Reviewerly/exHarmony huggingface dataset:
Outlier Detection Techniques: A Comparative Study

With the recently rising technologies and numerous applications, the necessity of outlier detection is increasing drastically. Currently, a major variant of outlier detection techniques is witnessed. These techniques played a crucial role in the advancement of fields like medical health, MasterCard fraud, and intrusion detection. However, it is a significant work to spot abnormal behaviours or patterns out from sophisticated data. This paper provides a summary of the outlier detection strategies for the high-dimensional dataset and offers a comprehensive understanding of all basic techniques of outlier detection. This paper provides a comprehensive summary of the ongoing work on anomaly detection techniques, particularly with high-dimensional datasets and data with mixed attributes. The detection of outliers from the given dataset with anomalous data is meaningful work in the area of big data as the data is increasing exponentially every year. Specifically, this paper discusses the current advancement in the field of anomaly detection methods and simultaneously discusses the strengths and limitations of each outlier detection method.
Offensiveness in Target Versions of Wuthering Heights

The aim of this study is to study how Heathcliff and Catherine’s unfulfilled love, in Wuthering Heights by Emily Brontë, and its dramatic consequences, hatred and revenge, can be shaped cross-culturally and inter-culturally through rendition of impoliteness. The study also considers the role of gender and social class in shaping the reality of the novel in target versions, which may have emotional consequences and cause offence (Culpeper in Impoliteness: Using Language to Cause Offence. Cambridge University Press, Cambridge, 2011). To this end, the study examines two Greek versions of the novel which are 36 years apart (Καζαντζής 1984; Μαντόγλου 2020) and a recent theatrical adaptation with respect to the level of impoliteness the target versions employ. The 1984 version seems to make use of a language which is less offensive and aggressive, in contrast to the more recent version of 2020. As the use of impoliteness may reflect conventions of the period a target version emerges in, potential shifts in the rendition of impoliteness could suggest a change in how society caused offence in 1984, as opposed to nowadays. Α questionnaire addressing 13 postgraduate students at NKUA confirmed the interpretation of findings. The study shows that impoliteness is a significant tool for shaping identities in translated fiction and seems to creatively interact with pragmatic parameters like gender and social class.
Neuro-Fuzzy based Safe Landing Control System for UAVs

The number of aerial drone users continue to increase due to its availability, usage, and depreciation. The low cost of drones results in low-quality components that are prone to damage. One of the most common problems of drones is the landing system, where most drones crash due to uncontrolled maneuvering of the drone. In this study, Adaptive Neuro-Fuzzy inference Systems (ANFIS) using MATLAB was developed to perform a safe landing system on low-cost drones where the Gaussian Bell Membership function was used due to a low training error of 0.0015693.
Detection of ADHD Disorder Using Dynamic Connectivity Tensors in Bidirectional Circular Reservoir Computing

attention deficit hyperactivity disorder (ADHD) is a type of neurodevelopmental disorder. These disorders affect the development of the human nervous system and lead to abnormal brain function, which may influence a person's emotions, ability to learn and memory. It is also possible that the effects of neurodevelopmental disorders will continue throughout a person's life. Due to the lack of biomarkers to diagnose ADHD, the risk of misdiagnosis is high. The physician diagnoses the disorder with a description given to him by the patient or designed tests. Since biological signals such as electroencephalography (EEG) can record and measure brain function, they can help diagnose this disorder. In this study, an innovative method for extracting the characteristics of EEG signals is presented, which includes two steps. EEG signals are first converted into temporal segments, and then the spatial features are encoded using a dot product between the time frames of the channels. This data constructs the input of the model, which consists of three parts. In the first stage, the data enters reservoir computing module to extract dynamic features of data. After performing the required calculations, we use the Principal Component Analysis (PCA) technique for dimensionality reduction. In the last step, a Support Vector Machine (SVM) classify the data. This method obtains 99% accuracy, which is the highest achieved accuracy on the data used in this study.
The digital design model of the basic educational programs of the University

This work represents the electronic information and educational environment "Electronic University" being developed at the Bauman Moscow State Technical University. The digital design model for basic educational programs has been created and implemented. This model makes it possible to form, control and adjust educational programs implemented by the University. At the modelling level, all the components of the main educational program of the University and its formation in one system are presented with the choice of an academic discipline as a unit of formation. It is shown that the developed digital model for the design of the basic educational programs of the Bauman Moscow State Technical University made it possible to create a powerful tool for managing not only the educational process, but also the University itself in all areas of its activity, making the analysis of the effectiveness of the University.
Application of Computer Internet Technology and Cloud Computing in the Intelligent Information Management Database of Students

