Reviewerly/exHarmony huggingface dataset:
Prediction of Breast Cancer Recurrence in Five Years using Machine Learning Techniques and SHAP

Cancer recurrence is the leading concern among the patients who are affected by breast cancer. Earlier detection of cancer relapse can help the clinicians in administering the right dose of treatment for the right duration of time. This can improve the prognosis of the patient and also reduce their fear regarding relapse. For this purpose, our work deals with using various machine learning (ML) models such as logistic regression, support vector machine (SVM), decision tree, and random forest to predict if a patient will have cancer relapse in 5 years. The performance of all the ML models is then compared by making use of some evaluation metrics such as C-index, accuracy, F1-score, precision, and recall. Random forest (RF) model produced the best performance when compared to all the other models. It gave a precision value of 0.75, accuracy of 0.69, recall of 0.66, C-index of 0.71, and F1-score of 0.70. Random forest model interpretation was done by using Shapley additive explanations (SHAP). This algorithm identifies the contribution given by each feature in cancer recurrence prediction by random forest model. Based on the SHAP values, the features which had high impact on model prediction were found. Features such as tumor size, mutation count, lymph nodes examined positive, age at diagnosis, Nottingham prognostic index (NPI), tumor stage, HER2 status, and cancer type detailed when they had higher values were responsible for causing cancer recurrence in 5 years according to random forest model predictions. Thus, prediction of cancer relapse helps the clinicians in administering the right treatment and improves patient prognosis.
Extreme Machine Learning Architectures Based on Correlation

The relationships between features can guide the architecture of pattern recognition systems. It is the case with images where the spatial relationship of pixels allows to use convolutions to filter images and extract high level features. In this paper, we consider extreme learning machine (ELM) classifiers and propose to modify the selection of the inputs of a hidden unit based on correlation matrix of the input features. The proposed approach is tested on databases of handwritten digits (MNIST and Devanagari). The results show that input selection based on the correlation matrix provides better performance and leads to a sparse representation of the weights in the network.
Quantumness of correlations in nanomaterials—experimental evidence and unconventional effects

&lt;abstract&gt;&lt;p&gt;Quantum correlations phenomena, such as entanglement, quantum discord and quantum coherence, are ubiquitous effects caused by interactions between physical systems—such as electrons and ions in a piece of metal, or H atoms/molecules adsorbed in nanoporous materials. Here, we address time-asymmetric quantumness of correlations (QoC), with particular emphasis on their energetic consequences for dynamics and non-equilibrium thermodynamics in condensed matter and/or many-body systems. Some known theoretical models—for example, the quantum Zeno effect and GKSL-type Markovian equations-of-motion, all of them being time-asymmetric—are shortly considered, with emphasis on the general character of one of their common and most intriguing result. Namely, that in clear contradistinction to conventional expectations, degradation (or destruction, decoherence, consumption, smearing out, coarse-graining) of quantum correlations can be a source of work (instead of heat production). The experimental relevance of the theoretical considerations is shown with the aid of a recent scattering experiment exploring the impulsively driven (by neutron collisions) translational dynamics of H$ _2 $ molecules in carbon nanotubes and other nanostructured materials—a topic of immediate relevance for material sciences and related technologies.&lt;/p&gt;&lt;/abstract&gt;
Block Designs

Let V be a n-dimensional vector space over the finite field $$\mathbb {F}_q$$ of q elements. Our first result determines the number $$\begin{bmatrix}{n}\\ {k}\end{bmatrix}_{q}$$ of k-dimensional vector subspaces of V.
Won’t you see my neighbor? User predictions, mental models, and similarity-based explanations of AI classifiers

