Reviewerly/exHarmony huggingface dataset:
Hierarchical Ensemble Based Imbalance Classification

In this paper, we propose a hierarchical ensemble method for improved imbalance classification. Specifically, we perform the first-level ensemble based on bootstrap sampling with replacement to create an ensemble. Then, the second-level ensemble is generated based on two different weighting strategies, where the strategy having better performance is selected for the subsequent analysis. Next, the third-level ensemble is obtained via the combination of two methods for obtaining mean and covariance of multivariate Gaussian distribution, where the oversampling is then realized via the fitted multivariate Gaussian distribution. Here, different subsets are created by (1) the cluster that the current instance belongs to, and (2) the current instance and its k nearest minority neighbors. Furthermore, Euclidean distance-based sample optimization is developed for improved imbalance classification. Finally, late fusion based on majority voting is utilized to obtain final predictions. Experiment results on 15 KEEL datasets demonstrate the great effectiveness of our proposed method.
Using Hybrid Scatterplots for Visualizing Multi-dimensional Data

Scatterplot visualization techniques are known as a useful method that shows the correlations of variables on the axes, as well as revealing patterns or abnormalities in the multidimensional data sets. They are often used in the early stage of the exploratory analysis. Scatterplot techniques have the drawback that they are not quite effective in showing a high number of dimensions where each plot in two-dimensional space can only present a pair-wise of two variables on the x-axis and y-axis. Scatterplot matrices and multiple scatterplots provide more plots that show more pair-wise variables, yet also compromise the space due to the space division for the plots. This chapter presents a comprehensive review of multi-dimensional visualization methods. We introduce a hybrid model to support multidimensional data visualization from which we present a hybrid scatterplots visualization to enable the greater capability of individual scatterplots in showing more information. Particularly, we integrate star plots with scatterplots for showing the selected attributes on each item for better comparison among and within individual items, while using scatterplots to show the correlation among the data items. We also demonstrate the effectiveness of this hybrid method through two case studies.
DevOps Sensoring Neuro Cluster

The quality of grains and other perishable items stored in a warehouse is usually degraded due to the lack of monitoring and proper maintenance of their required appropriate conditions in such a large area. The proposed methodology deals with storage grains, fruits, etc., and to recognize their pattern of temperature and humidity which will not cause degradation of them. To set up the environment regarding the nature of the crop so that an optimal threshold can be set with a proper format of monitoring and altering.
Ship Classification Using Swin Transformer for Surveillance on Shore

Ship image classification technology is one of the core technologies for intelligent maritime surveillance system. It is fundamental that ships and their types are accurately identified for analysing and understanding in maritime scenes. Recently, the transformer-based model successfully applied in the field of natural language processing, and they have surpassed convolutional neural networks in image classification tasks, with Swin Transformer as the leader. Swin Transformer builds a hierarchical pyramid structure and a shifted window scheme on the basis of multi-head self-attention mechanism. These qualities reduce the complexity of models, and makes it as a general backbone for computer vision. In this study, we use the well-known ship image dataset called Seaships to investigate the effectiveness of Swin Transformer. We find that its hierarchical pyramid structure, multi-head self-attention mechanism and shifted window scheme play a key role in ship image classification. The results show that Swin Transformer achieves an accuracy of 93.5% in ship image classification, and outperforms typical convolutional networks and Vision Transformer.
Using AR Headset Camera to Track Museum Visitor Attention: Initial Development Phase

Augmented Reality (AR) has become one of the mainstream media volumes. In museums, AR applications are being implemented to enhance visitor experience. Analyzing the museum visitor experience is crucial for a museum to provide better service to society. Head-mounted cameras and eye-tracking headsets can be used to analyze visitor experience. With the AR headsets becoming part of museum exhibitions, one can utilize these headsets to track and analyze visitor experience. This research aims to improve on the existing work on tracking museum visitor experience using AR headsets. Specifically, this paper discusses an experiment performed at the Jule Collins Museum of Fine Arts at Auburn, Alabama. Visitors’ attention towards exhibits was measured using an AR headset camera. Visitor exit surveys were collected reflecting on the visitor experience. This paper illustrates the applicability and failures of analyzing visitor experience with AR headset camera for different exhibits and scenarios. This paper also provides insight into the following issues: privacy concerns with using AR headsets in museums and the ways AR headsets affect visitor experience and behavior.
Corpora Modeling Time and Space Concepts

The article considers the linguistic concepts of "space" and "time" in English from the standpoint of well-known philosophical theories. It analyzes their definitions in the modern English dictionaries. The concepts "time" and "space" are modeled with the help of corpus tools on the English language corpus enTenTen20 on SketchEngine platform. The scientific significance is in the use of the corpus linguistics tools for the two key concepts analysis.
From Sketch to Simulation in Virtual Laboratory Using Deep Learning

