Reviewerly/exHarmony huggingface dataset:
A generative adversarial network for synthetization of regions of interest based on digital mammograms

Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training data, and skillful selection of hyperparameters. The application of deep learning models to medical image processing has yielded interesting performance, capable of correctly detecting abnormalities in medical digital images, making them surpass human physicians. However, advancing research in this domain largely relies on the availability of training datasets. These datasets are sometimes not publicly accessible, insufficient for training, and may also be characterized by a class imbalance among samples. As a result, inadequate training samples and difficulty in accessing new datasets for training deep learning models limit performance and research into new domains. Hence, generative adversarial networks (GANs) have been proposed to mediate this gap by synthesizing data similar to real sample images. However, we observed that benchmark datasets with regions of interest (ROIs) for characterizing abnormalities in breast cancer using digital mammography do not contain sufficient data with a fair distribution of all cases of abnormalities. For instance, the architectural distortion and breast asymmetry in digital mammograms are sparsely distributed across most publicly available datasets. This paper proposes a GAN model, named ROImammoGAN, which synthesizes ROI-based digital mammograms. Our approach involves the design of a GAN model consisting of both a generator and a discriminator to learn a hierarchy of representations for abnormalities in digital mammograms. Attention is given to architectural distortion, asymmetry, mass, and microcalcification abnormalities so that training distinctively learns the features of each abnormality and generates sufficient images for each category. The proposed GAN model was applied to MIAS datasets, and the performance evaluation yielded a competitive accuracy for the synthesized samples. In addition, the quality of the images generated was also evaluated using PSNR, SSIM, FSIM, BRISQUE, PQUE, NIQUE, FID, and geometry scores. The results showed that ROImammoGAN performed competitively with state-of-the-art GANs. The outcome of this study is a model for augmenting CNN models with ROI-centric image samples for the characterization of abnormalities in breast images.
Hyperopt-Sklearn

Hyperopt-sklearn is a software project that provides automated algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-Newsgroups, Convex Shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and Convex Shapes at the time of release.
The Effectiveness of Artificial Intelligence in Detection of Oral Cancer

The early detection of oral cancer (OC) at the earliest stage significantly increases survival rates. Recently, there has been an increasing interest in the use of artificial intelligence (AI) technologies in diagnostic medicine. This study aimed to critically analyse the available evidence concerning the utility of AI in the diagnosis of OC. Special consideration was given to the diagnostic accuracy of AI and its ability to identify the early stages of OC. From the date of inception to December 2021, 4 databases (PubMed, Scopus, EBSCO, and OVID) were searched. Three independent authors selected studies on the basis of strict inclusion criteria. The risk of bias and applicability were assessed using the prediction model risk of bias assessment tool. Of the 606 initial records, 17 studies with a total of 7245 patients and 69,425 images were included. Ten statistical methods were used to assess AI performance in the included studies. Six studies used supervised machine learning, whilst 11 used deep learning. The results of deep learning ranged with an accuracy of 81% to 99.7%, sensitivity 79% to 98.75%, specificity 82% to 100%, and area under the curve (AUC) 79% to 99.5%. Results obtained from supervised machine learning demonstrated an accuracy ranging from 43.5% to 100%, sensitivity of 94% to 100%, specificity 16% to 100%, and AUC of 93%. There is no clear consensus regarding the best AI method for OC detection. AI is a valuable diagnostic tool that represents a large evolutionary leap in the detection of OC in its early stages. Based on the evidence, deep learning, such as a deep convolutional neural network, is more accurate in the early detection of OC compared to supervised machine learning.
A Model for Predicting Cervical Cancer Using Machine Learning Algorithms

A growing number of individuals and organizations are turning to machine learning (ML) and deep learning (DL) to analyze massive amounts of data and produce actionable insights. Predicting the early stages of serious illnesses using ML-based schemes, including cancer, kidney failure, and heart attacks, is becoming increasingly common in medical practice. Cervical cancer is one of the most frequent diseases among women, and early diagnosis could be a possible solution for preventing this cancer. Thus, this study presents an astute way to predict cervical cancer with ML algorithms. Research dataset, data pre-processing, predictive model selection (PMS), and pseudo-code are the four phases of the proposed research technique. The PMS section reports experiments with a range of classic machine learning methods, including decision tree (DT), logistic regression (LR), support vector machine (SVM), K-nearest neighbors algorithm (KNN), adaptive boosting, gradient boosting, random forest, and XGBoost. In terms of cervical cancer prediction, the highest classification score of 100% is achieved with random forest (RF), decision tree (DT), adaptive boosting, and gradient boosting algorithms. In contrast, 99% accuracy has been found with SVM. The computational complexity of classic machine learning techniques is computed to assess the efficacy of the models. In addition, 132 Saudi Arabian volunteers were polled as part of this study to learn their thoughts about computer-assisted cervical cancer prediction, to focus attention on the human papillomavirus (HPV).
Brain tumor image generation using an aggregation of GAN models with style transfer

