Reviewerly/exHarmony huggingface dataset:
Hydro-geochemistry of groundwater and surface water in Dschang town (West Cameroon): Alkali and alkaline-earth elements ascertain lithological and anthropogenic constraints

This study focuses on the sources of alkali and alkaline-earth elements based on the geochemistry of groundwater and surface water in Dschang concerning environmental and anthropogenic constraints. A comprehensive set of 50 samples from groundwater and surface water were analyzed by ICP-MS and processed by spatial interpolation in a GIS environment. The results highlight a geochemical anomaly at the center of the densely inhabited area subject to a profusion of open dumps discharges. This anomaly with the highest spatial contents of Be (Cs, Rb, Mg) suggests an anthropogenic source that demarcates with the lowest alkali and alkaline-earth elements on the peripheral area of Dschang. Other findings include lithological constraints with volcanic rocks being the main source compared to granitoid. The study points out good correlations between Be, Cs, Rb and Mg spatial distributions and physico-chemical parameters of waters (K, EC, TDS), and inversely with the lowest pH. pH is established as the most functioning physico-chemical constraint of alkali and alkaline-earth mobility in Dschang. The pH lowest values within the geochemical anomaly also highlight the impact of human activities on water acidity, which later enhance elements mobility and enrichment. Despite low elements contents relative to WHO standards, our findings point out an example of anthropogenic impact on water geochemistry linked to solid waste pollution; it also demonstrates significant anthropogenic changes of environmental physico-chemical parameters of prime importance in the mobility and distribution of elements in the study area. Similar assessments should be extended in major towns in Cameroon.
Effect of Redundant Work on Machine

In the current scenario, a number of research is going on in the field of Artificial Intelligence. How can be the machine act as same as the Human brain? In this paper, one of the feature of Human Brain is compared with Machine. As the repetition of same work generally enhanced the accuracy until the saturation point is reached. After reached at the saturation point human brain accuracy becomes almost constant. This paper shows that Machine is also almost behave in the same way as the human brain. CIFAR10 and MNIST are two well-known datasets that we used in this research. This research showed how repetition of work enhances the accuracy of digit and image recognition task by Machine. A number of researches by the different researchers is going on to get the accuracy. Unfortunately still now maximum accuracy is not achieved. This research also tried to achieve this target. This research concentrates on how doing a thing again and again effects the accuracy. Artificial Neural Network technique is used to accomplish this task. As the experiment is on MNIST (Modified National Institute of Standards and Technology database) i.e. digit dataset and CIFAR10 (Canadian Institute For Advanced Research) i.e image dataset, Convolutional Neural Network (CNN) used. In this paper, experiments show how repletion of work effects the accuracy at the certain level.
Free-Form Laser Profilometry for Pipeline Inspection and 3D Cylindrical Reconstructions

Inspection of pipeline infrastructure is an essential part of pipeline integrity management to prevent from leakage and flow reduction is of great importance in water, gas, and oil distribution. However, current methods are limited in low resolution and low accuracy that fail to 3D reconstructions of pipelines. In this work, a theoretical laser propagation model based on a free-form laser profilometry method is established, and a fully functional laser system that incorporates a conic mirror within an integrated optical system containing all the elements necessary to retrieve the displacement information. The developed optical device exhibit high resolution, high accuracy, and high acquisition speed comparing with current technologies. Furthermore, the device can be completely sealed to be protected from the pipeline environment. This work paves the path towards autonomous laser scanners for underwater pipeline inspection.
INSTITUTIONAL COORDINATION OF FLOOD CONTROL AT MEDAN CITY IN MEBIDANGRO COOPERATION SCHEME

Flood management system in Medan City is very poor, because there is no institutional coordination resulting from sectoral egos. Effective institutional coordination in urban flood management is needed. Condition is based on problem of flooding in Medan City which has not been effectively implemented, so an alternative solution for integrated urban flood management is needed. Purpose of this research is to explain implementation of flood control policies in Medan City and how coordinate flood control institutions in Medan City in Mebidangro cooperation scheme. This type of qualitative research with descriptive methods is used in this research. Primary data collection techniques with deep interviews through FGD activities with stakeholders. In addition, secondary data is collected by citing journals, books, documents, online media, and other materials support research. Data analysis techniques are carried out through data reduction activities, data display and drawing conclusions. Results showed implementation Presidential Regulation No. 62 of 2011 on flood control in Medan City has not been optimally implemented. Condition is influenced lack of understanding authority and lack of coordination between related agencies to safeguard river basins, river norms and establish primary drainage channels. In addition, coordination of Medan City flood control institution in Mebidangro cooperation scheme has been carried out in a procedural manner. However, actualization still requires visionary leadership and command to support success program.
Signal Recognition Method of X-ray Pulsar Based on CNN and Attention Module CBAM

