Reviewerly/exHarmony huggingface dataset:
Statistical Characteristics of the Application of English Human-Computer Interaction Mathematical Algorithm in Online Courses

Abstract In recent years, with the rapid advancement of multi-mode teaching theory, this paper adopts an interactive large classroom method and derives from the interaction of mathematics. In combination with online course optimization, the performance is improved through the use of a multi-mode system and the use of interactivity The principle and empirical formula, and finally through the comparative analysis of mathematical statistics, proved that the human-computer interaction mathematical algorithm has a significant effect, “problem English human-computer interaction mathematical algorithm plays an active role in online courses.
Learning Automata for Online Function Evaluation Management in Evolutionary Multi-population Methods for Dynamic Optimization Problems

Multi-population (MP) approach is among the most successful methods for solving continuous dynamic optimization problems (DOPs). Nevertheless, the MP approach has to conquer several obstacles to reach its maximum performance. One of these obstacles, which is the subject of this chapter, is how the MP methods exploit function evaluations (FEs). Since the calculation of FEs is the most expensive component of the evolutionary computation (EC) methods for solving real-world DOPs, we should find a way to spend a major portion of FEs around the most promising search area space. In generic form, the MP approach as a sub-population located far away from the optimal solution(s) is assigned the same amount of FEs as that near-optimal solution(s), which in turn exert deleterious effects on the performance of the optimization process. Therefore, one major challenge is how to suitably assign the FEs to each sub-population to enhance MP methods’ efficiency for DOPs. This chapter generalizes the application of variable-structure learning automaton (VSLA) and fixed-structure learning automaton (FSLA) for FE management to improve MP methods for DOPs. The present work is applied to DE-based MP methods, MP version of particle swarm optimization (PSO), firefly algorithm (FFA), and JAYA.
Exploration of Blockchain Architecture, Applications, and Integrating Challenges

Internet is the technology that is highly used for communication as well as data transmission in the current scenario. Because of the rapid growth in such technology, the issues related to security are also increasing. Blockchain is emerging as a revolutionizing technology in this field. Basically, blockchain relies on the four components, namely decentralization, anonymity, persistence, and audibility. It focuses more on protecting data from unauthorized parties in performing any modification to the existing data. Blockchain technology is also defined as a digital ledger. It is not only limited to cryptocurrencies but with the potential to be transparent and fair, which opens many doors to various technologies like IoT, big data, and many more. This paper includes blockchain locution, and it will encounter some typical consensus algorithms that are ought to analyze blockchain applications and some technical challenges. The proposed research work will also concentrate more on the recent advances to tackle those challenges.
Classification of Benign and Malignant Pulmonary Nodules Using a Regularized Extreme Learning Machine

An L1/L2-norm-bound extreme learning machine classification algorithm is proposed to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. In this algorithm, features extracted from the segmented lung nodule using the histogram of oriented gradients method are used as inputs. L1-norm can promote sparsity in the weights of the output layer, and L2-norm can smooth output weights. The combination of the L1 norm and L2 norm can simplify the complexity of the network and prevent overfitting to improve classification accuracy. For each newly tested lung nodule, the algorithm outputs a class label of either benign or malignant. The accuracy, sensitivity, and specificity reached 94.12%, 93%, and 95% respectively over the lung image database consortium and image database resource initiative dataset. Compared with other algorithms, the average values of the three metrics increased by 6.5%, 7.94%, and 4.32%, respectively. An accuracy score of 95.83% can be achieved over a set of 120 urinary sediment images. Therefore, this algorithm has a good classification effect of pulmonary nodules.
CNN-based morphological decomposition of X-ray images for details and defects contrast enhancement

This paper introduces a new learning based framework for X-ray images that relies on a morphological decomposition of the signal into two main components, separating images into local textures and piecewise smooth (cartoon) parts. The piecewise smooth component corresponds to the spatial variation of the average density of the objects, whereas the local texture component presents the inspected objects singularities. Our method builds on two convolutional neural network (CNN) branches to decompose an input image into its two morphological components. This CNN is trained with synthetic data, generated by randomly picking piecewise smooth and singular patterns in a parametric dictionary and enforcing the sum of the CNN branches to approximate the identity mapping. We demonstrate the relevance of the decomposition by enhancing the local textures component compared to the piecewise smooth one. Those enhanced images compare favorably to the ones obtained with existing works destined to visualize High Dynamic Range (HDR) images such as tone-mapping algorithms.
k-Approximate Quasiperiodicity Under Hamming and Edit Distance