Humans should be able work more effectively with artificial intelligence-based systems when they can predict likely failures and form useful mental models of how the systems work (Johnson, et al. 2014, Klein, at al. 2005, Bansal et al. 2019, Tomsett, et al. 2020). We conducted a study of people’s mental models of artificial intelligence systems using a high-performing image classifier, focusing on participants’ ability to predict the classification result for a particular image. Participants viewed images in one of two classes and then predicted whether the classifier would label them correctly and indicated their confidence in their predictions. Participants also provided their own assessment of the correct class. In this experiment, we explored the effect of giving participants additional information. We showed them an array of the image’s nearest neighbors in a space representing the otherwise uninterpretable features extracted by the lower layers of the classifier’s neural network, using t-distributed stochastic neighbor embedding. We found that providing this neighborhood information did increase participants’ prediction performance, and that the performance improvement could be related to the neighbor images’ similarity to the target image. We also found indications that the presentation of this information may influence people’s own classification of the target image; in some cases after viewing the image’s neighbors, participants’ accuracy in identifying the actual class of the image was significantly worse when given the additional information, particularly when the set of neighbor images included images from the incorrect class. They became “mechanomorphized” in their own judgements, rather than anthropomorphizing the classifier’s process. There was also a significant relationship between reported confidence in predictions and accuracy, indicating that at a given level of confidence, participants in a control condition were significantly less accurate than experimental participants. In addition to the differences in mental models suggested by prediction accuracy and confidence, participants in the control and experimental conditions differed in how the described their mental models in comments on the image stimuli. Participants with less information tended to discuss image details, whereas those with more seemingly tried to find a pattern across the images and so focused more on the classifier itself and less on the image.
A Method for Image Registration via Broken Geodesics

Anatomical variabilities seen in longitudinal data or inter-subject data is usually described by the underlying deformation, captured by non-rigid registration of these images. Stationary Velocity Field (SVF) based non-rigid registration algorithms are widely used for registration. However, these methods cover only a limited degree of deformations. We address this limitation and define an approximate metric space for the manifold of diffeomorphisms $$\mathcal {G}$$ . We propose a method to break down the large deformation into finite set of small sequential deformations. This results in a broken geodesic path on $$\mathcal {G}$$ and its length now forms an approximate registration metric. We illustrate the method using a simple, intensity-based, log-demon implementation. Validation results of the proposed method show that it can capture large and complex deformations while producing qualitatively better results than state-of-the-art methods. The results also demonstrate that the proposed registration metric is a good indicator of the degree of deformation.
Avalanche RL: A Continual Reinforcement Learning Library

Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences). In this paper, we describe Avalanche RL, a library for Continual Reinforcement Learning which allows users to easily train agents on a continuous stream of tasks. Avalanche RL is based on PyTorch [23] and supports any OpenAI Gym [4] environment. Its design is based on Avalanche [16], one of the most popular continual learning libraries, which allow us to reuse a large number of continual learning strategies and improve the interaction between reinforcement learning and continual learning researchers. Additionally, we propose Continual Habitat-Lab, a novel benchmark and a high-level library which enables the usage of the photorealistic simulator Habitat-Sim [28] for CRL research. Overall, Avalanche RL attempts to unify under a common framework continual reinforcement learning applications, which we hope will foster the growth of the field.
The Effect of Varying Levels of Automation during Initial Triage of Intrusion Detection

With unrestrained optimism regarding the possibilities of artificial intelligence (AI) exceeding its actualization, AI developers are under increasing pressure to integrate AI into complex human decision-making tasks without fully understanding the implications of this automation. To investigate how automation may influence human performance in a high workload environment, this study utilizes a triage scenario from intrusion detection using a simulated SNORT interface. Participants classify a series of time-sensitive alerts as real intrusions or false alarms with the assistance of varying levels of automation (LOA) from no automation to fully autonomous. Preliminary results showed that participants tend to prefer and have some performance benefits with intermediate levels of automation.
Digital Storytelling to Help Improve Reading comprehension

Information and communication technologies have transformed the world, especially if they are aimed at promoting reading in children in their first years of learning. The problems generated by deficiencies in reading comprehension in the student body affect their school performance and their progress in the upper years, creating demotivation and low levels in their school performance. The objective of the research proposal is to design a virtual classroom with digital narratives as support to improve reading comprehension in the student body. Digital narratives are a technological resource that can be applied pedagogically, combining content with visual and auditory material, taking advantage of the functionality of virtual classrooms such as Google Classroom, in which the proposal was designed. It is concluded that teachers must identify the problems that their students present in the reading process and implement new methodological strategies, so that learning is different, participatory and innovative, this is where narratives play an important role, such as resource that provides a change in the learning system and support for students with school behind, in addition to strengthening cognitive development with audiovisual stimuli.
Expeirences of promoting journals into the information space

The key activities to promote university peer-reviewed journals in the world scientific space are discussed. These activities are to increase interest toward the publications and citation activity. The Russian segment of academic journals is characterized. The ways of M. V. Lomonosov Northern (Arctic) Ferderal University to promote its journals are described.
A Software Security Study among German Developers, Product Owners, and Managers

