Reviewerly/exHarmony huggingface dataset:
Application of GeoGebra in Stereometry teaching

The purpose of the paper is to improve methodology of teaching Mathematics via the use of cloud technology. The task of the paper is to identify the issues that require a theoretical and experimental solution. The objective of the paper is the educational process in the higher education institution, the subject of the paper is modern ICT. The result of the study is the learning tools of pedagogically considered and adequate bending of conventional and modern learning environment implemented into the educational process. The possibilities of using cloud technologies and Dynamic Mathematics system GeoGebra in the educational process through Stereometry specialized training have been revealed. The use of GeoGebra Dynamic Mathematics in Stereometry teaching will favourably influence the formation of students’ STEM competencies. In order to encourage Mathematics and Computer Science teachers to implement effectively the elements of STEM education, it is suggested that cloud-based learning tools such as GeoGebra be used in the teaching process.
Detecting twitter hate speech in COVID-19 era using machine learning and ensemble learning techniques

The COVID-19 pandemic has impacted every nation, and social isolation is the major protective method for the coronavirus. People express themselves via Facebook and Twitter. People disseminate disinformation and hate speech on Twitter. This research seeks to detect hate speech using machine learning and ensemble learning techniques during COVID-19. Twitter data was extracted from using its API with the help of trending hashtags during the COVID-19 pandemic. Tweets were manually annotated into two categories based on different factors. Features are extracted using TF/IDF, Bag of Words and Tweet Length. The study found the Decision Tree classifier to be effective. Compared to other typical ML classifiers, it has 98% precision, 97% recall, 97% F1-Score, and 97% accuracy. The Stochastic Gradient Boosting classifier outperforms all others with 99 percent precision, 97 percent recall, 98 percent F1-Score, and 98.04 percent accuracy.
Intelligent Water Level Monitoring and Water Pump Controlling System using IOT

Water is one of the most important elements of environment and essential for all living objects in planet earth. According to United State geological survey about 1.7 trillion of water are wasted ever year over world. A family can waste 9400 gallons of water every year. Water is also wasted when water pump runs even after water tank is full. Alongside this there is only 1% of drinking water available where water marks up around 71% of the earth surface and availability of drinking water is subsiding every year. It’s now a duty of every people on the earth to save water and ensure supply of pure drinking water. This paper introduces an IoT SMS enabled gadget that monitor water level and switches motor automatically when water goes above or below a certain level. This device also provides information about pH level of water additionally. All the stated information is sent to user through SMS.
Challenges and opportunities of augmented reality during the construction phase

Augmented reality (AR) is among the technologies in both Industry 4.0 and 5.0, which has the potential to play a key role in Architecture, Engineering, and Construction (AEC). Previous studies indicate that the use of AR, which is more prominent in other industries, is mainly focused on the construction phase. Additionally, this phase of the project faces performance, supervisory, and safety problems. Accordingly, this systematic review analyzes 50 related articles to assess the applications of this technology, its implementation, as well as its acceptance and non-acceptance factors in the construction phase. The results show that despite the advantages that AR brings, it is still not mature enough, and there are some inter-research conflicts in this field that require more profound studies to resolve them. Considering the existing challenges and research gaps, some future directions are provided in the final section of this review to clarify the research path for researchers.
Effectiveness of Machine Learning in Detecting Early-Stage Leukemia

The early identification and diagnosis of leukemia, i.e., the exact distinction of malignant leukocytes at the lowest possible cost in the early stages of the disease, is a key challenge in the domain of disease diagnostics. Considering the large frequency of leukemia, flow cytometry equipment is scarce, and the procedures available at laboratory diagnosis facilities are tedious, complex, and time-consuming. Inspired by the possibilities of machine learning (ML) in illness detection, the current critical search was done to examine the research attempting to find and categorize leukemia using ML algorithms. This research study provides a complete and systematic assessment of the current state of all available ML-based leukemia detection and classification algorithms that analyze PBS pictures. The accuracy rate of the ML techniques used in PBS image analysis to detect leukemia was greater than 93.5%, showing that the application of ML might lead to exceptional results in leukemia diagnosis from PBS pictures.
Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation With AlexNet