In the era of rapid development of the Internet, student education management will face more difficulties and challenges. How to achieve effective educational results is related to the stability of the university student population, the implementation of university management policies and the development of subject education. All aspects are closely related. In view of the problems of waste of resources, occupies a large amount of storage space, troublesome query and statistics in the current management mode of paper-based university archives; this system adopts the B/S mode combined with the synergy test of each module to realize the saving of development and the maintenance cost also reduces the workload of the client. In the end, a system with reasonable design, clean interface, simple operation and complete functions is obtained. This plays an important role for modernized file management in colleges and universities and has high practical value. We use the network platform as the core. This article establishes a differentiated student management database that records different types of students&#x0027; thoughts, behaviors, and psychological characteristics, systematically conducts scientific analysis and processing, and develops different educational management methods, and then we teach Carry out student education management.
An Online Model Integration Framework for Server Resource Workload Prediction

Workload forecasting is critical in cloud environments for the reason that cloud service providers must make timely decisions to meet the needs of users. We propose a load prediction method that integrates multiple models to get the advantages of online as well as offline modeling. During the process of forecasting, the data stream is passed through the LSTM model to output the offline prediction results, while the original data stream passes through the cluster and is sent to the bond Hoeffding Tree to output the online prediction results. After that, the outputs of the offline and the online models are integrated according to the integration algorithm, and the obtained prediction results are sent to the online error correction model, the final prediction result is achieved. Experiments show that our method is superior in terms of RMSE and MAE to some offline and online methods.
Handling Big Data in Relational Database Management Systems

Currently, relational database management systems (RDBMSs) face different challenges in application development due to the massive growth of unstructured and semi-structured data.This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, which do not adhere to the relational model.The migration from relational databases to NoSQL databases is challenging due to the data complexity.This study aims to enhance the storage performance of RDBMSs in handling a variety of data.The paper presents two approaches.The first approach proposes a convenient representation of unstructured data storage.Several extensive experiments were implemented to assess the efficiency of this approach that could result in substantial improvements in the RDBMSs storage.The second approach proposes using the JavaScript Object Notation (JSON) format to represent multivalued attributes and many to many (M:N) relationships in relational databases to create a flexible schema and store semi-structured data.The results indicate that the proposed approaches outperform similar approaches and improve data storage performance, which helps preserve software stability in huge organizations by improving existing software packages whose replacement may be highly costly.
Significance of Software Engineering Phases in the Development of a Software Application

Software engineering is the extensive study of how to build a software product using software development life cycle stages, beginning with requirement analysis phase to the last phase of testing and maintenance. This approach in the development of software application ensures completion of the project within the stipulated period without compromising quality of the software with effective utilization of resources. The most important is to develop a quality product. Case studies make readers understand the concept in a more impartial and tactile manner; one can make use of this experience in real-life circumstances for providing solutions to the complex problems. The significance of this case study is to represent the authors' experience of developing software by using method of software engineering, understanding how to apply them to build an application. This chapter gives a holistic review of the development of an Android application titled Vaccination Management System (VMS) “Vacc-IT” as a comprehensive case study.
On Large-Batch Training of Residual Networks with SignSGD

Large-batch training of deep neural networks (DNN) has recently been widely studied, since traversing the optimization landscape is faster with large batches and the emergence of parallel computing has made large-batch training feasible. However, its generalization is still worse compared to small-batch training, which is commonly attributed to the low gradient variance with large batches. We show that sign stochastic gradient descent (signSGD) is a suitable candidate for large-batch ResNet training with improved generalization compared to vanilla SGD. We further modify signSGD to improve the convergence for large batches while retaining its generalization properties. In particular, we propose r-signSGD, which restricts certain parameter updates of signSGD—r-signSGD updates a parameter of the neural network only if its gradient sign matches its gradient momentum sign. This prevents unnecessary/wrong updates given by signSGD especially for parameters with small gradients. We show that r-signSGD improves the convergence of signSGD while also performing better than signSGD with momentum, vanilla SGD, and even SGD with momentum in terms of test accuracy in the CIFAR10 image classification task.
iReportNow: A Mobile-Based Lost and Stolen Reporting System