Fast and accurate identification of X-ray pulsar signals is an important prerequisite for pulsar navigation. Most of the current identification methods are to extract the cumulative profile features and then compare them with features of the standard profile to complete the identification. However, the profile with high signal-to-noise ratio needs long observation time, which has a great impact on real-time identification. In this paper, the X-ray pulsar signal is converted into time interval sequences, and then the feature extraction and identification are completed by using one-dimensional convolution neural networks. In terms of network architecture design, we introduce convolutional block attention module (CBAM) and propose the CBAM-Inception module to construct the network. This structure combines the advantages of Inception and CBAM, and uses channel and spatial attention mechanisms to enhance the feature extraction capabilities of the Inception network. Experimental shows that the proposed method can greatly shorten the required observation time while ensuring high-accuracy X-ray pulsar signal identification. Moreover, the comparison of convolution blocks shows that the CBAM-Inception block can greatly improve network identification ability.
Convolutional Neural Networks for Malaria Image Classification

Image processing applications are becoming more popular day by day across various domains in real-time aspects. This directly or indirectly having a great influence on artificial intelligence-based applications. These applications are widely using in agriculture and bio-medical images to built automated frameworks in the recognition of various diseases. Not only that, image recognition, image to text transformation, and object recognition are also popular applications. To bring these applications into reality, convolutional neural networks (CNN) acts as a backbone. Even if a lot of popular CNN architectures also are in usage, still researchers are looking for more effective frameworks. These CNN architectures play a vital role in advanced concepts like transfer learning. The CNN working aspects are mentioned more clearly in this article. The concept of customized CNN is explained through the application of malaria cell images. This customized CNN model attained a model accuracy of 95.91% and validation accuracy of 94.52%.
An Efficient Sports Classification Technique Incorporating CNN Transfer Learning Models

Data must be accessible and transferable within time constraints; yet, due to the massive increase of sophisticated data in recent years, categorizing data appropriately has become increasingly challenging. Furthermore, with the enormous popularity of many sports genres in recent times, it has become imperative to categorize the data and improve the user’s search experience through the internet or other media with traditional machine learning approaches. This paper aims to develop an effective approach with deep learning techniques for classifying eight distinct forms of sports using a real-world data set derived from diverse sports videos. To identify the most suitable model, a comparison of four notable transfer learning models of convolutional neural networks, VGG16, VGG19, DenseNet2Ol, and InceptionV3, was performed with DenseNet2Ol yielding the most promising outcome of 99.08%. In addition, users can upload a sports video, and the sports-related tags will be generated automatically by a web application developing a magnificent recommendation process in the suggested system model.
Weapons Detection of Criminal Activities Based on Computer Vision

In contemporary the term 'computer vision' is applicable vastly for significant research arena. Many researchers have a keen interest in this field. In this computer vision-based system, there have managed to detect weapons of two types (different guns and knives) to inform the concerned authority of property about crimes ongoing inside the place. While a property is under such criminal activity that the criminals carry guns or/and knives with them, the system detects those weapons through a camera integrated module and runs the necessary functions accordingly. Due to witnessing weapons, the system immediately informs the concerned authority to alert them about the upcoming risk to the property via mobile message and call. Consequently, the proprietor is alerted about the forthcoming risk and takes possible measures to handle the situation. Apart from that, the system can make sound alarms around the place of crime so that the people near to the property can come forward to help. This research is applicable in various properties such as banks, offices, homes or anywhere to protect the valuable properties from being theft or robbed. In this research computer vision and machine learning approaches are applied for the optimal result prediction.
Double-stage methodology for activity recognition in manual assembly

The analysis and evaluation of manual assembly processes is related to high efforts and expenditures. Traditionally, assessments use visual and empirical methods with a low level of digitalization. These are often not cost-covering for small production quantities. This paper presents an approach to recognize assembly steps from individually detected sensory events. The approach can be integrated in a system for automatic analysis of manual assembly processes and is applicable when little training data is available. It is based on a hidden Markov model and combined with a decision logic. The methodology is tested on an exemplary use case.
MILP-Based Linear Attacks on Round-Reduced GIFT