Online news portals report almost daily on security incidents in all kinds of software products in finance, health, and engineering. Moreover, multiple security reports conclude that there is a growing number of security vulnerabilities, attacks, and incidents. This raises the question of the extent to which companies address software security while developing and operating their products. This paper reports on the results of an extensive study among developers, product owners, and managers in Germany. Our results show that ensuring security is a multi-faceted challenge for German companies, involving low awareness, inaccurate self-assessment, and a lack of competence on the topic of secure software development among all stakeholders. Thus, there is an urgent need to improve the current situation.
Covid-19 Face Mask Detection Using Deep Learning Techniques

Covid-19 Novel Coronavirus is the most hazardous virus, and its impact affects every countries economy. It affected and killed four million+ people around the world. Still, we didn’t find a medicine to cure this COVID. Even though we can’t cure it, we can reduce the spreading. So, I motivated myself to reduce these covid deaths and affections. Then I decided to do a Face Mask Detection system which is the foremost reason for this pandemic. The scope of our present investigation on Face Mask Detection can be performed around the entire campus present anywhere in the world. The main motive is to detect people's face masks and to reduce this covid. So, it can be performed anywhere in the world. My methodology is to collect the dataset efficiently. Then to detect the Face Mask by processing the dataset through deep learning techniques. I conclude that I had come up with a system that detects people's Face Mask. And I conclude that this system detects people's face mask with 95% accuracy. And I believe that Covid may reduce by this system.
Feature Selection and Feature Extraction-Aided Classification Approaches for Disease Diagnosis

In this paper, the application of machine learning approach in the construction of disease diagnosis system is introduced. So as to introduce machine learning approach into clinical medicine, and the performance of machine learning medical diagnosis model with different feature extraction or selection approaches are studied. The respective advantages of feature selection and feature extraction are employed to eliminate redundant information. Firstly, a feature extraction algorithm based on partial least squares is designed and proposed, and compared with the typical feature extraction approach principal component analysis. Then partial least square approach and recursive feature elimination are used to analyze the correlation of the original variable set, so as to analyze the possibility of the disease caused by the original disease variables collected. Finally, the proposed approach is verified on a cervical cancer data. Experimental results indicate that the proposed approach can achieve better feature extraction effect for cervical cancer diagnosis, which can improve the diagnosis accuracy on the premise of eliminating redundant information. In the aspect of correlation analysis of pathogenic factors, experimental results show that recursive feature elimination has better effect.
Developing Cost-effective Cybersecurity Management System for Academic Institutions in Saudi Arabia

Technology in the recent years has dramatically invaded the education system and profoundly changed the classroom landscape, especially in modern universities. Internet of Things (IoT) devices, such as automated attendance tracker, interactive whiteboards, etc., are significantly utilized to create a positive and engaging classroom atmosphere. Unfortunately, some emerging universities deploy these smart tools before establishing an effective cybersecurity management system to protect critical characteristics of information, i.e., confidentiality, integrity, and availability. The cybersecurity management system in academic institutions is the most-targeted system for cyberattacks, which is growing rapidly and hits businesses worldwide every day. To shed lights on the impact of this critical problem, we introduce a cybersecurity management system designed specifically for academic institutions based on Saudi Arabia. The paper also looks closely at the best practices and the adoption of international information security management standards deployed in worldwide academic institutions.
Neural Machine Translation of Low Resource Languages: Application to Transcriptions of Tunisian Dialect

With the evolution of speech technologies, the need to understand and process the poorly spoken language has gradually become a necessity. However, the lack of resources is the main computational processing challenge. We present, in this paper, an effort to create a Neural Machine Translation (NMT) model in order to translate the spoken language in Tunisia: The Tunisian Dialect (TD) into the Arabic Standard Language (MSA). Indeed, NMT tasks require an enormous amount of training data which represents a problematic for low resourced languages like TD. For this, two contributions will be presented in this paper, the first consists of the creating of a parallel corpus TD-MSA. Then, by exploiting the resulting corpus, we proposed a configuration of a neural translation model that achieved a BLEU score of 67.56%.
A Multi-organ Point Cloud Registration Algorithm for Abdominal CT Registration

Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning. It is also a challenging step because of the heterogeneous content of the human abdomen which implies complex deformations. In this work, we focus on accurately registering a subset of organs of interest. We register organ surface point clouds, as may typically be extracted from an automatic segmentation pipeline, by expanding the Bayesian Coherent Point Drift algorithm (BCPD). We introduce MO-BCPD, a multi-organ version of the BCPD algorithm which explicitly models three important aspects of this task: organ individual elastic properties, inter-organ motion coherence and segmentation inaccuracy. This model also provides an interpolation framework to estimate the deformation of the entire volume. We demonstrate the efficiency of our method by registering different patients from the LITS challenge dataset. The target registration error on anatomical landmarks is almost twice as small for MO-BCPD compared to standard BCPD while imposing the same constraints on individual organs deformation.
Improving Economic Security of Industry in Conditions of Informatization and Digitalization of Society