Abstract Quasiperiodicity in strings was introduced almost 30 years ago as an extension of string periodicity. The basic notions of quasiperiodicity are cover and seed. A cover of a text T is a string whose occurrences in T cover all positions of T . A seed of text T is a cover of a superstring of T . In various applications exact quasiperiodicity is still not sufficient due to the presence of errors. We consider approximate notions of quasiperiodicity, for which we allow approximate occurrences in T with a small Hamming, Levenshtein or weighted edit distance. In previous work Sim et al. (J Korea Inf Sci Soc 29(1):16–21, 2002) and Christodoulakis et al. (J Autom Lang Comb 10(5/6), 609–626, 2005) showed that computing approximate covers and seeds, respectively, under weighted edit distance is NP-hard. They, therefore, considered restricted approximate covers and seeds which need to be factors of the original string T and presented polynomial-time algorithms for computing them. Further algorithms, considering approximate occurrences with Hamming distance bounded by k , were given in several contributions by Guth et al. They also studied relaxed approximate quasiperiods. We present more efficient algorithms for computing restricted approximate covers and seeds. In particular, we improve upon the complexities of many of the aforementioned algorithms, also for relaxed quasiperiods. Our solutions are especially efficient if the number (or total cost) of allowed errors is small. We also show conditional lower bounds for computing restricted approximate covers and prove NP-hardness of computing non-restricted approximate covers and seeds under the Hamming distance.
Time-Scalable Low-Latency Video Bitrate Adaptation

High-quality real-time video requires large bandwidth and low latency. These requirements bring huge challenges to network performance. In this paper, a dynamically balanced framework for the bandwidth and delay of the video is proposed. The frame rate of the video segment being played will adaptively change with the download time. For example, When the throughput is slightly lower than the selectable bitrate, a longer download time is required, then the playback frame rate will slow down, and vice versa. This method not only reduces the buffer occupancy and enhances the real-time performance, but equivalent to increases the optional bitrate of the video. We have implemented our libVLC player using the SDK provided by VLC. The simulation results show that our method improves significantly the downloaded bitrate at low, medium, and high throughput.
Methods of Co-design Using Machine Learning as Design Materials

ML (machine learning) technology has customized products and services in different fields based on the data related to the user preference with the application of artificial intelligence technology in design. Although technology has unlimited potential to provide highly qualified products and services, we currently lack effective methods to work with ML in the design process, especially in co-design. This article aims to analyze the design methods in support of ML in the early co-design stages. This research presented an organized design workshop, in which all designers and programmers participated had more than two years of experience in ML product development. Eight users were also invited. The study discussed two methods: role-playing and scenarios, which can help co-designers understand and work with it in the fussy front end.
A Sentiment Analysis Approach to Process Civic Contributions

Crowdsourced civic engagement is a novel form of democratic participation that allows citizens to share their ideas and deliberate in a multitude of diverse participatory processes that are emerging all over the world, influencing, often with binding power, urban plans, city budgets, and even legislation, among many other forms of public policy decisions. As a result, hundreds of thousands of civic contributions are produced as ideas, comments, and proposals circulate among citizens and between them and government officials, generating an avalanche of mostly unstructured data, which decision-makers have difficulty to manage. Sentiment analysis techniques have the potential to process and classify these contributions in ways that can make it easier to make sense of them. In this paper, we present the design and implementation of a rule based sentiment analyzer that integrates a lexicon, optimized for the Spanish language and the application domain of civic contributions. We present the results of our first evaluation and discuss the aspects of the proposal that have room for improvement in future work.
Higher Lenses

We show that total, very well-behaved lenses are not very well-behaved when treated proof-relevantly in the setting of homotopy type theory/univalent foundations. In their place we propose something more well-behaved: higher lenses. Such a lens contains an equivalence between the lens's source type and the product of its view type and a remainder type, plus a function from the remainder type to the propositional truncation of the view type. It can equivalently be formulated as a getter function and a proof that its family of fibres is coherently constant, i.e. factors through propositional truncation.We explore the properties of higher lenses. For instance, we prove that higher lenses are equivalent to traditional ones for types that satisfy the principle of uniqueness of identity proofs. We also prove that higher lenses are n-truncated for n-truncated types, using a coinductive characterisation of coherently constant functions.
한국어 숙달도 평가 도구 개발을 위한 교사 요구 분석

