Reviewerly/exHarmony huggingface dataset:
Predicting of heavy metals in some areas of Iraq using spectral analysis techniques

Abstract Soil that has been contaminated by heavy metals is a serious environmental problem. A different approach for forecasting a variety of soil physical parameters is reflected spectroscopy is a low-cost, quick, and repeatable analytical method. The objectives of this paper are to predict heavy metal (Ti, Cr, Sr, Fe, Zn, Cu and Pb) soil contamination in central and southern Iraq using spectroscopy data. An XRF was used to quantify the levels of heavy metals in a total of 53 soil samples from Baghdad and ThiQar, and a spectrogram was used to examine how well spectral data might predict the presence of heavy metals metals. The partial least squares regression PLSR models performed well in predicting the Sr and Cr elements using spectroscopy, with coefficients R 2 = 0.73 and RMSE = 63.8 for the determination, and R 2 = 0.60 and RMSE = 16.4 for Cr, respectively. This research validates the detection of heavy metal contamination using reflectance spectroscopy. Results of the current study proved that some heavy elements have spectral features become either when their concentrations low or high, such as Cr, Sr, Cu and Zn. The current study opens new possibilities for studying these elements using remote sensing in the future.
A prototype sensorless dual axis solar tracker system based on Arduino programming technique

Abstract The research works on designing and manufacturing a device that can generate clean energy, taking into account the geographical location of Iraq, and be economical and cheap, this manufactured device addresses the permanent problems that this type of device encounters. Therefore, a solar tracker has been manufactured that tracks the sun’s rays, to reduce the number of solar panels used, and is an environmentally friendly generator, it works without the LDR photosensors. The work was done on different days, a sunny day, a partially cloudy day, and a cloudy day. Through the work, it was practically observed that the device responded to work and reduced the problem of dispersion, as it was directed by programming from the microcontroller, not by the sensors, from the obtained results, it was noted that the generation losses in the three days under study sequentially are (10.9%, 9%, 4.9% ) and the results are very acceptable, and the anticipated outcome was attained.
IMPACTS OF CYBER CRIME ON INTERNET BANKING: A STUDY

Internet banking, often called e-banking, is a service that make use of information and communication technology. Due to flexibility and convenience internet banking is growing more and more popular. Computer criminals attempts to obtain unauthorized access to the data of various sectors including financial and commercial sectors in order to engage in criminal activity. As a consequence, while using Internet banking and related services, users are always concerned for their security. Here security doesn’t mean physical security of the person rather it means security of financial information.&#x0D; The goal of this study is to determine which sectors are most vulnerable to cyber-attacks and to assure the development and modification of cyber security protocol.
An Analysis On The Teaching Strategy In Speaking Skill At Sman 2 Tembilahan

This study aims to find out what strategies are often used by teachers at SMAN 2 Tembilahan. The researcher found the problems is the strategies used by the teachers in teaching speaking at SMAN 2 Tembilahan have not been identified. The researcher chose SMAN 2 Tembilahan as the research location because the researcher saw that during PPL, the students’ ability in speaking was good and only a few students were somewhat low in speaking. The design of this research is descriptive research. The population of this study is English teachers at SMAN 2 Tembilahan. The sample for this study are two English teachers. To obtain data, researcher used interview and questionnaire as instruments, for interviews 5 question and for questionnaire 13 questionnaire. The researcher found strategies that are often used by teachers in teaching speaking. There are five strategies used by teachers in teaching speaking namely role play, storytelling, reporting, presentation and discussion group.
IMPLEMENTASI MODEL THINK TALK WRITE (TTW) DALAM PENINGKATAN HASIL BELAJAR SISWA MATERI PROCEDURE TEXT

The purposes of this study are to describe (1) the implementation of the Learning Implementation Plan during the learning process, (2) the learning outcomes of the product after the implementation of Think Talk Write, (3) student activities during the implementation of the Think Talk Write, and (4) students' responses to learning by applying the Think Talk Write learning model. This classroom action research was carried out for two cycles in class X MIPA 2 SMA Negeri 1 Puri involving 35 students. Each cycle consists of stages of planning, implementation, observation and reflection. The results showed that (1) the learning process by applying the Think Talk Write learning model could be carried out in good categories, namely in the first cycle with a value of 3.03 and the second cycle of 3.27., (2) student activities during the application of the Think Talk Write learning model showed students active with an activeness of 87.15% in the first cycle and 93.48% in the second cycle, (3) character behavior in both cycles showed good behavior with an average score of 3.23 (good) in the first cycle and 3.45 (good) in the second cycle, (4) results student learning after the implementation of the Think Talk Write learning model has increased with the percentage of completeness 77.14% in the first cycle increasing to 91.4% in the second cycle, and (5) student responses to learning by applying the Think Talk Write learning model are positive
Studying Indonesian-Speaking Politeness for Class XI Students at SMK Negeri Tapango, Polman Regency

