Reviewerly/exHarmony huggingface dataset:
FOOR: Be Careful for Outlier-Score Outliers When Using Unsupervised Outlier Ensembles

Outlier detection is a very important tool in analyzing patterns and detecting unexpected events in social systems. However, the process of outlier detection could be fraught with uncertainty, with difficulties in determining the veracity of an object’s outlier score. We propose a framework for outlier-score outlier removal (FOOR). FOOR is a selection method, which aims to remove inaccurate outlier scores prior to data processing by ensemble techniques, to improve the accuracy of all ensembles. FOOR has rigorously tested with 30 real-world datasets and seven state-of-the-art ensembles over 25 different base detectors. Simulated experiments showed that FOOR significantly improves the existing techniques, with an average (AVG) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> 0.05 AUC (from 0.81 to 0.86 AUC). Thus, we recommend FOOR as the new standard for outlier-score preprocessing before ensembles.
Enhancement of DWT-SVD digital image watermarking against noise attack using time–frequency representation

Information security has been defined as one of the most critical issues in the information era, as it is utilized to protect confidential information during transfers in real-world applications. In the case of image encryption, a variety of information security approaches were used. Domain spatial and domain frequency are two domains in which such techniques can be classified. This study uses a combination of the singular value de-composition (SVD) and discrete wavelet transformation (DWT) to construct an encryption method based on a traditional watermarking system. Compared with other traditional methods, the proposed DWT-SVD approach has excellent robustness, and it has been strengthened for having high degree of the robustness against the additive white Gaussian noise (AWGN) attacks by utilizing a de-noising strategy based upon S-transform approach. Compared with DWT algorithm denoising approach, the results reveal that the S-transform denoising algorithm that has been deployed in the present article has a robust protection towards the Gaussian noise attack for mean squared error (MSE) around 0.005 and peak signal to noise ratio (PSNR) around 24 dB.
Tractable depth-bounded approximations to <b>FDE</b> and its satellites

Abstract FDE, LP and $\mathbf {K_{3}}$ are closely related to each other and admit of an intuitive informational interpretation. However, all these logics are co-NP complete, and so idealized models of how an agent can think. We address this issue by shifting to signed formulae, where the signs express imprecise values associated with two bipartitions of the corresponding set of standard values. We present proof systems whose operational rules are all linear and have only two structural branching rules that express a generalized Principle of Bivalence. Each of these systems leads to defining an infinite hierarchy of tractable approximations to the respective logic, in terms of the maximum number of allowed nested applications of the two branching rules. Further, each resulting hierarchy admits of an intuitive 5-valued non-deterministic semantics.
On Evolvability and Behavior Landscapes in Neuroevolutionary Divergent Search

Evolvability refers to the ability of an individual genotype (solution) to produce offspring with mutually diverse phenotypes. Recent research has demonstrated that divergent search methods, particularly novelty search, promote evolvability by implicitly creating selective pressure for it. The main objective of this paper is to provide a novel perspective on the relationship between neuroevolutionary divergent search and evolvability. In order to achieve this, several types of walks from the literature on fitness landscape analysis are first adapted to this context. Subsequently, the interplay between neuroevolutionary divergent search and evolvability under varying amounts of evolutionary pressure and under different diversity metrics is investigated. To this end, experiments are performed on Fetch Pick and Place, a robotic arm task. Moreover, the performed study in particular sheds light on the structure of the genotype-phenotype mapping (the behavior landscape). Finally, a novel definition of evolvability that takes into account the evolvability of offspring and is appropriate for use with discretized behavior spaces is proposed, together with a Markov-chain-based estimation method for it.
Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations

Existing transaction data is only recorded and stored as a sales transaction memorandum, so it has not been utilized optimally. The data is only stored and used as transaction history. The availability of a lot of data and having a pattern of sales transactions that are similar to MSME Cafe Over Limit will be utilized by using data mining science. This research uses the association rules method. Implementation of fp-growth to get item combinations. The purpose of this research is to make it easier for MSMEs to determine menu recommendations for customers. The fp-growth algorithm is used to process as many as 2038 transaction data with a minimum support value of 10%, while for a minimum confidence value of 50%. So that there are 3 rules, namely "if you order Mariam chocolate cheese milk then the customer will order Kopsus Overlimit", from this rule it will form a support value of 10.79%, using a confidence value of 54.19% and a lift ratio of 0.93. Furthermore "if you order Kopsus Overlimit then you will order tofu at grandma's house", from the rule it will produce a support value of 34.69%, with a specified confidence value of 59.76%, so the lift ratio value is 1.15. The last rule "if you order tofu at grandma's house, the customer orders Kopsus Overlimit", from the rule that occurs, the support value is 34.69%, with a confidence value of 66.7% and a lift ratio of 1.15. The results of the study found the two best rules, namely "if the customer orders over-limit Kopsus, he will order tofu at grandma's house" and "if he orders tofu at grandma's house, the customer orders over-limit Kopsus". Based on the results of the rules formed, it can be concluded that only two rules can be categorized as valid and can be used as a reference in food and beverage menu recommendations at MSME Cafe Over Limit. So the results of this study can be useful to be applied to MSMEs, especially in terms of menu recommendations.
Digitalization of Higher Education in 2021 – Challenges for University Students In Russia

