Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Clustering Algorithm

Clustering is a technique of segregating the data points into certain clusters in such a manner that the data points present in the cluster are more similar in nature to the other data points in that cluster as compared to the data points that are segregated in different groups. In layman words, the purpose is to form distinct groups of data points having alike characteristics and allot it into the groups. The clustering algorithms are advocated for the task of classifying the spatial datasets into a set of groups. Generally, this algorithm can be distributed into two sections—Hard Clustering where every data object either corresponds to a group or cluster entirely and the other is Soft Clustering wherein the odds of that data object to be in those clusters is assigned rather than placing every data object into a distinct cluster. This paper focuses on comparative study of different clustering algorithms that are applied in data mining.
A Feature Engineering Focused System for Acoustic UAV Detection

The evolution of Unmanned Aerial Vehicles (UAVs) technology has made these devices suitable for a wide new range of applications, but it has also raised safety concerns as UAVs can be used for carrying explosives or weapons with malicious intentions. In this paper, Machine Learning (ML) algorithms are used to identify UAVs by using the sound signals they emit. We evaluate and propose a feature-based classification. Five individual features, and one combination of features are used to train four machine learning classification approaches: a neural network (NN), a support vector machine (SVM), a Gaussian Naive Bayes (GNB), and K-Nearest Neighbor (KNN). The training and testing dataset is composed of sound samples of UAVs and noise. The labels in the dataset we collected include the sound files of noise and UAVs. The results show that the combination of features outperforms the individual ones, with higher average accuracy scores.
Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today's world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure.Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage.In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user.In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner.Hence, the model is to be designed as a profit-driven framework to reduce cost and make span.With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS).A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds.Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation.The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines.The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.
Predicting the Presence of Newt-Amphibian Using Genetic Programming

In nature, aquatic ecosystems play a very important aspect. River valleys, wetlands, and water reservoirs are territories for various species of vegetation and wildlife. The prediction of these species is very important for natural resource planning. In this work, a publicly available UCI dataset containing extracted features from satellite imagery is used to classify the presence of newt-amphibians. We convert this multi-class classification problem to the binary classification problem. The transformation leads to being unbalanced classification problem. For the unbalanced classification, in the original form, most machine learning techniques give biased classification results, and their results are inclined in favor of the majority class. We use genetic programming with a newly proposed Euclidean distance and weight-based (EDWB) fitness function to resolve this problem. The result outcomes are compared with original work, support vector machine (SVM), and GP with the standard fitness function. The proposed approach achieves better results than the original work, SVM, and compared GP methods.
Subdivision of Maps of Digital Images

A digital image is a finite set of integer lattice points in an ambient Euclidean space together with a suitable adjacency relation between points. Subdivision, which is a process of enlarging a digital image in the photographic sense, provides a basic tool for operating with digital images. But given a map of digital images, there is as yet no general way to define a map of their subdivisions that might reasonably be called a subdivision of the map. In this paper, we construct such maps of subdivisions when the map of digital images has a 1- or 2-dimensional domain. From our constructions we deduce path covering and homotopy covering results that play a role in our development of the digital fundamental group.
Data Clustering Algorithms: Experimentation and Comparison

Due to increasing databases of all kinds, clustering has become one of the most essential tasks to classify the data. Clustering means to group or divide the data points based on their similarity to each other. Clustering can be stated as an unsupervised data mining technique that describes the nature of datasets. The main objective of data clustering is to obtain groups of similar entities. There are various methods of clustering such as hierarchical, partition-based, method-based, grid-based, and model-based. This paper provides a detailed study about clustering, its working processes. Along with basic information, detailed information about validity measures required to evaluate algorithms is discussed. This paper reviews clustering algorithms like K-Means, Agglomerative, and DBSCAN. A tabular comparison of algorithms is represented to acquire in-depth knowledge. The results obtained after experimenting with algorithms are also been discussed at the end.
Maritime Infrared Image Super-resolution Using Cascaded Residual Network and Novel Evaluation Metric