Modern society is faced with a growing degree of security threats both internally and externally, but access to the right and timely information can be a defining factor. Technological advancements have been advantageous to the police in terms of reporting, monitoring and responding to crime and criminality. At present, crime reportage is being done manually in most police stations via the use of pen and paper to document statements. This method is not only slow and ineffective but could lead to loss of valuable time and productivity. This work presents the design and implementation of “iReportNow”, a digital mobile application for reporting loss or theft. It leverages the current proliferation of mobile devices. The application makes information about reported cases of loss or theft readily available and accessible to the populace. The agile model of the system development life cycle (SDLC) was adopted throughout the stages of requirement gathering, to the design, analysis, implementation and testing. The application was implemented using Extensible Mark-up Language (XML) for the user interface, Java programming language for code binding, MySQL and PHP for a robust database. The application was deployed on 20 Android mobile devices for usability testing and it achieved a mean score of 83.5 based on the system usability scale (SUS). iReportNow will enhance efficiency in reporting and accessing the information on reported cases of loss and theft. Furthermore, it would help to allay uncertainties of buying stolen properties as users can easily check up on an item put up for sale.
Evaluating the Performance of Convolutional Neural Networks with Direct and Sequential Acyclic Graph Architectures in Automatic Segmentation of Breast Lesions in Ultrasound Images

Global health rates show that breast cancer has remained at the top of the biggest causes of death among women. Radiologists use images such as ultrasonography to aid diagnosis. In the era of big data, in which there is a large amount of data available and the use of artificial intelligence is omnipresent in assisting activities, automatic diagnosis aid is a topic on the agenda. Convolutional neural networks are efficient in the most medical tasks. In this work, the performance of two convolutional neural networks, one with sequential architecture and the other with a direct acyclic graph structure, are contrasted for the task of automatic segmentation of breast lesions in ultrasound images. For the development and evaluation of the proposals, two image banks were used, containing a total of 550 ultrasound images. Performance metrics already established in the literature, such as Global Accuracy and Dice Coefficient, were used to evaluate the network architectures. The best segmentation result shows a global accuracy of 96%.
What the literature says about social robots and AI technologies in health and social care

This chapter draws content from three literature reviews to provide an overview of the different types of humanoid and animal like robots used in health care settings. The factors that influence the acceptance of socially assistive robots by health and social care potential and actual users, family caregivers and healthcare professionals will be discussed with a special emphasis on culture. In addition, the current evidence on the views of healthcare professionals and the factors that impact on the implementation of these robots in health and social care environments will be presented.
A Real-time Activity Recognition System based on Dynamic Adaptive Windows using WiFi Signals

WiFi Chanel State Information (CSI)-based activity recognition has attracted much attention in recent years. And it is extremely vital to recognize activities in time, especially for dangerous activities such as fall. In this paper, we present a real-time activity recognition system. In this system, we design a dynamic threshold-based activity segmentation method, which can address the problems of the fixed threshold and single window, and accurately detect start and end points of activities. The experiments demonstrate that our system acquires expected recognition performance.
University Students’ Perception on the Usefulness of the Incorporation of Conversational Agents in Mathematics Learning

The open-ended interactions between the educator and student could definitely achieve a great success of teaching and learning. However, this is not easy to apply in the university classes as most classes are large size. Conversational agents, automated computer software that interact with human users through human language conversations, may implement into education to support the teaching and learning process. Unfortunately, this application in education domain is found scarce even though it has about 60 years’ history. This study is to design, develop and incorporate a conversational agent in the teaching and learning of undergraduate's mathematics subject. A quasi experimental design with a convenient sample was used. The students were brief and used it for learning outside the classroom as a blended learning tool for a month when they were taught by the lecturer in face-to-face classes. After that, they participated a 5 Likert-scale adapted questionnaire on perception toward this incorporation. Results shown that majority of them perceived positively on the aspect of usefulness of incorporation in their learning. The results cannot be generalized in accordance for all subjects, undergraduates and universities, but, may serve as a reference to design and develop educational conversational agent to be blended into learning for other subjects or universities.
Local filter-based sequential and distributed fusion state estimation for nonlinear multi-sensor systems with asynchronously correlated noises