Digital educational platforms emerged as an anticipated technological response to the need for the modernization of higher education. The digitalization of education was promoted at the state level as a strategy and an instrument to improve the quality and competitiveness of education. In 2020, the coronavirus pandemic accelerated the transition from the classroom to online-only, revealing the lack of purposeful online teaching methods, and insufficient digital competence among teachers.&#x0D; The authors analyzed publications by foreign and Russian researchers to highlight the common problems of higher education digitalization, including datafication, limited capabilities of digital educational platforms, and the changing role of the teachers in e-learning. The article focuses on students’ attitudes towards online education, and their voluntary or unwilling involvement in the digital educational environment, considering synchronous, asynchronous, and blended forms of learning before and during the pandemic.&#x0D; The achieved results suggest that such factors as the novelty and accessibility of online courses at Western universities initially served as a factor of attraction the students, inviting them to diversify their studies in their own creative way. However, increased stress and workload, various technical problems, tough teachers’ control on the one hand, and poor feedback, on the other, weakened the popularity of the digital educational environment. When asked to compare the pros and cons of online education, most students spoke in favour of maintaining the blended education format, as it allows for reducing the workload and – to some extent – enables the students to take control of their education trajectories.&#x0D; This work is intended for educators and researchers interested in the challenges caused by integrating digital technologies into traditional forms of education.
Protecting Your E-Commerce Business. Analysis on Cyber Security Threats

This paper aims to gather complete information needed for a retailer running an e-commerce website, with the intention of presenting some of the most common cyber security threats, such as malware, ransomware, SQL injection, and phishing, as well as ways to prevent them from happening and ways to manage the aftermath of a full-scale attack being carried out. Some best practices will be noted as a process that should always be considered when setting up an e-commerce business, and a risk management strategy will be outlined. An analysis will be performed on a data breach with one of the biggest number of victims in the last decade, which affected the Microsoft Exchange Servers.
Machine Predictive Maintenance by Using Support Vector Machines

Predictive Maintenance (PdM) is an adoptable worth strategy when we deal with the maintenance business, due to a necessity of minimizing stop time into a minimum and reduce expenses. Recently, the research of PdM is now begin in utilizing the artificial intelligence by using the machine data itself and sensors. Data collected then analyzed and modelled so that the decision can be made for the near and next future. One of the popular artificial intelligences in handling such classification problem is Support Vector Machines (SVM). The purpose of the study is to detect machine failure by using the SVM model. The study is using database approach from the model of Machine Learning. The data collection comes from the sensors installed on the machine itself, so that it can predict the failure of machine function. The study also to test the performance and seek for the best parameter value for building a detection model of machine predictive maintenance The result shows based on dataset AI4I 2020 Predictive Maintenance, SVM is able to detect machine failure with the accuracy of 80%.
Use of Mobile Applications for Foreign Language Learning Among University Students: Needs and Functions

Currently, mobile learning is an inseparable part of education since students use their mobile devices ubiquitously on a daily basis. This is also true for foreign language learning where smartphones and their applications are used for practicing all four language skills and language structures. The purpose of this article is to survey the needs of university students as far as the functions and content of a mobile app for foreign language learning are concerned. The methodology was based on an online questionnaire survey, which was conducted among 103 students of the University of Hradec Kralove, Czech Republic, in the period between 30 October 2020 and 24 January 2021. The findings reveal that the potential mobile applications for foreign language learning should be tailored to the language needs of students at the University of Hradec Králové; be targeted at their foreign language level between B1 and B2 of the Common European Reference Framework for Languages; be used at smartphones with Android operating system in particular; offer the possibility of repeating exercises and the combination of different teaching approaches; be free of charge and provide the possibility to choose a corresponding language level; be aimed at practicing specific language skills and knowledge; provide gamification features, offline version, online interaction, notifications; and serve as support to the face-to-face classes
Applicability of machine learning models for predicting soil organic carbon content and bulk density under different soil conditions