Infrared (IR) cameras have been important surveillance sensors for autonomous surface vessels; however, their detection ranges are limited by low resolution. In this study, we collect maritime IR images, analyze the characteristics of those images, and develop datasets for training and testing. Then, a new maritime IR image super-resolution network, maritime infrared super-resolution using cascaded residual network, is developed to reconstruct IR images using a scale of 4. Moreover, different loss functions have different effects on output images; a loss function is set to be a combination of three loss functions, including mean absolute error, mean squared error, and perceptual loss. Peak signal-to-noise ratio and structural similarity index measure cannot effectively describe super-resolution performance. As the novel evaluation metric, Canny edge detection method is used because edges are important for human and target detection algorithms. Finally, experiments are conducted and the results demonstrate that the developed residual network can achieve high-quality reconstructed maritime IR images.
Content-Based Collaborative Filtering with Hierarchical Agglomerative Clustering Using User/ Item based Ratings

The recommender system (RS) plays the major role in online networks, online shopping, and online services etc. The conventional RSs are suffering with the inaccurate quality of experience to the users, so the improper content is recommending to customers. The content based collaborative filtering (CBCF) method is introduced to solve the issues presented in the RSs. However, the CBCF method is suffering with the cold start problem for new users and suffering with data accuracy, data sparsity, and scalable data in clustering process. Thus, to solve these problems, this article proposes hierarchical agglomerative clustering (HAC) based collaborative filtering (HAC-CF) for RSs. The proposed HAC-CF based RS functions by utilizing the incentivized/ penalized user (IPU) model with user-based and item-based ratings. To this end, users are divided into several clusters through single link graph partitioning through minimum distance criteria. Then, the final item ranking is computed using Pearson correlation coefficient (PCC) similarity of users. Hence, recommendation efficiency and accuracy are increased at the end user by combining user, item models. The simulation results show the performance enhancement of proposed method with respect to F1-score, recall, and precision as compared to the conventional approaches.
Mapping and Path Planning Simulation of Mobile Robot Slam Based on Ros

Research on SLAM mapping and autonomous navigation of ROSmobile robots is increasing. It is very important to conduct simulation research on SLAM construction and autonomous navigation of mobile robot in real environment. It is helpful to discover and correct the problems existing in the mapping and autonomous navigation of MOBILE robot in SLAM in real time. The mobile robot simulation model is built by URDF and displayed in Gazebo simulation software. The simulation model of mobile robot shown in Gazebo is studied experimentally. The results show that the constructed mobile robot can construct 2d raster map in Gazebo simulation environment by using Gmapping function package in ROS combined with keyboard control, and display the constructed 2D raster map in Rviz. Finally, load yamL file under move_base function package in ROS in Launch file, and realize simple path planning of mobile robot in 2D raster map by combining 2D Nav Goal in Rviz.
Vision-guided Collision Avoidance Through Deep Reinforcement Learning

Collision avoidance is a crucial task in vision-guided autonomous navigation. Traditional solutions tend to be computationally expensive and difficult to adapt to new environments. In this work, we propose a novel collision avoidance solution for autonomous drones. Formulated under a deep reinforcement learning framework, our model relies on a pair of margin reward functions to ensure the drones fly smoothly while greatly reducing the chance of collision. Additional reward functions are designed to attract the drones to fly towards their destinations, as well as to follow predefined routes. Experiments using indoor simulation environments demonstrate the effectiveness of our overall design and the individual components.
The Efficient Implementation of Face Mask Detection Using MobileNet

Abstract Under the condition of the global pandemic, Wearing the mask in the public area is the direct and ordered action to prevent the spreading of the virus. Therefore, developing the machine learning model to detect whether the mask is appropriately wearing is practical and meaningful. This paper unfolded the mask recognition based on the MobileNet model, which belongs to one of the branches of Convolutional Neural Network, CNN for short, in the deep learning field. With the background and tremendous needs of face mask detection, in the beginning, we go through the review of the MobileNet’s history and the practicability of it on demanding functions. After that, we explained in a more detailed way to help gain a better understanding of MobileNet’s structure and how those differences make it outstanding. During the experiment, approximately 9000 images were used as inputs by the model for training and optimizing. The result turns to the less weighted model presenting an acceptable accuracy of 87.96% and 93.5% for testing whether a person wears a mask and whether the mask is covered correctly correspondingly. We also discovered several shortcomings and limitations during the testing process. Those problems and some of the possible further directions of MobileNet will be discussed. As a result, the MobileNet shows the huge probability for further improvement to be equipped into the small-scale but sophisticated utensil.
General data protection regulation