Automated recognition of handwritten characters and digits is a challenging task.Although a significant amount of literature exists for automatic recognition of handwritten characters of English and other major languages in the world, there exists a wide research gap due to lack of research for recognition of Urdu language.The variations in writing style, shape and size of individual characters and similarities with other characters add to the complexity for accurate classification of handwritten characters.Deep neural networks have emerged as a powerful technology for automated classification of character patters and object images.Although deep networks are known to provide remarkable results on large-scale datasets with millions of images, however the use of deep networks for small image datasets is still challenging.The purpose of this research is to present a classification framework for automatic recognition of handwritten Urdu character and digits with higher recognition accuracy by utilizing theory of transfer learning and pretrained Convolution Neural Networks (CNN).The performance of transfer learning is evaluated in different ways: by using pre-trained AlexNet CNN model with Support Vector Machine (SVM) classifier, and finetuned AlexNet for extracting features and classification.We have fine-tuned AlexNet hyper-parameters to achieve higher accuracy and data augmentation is performed to avoid over-fitting.Experimental results and the quantitative comparisons demonstrate the effectiveness of the proposed research for recognition of handwritten characters and digits using fine-tuned AlexNet.The proposed research based on fine-tuned AlexNet outperforms the related state-of-the-art research thereby achieving a classification accuracy of 97.08%, 98.21%, 94.92% for urdu characters, digits and hybrid datasets respectively.The presented methods can be applied for research on Urdu characters and in diverse domains such as handwritten text image retrieval, reading postal addresses, bank's cheque processing, preserving and digitization of manuscripts from old ages.
FedStack: Personalized activity monitoring using stacked federated learning

Recent advances in remote patient monitoring (RPM) systems can recognize various human activities to measure vital signs, including subtle motions from superficial vessels. There is a growing interest in applying artificial intelligence (AI) to this area of healthcare by addressing known limitations and challenges such as predicting and classifying vital signs and physical movements, which are considered crucial tasks. Federated learning is a relatively new AI technique designed to enhance data privacy by decentralizing traditional machine learning modeling. However, traditional federated learning requires identical architectural models to be trained across the local clients and global servers. This limits global model architecture due to the lack of local models heterogeneity. To overcome this, a novel federated learning architecture, FedStack, which supports ensembling heterogeneous architectural client models was proposed in this study. This work offers a protected privacy system for hospitalized in-patients in a decentralized approach and identifies optimum sensor placement. The proposed architecture was applied to a mobile health sensor benchmark dataset from 10 different subjects to classify 12 routine activities. Three AI models, ANN, CNN, and Bi-LSTM were trained on individual subject data. The federated learning architecture was applied to these models to build local and global models capable of state of the art performances. The local CNN model outperformed ANN and Bi-LSTM models on each subject data. Our proposed work has demonstrated better performance for heterogeneous stacking of the local models compared to homogeneous stacking. This work sets the stage to build an enhanced RPM system that incorporates client privacy to assist with clinical observations for patients in an acute mental health facility and ultimately help to prevent unexpected death.
A Hybrid Golden Jackal Optimization and Golden Sine Algorithm with Dynamic Lens-Imaging Learning for Global Optimization Problems

Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey’s position often depends on the male golden jackal and there is insufficient diversity of golden jackals in some cases, it is prone to falling into a local optimal optimum. In order to address these drawbacks of GJO, this paper proposes an improved algorithm, called a hybrid GJO and golden sine (S) algorithm (Gold-SA) with dynamic lens-imaging (L) learning (LSGJO). First, this paper proposes novel dual golden spiral update rules inspired by Gold-SA. These rules give GJO the ability to think like a human (Gold-SA), making the golden jackal more intelligent in the process of preying, and improving the ability and efficiency of optimization. Second, a novel nonlinear dynamic decreasing scaling factor is introduced into the lens-imaging learning operator to maintain the population diversity. The performance of LSGJO is verified through 23 classical benchmark functions and 3 complex design problems in real scenarios. The experimental results show that LSGJO converges faster and more accurately than 11 state-of-the-art optimization algorithms, the global and local search ability has improved significantly, and the proposed algorithm has shown superior performance in solving constrained problems.
LA-ICP-MS trace element analysis of sphalerite and pyrite from the Beishan Pb-Zn ore district, south China: Implications for ore genesis