A reliable overview of the spatial distribution of soil properties is a straightforward approach in soil policies and decision-making.Soil organic carbon (SOC) content, SOC stock and bulk density (BD) directly affect soil quality and fertility.Therefore, an accurate assessment of these crucial soil parameters is required.To do this, we used machine learning algorithms (MLAs) including, multiple linear regression (MLR), random forest (RF), artifi cial neural network (ANN), and support vector machine (SVM) with the help of environmental covariates to predict SOC content, BD, and SOC stock.The study was conducted in two different areas, Látókép and Westsik (East Hungary), both experimental research fi elds but different from physio geographic points of view.Thirty topsoils (0-10 cm) samples were collected for each study area using conditioned Latin Hypercube Sampling strategy.Environmental covariates were extracted from a digital elevation model (DEM) and satellite images based on the representation of soil forming factors.We validated the results by randomly splitting the dataset into a train (two-third) and test (one-third) and calculated the root mean square error and R 2 .Our results showed that RF provided the most accurate spatial prediction with R 2 of about 80% for each soil property in both study areas.This study highlighted the importance of terrain attributes (including plan and profi le curvature, elevation and valley depth) and NDVI derived from satellite images in presenting a spatial distribution of selected soil properties in two different areas.We conclude that comparing these methods can help to determine the most accurate maps under diverse geographical conditions and heterogeneities at different scales, which can be used in precision soil quality management.
Application of machine learning methods for filling and updating nuclear knowledge bases

The paper deals with issues of designing and creating knowledge bases in the field of nuclear science and technology. The authors present the results of searching for and testing optimal classification and semantic annotation algorithms applied to the textual network content for the convenience of computer-aided filling and updating of scalable semantic repositories (knowledge bases) in the field of nuclear physics and nuclear power engineering and, in the future, for other subject areas, both in Russian and English. The proposed algorithms will provide a methodological and technological basis for creating problem-oriented knowledge bases as artificial intelligence systems, as well as prerequisites for the development of semantic technologies for acquiring new knowledge on the Internet without direct human participation. Testing of the studied machine learning algorithms is carried out by the cross-validation method using corpora of specialized texts. The novelty of the presented study lies in the application of the Pareto optimality principle for multi-criteria evaluation and ranking of the studied algorithms in the absence of a priori information about the comparative significance of the criteria. The project is implemented in accordance with the Semantic Web standards (RDF, OWL, SPARQL, etc.). There are no technological restrictions for integrating the created knowledge bases with third-party data repositories as well as metasearch, library, reference or information and question-answer systems. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure the scalability of data warehouses and network services. The created software is in the public domain and can be freely replicated.
K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters

The Special Region of Yogyakarta (DIY) has rivers that cross rural and urban areas that are still used by the community and industry. However, cases of river water pollution in DIY are a major issue in 2021. It is very important to classify rivers according to class so that further analysis and action can be carried out. This study conducted a grouping analysis of rivers in DIY based on water quality parameters such as Total Suspended Solid (TSS), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Phosphate, Fecal Coli, and Total Coliform. The grouping method uses the K-means algorithm. The data source is secondary data from the DIY Provincial Environment and Forestry Service. The data is in the form of 56 river samples observed in November 2020. The description of the data shows that the average of the 56 river water samples is 24.95 for TSS, 8.84 for DO, 4.33 for BOD5, 20.36 for COD, 0 .54 for Phosphate, 22.820 for Fecal Coli, and 59.210 for Total Coliform. The results of grouping with k=6 are the best compared to k = 2, 3, 4, 5, 7, and 8. The number of members in this grouping is n1 = 14, n2 = 1, n3 = 1, n4 = 5, n5 = 18, and n6 = 17. The cluster that has the highest average TSS, BOD, and COD values is the 3rd cluster (Rivers in Bantul and Sleman Regencies). The cluster that has the highest DO value is the 6th cluster (Rivers in Bantul Regency). The cluster that has the highest average Phosphate value is the 2nd cluster (Rivers in Bantul, Sleman, and Gunungkidul Regencies). The cluster that has the highest average Fecal Coli and Total Coliform values are the 4th cluster (Rivers in Bantul Regency, Yogyakarta City, and Sleman Regency).
The Effectiveness of Zepeto Game to Improve Students Speaking Skill

