Reviewerly/exHarmony huggingface dataset:
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization

Structured CNN designed using the prior information of problems potentially improves efficiency over conventional CNNs in various tasks in solving PDEs and inverse problems in signal processing. This paper introduces BNet2, a simplified Butterfly-Net and inline with the conventional CNN. Moreover, a Fourier transform initialization is proposed for both BNet2 and CNN with guaranteed approximation power to represent the Fourier transform operator. Experimentally, BNet2 and the Fourier transform initialization strategy are tested on various tasks, including approximating Fourier transform operator, end-to-end solvers of linear and nonlinear PDEs, and denoising and deblurring of 1D signals. On all tasks, under the same initialization, BNet2 achieves similar accuracy as CNN but has fewer parameters. And Fourier transform initialized BNet2 and CNN consistently improve the training and testing accuracy over the randomly initialized CNN.
FDDetector: A Tool for Deduplicating Features in Software Product Lines

Duplication is one of the model defects that affect software product lines during their evolution. Many approaches have been proposed to deal with duplication in code level while duplication in features hasn’t received big interest in literature. At the aim of reducing maintenance cost and improving product quality in an early stage of a product line, we have proposed in previous work a tool support based on a conceptual framework. The main objective of this tool called FDDetector is to detect and correct duplication in product line models. In this paper, we recall the motivation behind creating a solution for feature deduplication and we present progress done in the design and implementation of FDDetector.
A Students’ Concentration Evaluation Algorithm Based on Facial Attitude Recognition via Classroom Surveillance Video

To detect the students’ concentration state in classroom, a DS (Dempster–Shafer theory)-based evaluation algorithm is proposed by measuring the students’ Euler angles of their facial attitude. The detection of facial attitude angles can be implemented under the surveillance video with lower pixels. Therefore, compared with other methods for students’ concentration evaluation, the proposed algorithm can be applied directly in most classrooms by the support of existing monitoring equipment. By using DS theory to fuse the concentration state of each student, the curve of students’ overall concentration score changing with time can be obtained to describe the overall classroom concentration state. The design of the algorithm is proved to be feasible and effective under the dataset provided by computer front camera. The realization of the overall function effect of the algorithm is tested under the 35-person classroom video dataset. Compared with the average score from the questionnaire given by 20 reviewers, the accuracy of the proposed algorithm is about 85.3%.
The Structural-Semantic Features of Computer Terms in English

The Structural-Semantic Features of Computer Terms in EnglishThe article is devoted to the study of the structural and semantic characteristics of computer terms in English. The peculiarities of the word-formation process and the functioning of English computer terms are analysed. Strukturalne i semantyczne cechy terminów komputerowych w języku angielskimArtykuł poświęcony jest badaniu cech strukturalnych i semantycznych terminów komputerowych w języku angielskim. Przeanalizowano specyfikę procesu słowotwórstwa i funkcjonowania angielskich terminów komputerowych.
Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization

Averaging scheme has attracted extensive attention in deep learning as well as traditional machine learning. It achieves theoretically optimal convergence and also improves the empirical model performance. However, there is still a lack of sufficient convergence analysis for strongly convex optimization. Typically, the convergence about the last iterate of gradient descent methods, which is referred to as individual convergence, fails to attain its optimality due to the existence of logarithmic factor. In order to remove this factor, we first develop gradient descent averaging (GDA), which is a general projection-based dual averaging algorithm in the strongly convex setting. We further present primal-dual averaging for strongly convex cases (SC-PDA), where primal and dual averaging schemes are simultaneously utilized. We prove that GDA yields the optimal convergence rate in terms of output averaging, while SC-PDA derives the optimal individual convergence. Several experiments on SVMs and deep learning models validate the correctness of theoretical analysis and effectiveness of algorithms.
TRAINING ENGINEERING MSC STUDENTS FOR TEACHING AND RESEARCH ACTIVITIES

