Reviewerly/exHarmony huggingface dataset:
Topography measurement of glass disk substrates with sub-nanometer resolution

With their high capacity and low cost, magnetic disks remain competitive for digital data storage. The low flying heights of the read/write head in the single nm-range place stringent requirements on the disk substrate topography. Interferometric surface metrology can provide the required topography data. For the measurement of state-of-the-art disk substrates with Angstrom-level waviness, the interferometer noise needs to be extremely low. A robust and cost-effective approach uses an LED as a light source providing temporally and spatially low-coherence illumination. The low temporal coherence leads to axially localized fringes, and clean single-surface fringes and topography maps are obtained even for transparent substrates. The low spatial coherence suppresses coherent noise, or speckle noise, in the fringe intensities and topography maps. Coherent noise cannot be determined by a simple repeatability measurement, since it is stable over some time for a given disk alignment. It does not appear in difference maps, but nevertheless is present in all acquired maps. An upper limit of low-level coherent noise is determined by looking at speckle decorrelation with increasing tilt of the test surface. In this presentation, the coherence issues are discussed together with the characterization of coherent noise and waviness filtering. Disk measurement examples are shown where the data were acquired with OptoFlat, an LED-based interferometer newly developed for the measurement of flat surfaces like disks and wafers.
Time Series Forecasting by Recommendation: An Empirical Analysis on Amazon Marketplace

Abstract This study proposes a forecasting methodology for univariate time series (TS) using a Recommender System (RS). The RS is built from a given TS as only input data and following an item-based Collaborative Filtering approach. A set of top- N values is recommended for this TS which represent the forecasts. The idea is to emulate RS elements (the users, items and ratings triple) from the TS. Two TS obtained from Italy’s Amazon webpage were used to evaluate this methodology and very promising performance results were obtained, even the difficult environment chosen to conduct forecasting (short length and unevenly spaced TS). This performance is dependent on the similarity measure used and suffers from the same problems that other RSs (e.g., cold-start). However, this approach does not require high computational power to perform and its intuitive conception allows for being deployed with any programming language.
LIFELONG LEARNING: MUSIC ADULT EDUCATION

During the last decades in the modern society becomes more popular the idea of lifelong education which means the possibility of adults to join the educational environments for further professional development. In the current article we disclose the essence of the lifelong education in common and in the field of artistic education. In addition, we specify the adulthood age-peculiarities according to the educational expectations and define the role of educator (teacher, tutor) in the context of musical studies for adults. The contemporary educational system is characterized by the openness and availability. It is represented through the threefold structure which includes formal, non-formal and informal education. These types of lifelong education provide the opportunity to practice music for all who want to achieve their skills and get new knowledge. Among the adult audience the most popular forms of musical lifelong education are both formal and non-formal. Some adults begin to practice music for their work and some people choose music as a hobby. But all of these students have the same goals – to realize their self-development ambitions and improve themselves. According to these goals students could choose the place where to get the music education: at musical colleges, universities, academies, musical schools, studious or courses, at the private lessons with a teacher or with online coacher etc. In consequence of musical studies, the student can develop his/her music and performing competence. It provides formation of students’ skills, knowledge and experience of musical activities. They learn how to do the everyday routines and prepare the interpretation of music pieces, how to organize and follow the self-guided work. In addition, during the musical classes students develop their aesthetic tastes and artistic tolerance, learning how to think about the artistic ideas in music pieces and how to build the interpretation plan according to composer’s view. Following the basic frameworks of andragogy (the science about adults’ education) the author highlights that music studies cannot be limited by the age of students. Contrary, the educational process obtains the new characteristics and qualities depend on who is a student – an adult or a child. But the author notices that teacher (tutor, educator) is a very important figure in the educational chain both with children and adults.
Comparative Analysis of Determination Methods of Glyphosate Degradation by Trichoderma asperellum Strain JK-28: A Multivariate Statistical Approach

