Reviewerly/exHarmony huggingface dataset:
Breast Cancer Classification: Features Investigation Using Machine Learning Approaches

Breast cancer is the second most common cancer after lung cancer and one of the main causes of death worldwide. Women have a higher risk of breast cancer as compared to men. Thus, one of the early diagnosis with an accurate and reliable system is critical in breast cancer treatment. Machine learning techniques are well known and popular among researchers, especially for classification and prediction. An investigation was conducted to evaluate the performance of breast cancer classification for malignant tumors and benign tumors using various machine learning techniques, namely k-Nearest Neighbors (k-NN), Random Forest, and Support Vector Machine (SVM) and ensemble techniques to compute the prediction of the breast cancer survival by implementing 10-fold cross validation. This study used a dataset obtained from Wisconsin Diagnostic Breast Cancer (WDBC) with 23 selected features measured from 569 patients, from which 212 patients have malignant tumors and 357 patients have benign tumors. The analysis was performed to investigate the feature of the tumors based on its mean, standard error, and worst. Each feature has ten properties which are radius, texture, perimeter, area, smoothness, compactness, concavity, concave, symmetry and fractal dimensions. The selection of features was considered a significant influence to the breast cancer. The analysis is compared and evaluated with thirty features to determine the features used for breast cancer classification. The result shown AdaBoost has obtained the highest accuracy for thirty features at 98.95%, ten features of mean at 98.07%, and ten features of worst at 98.77% with a lowest error rate. Additionally, the proposed methods are classified using 2-fold, 3-fold, and 5-fold cross validation to meet the best accuracy rate. Comparison results between all methods show that AdaBoost ensemble methods gave the highest accuracy at 98.77% for 10-fold cross validation, while 2-fold and 3-fold cross validation at 98.41% and 98.24%, respectively. Nevertheless, the result with 5-fold cross validation shows SVM produced the best accuracy rate at 98.60% with the lowest error rate.
Inclusive Education as a Tool For Implementing the Sustainable Development Goals on the Basis of Humanization of Society

The technogenic civilization, despite significant scientific and technical achievements, unfortunately, continues to face problems that entail irreparable consequences. Human activity based on the principle of immeasurable consumption produces an increase in inequality, poverty, hunger, diseases, armed conflicts, and global environmental changes. Weapons of mass destruction pose a global socio-environmental threat. All these trends indicate the need to move to a new level of progress based on sustainability, which in turn requires the formation of a new personality focused on the system of environmental values, and not on the values of the consumer society. Only a society consisting of people with a new worldview will be able to develop sustainably. In this regard, the author’s vision of inclusive education is formed in the study, which is understood as a system with a high integration ability, the role of which is reduced to creating a sustainable society with rational moral foundations. A doctrinal model of the implementation of the Sustainable Development Goals is proposed, the core of which is the humanization of society, contributing to moral, economic, social, cultural changes and influencing the life of society, while becoming the driving force of the economic, social and environmental aspects of sustainable development. The author’s approach allows us to expand the scientific vision of the essence and content of inclusive education, to determine the role of humanization in ensuring the implementation of the Sustainable Development Goals. The conceptual provisions of the article can be used as the basic foundations of a sustainable development strategy.
Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm

Abstract An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of subtasks that can serve as the stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial Multiobjective Evolutionary Algorithm (CMOEA), which allows all combinations of subtasks to be explored simultaneously. We compare CMOEA against three algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II, NSGA-III, and ε-Lexicase Selection. The algorithms are tested on a function-optimization problem with two subtasks, a simulated multimodal robot locomotion problem with six subtasks, and a simulated robot maze-navigation problem where a hundred random mazes are treated as subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. As a separate contribution, we show that adding a linear combination over all objectives can improve the ability of the control algorithms to solve these multimodal problems. Lastly, we show that CMOEA can leverage auxiliary objectives more effectively than the controls on the multimodal locomotion task. In general, our experiments suggest that CMOEA is a promising algorithm for solving multimodal problems.
Entanglement and scattering in quantum electrodynamics: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>S</mml:mi></mml:math> matrix information from an entangled spectator particle