Rapid digitalisation has resulted in a massive exchange of digital data between individuals and organisations, accelerating the importance of privacy-preserving legal frameworks, such as the General Data Protection Regulation (GDPR). Despite the importance of the implementation of such a framework, current research lacks evidence about how individuals perceive GDPR compliance. Given that, the objective of this study was to explore individuals' attitudes towards GDPR compliance in line with Protection Motivation Theory. This study employed a cross-sectional research design and collected 540 valid responses to test a model using structural equational modelling. The result of the analysis showed that perceived threat severity, response efficacy and self-efficacy have positive relationships with attitude towards GDPR compliance. In addition, it was found that attitude correlates with emotional empowerment. The findings of this paper contribute to the literature on privacy-preserving mechanisms by shedding light on individuals' perceptions of the GDPR. The evidence also adds to the current body of literature on information systems management by giving insights into the factors that determine the utilisation of privacy-preserving technologies. These pieces of evidence offer implications for policymakers by providing guidelines on how to communicate the benefits of the GDPR to the public.
On the existence of primitive normal elements of rational form over finite fields of even characteristic

Let [Formula: see text] be an even prime power and [Formula: see text] an integer. By [Formula: see text], we denote the finite field of order [Formula: see text] and by [Formula: see text] its extension of degree [Formula: see text]. In this paper, we investigate the existence of a primitive normal pair [Formula: see text], with [Formula: see text] where the rank of the matrix [Formula: see text] is 2. Namely, we establish sufficient conditions to show that nearly all fields of even characteristic possess such elements, except for [Formula: see text] if [Formula: see text] and [Formula: see text] is odd, and then we provide an explicit small list of possible and genuine exceptional pairs [Formula: see text].
The extent of the teacher academic development from the accreditation evaluation system perspective using machine learning

ABSTRACTABSTRACTThe purpose of this study to utilising Machine learning to discover knowledge which is called supervised and unsupervised learning when it is taught the actual outcome for the training instances like progressed or non-progressed performance and investigate the impact of the quality assurance process on the teacher's academic performance utilising teaching methods, student feedback, teacher portfolio, and academic benchmarks. Moreover, it aims to assess and improve the academic staff members performing the Accreditation Evaluation System (AES) that involves Student Feedback System (SFS), Teacher Portfolio Assessment (TPA), as well as Continuous Academic Development (CAD) for the academic year (2016–2017) which compiled of (1556) academic staff at the University of Sulaimani. Overall, the conclusions of this study confirmed that the quality assurance has progressed, and enhanced the quality of the teacher performance, also reinforces all dimensions of the teaching, academic, and research performance of teachers by applying the K-Means Clustering Algorithm methodology to analyse and assemble a big data according to the teacher academic titles. In addition, the binary logistic regression analysis was executed to reveal and prophesy the significant influences of academic titles on the teacher progression of the Accreditation Evaluation System performance. The K-Means Clustering Algorithm showed better results than Logistic regression by having 90% testing accuracy. In the future, Un-Supervised Learning can be used for better accuracy.KEYWORDS: Machine learningk-means clusteringbinary logistic regressionaccreditation evaluation systemquality assurance processacademic benchmarks Disclosure statementNo potential conflict of interest was reported by the author(s).AcknowledgmentsThe author is very grateful to theUniversity of Sulaimani and Teaching Quality Assurance, and Curriculum Development Director at the University of Sulaimani who participated in preparing the academic staff data.Correction StatementThis article has been republished with minor changes. These changes do not impact the academic content of the article.
Security Issues in Cloud and Mobile cloud: A Comprehensive Survey

ABSTRACTABSTRACTCloud computing is one of the rapidly growing technologies in computing. It includes many benefits such as improved reliability, enormous scalability, decreased costs, portability, enhanced geographic coverage with fastest time, less infrastructure investment though it has challenges such as data security, insufficient resources, skill, etc. For past few years, cloud computing has grown considerably in information technology. Safety of information is a great concern as enormous information of individuals and companies was being stored in cloud. Many software giants such as Microsoft joined hands to build cloud services. The growth of cloud computing had been affected by data security issues, which leads to complexity with data privacy and protection. The objective of this work is to study and analyze the security issues in cloud computing. The security issues pertaining to network, data, application, storage, and information in cloud domain with the algorithms and tools deployed were addressed and reviewed elaborately.KEYWORDS: E-healthcarebiometric identificationcipher text policyidentity proofingauthenticationbig data security and privacycryptographystorage auditingcloud service providers (CSPs) Disclosure statementNo potential conflict of interest was reported by the author(s).
Non-contact strain measurement to eliminate strain gages in vibration-based high cycle fatigue testing