In the recent past, deep learning-based models have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated datasets. An interesting application of deep learning is synthetic data generation, especially in the domain of medical image analysis. The need for such a task arises due to the scarcity of original data. Class imbalance is another reason for applying data augmentation techniques. Generative Adversarial Networks (GANs) are beneficial for synthetic image generation in various fields. However, stand-alone GANs may only fetch the localized features in the latent representation of an image, whereas combining different GANs might understand the distributed features. To this end, we have proposed AGGrGAN, an aggregation of three base GAN models-two variants of Deep Convolutional Generative Adversarial Network (DCGAN) and a Wasserstein GAN (WGAN) to generate synthetic MRI scans of brain tumors. Further, we have applied the style transfer technique to enhance the image resemblance. Our proposed model efficiently overcomes the limitation of data unavailability and can understand the information variance in multiple representations of the raw images. We have conducted all the experiments on the two publicly available datasets - the brain tumor dataset and the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. Results show that the proposed model can generate fine-quality images with maximum Structural Similarity Index Measure (SSIM) scores of 0.57 and 0.83 on the said two datasets.
Design of a Novel IoT Framework for Home Automation using Google Assistant

It is now possible to build a Home Automation System (HAS) using sophisticated computing devices, wireless sensor networks (WSNs), and Internet of Things (IoT)-based Smart Banks to automate your home. Home Automation can also make use of Gesture Detection and Controls. It is the primary objective of this article to build a house. Electronic home gadgets like TVs, fans, and lights can all be controlled using the internet, and at a very low cost. So, the objective is to assemble ARDUINO board and Bluetooth are used to create a home automation system that can be controlled from an Android smartphone.
How Cognitive Biases Affect XAI-assisted Decision-making

The field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to complex AI systems. Although it is usually considered an essentially technical field, effort has been made recently to better understand users' human explanation methods and cognitive constraints. Despite these advances, the community lacks a general vision of what and how cognitive biases affect explainability systems. To address this gap, we present a heuristic map which matches human cognitive biases with explainability techniques from the XAI literature, structured around XAI-aided decision-making. We identify four main ways cognitive biases affect or are affected by XAI systems: 1) cognitive biases affect how XAI methods are designed, 2) they can distort how XAI techniques are evaluated in user studies, 3) some cognitive biases can be successfully mitigated by XAI techniques, and, on the contrary, 4) some cognitive biases can be exacerbated by XAI techniques. We construct this heuristic map through the systematic review of 37 papers-drawn from a corpus of 285-that reveal cognitive biases in XAI systems, including the explainability method and the user and task types in which they arise. We use the findings from our review to structure directions for future XAI systems to better align with people's cognitive processes.
SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization

Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially through fine-tuning large pre-trained language models on the downstream dataset.These models are typically decoded with beam search to generate a unique summary.However, the search space is very large, and with the exposure bias, such decoding is not optimal.In this paper, we show that it is possible to directly train a secondstage model performing re-ranking on a set of summary candidates.Our mixture-of-experts SummaReranker learns to select a better candidate and consistently improves the performance of the base model.With a base PEGASUS, we push ROUGE scores by 5.44% on CNN-DailyMail (47.16 ROUGE-1), 1.31% on XSum (48.12 ROUGE-1) and 9.34% on Reddit TIFU (29.83 ROUGE-1), reaching a new state-of-theart.Our code and checkpoints will be available at https://github.com/ntunlp/SummaReranker.
Recommendation of Multimedia Items by Link Analysis and Collaborative Filtering