Aims: This study aimed at demonstrating the ability of a mold species, isolated from a rice farm agricultural soil, chronically treated with glyphosate herbicide, to degrade glyphosate and to compare different evaluation methods of biodegradation.&#x0D; Study Design: A completely randomized design of a factorial experiment was used.&#x0D; Place and Duration of Study: The study was conducted at the Department of Microbiology, University of Calabar, Nigeria between February and April.&#x0D; Methodology: Molds were isolated from nine rice agricultural soil samples using potato dextrose agar. The molds were screened for glyphosate degradation using different media to determine their requirement for supplementation or not at all. A completely randomized design of a factorial experiment involving 3 factors at 3 levels each, with three replications, was adopted for glyphosate degradation studies. Degradation was monitored by dehydrogenase activity, carbon dioxide evolution, pH changes, fungal dry weight and residual glyphosate determination. A comparative evaluation of the different methods was made using multivariate statistics.&#x0D; Results: The mold, identified as Trichoderma asperellum strain JK-28, was incapable of using glyphosate as sole sources of phosphorus and nitrogen and so required nutrient supplementation. Largest significant correlation between dependent variables (P &lt; .001; r = - 779) occurred between dehydrogenase activity and fungal dry weight while the lowest was between residual glyphosate and fungal dry weight. Residual glyphosate and pH had negative correlations with other dependent variables. Principal component analysis extracted only one component with a cumulative variance of 70%. Multivariate analysis of variance (three-way MANOVA) of data, interpreted from the Pillai’s Trace test, was significant at P &lt;.001 for the main, two- and three-way interactions.&#x0D; Conclusion: Trichoderma asperellum strain JK-28 could degrade glyphosate using it as sole carbon source but with nutrient supplementation. Residual glyphosate determination is recommended as most reliable method for evaluating organic pollutant biodegradation.
Climate Study of Owerri and Comparative Analysis of Different Methods of Base Factor Computation

This study analyzed three decades of the temperature and rainfall data of Owerri, South Eastern Nigeria and in the process carried out a comparative analysis of using three different methods namely the mean, median and jackknife to derive the base data upon which the anomalies are computed with a view to ascertaining if any significant differences exist in the obtained results.And for rainfall which is seasonal, a further z-score method was employed to remove the effect of seasonality by dividing the anomalies with its standard deviation.Linear regression analysis was used to interpret the anomaly plots and the trend in climate from 1986 to 2015 in Owerri extracted.For the temperature analysis, the mean and median methods gave similar result which makes the mean method an acceptable method due to its simplicity, taking cognizance of the heavy computation in the other methods.Interestingly, for the rainfall analysis, the mean and jackknife methods gave the same result which also makes the mean method acceptable to avoid the heavy computations of the other two.ANOVA on the anomalies so derived showed no significant difference in their mean, thus underpinning the choice of the mean method.Using the outcome of the mean method therefore, the analyzed mean minimum temperature and mean maximum temperature indicate that the night time temperature has increased in Owerri within the period under consideration by 0.0186˚C each year and the daytime temperature increased by 0.01˚C per year respectively, thus indicating that a warming climate change has taken place in Owerri.Within the same period, the rate for rainfall on the other hand showed a decrease.In summary, the simple mean method of base computation suffices over the median and jackknife methods in climate studies for Owerri, a trend of warming climate has taken place in Owerri with increases in both daytime and night temperatures but with a reverse trend in rainfall which will require further inquiry.
Facial Emoji Recognition

Facial emoji recognition is a human-computer interaction system.In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields.Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera.The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions.Facial expressions are important in human communication and interactions.Also, they are used as an important tool in studies about behavior and in medical fields.Facial emoji recognizer provides a fast and practical approach for non-meddlesome emotion detection.The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm.Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output.
Why Pedagogy Needs Clarity in Definition of the Concept of Profession

The article discusses the pedagogical understanding of the concept of profession as extremely important for the pedagogical community in the conditions of the modern state of education. The authors believe that the current situation in pedagogy is characterized by the confusion of the concepts profession, specialty, qualification, position , imprecise identification of subjects of education (teacher, educator, tutor, pedagogical worker, etc.), which makes it difficult to develop professional and educational standards, ensure their continuity, define labor actions and competencies clearly. For this reason, it would seem, a private matter acquires social, theoretical and practical significance. The logic and content of the article determine the authors’ position in the consideration of the stated topic – they believe that analyzing the specifics of the teaching profession and related concepts requires taking into account the achievements and recommendations of sociology and psychology of professions, studying the possibilities of the pedagogical professiology. Consideration of the problem only from within the pedagogy itself limits the possibilities of disciplinary analysis, the activity of scientific reflection, the creation of holistic ideas on the teaching profession as a significant humanitarian activity in society, which initially carries humanitarianism as a leading quality. The authors critically consider the Professional standard of a teacher, in which they find contradictory positions, and come to a conclusion about the need for the clear scientific and pedagogical understanding of the concepts of profession, professionalism, professional, specialty, qualification, activity, labor functions, competencies.
PROTECTION OF SCIENTIFIC AND EDUCATIONAL RESOURCES IN INFORMATION AND LIBRARY SYSTEMS