The purpose of this study was to determine the impact of Zepeto Game learning media on speaking skills in the second grade of MA Annida Al-Islamy Jakarta Barat. This study employed a quasi-experimental design with two classes as the sample. Furthermore, the research design of this study includes a pre-test, treatment, and post-test, with the XI MIPA 1 class acting as the experimental class and the XI MIPA 2 class acting as the control class. The researcher used 60 second-grade students as respondents to accomplish the goal of this study. They were then split into two groups: experimental and control. During the treatment process, the experimental group received Zepeto Game as learning media, while the control group received traditional instruction. According to the t-test calculation, to = 6,06 is greater than tt = 2,39, in the degree of significance 1% = 2,39, and in the degree of significance 5% = 1,67.
Education and Professional Training as Tools for the Formation of Human Capital

The subject of the research is approaches to the disclosure of the essential aspects of human capital in the conditions of the emerging new Russian model of economic development. The purpose of the work is to consider the features of the accumulation of human capital, manifested in the process of objective aspirations of the population of different age groups to receive education of a certain level and qualification, in the conditions of expanding opportunities of the transforming economy. The novelty of the research consists in the objective representation of the place occupied by human capital in the composition of human potential as a fundamental scientific category and its interrelations with intellectual capital and education as the basic sphere of its formation. The article establishes the reasons for the differences in views on the conceptual understanding of human capital, reveals the content of this structurally complex definition. To characterize the basic elements reflecting certain aspects of human capital, a number of domestic and foreign scientific publications are analyzed. When substantiating the importance and role of education and vocational training in the accumulation of human capital, dialectical and systematic approaches, methods of comparative and content analysis are used. The measures recommended for the adoption of vocational guidance of students, the creation of a system of motivation and a set of moral and material incentives for the teaching staff of higher education in order to bring the structure of personnel training in line with the dynamically changing demands of the labor market are reasoned. Conclusions are drawn about the need to develop an updated concept for the development of Russian higher education, the concentration of creative efforts of scientific and pedagogical workers, the entire professional community on the fundamental vectors of the formation and accumulation of human capital.
Inflating 2D convolution weights for efficient generation of 3D medical images

Background and Objective: The generation of three-dimensional (3D) medical images has great application potential since it takes into account the 3D anatomical structure. Two problems prevent effective training of a 3D medical generative model: (1) 3D medical images are expensive to acquire and annotate, resulting in an insufficient number of training images, and (2) a large number of parameters are involved in 3D convolution. Methods: We propose a novel GAN model called 3D Split&Shuffle-GAN. To address the 3D data scarcity issue, we first pre-train a two-dimensional (2D) GAN model using abundant image slices and inflate the 2D convolution weights to improve the initialization of the 3D GAN. Novel 3D network architectures are proposed for both the generator and discriminator of the GAN model to significantly reduce the number of parameters while maintaining the quality of image generation. Several weight inflation strategies and parameter-efficient 3D architectures are investigated. Results: Experiments on both heart (Stanford AIMI Coronary Calcium) and brain (Alzheimer’s Disease Neuroimaging Initiative) datasets show that our method leads to improved 3D image generation quality (14.7 improvements on Frchet inception distance) with significantly fewer parameters (only 48.5% of the baseline method). Conclusions: We built a parameter-efficient 3D medical image generation model. Due to the efficiency and effectiveness, it has the potential to generate high-quality 3D brain and heart images for real use cases.
Zero-watermark scheme for medical image protection based on style feature and ResNet

Zero-watermarking technology is of major interest within the field of medical image copyright protection because this embedding method does not cause loss of images. However, the features used to construct the zero-watermark in existing methods are destroyed after geometric attacks, particularly strong geometric attacks. This destruction causes the final extracted watermark image to lose its details and fail copyright protection. This study proposes a zero-watermark medical image protection scheme based on style features and a residual network to solve this concern. The style features extracted based on deep neural networks are high-order statistics of image details and abstract features and have good robustness against geometric attacks, such as rotation and scale. Simultaneously, this study proposes a zero-watermark verification method based on the residual network to replace the traditional XOR1 operation. The residual network can iteratively learn to extract the watermark image from the zero-watermark image and enhance the quality of the watermark image using the designed loss function. We experimentally demonstrated that the residual network outperformed in reconstructing watermark images. This scheme can resist common geometric and strong image attacks, performance is superior to that of similar zero-watermarking schemes.