&#x0D; &#x0D; &#x0D; We investigate two recommendation approaches suitable for online multimedia sharing services. Our first approach, UserRank, recommends items by global interestingness irrespective of user preferences and is based on the analysis of ownership and evaluation link structure. We also present a personalized interestingness algorithm that combines UserRank with collaborative filtering which enables a single parameter to control the degree of personalization in the recommendations. Our initial results from an informal user study are encouraging.&#x0D; &#x0D; &#x0D;
A Practical Approach to expressing digitally signed documents

Initially developed and considered for providing authentication and integrity functions, digital signatures are studied nowadays in relation to electronic documents (edocs) so that they can be considered equivalent to handwritten signatures applied on paper documents. Nevertheless, a standardized format to be used specifically for e-doc representation was not yet specified. Each document management system is free to choose whatever e-doc format is suitable for its requirements (e.g. ASCII, Word, PDF, binary). So far, some solutions for document management systems were found but none of them was designed to consider security an important requirement and to enable digital signing and easy management of e-docs. A possible solution to this problem is our secure document management system named AIDA. This paper focuses on the use of XML and XML Signature for the representation of e-docs in AIDA.
THE SPEECH ACT OF APOLOGY IN SETSWANA EDUCATIONAL CONTEXTS

The main purpose of this paper encompasses the analysis of certain aspects of the speech act of apology in Setswana.Those aspects include the selection of the strategy used in the expression of apologizing and the participation of gender.The model of Brown and Levinson provides social factors which affects the way a speaker apologizes for an offence.The data for this study was collected by means of quantitative analysis, which was administered among 22 B.Ed. second year Setswana students.The participants were provided with the questionnaire that consists of a social situation and in each situation the respondent has committed an offence to someone and has to apologize to the person.The offence affected persons belong to different social backgrounds and have different relations with the respondents from formal to informal situations.The researcher analyses the effects that different aspects have on the apology strategies which males and females use in different contexts.The findings in terms of apology situations and apology strategies displayed certain similarities and differences as a result.
Future Growth Of Software Engineering Baccalaureate Programs In The United States

Despite the large current number of software engineering professionals in the United States, as well as projections that it is one of the largest-growing fields in the nation for the current decade, growth in the number of Bachelor's degree programs in the United States has recently declined.There are currently only about thirty schools in the United States that offer a baccalaureate degree in software engineering (including several online programs), without many more in sight.This paper looks at the growth and development of the Bachelor's degree programs in software engineering in the United States, possible causes for the paucity of new programs, and what this might mean for the future.Included is a survey of software engineering educators in programs which do not currently have a Bachelor's degree program in software engineering, as well as comparisons with other computing fields when they were in similar stages of development.
Types of personality motivation structure of women who are acquiring higher education in psychology

The article deals with the results of empirical research on the types of personality moti-vation structure of women who are acquiring higher education in psychology. Two types have been singled out: expressive and impulsive. The expressive type comprises two com-ponents: ideal motivation aimed at life necessities, general activity and social utility, and real motivation aimed at comfort, social status and communication. In the structure of the impulsive type there are three components: real motivation aimed at life necessities, so-cial status, general activity and social utility; real motivation aimed at communication and comfort; creativity motivation aimed at self-development and creative activity. Women belonging to the expressive type of personality motivation structure have more positive self-attitude and a higher level of psychological well-being than women belonging to the impulsive type.
Media education in the school system

The diagnosis of the problem of the contemporary world in the area of global media communication is a basic challenge for existing and modernized educational systems. Without a complete vision of the world, people cannot exist in a fully aware way. Hence, the most important questions related to such social development in which no one would be excluded from the world-wide circulation of information. The general assumptions of common media education have been presented in the article.
Is Social Software a New Mode of Second-Language Learning in the Information Age?

The basic characteristics of social media mirror much of the rhetoric and thinking aro-und educational reform in the modern world context. The basic cultural relevance of social media to young people enables them to learn how to bring their social life into the matrix of school activities. It also provides young learners with different audiences for different activities. Today, social media are probably the critical component in enabling individuals, both teachers and learners, to construct their own personal learning networks. Joining, building, growing and managing digital communities, whether relatively open or relatively closed, enables teachers and learners to connect with others with similar interests and to learn from other participants in those communities.
Social Access Control for Social Media Using Shared Knowledge Questions

Managing privacy of online content is difficult. We present a simple social access control where sharers specify test questions of shared knowledge, such as "what is our school mascot," instead of creating authenticated accounts and specifying explicit access control rules for all potential accessors. This demo will let attendees interact with our Facebook prototype. We will also explain prior studies that elucidate the context of photo sharing security, gauge the difficulty of creating shared knowledge questions, measure their resilience to adversarial attack, and evaluate users' abilities to understand and predict this resilience.
Software Fault Reduction Methodology for Reliable Knowledge Bases