GIFT is a lightweight block cipher with an SPN structure proposed in CHES 2017. It has two different versions whose block sizes are 64 and 128 respectively. In RSA 2019, Zhu et al. found some differential characteristics of GIFT with MILP method and presented corresponding differential attacks. In this paper, we further find some linear characteristics with MILP method. For GIFT-64, we find two 11-round linear characteristics with correlation \begin{document}$2^{-29}$\end{document} , and use one of them to present a 16-round linear attack on GIFT-64 by adding 4 rounds before and one round after the linear characteristic. For GIFT-128, we find a 16-round linear characteristic with correlation \begin{document}$2^{-62}$\end{document} . As far as we know, it is the longest linear characteristic found for GIFT-128. Using the 16-round linear characteristic, we present a 20-round linear attack on GIFT-128 by adding 2 rounds before and 2 rounds after the linear characteristic.
Shaping the future of microsurgery: Combination of exoscope and smart glasses

The exoscope is increasingly being used across multiple surgical specialties including plastic surgery, otolaryngology, gynecology, and neurosurgery driven by the growing advancements in 3D video photography technology.1,2 The compact nature, excellent maneuverability and enhanced ergonomics have positioned it as the future of operative microsurgery.
Intelligent programming support system: machine learning feat. Fast development of secure programs.

The article is devoted to the field of software development. The considered scientific contradiction lies in the fact that, on the one hand, the use of manual labor of a programmer is necessary in this area, and on the other hand, the presence of a human factor negatively affects the safety of the resulting code. To resolve the contradiction, it is proposed to use machine learning, which is traditionally used to solve the problem of classification, regression, search for anomalies, clustering, generalization and search for associations. It is shown that the majority of publications on this solution are of a private nature and do not cover the entire spectrum of possibilities. Various ways of automating the programming process using solutions for the specified machine learning problems are considered and substantiated. The demand for a system that combines such methods is indicated; Also, for the first time, its author’s definition is introduced: «Intelligent Programming Support System – a computer automated system based on artificial intelligence technologies, the purpose of which is to help developers of program code in the interests of reducing and simplifying manual labor, as well as increasing the safety of the final product». A comparative analysis of automation methods based on machine learning is given according to 8 criteria that this intelligent system must meet. The ways of further continuation of the research are indicated.
Pictorial interpretation of quantum-enhanced measurements with wave functions

Quantum-enhanced measurements using squeezed states of light provide sensitivities beyond the shot noise limit. Although the mechanism of quantum enhancement is well described by quantum mechanics, it is difficult to intuitively understand how squeezed states are exploited. In this paper, we present a pictorial interpretation of quantum-enhanced measurements using wave functions, which provides intuitive pictures of squeezed states in various experimental systems.
Analysis of alternative flat representations of a UAV for trajectory generation and tracking

Motion planning problems benefit greatly from the properties of differential flatness, which are widely employed for trajectory generation and controller design. This paper aims to highlight the fact that various flat representations of a system can have different implications in the trajectory generation and tracking objectives. In particular, we consider a fixed-wing UAV (Unmanned Aerial Vehicle) and analyze various flat representations. We reformulate the trajectory generation and tracking problem in terms of different flat outputs and analyze the optimal cost, constraints satisfaction, tracking error and computational complexity. Insights on the future work complete the analysis. The research shows promising directions, especially in the area of disturbance rejection and robust control.
Co-AI: A Colab-Based Tool for Abstraction Identification

Abstraction identification is aimed at discovering significant domain terms. Prior work, notably AbstFinder and RAI (relevance-driven abstraction identification), has introduced the core ideas, but offered only limited tool support. This paper presents our abstraction identification tool, Co-AI, built on the Google Colab environment allowing the users to run the tool within their web browsers, promoting tool adoption and extension. Co-AI integrates the Wikipedia pages as the domain corpus, and identifies the candidate abstractions with a set of natural language processing (NLP) patterns. Co-AI is available at: https://colab.research.google.com/drive/1ur5KILoi_n-3KY0_vJcMBQDtiSYgcYeP?usp=sharing and we welcome the community’s feedback of our tool.
Neural network architectures based on the classical XY model

Classical XY model is a lattice model of statistical mechanics notable for its universality in the rich hierarchy of the optical, laser, and condensed matter systems. We show how to build complex structures for machine learning based on the XY model's nonlinear blocks. The final target is to reproduce the deep learning architectures, which can perform complicated tasks usually attributed to such architectures: speech recognition, visual processing, or other complex classification types with high quality. We developed a robust and transparent approach for the construction of such models, which has universal applicability (i.e., does not strongly connect to any particular physical system) and allows many possible extensions, while at the same time preserving the simplicity of the methodology.
Inequality distance hyperplane multiclass support vector machines