The purpose of this study is to propose directions for the development of Korean language proficiency assessment tools based on the analysis of instructor needs for such tools. In conducting this study, the needs of a subject group of 257 domestic and overseas instructors were observed regarding the language proficiency evaluation system and evaluation components of the current TOPIK. According to the results of the study, instructors considered the TOPIK, for the most part, to be an adequate tool for language proficiency assessment. However, the study results also showed that, regarding the TOPIK I, instructors found the lack of a writing section to be an issue. As for the TOPIK II, results showed that instructors found the reading section too difficult and the allocated time for the writing section to be somewhat short. When asked about the development of proficiency assessment, the majority of instructors emphasized the need for the development of new tools that would be appropriate for a new type of assessment. They also expressed that proficiency assessment should be applied for each of the four evaluation areas - speaking, listening, reading, and writing - and that the evaluation system should be divided into intermediate and advanced levels to take difficulty into consideration. Lastly, instructors mentioned that test results should show performance rankings and scores for each of the four aforementioned language evaluation areas. Based on the results outlined above, this study proposes the following directions for the development of Korean language proficiency assessment tools: first, adequate evaluation tools should be developed for the purpose of language proficiency assessment; second, all four language evaluation areas should be included from the beginner level; third, the evaluation system should take difficulty and practicality into consideration by providing two separate tests, one for the beginner and intermediate levels, one for the advanced level; fourth, evaluation components such as allotted question times, number of questions, difficulty, and answer choices, should be similar to that of existing evaluation method, with slight adjustments made to the difficulty of reading and allotted time of writing for the advanced level; fifth, the assessment results should offer performance rankings and scores for each of the four language evaluation areas.
Research on intelligent early warning technology for natural disasters of transmission lines based on 5G and artificial intelligence

In the application process of the existing intelligent disaster early warning technology, the problem of fuzzy current distribution characteristics leads to the short battery life of the tower terminal. This paper designs an intelligent early warning technology for natural disasters of transmission lines based on 5G and artificial intelligence. The catastrophe prediction results as auxiliary indexes in early warning, establishing database of transmission line of natural disasters, with the method of monitoring the fault current, setting axis path transmission lines, detection and separation of moving targets, using 5 g and artificial intelligence technology to extract the characteristics of current distribution, according to the values of worshiping information, building intelligent early warning model of transmission line natural disasters. Experimental results: The battery life of the pole and tower terminal of the intelligent early warning technology designed this time is 11.88h and 12.56h longer than the other two existing early warning technologies respectively, proving that the practical application effect of the intelligent early warning technology of transmission line integrating 5G and artificial intelligence is better.
Encouraging Compiler Optimization Practice for Undergraduate Students through Competition

AI and other emerging applications demand domain-specific architectures which require compiler techniques such as back-end generation for different architectures and optimizations. However, traditional undergraduate compiler courses emphasize the front-end, while code generation and optimization are rarely involved. To motivate universities to have more industry-friendly compiler courses, we have designed a national compiler design competition for undergraduates to include compiler techniques beyond parsing. Moreover, we provided reliable, continuous cloud storage and an online evaluation platform for distributed competitors. In the 9-week Competition in 2020, each team (up to 4 students) was required to implement a compiler for a given SysY language and given target hardware (Raspberry Pi 4B). The performance was evaluated by executing code generated by the compiler on real hardware. Finally,21 of 72 teams successfully passed all functional test cases; 12 of 21 teams implemented optimizations showing significant speedup over gcc -O0; furthermore, compilers of the top 3 teams performed better than gcc -O2 on the given 10 performance test cases. Some advanced optimization techniques, such as multithreading and SIMD, were used by some teams. This paper summarizes the competition and further thoughts on compiler courses.
The research trends in elementary education: a bibliometric and content analysis from Indonesia accredited journals

Abstract Understanding the research trend in the elementary education level is essential for future research directions that could strongly contribute to the educational field. The purpose of this research was to analyze the research trend in elementary education level in Indonesia. Bibliometric and content analysis was applied to answer the research questions. The data source of this study was articles published by Indonesian Journal that accredited by the science and technology index (SINTA). In order to maintain the credibility of the data, we were selected the journal article only for those published in Journal with SINTA level 2 during the period of 2017 to 2019. The bibliometric analysis was conducted using the VOSviewer program. Moreover, the content analysis was conducted using unitizing-sampling-recording-reducing-inferring-and narrating. The finding of our study describes the research trend in Indonesia based on aspects: 1) researcher network; 2) topic; 3) method; 4) subject; 5) research location; 6) data collection; and 7) analyses. Therefore, as our recommendation, there is a need for elementary education researcher to conduct their study that drives to the research trend as we reported.
Research on Interval Estimation of Trip Fuel Consumption Based on Irregularly Distributed Samples