We propose a systematic methodology named “software fault reduction methodology” to design software development/V&V plan for a target knowledgebase. It focuses on the types of faults in the outputs (documents, programs etc.) of each development stage and selects proper fault-detection methods or prevention methods by their detective / preventive power and coverage.
Social Equality Through Multicultural Education – a Means for Reducing Prejudices and Stereotypes in the Educational Process

The paper deals with the topic of social equality. It defines the basic terminology, clarifies the issue in the context of reducing prejudices and stereotypes. It emphasizes the importance of developing the processes and mechanisms for social equality, for fostering humanity in human beings. One of the key mechanisms is multicultural education. The author of the paper interprets it in the educational process, beginning with preschool education and ending with tertiary education.
Identifying Vulnerable Security Practices In Small Scale Computer Networks

Twenty years ago, the Internet and networking was technology only accessible to large corporations, educational institutions and researchers.In today's information marketplace highspeed Internet reaches into the smallest businesses and households.To extend Internet connectivity, individuals purchase small routers and wireless access points from local electronic stores.This allows sharing of the Internet connection with multiple employees and family members simultaneously.Improper installation of such data equipment creates a vulnerability to personal information and security.In an effort to better serve this growing market, student training has been implemented in the security of small scale networks and routers.Traditional security training has typically concentrated on virtual simulation and larger scale routers/firewalls.Small inexpensive routers/firewalls and access points can be a powerful tool, which possesses many of the same security principles as their more expensive counterparts.A majority of students already have this equipment available in their own homes because of the popularity of DSL and Cable Internet access.Students, many times, disconnect the principles of home security from that of corporate or large scale network security.They learn the practice yet fail to implement this knowledge into their own life.Even the smallest leak of information may result in catastrophic circumstances.This paper will discuss the use of small scale routers and access points in the training of a security specialist in a Computer Engineering Technology program.Examples will be given on how these small inexpensive communication devices can be used to demonstrate extensive security principles.The result is a personal connection for the student to exercise good security practices on a daily basis.
Why bitcoin can never be money

In On the Origins of Money (1892), Carl Menger explains that in a barter situation, some commodities are more commonly demanded than others, are more saleable. In order to overcome the double coincidence of wants, people naturally begin trading their goods first for a more saleable good in order to then trade for their final objective. Menger (1892) describes it thus, «Men have been led, with increasing knowledge of their individual interests, each by his own economic interests, without convention, without legal compulsion, nay, even without any regard to the common interest, to exchange goods destined for exchange (their “wares”) for other goods equally destined for exchange, but more saleable.» Lud-wig von Mises restated the same insight in Human Action (1940), «[Money] is the most marketable good which people acquire be-cause they want to offer it in later acts of interpersonal exchange. Money is the thing which serves as the generally accepted and commonly used medium of exchange.»&#x0D; As ever more people discovered the advantages of using a more saleable good in indirect exchange, one commodity became increasingly adopted until it eventually became money, the most marketable of all goods, the good that can generally be traded for all other goods within the market. As Menger said (1892), «And so it has come to pass, that as man became increasingly conversant with these economic advantages… those commodities, which relatively to both space and time are most saleable, have in every market become the wares, which it is not only in the interest of every one to accept in exchange for his own less saleable goods, but which also are those he actually does readily accept.»
Expected Path Degradation when Searching over a Sparse Grid Hierarchy

The traditional focus of combinatorial search research is to speed up the search algorithm. An alternative, however, is to create a sparser representation of the search space. This relates to the idea of spanners from graph theory. These are subgraphs which retain paths between any two vertices of the original graph while guaranteeing a maximum stretch in path length. In practice, the path degradation of graph spanners is significantly smaller than the theoretical bound. Even so, expected path degradation of graph spanners is typically not studied. This work focuses on grid path-finding to propose an algorithm that constructs a grid spanner, where analysis for the obstacle-free case shows that significant performance gains can be achieved with a small decrease in expected path quality. This is an important first step towards studying the expected performance of spanners. Experiments on game maps show that expected path quality with obstacles is only sometimes marginally lower than that in the obstacle-free case and that a significant reduction in the size of the search space can be achieved.