Virtual labs are simulated learning environment softwares that allow students to complete laboratory experiments online or offline to explore concepts and theories without stepping into a physical science lab. Developing such a software code is a long process and a hard task to achieve that requires a skilled expert developer. Human free-hand sketches have been studied in various fields including sketch recognition, synthesis, sketch-based image retrieval, etc. This paper will present a new sketch paradigm to generate a simulation from a Hand sketch. We propose a novel method that uses Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNN) combined with a Laboratory as a Service (LaaS) in order to transform a user-drawing sketch into a simulation code. Our approach is based on the use of both SSD (Single Shut Detector) and Mobile-Net as feature extractions for object identification in the sketch, called corresponding web services, which eventually regenerate a simulation software. Our method has been trained and evaluated using the “Quick, Draw” dataset. Experimental results have demonstrated that the approach has achieved over 80% of accuracy.
Spenser and Logic: Gigantomachia and Contentlessness in<i>The Faerie Queene</i>

Figuring the enforcement of authority against rebellion, the war between the Olympians and the earth-spawned Giants is typically read as a marker of ideology. In The Faerie Queene, Spenser’s abundant allusions to the Gigantomachia can seem straightforwardly ideological, aligning Olympian rule with his virtue-knights, avatars of Elizabethan hegemony, and his giants with subversion. This essay explores another significance for the Gigantomachia, reviewing a different tradition of meaning for the myth-pattern and locating it in the poem—a tradition wherein, rather than liberation in the political realm, the Giants portend the radical oversimplification and even the nullification of thought within the mind. Through conflict with giants, Spenser argues the importance of logic: investigating, idea inventing, discriminating, dialoguing. Giants help clarify the picture of the place of logic, particularly in a Ramist vein, in The Faerie Queene. The foci are the Egalitarian Giant and the correspondences between Orgoglio and Disdaine.
Fuzzy Adaptive PID Control of Waste Heat Power Generation

In the cogeneration control system based on oxygen bottom blowing process, the highest temperature point in the whole system is the superheated vapor temperature at the outlet of superheater, and it is one of the parameters that represent the operating state of the system. The temperature of superheated steam affects the safe and economic operation of the whole system and the service life of the equipment. This paper analyzes the temperature characteristics of superheated steam and establishes the mathematical model of superheated vapor temperature control system. According to the characteristics of large control delay and inertia, a fuzzy adaptive PID controller is designed. A variable proportion factor fuzzy adaptive PID controller was proposed, and the SIMULINK tool was used to simulate the control system under different states.
Performance Analysis of Machine Learning Algorithms for Website Anti-phishing

Phishing has become the main hazard to most of the web users, and website phishing makes people to lose millions of dollars every year. In today's world, most of the files are placed on web. Security of these files is not guaranteed. In the same way, phishing makes easier to steal the data. One simple approach is not sufficient to solve this problem. This paper provides the overview of different anti-phishing techniques using machine learning approach to solve the website phishing. Machine learning is technique of learning from experience. Machine learning has different paradigms like supervised, unsupervised, semi-supervised, and reinforcement learning. This paper follows supervised learning approach to provide solution to the website phishing problems. Supervised learning is used in classification and regression. The comparison of accuracy levels of these anti-phishing techniques is discussed in this paper.
Real-Time Performance Analysis and Tuning of Embedded System Virtualization Architecture Based on KVM

Embedded systems are proceeding towards exploiting virtualization technology to have the benefits of Real-Time Operating System (RTOS) and General-Purpose Operating System (GPOS) in the same system. This combination provides both a timely and deterministic behavior and a general-purpose application codebase. There still exist concerns about the real-time responsiveness of RTOS running inside a Virtual Machine (VM). In this paper, the real-time performance of Kernel-based Virtual Machine (KVM) virtualization architecture is analyzed on a multi-core system. Here, a preemptible Linux kernel with the PREEMPT_RT patch is used for RTOS, while a standard Linux kernel is used for GPOS. The interrupt latency inside the real-time guest VM is analyzed by applying various amounts of CPU, memory, and I/O stresses on the guest and host systems. A VM resource monitoring tool ‘VM_stat’ is developed to know the resource usage of the guest VMs, which is useful for effectively tuning the system. Different real-time tuning measures are applied on the host/guest systems and the performance is analyzed.
Multi-Criteria Analysis for Selecting the Optimum Chatbot for Customer Service Under Uncertainty: Framework, Insights, and Case Study