Interval estimation of aircraft fuel consumption is an important basis for airlines to make flight plans and to save energy and reduce emissions. Aiming at the problem that the fuel consumption samples under the influence of multiple factors presented irregular distribution, which would reduce the rationality and accuracy of fuel consumption estimation, an interval estimation method of trip fuel consumption based on the combination of support vector quantile regression and Bootstrap (SVQR-Bootstrap) was proposed. The method based on quantile regression residual error minimization of the absolute value of asymmetric form thoughts to obtain the conditional quantile of trip fuel consumption and on this basis, through the Bootstrap estimate mean and standard deviation of trip fuel consumption population, and then, according to fixed aircraft type and segment, the population of fuel consumption meets the normal distribution, building estimated interval of trip fuel consumption under a constant confidence level. According to simulation comparisons, the SVQR-Bootstrap method can effectively reduce the length of the estimation range, pointing to the clear, which is better than the interval estimation method which requires assumed data distribution.
Construction of cubic splines for interpolating functional dependencies and processing the results of experimental studies

Abstract Algorithms for constructing cubic splines with different boundary conditions have been developed: 1. The second derivative takes arbitrary values at the ends of the spline. A special case is the “natural spline”, when the second derivative at its ends is equal to zero. 2. At one end of the spline, the first derivative is given, and at the opposite end – the second derivative. 3. The first derivative is given at the two ends of the spline without any restrictions on the second derivatives. The proposed methods were tested on the example of interpolation of the function 10 x sin xe −x for the interval [0, 1]. The relative calculation error for the number of nodes n = 10 was about 10 −2 . With an increase in the number of nodes to 1000 and 10000, the error increases, respectively, to 10 −6 and 10 −7 −10 −8 . It is shown that a number of problems with other boundary conditions can be solved using the proposed methods. The considered algorithms can be used for interpolating functional dependencies and processing the results of experimental studies, represented as a discrete set of pairs of numbers.
Scalable Graph Convolutional Variational Autoencoders

Autoencoders are widely used for self-supervised representation learning. Variational autoencoders (VAEs), a special type of autoencoders, are proven to be effective in estimating the underlying probability distribution of the training data. Even though VAEs are well explored in many application domains, their utilization for graph-structured data is still under extensive research. Graph variational autoencoders achieved competitive results on various graph-related modeling tasks (e.g. link prediction and node clustering) by incorporating node features. However, current graph VAEs are unable to scale efficiently for larger graphs.In this paper, we propose a novel method that adapts the stochastic multiple partitions (SMP) algorithm to improve on scalability. We also introduce novel graph convolutional layers with general graph filters, which significantly improve the predictive performance of the neural network. The proposed method is evaluated on two popular large-graph datasets. According to the results, the proposed filters outperform the baseline filter in link prediction and node clustering for both datasets.
“SIAB” smart E-stoichiometry as a chemistry learning acid base on Android mobile

In this millennial era, technology is growing rapidly, it is hoped that everything can be digitized in order to facilitate the access process. Likewise in the education sector, digital media is expected to facilitate the work of teachers and students’ understanding of material at school. Therefore we want to create an educational platform that contains a lesson in school, and we choose chemistry. After conducting the survey, the subject of chemistry lesson chosen by the respondents was stoichiometry. The name of this app is “SIAB”. SIAB “Stoichiometry Acid Base” is a chemical calculator application that is useful for calculating the quantitatively between reagents (reactants) and products (reaction products) in acid base reaction. There are 3 steps methodology to implement it, first step is problem survey, to find out whether the chosen problem is related to the target. Second step is analysis, to make sure that the problem relates to the target, and the target is experiencing the chosen problem. Last step is application design to build and design SIAB apps, for the application design step there are 3 part of step. The first step, we search and gather sources that contain about acid and base chemical reaction theory, which will be used as a reference material to be displayed in the application. Then the second step is the fulfillment of the required application assets including image and audio which for help user to make more interesting and understanding easily. The third step is determining the core software used to make the application. The result of the implementation test of our application is 90% of students find this application helpful to determine the results of chemical reactions between several molecules and easier to study. This mobile application can be used to simplify the calculation of acid-base stoichiometry anytime and anywhere.