Introduction. The authors of the article highlight the results of research on the development of methods and tools for ensuring the information security of scientific and educational resources in information library systems and corporate information networks. The analysis of the state of the problem and the existing approaches on this topic are given too. Materials and Methods. The approach justifies the need to protect scientific resources from unauthorized access and the use of fuzzy logic to solve the problem. As a model and solution method, a fuzzy model correspondences is proposed, which makes it possible to comprehensively solve the problem of identifying threats in the event of a situation, as well as provide recommendations on how to eliminate them. For the formation of a knowledge base, experts are involved who determine the functions of belonging to knowlege base “Situation — Threats — Actions to eliminate the threats”. Results. The research results are being implemented as part of the ARMAT++ information library system of the corporate network of electronic libraries of 63 Uzbekistan universities for the protection of scientific and educational information from unauthorized access. Testing of the methods, programs of the subsystem and the expert knowledge base are carried out on the basis of information and resource centers under the project “Virtual electronic library of the Tashkent University of Information Technologies named after Muhammad Al-Khorezmi and its five branches”. Discussion and Conclusions. The use of the apparatus of fuzzy logic for the formation of a knowledge base of the “Situation — Threats — Actions to eliminate the threats” type in information library systems significantly increases the degree of protection of valuable information resources from unauthorized access.
Quantitative Evaluation of Cyber-Attacks on a Hypothetical School Computer Network

This paper presents the attack tree modeling technique of quantifying cyber-attacks on a hypothetical school network system.Attack trees are constructed by decomposing the path in the network system where attacks are plausible.Considered for the network system are two possible network attack paths.One network path represents an attack through the Internet, and the other represents an attack through the Wireless Access Points (WAPs) in the school network.The probabilities of success of the events, that is, 1) the attack payoff, and 2) the commitment of the attacker to infiltrate the network are estimated for the leaf nodes.These are used to calculate the Returns on Attacks (ROAs) at the Root Nodes.For Phase I, the "As Is" network, the ROA values for both attack paths, are higher than 7 (8.00 and 9.35 respectively), which are high values and unacceptable operationally.In Phase II, countermeasures are implemented, and the two attack trees reevaluated.The probabilities of success of the events, the attack payoff and the commitment of the attacker are then re-estimated.Also, the Returns on Attacks (ROAs) for the Root Nodes are re-assessed after executing the countermeasures.For one attack tree, the ROA value of the Root Node was reduced to 4.83 from 8.0, while, for the other attack tree, the ROA value of the Root Node changed to 3.30 from 9.35.ROA values of 4.83 and 3.30 are acceptable as they fall within the medium value range.The efficacy of this method whereby, attack trees are deployed to mitigate computer network risks, as well as using it to assess the vulnerability of computer networks is quantitatively substantiated.
DeepFault: Fault Localization for Deep Neural Networks

Deep Neural Networks (DNNs) are increasingly deployed in safety-critical applications including autonomous vehicles and medical diagnostics. To reduce the residual risk for unexpected DNN behaviour and provide evidence for their trustworthy operation, DNNs should be thoroughly tested. The DeepFault whitebox DNN testing approach presented in our paper addresses this challenge by employing suspiciousness measures inspired by fault localization to establish the hit spectrum of neurons and identify suspicious neurons whose weights have not been calibrated correctly and thus are considered responsible for inadequate DNN performance. DeepFault also uses a suspiciousness-guided algorithm to synthesize new inputs, from correctly classified inputs, that increase the activation values of suspicious neurons. Our empirical evaluation on several DNN instances trained on MNIST and CIFAR-10 datasets shows that DeepFault is effective in identifying suspicious neurons. Also, the inputs synthesized by DeepFault closely resemble the original inputs, exercise the identified suspicious neurons and are highly adversarial.
On the Ability of the Modern University to Meet the Challenges of Sustainable Development