The Beishan Pb-Zn ore district, located in northern Guangxi, China, is a giant carbonate-hosted Pb-Zn ore district. In-situ trace element analysis was performed using LA-ICP-MS on sphalerite and pyrite from three representative Pb-Zn deposits in the Beishan Pb-Zn ore district. The analysis of these trace elements reveals the trace element compositions of the sulfides, the distributions and the substitution mechanisms of trace elements, physicochemical characteristics of the ore-forming fluids, and ore genesis. Sphalerite is enriched in Mn, Fe, Ge, and Cd, while it is poor in Co, Ni, Ga, As, In. Pyrite is enriched in Co, Ni, As, Tl, Sb, and Pb, but poor in Mn, Ga, Ge, Cd, In, Sn. Mn, Fe, Cd, Ga, Ge, Sb, Ag, and As in sphalerite enter the mineral lattice mainly by simple or coupled substitution mechanisms, while Cu and Pb can enter the mineral lattice in the form of solid solution or micro-inclusions. Mn, Co, Ni, As, Tl, and Sb in pyrite substituted Fe2+ by simple or coupled mechanisms, while Pb can enter the mineral lattice in the form of solid solution or micro-inclusions. Studies of trace elements related to temperature (e.g., Mn, Fe, Ga, Ge, In) in sphalerite and pyrite indicated that mineralization in the Beishan Pb-Zn ore district occurred under a low-temperature condition. And according to the sphalerite GGIMFis geothermometer, the mineralization temperature of the Beishan Pb-Zn ore district was 55–268 °C (average 153 °C), of which 110–230 °C(average 153 °C) in Duchuan, 92–268 °C (average 149 °C) in Beishan, and 55–243 °C (average 167 °C) in Dongyong. The Mn, Fe, Ga, Ge, and In contents of sphalerite are similar to those of MVT deposits. The combination of some binary pattern diagrams, principal component analysis (PCA), and Co/Ni ratios of pyrite, with the geological features and physicochemical properties of the ore-forming fluids, indicates that the Beishan Pb-Zn ore district is an MVT district. This paper, combined with previous studies, shows that the Co/Ni ratio of pyrite has some guiding significance for mineral exploration of this kind of Pb-Zn deposit, with Co/Ni values around 0.1 indicating that the deposit has a better prospect of developing into a large deposit, and Co/Ni much less than 0.1 indicating that the deposit may not have a good prospect.
Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization

In many practical applications, the end-goal of multi-objective optimization is to select an implementable solution that is close to the Pareto-optimal front while satisfying the decision maker’s preferences. The decision making process is challenging since it involves the manual consideration of all solutions. The field of multi-criteria decision making offers many methods that help the decision maker in this process. However, most methods only focus on analyzing the solutions’ objective values. A more informed decision generally requires the additional knowledge of how different preferences affect the variable values. One difficulty in realizing this is that while the preferences are often expressed in the objective space, the knowledge required to implement a preferred solution exists in the decision space. In this paper, we propose a decision support system that allows interactive knowledge discovery and knowledge visualization to support practitioners by simultaneously considering preferences in the objective space and their impact in the decision space. The knowledge discovery step can use either of two recently proposed data mining techniques for extracting decision rules that conform to given preferences, while the extracted knowledge is visualized via a novel graph-based approach that allows the discovery of important variables, their values and their interactions with other variables. The result is an intuitive and interactive decision support system that aids the entire decision making process — from solution visualization to knowledge visualization. We demonstrate the usefulness of this system on benchmark optimization problems up to 10 objectives and real-world problems with up to six objectives.
A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields

Image segmentation is the process of partitioning a digital image into a set of homogeneous regions (according to some homogeneity criterion) to facilitate a subsequent higher-level analysis. In this context, the present paper proposes an unsupervised and graph-based method of image segmentation, which is driven by an application goal, namely, the generation of image segments associated with a user-defined and application-specific goal. A graph, together with a random grid of source elements, is defined on top of the input image. From each source satisfying a goal-driven predicate, called seed, a propagation algorithm assigns a cost to each pixel on the basis of similarity and topological connectivity, measuring the degree of association with the reference seed. Then, the set of most significant regions is automatically extracted and used to estimate a statistical model for each region. Finally, the segmentation problem is expressed in a Bayesian framework in terms of probabilistic Markov random field (MRF) graphical modeling. An ad hoc energy function is defined based on parametric models, a seed-specific spatial feature, a background-specific potential, and local-contextual information. This energy function is minimized through graph cuts and, more specifically, the alpha-beta swap algorithm, yielding the final goal-driven segmentation based on the maximum a posteriori (MAP) decision rule. The proposed method does not require deep a priori knowledge (e.g., labelled datasets), as it only requires the choice of a goal-driven predicate and a suited parametric model for the data. In the experimental validation with both magnetic resonance (MR) and synthetic aperture radar (SAR) images, the method demonstrates robustness, versatility, and applicability to different domains, thus allowing for further analyses guided by the generated products.
ICT and agricultural sector performance: empirical evidence from sub-Saharan Africa