We consider a general quantum field relativistic scattering involving two half-spin fermions, $A$ and $B$, which are initially entangled with another fermion $C$ that does not participate in the scattering dynamics. We construct general expressions for the reduced spin matrices for the out-state considering a general tripartite spin-entangled state. In particular we study an inelastic QED process at tree-level, namely ${e}^{\ensuremath{-}}{e}^{+}\ensuremath{\rightarrow}{\ensuremath{\mu}}^{\ensuremath{-}}{\ensuremath{\mu}}^{+}$ and a half-spin fermion $C$ as a spectator particle which can be entangled to the $AB$ system in the following ways: W state, GHZ state, $|{\mathrm{A}}^{\ensuremath{\alpha}}⟩\ensuremath{\bigotimes}|{\mathrm{\ensuremath{\Psi}}}^{\ifmmode\pm\else\textpm\fi{}}{⟩}_{\mathrm{BC}}$, and $|{\mathrm{A}}^{\ensuremath{\alpha}}⟩\ensuremath{\bigotimes}|{\mathrm{\ensuremath{\Phi}}}^{\ifmmode\pm\else\textpm\fi{}}{⟩}_{\mathrm{BC}}$, where ${|{\mathrm{\ensuremath{\Psi}}}^{\ifmmode\pm\else\textpm\fi{}}⟩,|{\mathrm{\ensuremath{\Phi}}}^{\ifmmode\pm\else\textpm\fi{}}⟩}$ are the Bell basis states and $|{\mathrm{A}}^{\ensuremath{\alpha}}⟩$ is a spin superposition state of system $A$. We calculate the von Neumann entropy variation before and after the scattering for the particle $C$ and show that spin measurements in $C$ contain numerical information about the total cross section of the process. We compare the initial states W and GHZ as well as study the role played by the parameter $\ensuremath{\alpha}$ in the evaluation of the entropy variations and the cross section encoded in the spectator particle.
Towards a universal continuous knowledge base

In artificial intelligence (AI), knowledge is the information required by an intelligent system to accomplish tasks. While traditional knowledge bases use discrete, symbolic representations, detecting knowledge encoded in the continuous representations learned from data has received increasing attention recently. In this work, we propose a method for building a continuous knowledge base (CKB) that can store knowledge imported from multiple, diverse neural networks. The key idea of our approach is to define an interface for each neural network and cast knowledge transferring as a function simulation problem. Experiments on text classification show promising results: the CKB imports knowledge from a single model and then exports the knowledge to a new model, achieving comparable performance with the original model. More interesting, we import the knowledge from multiple models to the knowledge base, from which the fused knowledge is exported back to a single model, achieving a higher accuracy than the original model. With the CKB, it is also easy to achieve knowledge distillation and transfer learning. Our work opens the door to building a universal continuous knowledge base to collect, store, and organize all continuous knowledge encoded in various neural networks trained for different AI tasks.
Detecting security vulnerabilities with static analysis – A case study

Abstract Many security vulnerabilities can be detected by static analysis. This paper is a case study and a performance comparison of four open-source static analysis tools and plugins (PMD, SpotBugs, Find Security Bugs, and SonarQube) on Java source code. Experiments have been conducted on the widely used Juliet Test Suite with respect to six selected weaknesses from the official Top 25 list of Common Weakness Enumeration. In this study, analysis metrics have been calculated for helping Java developers decide which tools can be used when checking their programs for security vulnerabilities. It turned out that particular weaknesses are best detected with particular tools.
Smoky Vehicle Detection Based on Improved Vision Transformer

research-article Smoky Vehicle Detection Based on Improved Vision Transformer Share on Authors: Li Yuan School of Automation, Southeast University, China and Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, China School of Automation, Southeast University, China and Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, ChinaView Profile , Shuzhen Tong School of Automation, Southeast University, China and Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, China School of Automation, Southeast University, China and Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, ChinaView Profile , Xiaobo Lu School of Automation, Southeast University, China and Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, China School of Automation, Southeast University, China and Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, ChinaView Profile Authors Info & Claims CSAE 2021: The 5th International Conference on Computer Science and Application EngineeringOctober 2021 Article No.: 97Pages 1–5https://doi.org/10.1145/3487075.3487172Online:07 December 2021Publication History 0citation27DownloadsMetricsTotal Citations0Total Downloads27Last 12 Months27Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Trend analyses of regional time series of temperatures and rainfall of the Tapi basin