In this study, inequality distance hyperplane multiclass support vector machines (IDH-MSVM) algorithm is proposed on the basis of multiclassification support vector machine (MSVM) which was proposed by J. Weston and C. Watkins in 1999. It only needs to solve a single objective optimization problem to deal with multiclassification problems. For original MSVM, the hyperplane distance refers to the classification interval between classical margin and hyperplane, which is equality. However, the IDH-MSVM introduces parameters to adjust distance between every classification hyperplane and classical margin, which makes the hyperplane distance inequality. The effectiveness of the proposed method is experimented on UCI standard data sets and compared with several multiclassification algorithms. Experimental results show that this method has a better classification effect on multiclassification data.
Implementasi Pojok Baca untuk Meningkatkan Minat Baca Siswa MI Muhammadiyah Kartasura

This research aims to illustrate the implementation of the reading corner to increase the interest in reading the MI Muhammadiyah Kartasura students. The study uses a qualitative inductive approach. Data and collection techniques consist of primary data and secondary data obtained through observations, interviews, polls or questionnaires and documentation. Data validation uses the theory and techniques of the source triangulation and triangulation method. Data analysis uses interactive models including data collection, data reduction, data presentation and withdrawal of conclusions. Results from the study showed that. 1) The implementation of reading corners can increase student interest in the school environment with habituation, learning, development and school programs for students to read. 2) Student reading interest increases with a reading corner that encourages students to better appreciate their time reading books in the classroom reading corner. 3) The implementation of this reading corner is effective enough to increase the interest of reading students in the school environment and facilitate teachers to implement and relate lessons to create varied learning.
The Results Analysis of Innovative Educational Tools Use in Teacher Professional Activity

The article discusses one of the current problems of the education system modernization – the problem of teacher readiness to innovative educational tools use in professional activity. Practical, theoretical and methodological aspects of scientific research were analyzed in the framework of the use of innovative tools in professional activities. The author’s attention is focused on the e-learning tools and information technologies toolkit in the information and educational environment and the entire education system modernization and digitalization. The analysis showed that the discussed topics are widely represented in the professional pedagogical community scientific research. At the same time, the innovative tools themselves, which include e-learning tools, have evolved from technical tools to pedagogical tools. The e-learning pedagogical tools development in the open education framework requires the traditional teaching and innovative means integration. However, regardless of innovative tools type, there is an increasing need of these pedagogical tools use competent implementation in educational organizations. This thesis is confirmed by a number of tasks: the need to determine the effective characteristics that ensure the successful teacher professional activities self-realization; the need for a scientifically based effective characteristic structure; the need to determine the methodological foundations of the effective characteristics formation; the need to resolve the contradiction between the innovative approaches implementation trend in the educational and the unwillingness of the teacher to solve these innovative problems. Also, in the course of the research results analysis in the vocational education field, we conclude that readiness, as an effective characteristic and in its essence seems to be a broader concept than competence. The author concludes that the readiness includes competence in its structure.
A Virtual Clown Behavior Model Based on Emotional Biologically Inspired Cognitive Architecture

A cognitive model of the virtual clownery paradigm is developed, in which two clowns perform on a virtual stage, interacting with each other. Agents controlling virtual clown behavior were designed and implemented based on the eBICA cognitive architecture and embedded into the virtual environment, where they controlled two avatars. Clownery action was generated using the paradigm of ad hoc improvisation, without any given scenario. In this study, the output was produced as text, and the action was not visualized. In parallel, a number of scenarios were written by a group of experts using the same paradigm and behavioral repertoire. The two sets of scenarios were compared to each other in a number of characteristics. Results show which particular features of the model result in believable and socially attractive behavior. Implications concern socially-emotional intelligent agents for practically useful domains.
An Ensemble Learning-Based Architecture for Security Detection in IoT Infrastructures

The Internet of Things has known an important development. However, security management is still a key challenge in particular for deploying complex IoT systems that provide sophisticated services. In this paper, we design an ensemble learning-based architecture to support early security detection in the context of multi-step attacks, by leveraging the performance of different detection techniques. The architecture relies on a total of five major methods, including process mining, elliptic envelope, one class support vector machine, local outlier factor and isolation forest. We describe the main components of this architecture and their interactions, from the data preprocessing to the generation of alerts, through the calculation of scores. The different detection methods are executed in parallel, and their results are combined by an ensemble learning strategy in order to improve the overall detection performance. We develop a proof-of-concept prototype and perform a large set of experiments to quantify the benefits and limits of this approach based on industrial datasets.