the image is deblurred and enhanced by using the dark channel prior principle. Comparative experimental results indicate that the method can effectively compensate for the color distortion of underwater images and significantly improves the visibility of underwater targets.
Quantum Encrypted Communication between Two IBMQ Systems Using Quantum Permutation Pad

We demonstrate an early functional implementation of the Kuang and Barbeau’s Quantum Permutation Pad (QPP) algorithm on the IBMQ physical quantum computers using the Qiskit development kit. Our implementation of the quantum encryption QPP algorithm uses 2-qubit permutation operators created from the compositions of a few CNOT and NOT gates. With inability to physically transfer qubits between two IBMQ machines we acted as two separate IBMQ computers and described how ideally two quantum systems can securely communicate using QPP. Since the physical qubits are still noisy, we use a simple error correction technique by choosing the correct state with the highest probability. Our implementation can be extended to a hybrid system that consist of a quantum computer communicating with a classical computer securely using QPP. This work can be considered as a toy example of the fully secure implementation of quantum encryption using QPP. Nevertheless, it is a promising first step towards secure quantum communication.
Shapley Value as an Aid to Biomedical Machine Learning: a Heart Disease Dataset Analysis

This paper investigates the decision making process aided by machine learning for biomedical problems and how to improve it through meta assessments of the most relevant features. Classification algorithms are usually trained and exploited with high dimensional datasets (i.e., with an extremely large number of features), which is inefficient and costly. It would be beneficial to identify the most meaningful features that contribute the most to assigning a category to a subject, and in particular, diagnosing a pathological condition. A helpful support can come from cooperative game theory, through the computation of the Shapley value, an indicator of desirable properties according to which the players, in our case the input features, can be ranked. We apply such a framework to a supervised machine learning scenario of a random forest tree classifier applied to heart disease detection. From a publicly available dataset, we identify the most relevant features that can affect the decision, thus obtaining practical guidelines for a compact yet efficient description based on an analytical rationale.
A Systematic Review On Sentiment Analysis Approaches

With the rise of social media on the internet, one of the most important study fields is sentiment analysis. Millions of people trade their thoughts, ideas, expressions, sentiments, and opinions on social media sites such as Twitter, YouTube, Facebook, and others, infusing a lot of passion into human life these days. Sentiment analysis, often known as opinion mining, is primarily concerned withthe classification and prediction of people's attitudes toward a certain topic. It's also known as “emotion mining” or “feeling mining.” It entails categorizing written documents or sentences into positive or negative categories based on the expressed perspective on a certain issue. Although sentiment analysis appears to be comparable to text classification, it confronts a number of obstacles that have prompted a lot of research in this area. Different machine learning (ML) & lexicon-based algorithms have been developed in the narrative to automate the sentiment analysis task. We provide the findings of a tertiary study in this paper, which intends to investigate the current level of research. So that future investigators with better knowledge might create new automated approaches that solve all of the problems and provide the greatest results.
The Role of Innovative Data Mining Approaches for Analyzing and Estimating the Crop Yield in Agriculture Among Emerging Nations

Agriculture is the study or practise of cultivating land, including that of the gradual improved soil condition in order to generate harvests for food, fleece, as well as other products. The area of land under cultivation has decreased dramatically over time as a result of expanding urbanisation and industrialisation; also, the agriculture industry has been severely impacted by population reduction and climate change. Data mining is a rapidly new and rapidly growing research-oriented subject in agriculture that is used to formulate and analyse diverse agricultural production situations. In agriculture, data mining might aid in yield prediction, climate and rainfall forecasts, seed and soil conditions, and crop production. In agriculture, predictive data mining is designed to estimate upcoming crop, fertilisers and pesticides that can be used, and income to be made for healthy crop development and function. Different “data mining techniques”, including “k-nearest neighbour (KNN) ”, “support vector machine (SVM)”, and “artificial neural network (ANN)”, are used to analyse different ways and boost agricultural growth. Each analysis technique will have its own unique way of perceiving numerous difficulties and leads to the development of a suitable solution for every agronomic challenge. Aim of this paper is to defining role of “Data Mining Techniques” for estimating crop yield in agricultural context among emerging nations. This research paper has considered mixed method technique (primary quantitative and secondary qualitative) to gather relevant and factual data.
Intelligent Agents based Integration of Machine Learning and Case Base Reasoning System

Agents technologies are essential in learning and case base reasoning components to use incritical elements of the Cognitive knowledge base. The possibility of integrating several technologies into a basic structure that makes a kernel of an intelligent strategy. An alternative design is proposed in this paper to form the basis intelligent system that would operate in complex environment. This structure is very flexible because it allows adaptation via learning and alteration of the user knowledge. This paper used intelligent agent-based machine learning techniques to select the best suppliers and for further process seed it in case base. In this paper, we will also discuss the framework of machine learning techniques and CBR (Case Based-Reasoning) integrates with intelligent agents improving the resulting quality to solve highly complex problems.
Disease Prediction using machine learning algorithms

Extensive use of computer-based automation in the healthcare sector has resulted in the collection of huge amounts of digital data. Due to which, healthcare professionals find it easy to examine symptoms precisely and identify diseases at early stage but there is always certain human error. Comparatively, supervised machine learning (ML) algorithms has shown notable capability in exceeding standard approach for disease detection and helps medical experts in the early detection of high-risk diseases. In this paper, algorithms discussed were K- Nearest Neighbor, Naïve Bayes, Support Vector Machine and Decision Trees.