Climate change information at the scale of basin is vital for planning, development and use of water. The Tapi basin is climatically responsive. Hydrological response of a basin is based mainly on rainfall and temperature. Variations in climate at regional scales impacts fundamental features of our life. Thus, in the present work, trend analyses of regional time series (1971-2004) of minimum, mean, maximum temperatures and rainfallis performed for monthly, annual and seasonal scales for the Tapi basin. Correlogram is utilized for evaluation of dependence of data. Mann-Kendall test and Mann-Kendall test with block bootstrapping are applied for the evaluation of trend significance. Sen’s slope test is applied for the evaluation of trend magnitude. Sequential Mann-Kendall test is applied for assessment of beginning and end of the trend. Statistically significant positive trends are detected in regional annual and winter Tmean time series with their beginning in years 1974 and 1972, respectively.
A DICOM Standard Pipeline for Microscope Imaging Modalities

In the nineties, the adoption of the DICOM standard format in radiology departments brought numerous advantages to clinical practice. The setup of PACS with standard communication processes and data formats allowed the creation of central repositories, fast retrieval of images, visualization of images acquired with several modalities, and simultaneous access at distributed places. Nowadays, microscopy imagining faces the same normalization challenge with the proliferation of equipment that stores data in a proprietary format and provides dedicated visualization software. This reality severely limits the implementation of vendor-neutral archives with common visualization processes, conditioning the research work and its integration in clinical environments. This paper proposed a pipeline for the integration of multiple microscopy imaging modalities into the PACS-DICOM universe, including the numerous metadata elements. A proof-of-concept system was developed, for validation purposes, and integrated with the Dicoogle open-source PACS, providing image storage, metadata indexing and visualization.
Towards transparent and trustworthy cloud carbon accounting

Climate Change is arguably the biggest challenge that humanity faces today. Multiple trends such as the exponential explosion of data transfer, the emergence and popularity of power intensive workloads such as AI, and the flattening of Moore's law contribute to a rising concern over the increasing carbon footprint cost of digital computation. Any effective strategy to reduce the energy consumption and associated carbon footprint of computations must begin with an accurate and transparent quantification method. However, while most businesses today run a significant portion of their workloads on third party cloud environments, transparent carbon quantification of tenant workloads in cloud environments is lacking. This regretful situation inhibits reliable reporting of Scope 3 Green House Gas (GHG) by cloud users, meaningful comparison of cloud carbon efficiencies, and measurable reduction strategies. In this extended abstract we explain the unique challenges that arise in multi-tenant cloud environments, and propose and discuss an approach, consistent with the GHG Protocol, for cloud carbon footprint quantification. The quantification is a first step towards sustainable cloud environments, that employ dynamic controllers to quantify and reduce the carbon footprint at every layer of the cloud stack.
Benefits of Organizing Remote Education in the Design of a University's Unified Information and Analytical Space Using Artificial Intelligence

The study aims to substantiate the importance of using a UIAS of the university for organizing remote education. The creation of a UIAS allows organizing accounting, storage and accessing the data collected in the course of the work of the structural divisions of the university online. It helps consolidating the date which is relevant and important for generating reports and analytical documentation of divisions when organizing remote education.
Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis

Sentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Unlike the methods that lexical encode or add information to the corpus, this method adds presentation of raw data based on the expert's knowledge in the ontology. Once the data has a rich knowledge of the topic, the efficiency of the machine learning algorithms is significantly enhanced. Thus, this method is appliable to embed knowledge in datasets in other languages. The test results show that deep learning methods achieved considerably higher accuracy when trained with the KPRO method's dataset than when trained with datasets not processed by this method. Therefore, this method is a novel approach to improve the accuracy of deep learning algorithms and increase the reliability of new datasets, thus making them ready for mining.
Digital instruments in educational activities