In this paper, sequential and distributed fusion state estimation algorithms based on local filter are proposed for nonlinear multi-sensor systems with asynchronously correlated noises. At first, in order to avoid the computational burden of measurement augmentation in centralized fusion filter, a sequential fusion filter is deduced by correcting measurement noises recursively. Subsequently, considering the problem that the parameters in covariance intersection (CI) fusion algorithm are difficult to estimate and the estimation accuracy of the sequential fast CI (SFCI) fusion algorithm is poor, a novel distributed fusion filter is proposed, where local filters are sent to the fusion center as the measurements and fusion predictor in the fusion center is sent to local filters by feedback. Then, to be suitable for practical applications, the numerical implementation based on third-degree spherical-radial rule is given. Finally, the superiority of the proposed algorithms is shown by using a nonlinear model.
An image translation algorithm based on Generative Adversarial Networks

Image translation is a hot research topic in the field of computer vision. Unlike the traditional method, which may lead to blur and distortion, we aim to investigate a general image-to-image translation algorithm based on the generative adversarial networks in this paper. We introduce the Patch GAN to make sure the image content is high quality in local details. The whole network also learns a loss function to realize the image reconstruction from label maps and edge maps. Through extensive experiments, we demonstrate that the translation effect can be improved by improving the clarity of images.
The Challenges of Digitalization of the Teaching/Learning Process During Covid-19

At present teachers are facing a number of challenges caused by the reform processes in education that require acquisition of new competencies. Digital competence, that includes both technical skills needed to manage the software and cognitive skills that are related to education, has been gradually introduced in the curricula over the past decade. Teachers’ digital competence includes the ability to use technologies efficiently, developing digital content and inspiring students to work in a digital environment. This also requires a wider conceptualization of digital competence in the context of re-conceptualization of flexible learning. The methodology employed in this study is three focus group interviews with forty-three teachers of comprehensive and vocational schools in Latvia on their experience in integrating digital technologies into the remote teaching process during the emergency situation due to coronavirus disease COVID-19 from March until June 2020. The study focuses on digital transformations in education process at schools in Latvia and is aimed at exploring difficulties and challenges faced by teachers while ensuring remote teaching/learning process. COVID-19 was a big push towards the introduction and wider usage of technologies into education process that also greatly impacted the level of pressure and stress experienced by educators due to the demand to adapt to urgent and swift shift.
Ecosystem of Health Care Software Engineering in 2050

We Emerging technologies towards digitalization and clinical technology are shifting health services to a different level of advancements. The intensification of trending technologies is enlightening patient aligned care, introducing new ways and means to improve health care at the same time cut actual cost of operation by enhancements towards automated information management, data and information repository, enabling instantaneous availability and accessibility of information to clinicians and last but not least technology that can proactively perform tasks consuming less space, time and cost. The purpose of the paper is to discuss the latest trends in clinical technology, tools that can enhance monitoring and diagnostic proficiencies, and healthcare needs that are growing apace as innovation them could play a key role in shaping the future of healthcare systems? A comprehensive analysis approach of healthcare policies and applications literature was used to identify the growing cost of healthcare and operations. The paper concludes that emerging technologies, hospitals and facilities architecture are key points or should we say pillars that could play vital role towards health care future. The paper will provide a summary of some of the technological trends that are making an impact in improving the quality of healthcare today and their contribution to health eco system in the future (2050). Why do we need these trends and technologies? Some of the hurdles, issues complications currently faced by the healthcare industry and how they can be resolved to shape the future of healthcare industry.
Performance Analysis of Deep Learning Approaches for Offline Signature Verification

Signature verification plays a significant role in many biometric authentication places. Many financial institutes require a robust signature verification process for check clearance, loan sanction, processing, pension relation documents, etc. Expert forgeries make it hard to authenticate an individual's identification based on signatures. Typically, this occurs when the forger understands the user's intricate features of the signature and strives to mimic it. Online signature verification approaches can extract various features such as keystrokes, pressure of the pointer, duration between the strokes and the lettering styles, so that verification becomes effective. However, the lack of these intricate details in offline signature, the authentication process becomes much more difficult. To address these issues, in this paper we propose deep learning-based approaches for offline signature verification. In this regard, we have used ZFNet, LeNet and AlexNet architectures with CEDAR, BHSig20 and UTsig datasets for our extensive. experimentation. We propose a learning model in which the dataset consists of multiple genuine and forged signatures. Further, performance analysis of these techniques has been carried out. It was found that LeNet has provided better training and testing accuracy with above 82% performance.