The paper discusses the organization features of training engineering MSc students for teaching and research activity. The authors have analyzed the historical and pedagogical aspects of the origin and development of master’s degree programs in Russia as one of the most important resources for providing the higher education system with teaching staff . The modern requirements of society for training university teaching staff in changing conditions and increasing their professional mobility are revealed. Training nonpedagogical MSc students for teaching and research activity at a university involves a pedagogical component in the teaching content structure, as exemplifi ed by “Fundamentals of Pedagogical Activity” or “Methods of Vocational Training” courses and teaching practice programs. Research conducted by the authors at the the Goryachkin Institute of Mechanical and Power Engineering at Russian Timiryazev State Agrarian University for three years, showed a high demand for lecturers with a master’s degree in modern technical universities, as well as high motivation of Msc students for teaching and research activity. The study involved 109 MSc students in the areas of training “Agroengineering”, “Heat Power Engineering and Heat Engineering”, “Electric Power Engineering and Electrical Engineering”. All the participants were taking a course of “Fundamentals of Pedagogical Activity” and having teaching practice at the university. As a result of the research, the authors have off ered some ways to solve the problem of training teaching staff with a master’s degree for technical universities. They include conducting fundamental research on the methodological issues of master’s degree training, considering past experience and modern trends; developing theoretical and applied issues of the master’s degree training content structure in order to increase the eff ectiveness of training programs, including the pedagogical component; identifying and implementing eff ective forms, models, and adaptive programs in the university study process to train MSc students for teaching and research activities and ensure their further professional development.
Multi-frequency sinusoidal chaotic neural network and its complex dynamics

A large number of animal experiments show that there is irregular chaos in the biological nervous systems. An artificial chaotic neural network is a highly nonlinear dynamic system, which can realize a series of complex dynamic behaviors, optimize global search and neural computation, and generate pseudo-random sequences for information encryption. According to the superposition theory of sinusoidal signals with different frequencies of brain waves, a non-monotone activation function based on the multifrequency-frequency conversion sinusoidal function and a piecewise function is proposed to make a neural network more consistent with the biological characteristics. The analysis shows that by adjusting the parameters, the activation function can exhibit the EEG signals in its different states, which can simulate the rich and varying brain activities when the brain waves of different frequencies and types work at the same time. According to the activation function we design a new chaotic cellular neural network. The complexity of the chaotic neural network is analyzed by the structural complexity based SE algorithm and C0 algorithm. By means of Lyapunov exponential spectrum, bifurcation diagram and basin of attraction, the effects of the activation function’s parameters on its dynamic characteristics are analyzed in detail, and it is found that a series of complex phenomena appears in the chaotic neural network, such as many different types of chaotic attractors, coexistent chaotic attractors and coexistence limit cycles, which improves the performance of the chaotic neural network, and proves that the multi-frequency sinusoidal chaotic neural network has rich dynamic characteristics, so it has a good prospect in information processing, information encryption and other aspects.
Formation of Competencies of Heads for Management of Digital Culture Devel-opment of Employees of Educational Organizations

In the conditions of the intensive transformation of modern culture, due to the mass dig-italization of various spheres of society, the traditional education-culture relationship acquires significant features. A significant cultural layer (especially mass culture) is formed in the digital environment. Today's education requires that all subjects of the educational process not only correctly use the artifacts of this culture, but also successfully participate in its creation. One of the key conditions for solving this problem is the formation of a digital culture of employees of the educational organization. And effective management of the development of such a culture becomes an important managerial task of its leader. Of course, such a task requires the head of special competencies, which can be mastered by an effective system of continuing education of the education manager.
Pengembangan Media Pembelajaran Berbasis Android pada Mata Pelajaran Simulasi Digital

Based on the observations, it was found that vocational schools in Padang have an adequate facilities for technology-based learning activities. However, the learning media which used were in conventional form, as found at digital simulation subjects. It was assumed that technology-based learning media could help learning activities, and the technology chosen is Android. The research objective is to test the validity of the development of learning aids in the form of android for digital simulation learning activities. This research using the Research and Development method 4D developments (Define, Design, Develop, and Disseminate). Validity testing is one of the 4D development steps, namely define. The validity test was conducted with five experts of media, content, and programming. Validity testing shows the mean validation results of 93.40% are included in the very valid category. The conclusion is that android props for digital simulation learning activities are valid for use in vocational schools in Padang.
Encryption Image by Using RC6 and Hybrid Chaotic Map

Cryptography is data processed in a way that becomes incomprehensible and unavailable to unauthorized persons. In this paper instructed method to encryption image by using RC6 algorithm and generated key by using hybrid chaotic map (tent and logistic map). Used some measures such as frequency test within a block, entropy, serial test (two-bit test), and frequency test (monobit test); to demonstrate the strength of the algorithm proposed in the image coding and protection. The MATLAB program was used as a work environment.
The PACE 2020 Parameterized Algorithms and Computational Experiments Challenge: Treedepth.