The main problem in this study is how to use the principle of politeness in student interactions and the types of speech acts contained in the interaction of class XI students at SMK Negeri Tapango. This study aims to describe the use of politeness principles and describe the types of speech acts contained in the interaction of class XI students at SMK Negeri Tapango. This research is a qualitative descriptive study with the subject of verbal interaction of class XI students at SMK Negeri Tapango, Polewali Mandar Regency. Data collection techniques used are observation techniques, note-taking techniques and recording techniques. Data analysis techniques by transcribing data from observations, identifying and clarifying data, copying into data cards, analyzing data cards and concluding. The results of this study indicate the number of utterances collected is 35 utterances. The politeness principles include: (1) maxim of wisdom as many as 6 utterances (2) maxim of generosity as much as 8 (3) maxim of appreciation as many as 3 utterances (4) maxim of simplicity as many as 2 utterances (5) maxim of consensus as many as 11 utterances and (6) maxim of sympathy as much as 5 utterances. The maxim that is widely used is the maxim of consensus. The maxim that is widely used is the maxim of consensus. This shows that the speaker and the interlocutor in the interaction maximize the compatibility of goals or opinions in interacting. While the maxim that is least used in the speech of the students of SMK Negeri Tapango is the maxim of simplicity.
Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models

Statistical shape modeling (SSM) is an enabling quantitative tool to study anatomical shapes in various medical applications. However, directly using 3D images in these applications still has a long way to go. Recent deep learning methods have paved the way for reducing the substantial preprocessing steps to construct SSMs directly from unsegmented images. Nevertheless, the performance of these models is not up to the mark. Inspired by multiscale/multiresolution learning, we propose a new training strategy, progressive DeepSSM, to train image-to-shape deep learning models. The training is performed in multiple scales, and each scale utilizes the output from the previous scale. This strategy enables the model to learn coarse shape features in the first scales and gradually learn detailed fine shape features in the later scales. We leverage shape priors via segmentation-guided multi-task learning and employ deep supervision loss to ensure learning at each scale. Experiments show the superiority of models trained by the proposed strategy from both quantitative and qualitative perspectives. This training methodology can be employed to improve the stability and accuracy of any deep learning method for inferring statistical representations of anatomies from medical images and can be adopted by existing deep learning methods to improve model accuracy and training stability.
ADASSM: Adversarial Data Augmentation in Statistical Shape Models from Images

Statistical shape models (SSM) have been well-established as an excellent tool for identifying variations in the morphology of anatomy across the underlying population. Shape models use consistent shape representation across all the samples in a given cohort, which helps to compare shapes and identify the variations that can detect pathologies and help in formulating treatment plans. In medical imaging, computing these shape representations from CT/MRI scans requires time-intensive preprocessing operations, including but not limited to anatomy segmentation annotations, registration, and texture denoising. Deep learning models have demonstrated exceptional capabilities in learning shape representations directly from volumetric images, giving rise to highly effective and efficient Image-to-SSM networks. Nevertheless, these models are data-hungry and due to the limited availability of medical data, deep learning models tend to overfit. Offline data augmentation techniques, that use kernel density estimation based (KDE) methods for generating shape-augmented samples, have successfully aided Image-to-SSM networks in achieving comparable accuracy to traditional SSM methods. However, these augmentation methods focus on shape augmentation, whereas deep learning models exhibit image-based texture bias resulting in sub-optimal models. This paper introduces a novel strategy for on-the-fly data augmentation for the Image-to-SSM framework by leveraging data-dependent noise generation or texture augmentation. The proposed framework is trained as an adversary to the Image-to-SSM network, augmenting diverse and challenging noisy samples. Our approach achieves improved accuracy by encouraging the model to focus on the underlying geometry rather than relying solely on pixel values.
Muscle Volume Quantification: Guiding Transformers with Anatomical Priors

Muscle volume is a useful quantitative biomarker in sports, but also for the follow-up of degenerative musculo-skelletal diseases. In addition to volume, other shape biomarkers can be extracted by segmenting the muscles of interest from medical images. Manual segmentation is still today the gold standard for such measurements despite being very time-consuming. We propose a method for automatic segmentation of 18 muscles of the lower limb on 3D Magnetic Resonance Images to assist such morphometric analysis. By their nature, the tissue of different muscles is undistinguishable when observed in MR Images. Thus, muscle segmentation algorithms cannot rely on appearance but only on contour cues. However, such contours are hard to detect and their thickness varies across subjects. To cope with the above challenges, we propose a segmentation approach based on a hybrid architecture, combining convolutional and visual transformer blocks. We investigate for the first time the behaviour of such hybrid architectures in the context of muscle segmentation for shape analysis. Considering the consistent anatomical muscle configuration, we rely on transformer blocks to capture the long-range relations between the muscles. To further exploit the anatomical priors, a second contribution of this work consists in adding a regularisation loss based on an adjacency matrix of plausible muscle neighbourhoods estimated from the training data. Our experimental results on a unique database of elite athletes show it is possible to train complex hybrid models from a relatively small database of large volumes, while the anatomical prior regularisation favours better predictions.
Using Python to Predict Global City Temperatures for 400+ Cities