Because of the advancement of Artificial Intelligence technology, Chatbot platforms can now be integrated into many aspects of customer support. This technology is increasingly being used for customer support. Chatbot technology may benefit anybody in the region, including employees and consumers, by providing quick and tailored services. This paper employed relevant attributes for a decision-making instance of an Egypt Telecommunication Company's chatbot selection problem. The best-suited chatbot was chosen using a new combination strategy combining Combined Compromise Solution (CoCoSo) and Analytic Hierarchy Process (AHP) in the context of Single-Valued Neutrosophic Sets (SVNSs). This technique uses the AHP method to give a novel procedure for generating attribute weights, and CoCoSo is used to rank the options and select the best chatbot. The usefulness and viability of the current study are statistically demonstrated by solving an explanatory case study of chatbot selection within the framework of SVNSs. The method's resilience and strength are further demonstrated through sensitivity analysis and comparisons with previously proposed approaches. The findings of this study reveal that the approach used might lead to more realistic results when dealing with uncertain and irregular information.
CT/MRI 3D Fusion for Cerebral System Analysis

The main idea of our system is to create a fusion of CT & MRI data of the same patient. We care about the accuracy of fused data correlation, emphasising vascular structures. We propose volumetric correlation method, vascular system reconstruction using tubular structures, error measurement and analysis using surface distance measurements. The resulting data from the fusion system combines bone structures from CT imaging and the reconstructed vascular system from MRI. We ensure to match correctly cerebral vessels with corresponding data from CT. Such data enables extended analysis of vascular segments relative to bones. In this work, we also propose a new semi-circular coordinate system to unify the orientation of our fusion models. Such a system allows for a more convenient and precise numerical analysis of anatomical correspondence to the principal axes of the coordinate system. It is also possible to define precisely the left and right sides of the human head.
Partial Cloud Dependent Hosting Performance Measurement and Analysis

Cloud services provide numerous advantages over private servers, but there are certain drawbacks, such as vendor lock, budget overrun due to an unexpected spike in computing demand and migrating existing system to the cloud. In this study, we deployed , measured and compared the performance of application hosting on virtual private servers with integrated cloud storage and cloud-based hosting. Cloud services and virtual private servers (VPS) may be evaluated using a range of performance indicators that can be applied to various components of the service. We recommend combining a cloud service with an application running on a virtual private server to store and retrieve file objects, allowing the application to grow by utilising cloud platforms like Amazon web services and other public cloud technology. Organizations may create safe and scalable apps while avoiding budget overruns and moving an existing system from a private server.
Fabric Image Retrieval Based on Decoupling of Texture and Color Feature

Fabric image retrieval, a form of content based image retrieval, is a high value research with the potential to be applied in many fields, such as e-commerce and inventory management. However, this research hotspot is plagued by two major challenges, namely the high requirements for retrieval results and the peculiarities of fabric images. Unlike general image retrieval, fabric image retrieval systems have to pay more attention to texture and color features. To address these challenges, we propose a novel framework for fabric retrieval by using self-supervised and deep hashing techniques. The framework consists of two modules for feature learning and hashing learning. During the feature learning phase, the color and texture information in the image is decoupled under the drive of augmented based pretext tasks. In hashing learning, Bi-half layer is introduced to generate high-quality hash codes. The visualization results indicate that the proposed method performs well for the representation of fabric images. And the experimental results show that the proposed retrieval framework can achieve a good performance and outperforms other methods, including several deep hashing methods and our previous work.
Revisiting State-of-the-Art Applications of Blockchain Technology in Business Ecosystems

In this paper, the authors provided a comprehensive analysis of blockchain technology and its state-of-the-art applications. The authors defined types of blockchain-enabled applications across various domains and research areas. Despite blockchain-enabled applications are widely deployed, the authors identified open issues and proposed potential solutions that are of high relevance and value for both academics and practitioners. As outstanding issues of the blockchain technology identified in this paper become addressed and as blockchain develops into more efficient, scalable, durable, and mature, its state-of-the-art applications will break into many more domains.
Detecting Dengue Disease Using Ensemble Classification Algorithms

Health care has grown beyond imagination in the last few years with the impact of artificial intelligence. Artificial intelligence applications are used to solve many health issues in the society. However, developing these applications involves transforming the data from the original format to a format that is understandable by the system. It also involves using suitable algorithms appropriate for the problem to be solved. This work discusses an approach used to detect dengue. Performance evaluation was done with the real-time dataset. Few classification algorithms have been used over the dataset. To have a better accuracy, ensemble learning methods were used. Out of the three ensemble machine learning algorithms, like light gradient boost classifier, logistic regression, and support vector machine classifier that were used, the experimental study reveals that light gradient boost classifier gives a better accuracy of 94.47% compared with the other algorithms that are used.
Research on targeted service technology for early warning information of meteorological disaster based on NLP