This year’s Parameterized Algorithms and Computational Experiments challenge (PACE 2020) was devoted to the problem of computing the treedepth of a given graph. Altogether 51 participants from 20 teams, 12 countries and 3 continents submitted their implementations to the competition.In this report, we describe the setup of the challenge, the selection of benchmark instances and the ranking of the participating teams. We also briefly discuss the approaches used in the submitted solvers and the differences in their performance on our benchmark dataset.
Spatial and Temporal Evolution in the Unsaturated Zone of Pollutants Resulting from Accidental Discharges of Petroleum Products

Abstract The exploitation and processing of oil resources are factors of regional progress and development; in this regard, the final products resulting from exploitation and subsequent refining can be accidentally spilt on the ground destabilizing the relationships between flora, fauna, water and soil, generating irreversible effects on terrestrial and aquatic ecosystems. The impact of the effects generated by petroleum products on soil and water resources is limiting nature and imposes severe measures to restrict the resources used for economic, administrative and consumption purposes. Experimental research involves the usage of geostatistical methods for modelling the unsaturated area and the usage of specialized applications for estimating the dispersion of pollutants in the studied area based on an accidental pollution scenario. The application of the method used in the areas with a significant risk of contamination (oil fields) it aims to identify the movement of the contaminant at various times and concentrations in order to choose the best measures to reduce and minimize pollution of the unsaturated (vadose) zone. The objectives of the reduction and minimization measures are to limit the transfer of contaminants in the saturated area or to the strategic drinking water reserves. The applied method supports researchers, hydrologists, hydrogeologists, environmental engineers who want to deepen the transition elements of the pollutant in the unsaturated area, the phenomenology and dispersion of pollutants in waters located in the unsaturated area and the problems caused by pollutants on the environment.
Unsupervised Method for Calculating Diameter and Number of Leukocyte Cells

Abstract Leukocyte has an essential role in the body. Leukocyte has functioned as the body’s immune system function to protect the body from viruses, bacteria, and pathogens. Under normal circumstances, leukocyte has a relatively constant amount. If the number of leukocytes exceeds or is less than usual, health problems can occur. So that the leukocyte cell counts as an indicator of abnormalities and diseases. Digital image processing is an alternative method to identify leukocyte in addition to a hematology analyzer or manually by medical personnel by processing blood images so that the desired information is obtained. Blood images have a variety of lighting conditions, staining time, and cell thickness that cause the various attributes of the blood image. This experiment segmented leukocyte using unsupervised method especially the K-Means clustering method on the hue component of the image after being transformed into HSV color coordinates by grouping leukocyte cell images (great hue value) into one cluster. The results show that the performance of our system as follows, sensitivity 84%, specificity 94%, and accuracy 97%.
A Faster Subquadratic Algorithm for the Longest Common Increasing Subsequence Problem

The Longest Common Increasing Subsequence (LCIS) is a variant of the classical Longest Common Subsequence (LCS), in which we additionally require the common subsequence to be strictly increasing. While the well-known Four Russians technique can be used to find LCS in subquadratic time, it does not seem directly applicable to LCIS. Recently, Duraj [STACS 2020] used a completely different method based on the combinatorial properties of LCIS to design an 𝒪(n²(log log n)²/log^{1/6}n) time algorithm. We show that an approach based on exploiting tabulation (more involved than Four Russians) can be used to construct an asymptotically faster 𝒪(n² log log n/√{log n}) time algorithm. As our solution avoids using the specific combinatorial properties of LCIS, it can be also adapted for the Longest Common Weakly Increasing Subsequence (LCWIS).
Mobility Prediction Based Computation Offloading Handoff Strategy for Vehicular Edge Computing

In the vehicular cloud computing environments, computation offloading faces the problems such as high network delay and large load of the remote cloud. The vehicular edge computing takes advantage of the edge servers to be close to the vehicular terminals, and provides the cloud computing service to solve the problem mentioned above. However, due to the dynamic change of communication environment caused by vehicle movement, the task completion time will increase. For this reason, this paper proposes a Mobility Prediction-based computation Offloading Handoff Strategy (MPOHS), which tries to minimize the average completion time of offloaded tasks by migrating tasks among edge servers according to the prediction of vehicle movement. The experimental results show that, compared with the existing research, the proposed strategy can reduce the average task completion time, cut down the handoff times and handoff time overhead, and effectively reduce the impact of vehicle movement on the performance of computation offloading.
Huawei’s Submissions to the WMT20 Biomedical Translation Task