The purpose of this investigation was to use Python to model global city temperatures for 400+ cities for many decades. The process used a compilation of secondary data to find my renowned sources and use different regression models to plot temperatures. Climate change is an impending crisis for our Earth, and modeling its changes using Machine Learning will be crucial to understanding the next steps to combat it. With this model, researchers can understand which area is most harshly affected by climate change leading to prioritization and solutions. They can also figure out the next sustainable solutions based on climate needs. By using KNeighbors and other regressors, we can see an increase in temperature worldwide. Although there is some error, which is inevitable, this is mitigated through several measures. This paper provides a simple yet critical understanding of how our global temperatures will increase, based on the last 200+ years.
Introduction to the Mycorrhiza Optimization Algorithm

HeuristicHeuristic methods are used to search for solutions by trial and error, while metaheuristicsMetaheuristic are considered high-level methods because they use information and solution selection to guide the search process (Yang in Studies in computational intelligence, pp 1–26, 2014 [1]). At present, most metaheuristics imitate nature, in particular they imitate the biological systems that have evolved the most from natural selection over time (Yang and Deb in 2009 world congress on nature & biologically inspired computing (NaBIC), pp 210–214, 2009 [2]). For example, in the world of the deaf and blind, temperature and body odor are very important indicators, or for bats that live in caves, what is important are the air compression waves that allow them to echo, these special characteristics that these beings have developed alive are the ones that take the algorithms inspired by nature for their development.
Background for the Mycorrhiza Optimization Algorithm

Nature-inspired optimization algorithms such as Particle Swarm OptimizationParticle Swarm Optimization (PSO) (El-Shorbagy and Hassanien in Int J Rough Sets Data Anal 5:1–24, 2018 [1]), Flower Pollination Algorithm (FPA) (Yang in Flower pollination algorithm for global optimization, pp 240–249, 2012 [2]; Balasubramani and Marcus in A study on flower pollination algorithm and its applications, 2014 [3]), Ant Colony OptimizationAnt Colony Optimization (ACO) (Dorigo et al. in IEEE Comput Intell Mag 1:28–39, 2006 [4]), Artificial Bee ColonyArtificial Bee Colony Algorithm (ABC) (Bansal et al. in Int J Adv Intell Paradig 5:123, 2013 [5]), Firefly AlgorithmFirefly Algorithm (FA) (Ali et al. in ARPN J Eng Appl Sci 9:1732–1736, 2014 [6]), etc., have demonstrated flexibility, efficiency and adaptability, in solving a wide spectrum of problems in real world applications, their merits and successes have inspired researchers to continuously develop these algorithms innovative.
Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm Using GPUs

Bundle adjustment is a camera and point refinement technique in a 3D scene reconstruction pipeline. The camera parameters and the 3D points are refined by minimizing the difference between computed projection and observed projection of the image points formulated as a non-linear least-square problem. Levenberg-Marquardt method is used to solve the non-linear least-square problem. Solving the non-linear least-square problem is computationally expensive, proportional to the number of cameras, points, and projections. In this paper, we implement the Bundle Adjustment (BA) algorithm and analyze techniques to improve algorithmic performance by reducing the mean square error. We investigate using an additional radial distortion camera parameter in the BA algorithm and demonstrate better convergence of the mean square error. We also demonstrate the use of explicitly computed analytical derivatives. In addition, we implement the BA algorithm on GPUs using the CUDA parallel programming model to reduce the computational time burden of the BA algorithm. CUDA Streams, atomic operations, and cuBLAS library in the CUDA programming model are proposed, implemented, and demonstrated to improve the performance of the BA algorithm. Our implementation has demonstrated better convergence of the BA algorithm and achieved a speedup of up to 16× on the use of the BA algorithm on various datasets.
Importance of Machine Learning Models in Healthcare Fraud Detection