Affected by global climate change and the superposition of urban construction, the climate of city meteorological guarantee service puts forward new requirements and new challenges. As the first line of defense of meteorological disaster prevention and reduction, it is very important to realize the refinement targeted service of early warning information. There are some problems in the traditional early warning information service, such as the difficulty of accurate service of early warning information, the lack of precision, and the insufficient mining of early warning text information. This paper mainly analyzes the text description characteristics of early warning information of meteorological disaster, constructs the early warning information knowledge extraction process, constructs the early warning information labeling system, and realizes the early warning effective time extraction method based on conditional random field model, Early warning affected areas extraction method based on bidirectional long-term and short-term memory neural network and early warning cautions extraction method based on bootstrapping weak supervised learning method. Finally, taking the early warning information targeting service of meteorological information decision support system as an example, this paper tests the early warning information extraction methods, and preliminarily realizes the early warning precision targeting service in the decision support service of meteorological disaster prevention and reduction.
Fusing AutoML Models: A Case Study in Medical Image Classification

Automated Machine Learning (AutoML) tools are being increasingly used by researchers to solve a large number of pattern recognition tasks. A typical AutoML tool efficiently evaluates the performance of classifiers (e.g., SVM, Neural Network, Decision Tree, etc.) on an input dataset and determines the best performing classifier, along with the corresponding optimized parameters. In this work, we explore the use of a fusion scheme to combine the outputs of multiple classifier models generated and optimized by an AutoML tool known as Auto Tuned Models (ATM). We show that this approach provides a flexible framework that can be successfully applied to a wide variety of medical image classification tasks. Because each individual classification model is optimized for the given dataset, regardless of the size of the dataset, there is no requirement to identify and transfer knowledge from other domains. We generate up to 3,600 models for each dataset and explore the efficiency of fusion on subsets of models, as well as present two methods to automatically sort the classifier models for fusion. Experiments conducted on three medical imaging datasets, viz., stem cell, brain tumor and prostate cancer, convey the benefits of the proposed fusion scheme in improving classification accuracy.
Understanding Digital Literacy, Digital Competence, and Pedagogical Digital Competence: Implementing Online Teaching for Filipino Tertiary Educators During COVID-19

This chapter aims to trace and clarify the meaning of digital literacy (DL), digital competence (DC), and pedagogical digital competence (PDC) in the global literature, as well as their relevance to the Filipino tertiary education teachers’ PDC during the current COVID-19 pandemic that suspended face-to-face classes in favor of flexible, blended, and online teaching as mandated by the Philippines’ Commission on Higher Education (CHED). It also examines the major obstacles that hinder the development of the Filipino teachers’ PDC to be able to implement the CHED’s long-distance learning requirement as part of its recommended flexible learning to minimize COVID-19 infection. Using a systematic literature review as the primary method and peer-reviewed journal articles, reports, and books as sources of textual data, it argues that responding adequately to the CHED’s teaching mandate during the pandemic requires addressing the problems of lack of logistical support for teachers and students, such as weak and unstable Internet connection and lack of access to digital devices and educational technologies. It also needs to strengthen institutional support for information communication technology (ICT) infrastructure in colleges and universities and teachers’ ICT training to enhance their PDC and attitude toward online and long-distance learning. This chapter recommends that the Philippine government through the CHED must encourage more studies to understand fully the implementation barriers to improve the Filipino tertiary educators’ PDC during the current COVID-19.
Tightly Coupled Distributed Kalman Filter Under Non-Gaussian Noises

Distributed Kalman filter (DKF) is an effective method to solve the problem of state estimation in multi-sensor systems. However, the accuracy of conventional DKF may be influenced by the non-Gaussian noises and the accumulated errors in local Kalman filters (LKFs). To encounter the above challenges, a tightly coupled DKF with covariance intersection is proposed. In our method, using the computation outputs of LKFs, a Gaussian Mixture Model is formulated to address the influence of non-Gaussian noises. In order to reduce the cumulative errors from LKFs, covariance intersection fusion is utilized. Furthermore, an index-Huber function is designed to reduce the impact of large covariance generated by the LKFs. Several simulation and real-world experiments are conducted to show the effectiveness of our methods. The method we proposed outperforms three other DKF algorithms in the metrics of RMSE and cumulative error. In addition, a real-world multi-sensor state estimation experiment is conducted on a hexapod robot.