This paper describes Huawei’s submissions to the WMT20 biomedical translation shared task. Apart from experimenting with finetuning on domain-specific bitexts, we explore effects of in-domain dictionaries on enhancing cross-domain neural machine translation performance. We utilize a transfer learning strategy through pre-trained machine translation models and extensive scope of engineering endeavors. Four of our ten submissions achieve state-of-the-art performance according to the official automatic evaluation results, namely translation directions on English French, English->German and English->Italian.
MANorm: A Normalization Dictionary for Moroccan Arabic Dialect Written in Latin Script

Social media user generated text is actually the main resource for many NLP tasks. This text, however, does not follow the standard rules of writing. Moreover, the use of dialect such as Moroccan Arabic in written communications increases further NLP tasks complexity. A dialect is a verbal language that does not have a standard orthography. The written dialect is based on the phonetic transliteration of spoken words which leads users to improvise spelling while writing. Thus, for the same word we can find multiple forms of transliterations. Subsequently, it is mandatory to normalize these different transliterations to one canonical word form. To reach this goal, we have exploited the powerfulness of word embedding models generated with a corpus of YouTube comments. Besides, using a Moroccan Arabic dialect dictionary that provides the canonical forms, we have built a normalization dictionary that we refer to as MANorm. We have conducted several experiments to demonstrate the efficiency of MANorm, which have shown its usefulness in dialect normalization. We made MANorm freely available online.
Categories – an Attribute of Scientific and Sociotransformative Activity

Introduction. Cognition is an essentially human attribute; a cultural entity and a precondition for social progress, in which categorisation and methodological significance play a key role. But the essence and functions of such categories can also cause disagreement in the scientific community. Hence the importance of understanding the origin, essence and meaning of categories by implication. Theoretical Basis. Methods. In order to fully and thoroughly comprehend the essence, role, types and functions of categories, it is necessary to employ rational systemic and dialectic methods, and also semantic analysis. Results. After due consideration of these issues, there was a increased conviction that categories are the most important tools, guidelines, foundations and outcomes of cognitive activity. The importance of categories in scientific research, in ensuring day-to-day life of society, as well as in the development of special knowledge systems and the implementation of specific types of social practice, is high. It is determined by the place of categories in the lexicon of science and social reproduction, in the organization of essential social activities, all of which would be impossible without having a theoretical, political and organizational uniqueness. A more complete and deeper understanding of the place of categories can be found in their origin and etymology, in the very concept of ”category” and in the essence of certain categories. Of great importance is also the typology of categories, as well as the functions and limits of their application. An important result of the study is the conclusion that categories do not ultimately give rise to fundamental contradictions between researchers of various specialisations. On the contrary, good and skilful use of categories makes it possible to gain a full perception of reality and interaction. Discussion and Conclusion. Understanding categories has become an important factor in the development of science and society. These findings present further opportunities for the development of science, technology, and the improvement of social relationships.
A simplified imaging model of bi-telecentric lenses under Scheimpflug condition and its calibration

In the lens-based imaging model, the Scheimpflug principle is expressed as the object plane, the image plane, and the lens plane intersect in a line. With this principle, the object surface in front of the lens can be tilted by installing a tilted sensor, thereby significantly extending the axial distribution of the clear-imaging area. In order to calibrate the bi-telecentric lens under the Scheimpflug condition, we derived a concise imaging model, and the corresponding calibration method without solving the rotation or tilt angle is proposed. The re-projection error is calculated in the experiment to verify the effectiveness of our method.
Improving the Trustworthiness of Software Through Rigorous Data Type Design

We discuss the importance of ensuring that software developers take a measured approach to making design decisions related to data. Ada can be used to reduce cyclomatic complexity to improve the trustworthiness of software.
Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules

This paper describes the method we developed for the Task 2 English variation of the Social Media Mining for Health Applications (SMM4H) 2020 shared task. The task was to classify tweets containing adverse effects (AE) after medication intake. Our approach combined transfer learning using a RoBERTa Large Transformer model with a rule-based post-prediction correction to improve model precision. The model’s F1-Score of 0.56 on the test dataset was 10% better than the mean of the F1-Score of the best submissions in the task.
Dynamic estimation model of insurance product recommendation based on Naive Bayesian model

Aiming at the dynamic estimation of insurance product recommendation, considering the particularity and complexity of purchasing insurance product and the uncertainty of influencing factors, a dynamic estimation model of insurance product recommendation based on Naive Bayes is proposed. The model combines customer insurance information with machine learning. The results show that the naive Bayesian classification algorithm can be compared with the decision tree and neural network classification algorithm, showing high accuracy and high speed.