In the article technologies of knowledge transfer at different stages of social development are discussed. The priority and effectiveness of a particular technology of knowledge transfer is determined by the conditions and the state of the external environment. The technology of knowledge transfer is relevant in the conditions of post-industrial development, which is characterized by adaptability and provides a high speed of learning appropriated to the trends of the development of the external environment.
Modeling Semantic Compositionality with Sememe Knowledge

Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models. In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment. Furthermore, we make the first attempt to incorporate sememe knowledge into SC models, and employ the sememeincorporated models in learning representations of multiword expressions, a typical task of SC. In experiments, we implement our models by incorporating knowledge from a famous sememe knowledge base HowNet and perform both intrinsic and extrinsic evaluations. Experimental results show that our models achieve significant performance boost as compared to the baseline methods without considering sememe knowledge. We further conduct quantitative analysis and case studies to demonstrate the effectiveness of applying sememe knowledge in modeling SC. All the code and data of this paper can be obtained on https://github.com/thunlp/Sememe-SC.
Bug Severity Prediction Using a Hierarchical One-vs.-Remainder Approach

Assigning severity level to reported bugs is a critical part of software maintenance to ensure an efficient resolution process. In many bug trackers, e.g. Bugzilla, this is a time consuming process, because bug reporters must manually assign one of seven severity levels to each bug. In addition, some bug types may be reported more often than others, leading to a disproportionate distribution of severity labels. Machine learning techniques can be used to predict the label of a newly reported bug automatically. However, learning from imbalanced data in a multi-class task remains one of the major difficulties for machine learning classifiers. In this paper, we propose a hierarchical classification approach that exploits class imbalance in the training data, to reduce classification bias. Specifically, we designed a classification tree that consists of multiple binary classifiers organised hierarchically, such that instances from the most dominant class are trained against the remaining classes but are not used for training the next level of the classification tree. We used FastText classifier to test and compare between the hierarchical and standard classification approaches. Based on 93,051 bug reports from 38 Eclipse open-source products, the hierarchical approach was shown to perform relatively well with $$65\%$$ Micro F-Score and $$45\%$$ Macro F-Score.
An Effective Track Designing Approach for a Mobile Robot

Advancements of technology in day to day life demands upgradation in the existing soft computing approaches, for enhancing the accuracy. So, the existing particle swarm optimization (PSO) has been upgraded in this article and the new approach is adaptive particle swarm optimization (APSO). Designing an effective track which is shorter in length, takes less travel time, computation time, smooth, feasible and has zero collision risk with obstacles is always a crucial issue. To solve these issues APSO approach has been adopted in this work. A static environment has been implemented in this article for conducting the simulation study. Fifteen numbers of obstacles have been taken into consideration for designing the environment. A comparability study has been stuck between PSO and APSO to recognize the fittest approach for track design (less track size and travel time) with the shortest computation time. The APSO approach is identified as the best suited track designing tool for mobile robots.
Collaborative Evidential Clustering

Different companies may not be allowed to treat data together given restrictions of security, privacy or other technical reasons. In order to make better use of information from different sources, clustering algorithms based on collaboration mechanisms have been widely used. We propose the concept of collaborative evidential clustering under the framework of evidence theory. The key point is to establish collaboration among the credal partition matrices of each data site to meet the data confidentiality requirements. Considering the problems of excessive information interaction and insufficient information interaction, we design single-step and multi-step collaborative evidential clustering algorithms. Our algorithms were validated on real data sets.
STRUCTURE TOPOLOGICAL MODEL OF SEWAGE SYSTEMS SETTLEMENT AND ITS FUNCTIONAL DESCRIPTION

A topological model of the structure of the technical system of sewage settlement, which is displayed using graphs, is proposed. The functional description of the settlement technical system is verbally executed and reflects a multilevel hierarchy of sewer system functions. The proposed topological model of a technical system and its functional description allow the analysis of the real sewerage system of a settlement and modeling to solve its modernization problems and determine the direction of its prospective development, taking into account the many internal and external factors and relations with the surrounding knowledge space and technology complexes. The factors influencing the improvement of all complexes of the settlement sewerage system, as well as the choice of technological solutions at the stage of architectural and construction design of these technical systems are described in detail.