Abstract ICT infrastructure is considered crucial to performance and overall development of many sectors in sub-Saharan Africa (SSA). Yet, there exists insufficient evidence on the effect of ICT on agriculture real output and export performance in the African continent. This study investigates the effect of ICT infrastructure on agriculture sector performances in SSA using panel autoregressive distributed lag (ARDL) approach. Panel data were collected over the period of twenty-three (23) years (1995–2017) in 39 SSA countries. Two models were specified using agricultural value addition and agriculture products as a percentage of total merchandise export as dependent variables. Key independent variables include mobile-cellular telephone subscription and individual using the internet. The study also introduced important control variables such as livestock production index and crop production index. The results from the estimation provide substantial evidence to show that ICT infrastructure has positive externality on agricultural sector performances in the long run. However, there is no evidence to maintain this position in the short run. The study, therefore, recommends that there should be a cautious approach to increasing investment in ICT infrastructure. Provision of in ICT infrastructure alone may not automatically improve agricultural output. Thus, there is a need for extension services to propagate and educate farmers on the importance of continuous adoption of ICT infrastructure for agricultural practices in SSA.
Optimal rates of statistical seriation

Given a matrix, the seriation problem consists in permuting its rows in such way that all its columns have the same shape, for example, they are monotone increasing.We propose a statistical approach to this problem where the matrix of interest is observed with noise and study the corresponding minimax rate of estimation of the matrices.Specifically, when the columns are either unimodal or monotone, we show that the least squares estimator is optimal up to logarithmic factors and adapts to matrices with a certain natural structure.Finally, we propose a computationally efficient estimator in the monotonic case and study its performance both theoretically and experimentally.Our work is at the intersection of shape constrained estimation and recent work that involves permutation learning, such as graph denoising and ranking.
A Design Model of Copyright Protection System Based on Distributed Ledger Technology

Nowadays, to protect and secure interest of innovators and research information in decentralized ledger-based echo system using blockchain technology to minimize the challenges and risks faced by conventional copyright system called intellectual property systems, the authors proposed a model using programmable contracts that ensures to extract the features provenance for tracing of ownership, transfer histories which in turns ensures system transparency and traceability. This paper covers basic understanding of blockchain, existing copyright system, and a new echo system can be implemented using blockchain technology effectively. A solution model is designed for the system along with the explanation of the procedure to build the system.
On the integration of similarity measures with machine learning models to enhance text classification performance

Several techniques have long been proposed to enhance text classification performance, such as: classifier ensembles, feature selection, the integration of similarity measures with classifiers, and meta-heuristic algorithms. The integration of similarity measures with machine learning models (ML), however, has not yet received thorough analysis for text classification. As a result, in an effort to thoroughly investigate the impact of similarity measures integration with ML models, this work makes three major contributions: (1) proposing newly-integrated models and presenting benchmarking studies for integration methodology over balanced/imbalanced datasets; (2) offering detailed analysis for dozens of integrated models that are established, and experimentally proven, to significantly outperform state-of-the-art performance. The models' construction used fourteen similarity measures, three knowledge representations (BoW, TFIDF, and Word embedding), and five models (Support Vector Machine, N-Centroid-based Classifier, Multinomial Naïve Bayesian, Convolutional Neural Network, and Artificial Neural Network); and (3) introducing significantly-effective and highly-efficient variations of these five models. The evaluation study has been conducted internally for integrated models against their baselines, and externally against the state-of-the-art models. While the internal evaluation constantly showed a total enhancement rate of 49.3% and 59% over the balanced and imbalanced datasets, respectively, the external evaluation attested to the superiority of the integrated models.
A Human-Detection Method Based on YOLOv5 and Transfer Learning Using Thermal Image Data from UAV Perspective for Surveillance System

At this time, many illegal activities are being been carried out, such as illegal mining, hunting, logging, and forest burning. These things can have a substantial negative impact on the environment. These illegal activities are increasingly rampant because of the limited number of officers and the high cost required to monitor them. One possible solution is to create a surveillance system that utilizes artificial intelligence to monitor the area. Unmanned aerial vehicles (UAV) and NVIDIA Jetson modules (general-purpose GPUs) can be inexpensive and efficient because they use few resources. The problem from the object-detection field utilizing the drone’s perspective is that the objects are relatively small compared to the observation space, and there are also illumination and environmental challenges. In this study, we will demonstrate the use of the state-of-the-art object-detection method you only look once (YOLO) v5 using a dataset of visual images taken from a UAV (RGB-image), along with thermal infrared information (TIR), to find poachers. There are seven scenario training methods that we have employed in this research with RGB and thermal infrared data to find the best model that we will deploy on the Jetson Nano module later. The experimental result shows that a new model with pre-trained model transfer learning from the MS COCO dataset can improve YOLOv5 to detect the human–object in the RGBT image dataset.