Peculiarities of economic security of industry lie in the separation of this process within the framework of differentiated factors that make it possible to provide this industry. Identified features of the economic safety of industry include maintaining the functional state of production industries, maintaining the steady growth of the industrial industry, generating demand and supply for industrial products, regulating processes at industrial enterprises, and eliminating the main problems of transforming industrial production. The risk component considered in the study identifies key issues in the field of transforming the conditions of informatization and digitalization of the public environment. Risks reflecting information conditions are aimed at the emergence of technological and competitive problems of economic safety of industry. Risks of the conditions of digitalization of the company are financial and operational in nature. Their minimization is aimed at identifying activities that are of the nature of financial support for the industrial industry. Measures to improve the economic safety of industry include a set of proposals for the development of conditions for informatization and digitalization of society. The author's measures to improve the economic safety of industry include: eliminating the poor quality of the work of institutions, effective mechanisms of interaction between IT, financial, and industrial companies, formation of state programs of information and digital modernization of industry, moving away from the concept of “innovation industry” to “information industry” and “digital industry”, creating pools of enterprises of technological and industrial industries, and differentiating industrial products.
Deep Residual Network-Based Sentiment Analysis of Amazon Cell Phone Reviews

Sentiment analysis (SA) is a progressive research area in the process of text mining. SA or the opinion mining (OM) is the process of examining people’s opinions, emotions, and attitudes toward the entity. The entity represents the events, individuals, and topics. This paper focuses on designing a novel classifier approach for the sentimental classification based on the Amazon cell phone reviews. Initially, the input cell phone review is preprocessed by employing the stop word removal and stemming technique. In the feature extraction phase, the all-caps, emoticon, numerical words, hash tags, SentiWordNet, elongated words, punctuation marks, and negation features are extracted in an efficient way from the preprocessed output. Moreover, the sentimental classification performance is carried out using the developed deep residual network (DRN) classifier approach. Meanwhile, the efficiency of the developed DRN technique is evaluated based on the performance metrics including accuracy, sensitivity, and specificity. The proposed DRN approach obtained high classification performance with respect to the maximum accuracy of 0.953, higher sensitivity of 0.944, and maximal specificity of 0.944, respectively.
Implementing the University’s Sustainable Development Goals in the Context of Regional Economic Growth

The paper aims to identify the strongest links between the impact of regional economic growth and universities according to the UN Sustainable Development Agenda. The study analyzes the basic normative documents related to sustainable development, the goals and main objectives of sustainable development in the context of the development potential of universities, the state of innovation development of regions (including the Republic of Bashkortostan), as well as sustainable development programs and the results of their implementation in the leading universities of the world and Russia. In addition to general scientific methods of analysis and synthesis, the authors use a comparative method, the method of identifying cause-effect relationships, and systemic, process, and dynamic approaches. The study allowed the authors to obtain several definite conclusions and practical recommendations on the need to build the UN Sustainable Development Goals into the development of universities. The main research proposals are related to the synchronization of the university strategy in terms of research, education, personnel, youth, campus, information, and other policies with the objectives of sustainable development as the main element of strengthening the position of the university and the economic growth of the region where the university is located. The study provides basic recommendations for incorporating sustainability policy into the strategic map of university development directions.
Rotating Target Detection for Remote Sensing Images Based on Dense Attention

With the development of remote sensing imaging technology, which makes automatic interpretation of remote sensing images possible. Although object detection algorithm has achieved excellent performance in natural scenes, there are many inadaptations when it is directly applied to remote sensing scenes. Therefore, in order to address the above problems, we propose a lot of common modules of rotating object detection for remote sensing images. More specifically, including the label format conversion method, an attention module to strengthen target features and the modification of YOLOv5 head for densely rotating target detection. Finally, taking YOLOv5 as an example to verify the validity of the methods. The experimental results show that the detection accuracy of the improved YOLOV5 algorithm is 60.91%. Compared with SSD and Faster R-CNN, the detection accuracy of YOLOV5 algorithm is improved by 7.98% and 4.48%, respectively.