The objective of this article is analysis of the experience of implementing digital tools in educational activities and identifying the possibilities of modern digital tools in educational activities. Among the participants are students of higher educational institutions. The results allowed determining that Digital tools introduced and used in educational activities contribute to the training of a highly qualified specialist in demand in the labor market.
Independent assessment of the several digital skills of secondary school students: Approaches and results

The article presents and comprehends the results of an independent assessment of several digital skills of students of general education organizations. The problem of assessing digital skills is relevant the PISA international research on the quality of education, which includes the study of this competence various aspects. Digital literacy is a strategic education system direction of the Russian Federation, the Perm Region, and the city of Perm. The study provides an assessment of the digital skills of eighth graders in terms of the computational thinking and safe behaviorin a digital environment (sample of schoolchildren in the city of Perm). The research is based on a competence-based approach, consideration of digital skills in the context of assessing functional literacy, the use of big data for making management decisions in education, approaches to implementing an independent assessment in education. The most scarce trainees’ skills in the aspects of digital security and computational thinking are highlighted: critical attitude to information on the Internet; selection of Internet sources for a practical task; assessment of the capabilities of digital tools for solving practical problems. The study shows the need to improve the training of students in the use of digital technologies for solving practical problems. The authors formulate recommendations for the training of teachers of general education organizations teaching various subjects, as well as students studying in pedagogical areas of training. The revealed deficient skills of students show the need to improve the methods of teaching informatics. The authors emphasize the need to comprehend the problem of independent assessment of digital skills as an interdisciplinary problem affecting general pedagogy, teaching methods of informatics, pedagogy of vocational education, meta-pedagogy and metamethodology, big data analysis for making management decisions in education.
Performance Evaluation of Blockchain Based Agricultural Input Voucher System

Large portion of rural small-scale farmers in Tanzania can’t afford to buy quality agricultural inputs (fertilizers and seeds), therefore the government of Tanzania provides these farmers with paper-based subsidy vouchers which allow them to buy the agricultural inputs at only half price. However, challenges like misuse of the vouchers by some government officials (stealing and favoritism in vouchers’ allocation) and lack of trust (difficulty of verifying vouchers’ authenticity) have resulted into low crop productivity for the farmers and loss of government funds. In our previous work, we developed agricultural input voucher system which stores and allocates digital subsidy vouchers to farmers in the Hyperledger Fabric blockchain network and we evaluated its performance based on transaction throughput and latency metrics which are important to farmers, who access the system through Short Message Service (SMS) in their mobile phones. The blockchain network’s features such as hashing and digital signature prevent misuse and ensure trust of the digital vouchers. In this paper, we evaluate performance of the developed blockchain network based on resources (CPU and memory) consumption metrics which are important in planning resources during system deployment. The evaluation findings reveal that, Raft consensus protocol consumes resources more efficiently than Kafka consensus protocol.
Partial stability of induced entanglement in an off-resonance qutrit-cavity system with nonlinear coupling and intrinsic decoherence

In this article, we introduce an analytical description for the nonlinear off-resonance qutrit-cavity system through intensity-dependent nonlinear coupling under intrinsic decoherence. When the cavity is initially in coherent and even coherent states. The dynamics of the negativity entanglement, in the off-resonance qutrit-cavity system, is investigated under the physical parameters of the qutrit-cavity detuning, the intrinsic decoherence as well as the initial cavity coherent states. It is found that the ability of the off-resonance qutrit-cavity interaction to generate the entanglement, depends on the physical parameters. The high nonclassicality of the even coherent state can enhance the generated entanglement and its stability. For the intrinsic decoherence, the amount and the stability of the generated qutrit-cavity entanglement can be controlled by the coherent field intensity of the initial cavity states.
New Discrete Crow Search Algorithm for Class Association Rule Mining

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.