Digital Image Correlation (DIC) is a non-contacting, camera-based technique that calculates full-field displacements and strains by comparing digital images taken before and after an object is deformed. During a vibration-based fatigue test, DIC has an advantage over strain gages in that it is non-contacting and does not accumulate damage during the test. In this work, DIC was implemented to build strain-velocity calibration curves as an alternative to strain gages. First, a curve fit was applied to DIC displacements and strains along the free edge of the plate using an approximate solution for the mode shape of a cantilevered plate. In total, the curve fits were applied to three sets of DIC data: (i) the raw strains calculated with DIC; (ii) the in-plane U-displacements from which the raw DIC strains were computed; and (iii) the out-of-plane W-displacements observed in the direction of motion. Second, classical plate theory was used to calculate strains by taking derivatives of each of the applied curve fits. Third, the peak strains from each curve fit were used to build the strain-velocity calibration curves. Further, a Monte Carlo Method uncertainty analysis was performed to estimate the uncertainty of the curve fitted DIC and strain gage measurements. Of the three curve-fits, the DIC strains derived from the out-of-plane displacements provided the most precise measurements relative to a strain gage at all excitation levels used to build the calibration curves.
Review of ensemble classification over data streams based on supervised and semi-supervised

Most data stream ensemble classification algorithms use supervised learning. This method needs to use a large number of labeled data to train the classifier, and the cost of obtaining labeled data is very high. Therefore, the semi supervised learning algorithm using labeled data and unlabeled data to train the classifier becomes more and more popular. This article is the first to review data stream ensemble classification methods from the perspectives of supervised learning and semi-supervised learning. Firstly, basic classifiers such as decision trees, neural networks, and support vector machines are introduced from the perspective of supervised learning and semi-supervised learning. Secondly, the key technologies in data stream ensemble classification are explained from the two aspects of incremental and online. Finally, the majority voting and weight voting are explained in the ensemble strategies. The different ensemble methods are summarized and the classic algorithms are quantitatively analyzed. Further research directions are given, including the handling of concept drift under supervised and semi-supervised learning, the study of homogeneous ensemble and heterogeneous ensemble, and the classification of data stream ensemble under unsupervised learning.
Modeling context appearance changes for person re-identification via IPES-GCN

Most of the previous person re-identification works focus on learning discriminative features for individuals, and retrieve a query person only based on pair-wise individual feature similarities, ignoring context relationships among gallery images. Consequently, it is hard to re-identify a query person when large appearance changes occur. To address this problem, we propose the IPES-GCN model to exploit context relationships across gallery images. In the IPES-GCN, we first construct an Individual Pivot Expansion Subgraph (IPES) to enrich the context representation of each individual. By taking high-order nearest neighbors of each individual into account, we can model more positive appearance changes into the representation. Then GCN is employed to explicitly model various appearance changes contained in the IPES into a graph embedding for each enriched individual. Finally, the graph embeddings are utilized to re-rank the gallery images. Experiments on the Market1501, DukeMTMC and MSMT17 datasets show that the proposed method has strong generalization and can yield favorable results to the state-of-the-art methods.
Constructing Embodied Interaction of Intangible Cultural Heritage Course through Immersive Virtual Reality

With the development of digital technology, intangible cultural heritage (ICH) enthusiasts have an increasingly high demand for the learning experience of intangible cultural heritage. The appearance of intangible cultural heritage experience courses meets the demand of intangible cultural heritage enthusiasts for experiential learning to a certain extent, which plays a positive role in promoting the protection and inheritance of intangible cultural heritage. However, there is no systematic research and design specification on how to convey the intangible cultural heritage knowledge and value in experiential courses, which leads to learners' emphasis on the experiential process and lack of cognition of the content of intangible cultural heritage itself. In this research, learners will build personalized knowledge system for intangible cultural heritage based on embodied interaction. Immersive virtual reality will be applied in the classroom as a teaching tool to enable learners to do embodied creation practice. Through practice and qualitative analysis, it has been proved that this method deepens learners' cognition of intangible cultural heritage and learners can complete high-quality artworks.