With the advent of technology and the improvements in AI, many healthcare institutions are struggling with the threat of fraud. As such, Healthcare fraud poses a significant threat to the healthcare industry, as it has led to numerous financial losses. In addition, there have been cases of compromised patient care due to the fraudsters being so advanced in their systems. The purpose of this research is to investigate the pivotal role of machine learning models and how they can be used to address the challenge of fraud. Many professionals have stated that machine learning models can enhance the accuracy and fairness of healthcare fraud detection. The ideas stem from the ability to leverage a diverse dataset of healthcare transactions, including claims and billing records. Other ideas include patient demographics, where a range of machine learning algorithms, like (Random et al.) and deep learning models (CNN, RNN), are significant in evaluating the performance of the technology. The results from this research show that machine learning models are better when compared to traditional approaches. These models can achieve high precision and recall scores. The models exhibit robustness, and they are able to show an ability to adapt to variations in fraud patterns. Therefore, machine learning models offer a promising avenue for healthcare organizations to combat fraud.
Neural Networks with Variational Quantum Circuits

The field of machine learning is an interdisciplinary area that aims to extract useful information from data through mathematical means. Integrating quantum computing with machine learning has led to exciting new avenues of research, where quantum mechanics principles are applied to enhance and optimize classical machine learning algorithms. In this study, we explore hybrid quantum-classical neural networks with an approach that combines both classical and quantum computing. We achieve this by implementing a variational quantum circuit as the output layer of a classical convolutional neural network. We use this hybrid neural network to classify images of digits from the MNIST dataset. Using this approach, we were able to classify images with high accuracy. Furthermore, due to its flexibility, this hybrid algorithm can be adapted to explore the potential of quantum computing especially in the era of noisy intermediate-scale quantum devices.
Digital Curation in the postmodern social perspective: curatorial facets, concepts and participatory techniques

Abstract: Information and Communication Technologies transform the spaces for presenting cultural heritage information and communication from brick-and-mortar spaces into hybrid environments: both on-site and digital-virtual. This transformation demands a more specific Digital Curation on the Web 2.0 context, platform on which participatory approaches emerge in the curatorial process, such as Social, Collective and Citizen Curations, which use crowdsourcing and storytelling. In this scenario, this article questions the difference between Digital Curation and other participatory curations which have emerged in postmodernity. The objective was to explore the Digital Curation facets in the participatory approach in the scope of Information Science. The scientific relevance of the paper was justified by the need for stakeholders’ participation in the curation of cultural contents and the necessary approach by Information Science. The methodology was qualitative in nature, theoretical and exploratory. The theoretical research was carried out on the proposed themes, and their similarities and differences were explored. Digital Curation expands by presenting new curatorial facets - Social, Citizen and Collective -, which present different levels of social involvement and can converge with each other, according to the objective of the proposed curation project. Crowdsourcing and storytelling techniques are used for the curatorial process with a participatory approach.
Data Science in a Pandemic

Data Science has the potential to provide humanity with critical insight into the massive data being collected during a pandemic. The COVID-19 pandemic presented that opportunity, and Data Science supported an international audience promptly, reliably, effectively, and frequently during that difficult time. The most significant contributions were data visualizations and data dashboards, however, other tools, such as predictive and prescriptive analytics, were equally critical to the effort. The urgency at the start of the pandemic was to quickly communicate information to citizens, governments, and institutions. The change in modality from traditional statistical metrics and tables to data visualizations was extremely significant and helpful to so many. This paper reviews these contributions by demonstrating how the COVID-19 story unfolded through author-generated data visualizations and dashboards, and by providing the community with open-source access to the scripts that generated these visualizations. The open-source access to the (R language) scripts reflects this article&rsquo;s novelty in the literature. Using publicly available datasets from multiple sources, and employing R toolkits, the author validates the role that Data Science can play in a pandemic, and that can be implemented by anyone with some basic knowledge of scripting languages, like R. The intent is to provide these valuable tools to the community and to demonstrate their effectiveness in the likely event when there is another crisis.
$$\textsf{ATM}$$: A Logic for Quantitative Security Properties on Attack Trees

Critical infrastructure systems—for which high reliability and availability are paramount—must operate securely. Attack trees (ATs) are hierarchical diagrams that offer a flexible modelling language used to assess how systems can be attacked. ATs are widely employed both in industry and academia but—in spite of their popularity—little work has been done to give practitioners instruments to formulate queries on ATs in an understandable yet powerful way. In this paper we fill this gap by presenting $$\textsf{ATM}$$ , a logic to express quantitative security properties on ATs. $$\textsf{ATM}$$ allows for the specification of properties involved with security metrics that include “cost”, “probability” and “skill” and permits the formulation of insightful what-if scenarios. To showcase its potential, we apply $$\textsf{ATM}$$ to the case study of a CubeSAT, presenting three different ways in which an attacker can compromise its availability. We showcase property specification on the corresponding attack tree and we present theory and algorithms—based on binary decision diagrams—to check properties and compute metrics of $$\textsf{ATM}$$ -formulae.