Reviewerly/exHarmony huggingface dataset:
Scrutability, Control and Learner Models: Foundations for Learner-Centred Design in AIED

There is a huge, and growing, amount of personal data that has the potential to help people learn. There is also a growing and broad concern about the ways that personal data is harvested and used. This makes it timely to draw on the decades of AIED research towards creating systems and interfaces that enable learners to truly harness and control their learning data. This invited keynote will present a whirlwind tour of my learner modelling research and a selection of other work that has influenced my own towards the goal of putting people in control of their own learning data and its use. I will explain the rationale for my focus on scrutability, as a foundation for users to harness and control their learning data, especially for learning contexts. I will share key lessons from my work for creating AIED systems that are deeply learner centred. Building on this, I will present a vision for AIED, one that takes a learner-centred perspective to designing AIED systems and recognises the inherent limitations of learning data. This is a broad view of AIED that returns its founding goals to create advanced learning technologies.
Explanations in Digital Health: The Case of Supporting People Lifestyles

Systems that aim at supporting users on behavior change are expected to implement strategies that can both motivate and gain the users’ trust, like the use of human understandable justifications for system’s decisions. While the literature has dedicated great effort on providing accurate system’s decisions, less focus has been given on addressing the problem of explaining to the user the reasons for a decision. This work presents a SPARQL-based reasoner enabling explainability on systems thought for supporting users in following healthy lifestyles. Our results demonstrate that users that received such information were able to reduce unhealthy behaviors over time.
Finance, Growth, and Inequality

Finance and growth emerged as a distinct field of economics during the last three decades as economists integrated the fields of finance and economic growth and then explored the ramifications of the functioning of financial systems on economic growth, income distribution, and poverty. In this paper, I review theoretical and empirical research on the connections between the operation of the financial system and economic growth and inequality. While subject to ample qualifications, the preponderance of evidence suggests that (1) financial development—both the development of banks and stock markets—spurs economic growth and (2) better functioning financial systems foster growth primarily by improving resource allocation and technological change, not by increasing saving rates. Some research also suggests that financial development expands economic opportunities and tightens income distribution, primarily by boosting the incomes of the poor. This work implies that financial development fosters growth by expanding opportunities. Finally, and more tentatively, financial innovation—improvements in the ability of financial systems to ameliorate information and transaction costs—may be necessary for sustaining growth.
A Note on the Optimal Convergence Rate of Descent Methods with Fixed Step Sizes for Smooth Strongly Convex Functions

Abstract Based on a result by Taylor et al. (J Optim Theory Appl 178(2):455–476, 2018) on the attainable convergence rate of gradient descent for smooth and strongly convex functions in terms of function values, an elementary convergence analysis for general descent methods with fixed step sizes is presented. It covers general variable metric methods, gradient-related search directions under angle and scaling conditions, as well as inexact gradient methods. In all cases, optimal rates are obtained.
Weight Forests-Based Learning Algorithm for Small-Scaled Data Processing

This paper proposes an ensemble learning algorithm that assigns a weight to the prediction of trees in each category, and these trees are generated by a specific feature to establish weight forests. The weight of forests is composed of a weight matrix and a weight of full connections, and two types of weight updating methods are employed. On the CICD2017, the generalized weight forest method outperforms the random forests method. The weight forests method can achieve better performance by using incremental learning for weight update. In this paper, we propose an incremental tree algorithm based on weight forests to solve the drift problem, and the occurrence of concept drift can be detected by adjusting the coefficients of incremental trees at a high level of accuracy.
Factorization of Dickson polynomials over finite fields

Let $$D_n(x;a)$$ and $$E_n(x;a)\in {\mathbb {F}}_q[x]$$ be Dickson polynomials of first and second kind respectively, where $${\mathbb {F}}_q$$ is a finite field with q elements. In this article we show explicitly the irreducible factors of these polynomials in the case that every prime divisor of n divides $$q-1$$ . This result generalizes the results found in (Finite Fields Appl. 3:84–96, 1997; Explicit factorization of cyclotomic and Dickson polynomials over finite fields, Springer, Berlin, 2007; Finite Fields Appl. 38:40-56, 2016; Discrete Math. 342:111618, 2019).
Bibliometric analysis of the core thesis system of Interaction Design Research on Human-Computer Interaction

Human-computer interaction (HCI) attracts more and more attention in automation, multimedia retrieval, computer system, universal computing. Meanwhile, it also poses new challenges to interaction design based on HCI in user experience, aesthetics, game design, and education]. This study aimed to analyze and assess literature published in the field of interaction design of HCI. <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> Web of science was used to retrieve literature of interaction design. The study period was set from 1985 to 2020, a total of 3243 documents were published. Pyecharts were used to present geographical distribution of publications and VOSviewer was used for data visualization. <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> This article uses standard bibliometric indicators to demonstrate five main aspects: Publications growing, Geographical distribution of publications, citation analysis, research productivity, and author keywords clusters analyze. <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> Through scientometrics analysis, it is found that the current research hotspots are mainly concentrated in 5 clusters: (a) UI for user-centered design, (b)HCI. (c)interaction design, (d)Intelligent interactive recognition research, (e)e-health and health information. According to the research frontier of interaction design research on HCI, researchers can get a clearer research status. Also, by showing the research fronts of interaction design of HCI, the scientometric analysis also highlights the research gap at the increasing importance of user health and the sustainability of interaction.
Cloud Security in Middleware Architecture

The new Internet of Things (IoT) has increased the need for computing, connectivity, and storage capacities as the amount of sensitive data grows. Since it provides on-demand access to a common pool of resources such as processors, storage, software, and services, cloud computing can seem to be a convenient solution. However, there is a cost, as excessive communications burden not only the core network, but also the cloud data centre. As a result, it’s critical to consider appropriate approaches and security middleware solutions. In this chapter, we define a middleware architecture to address security concerns and explore the general concept of cloud to achieve a higher level of security. Since it is designed to pre-process data at the network’s edge, this security middleware functions as a smart gateway. Data can be processed and stored locally on fog or sent to the cloud for further processing, depending on the information obtained. Furthermore, the devices communicate via middleware, which gives them access to more computing power and improved security capabilities, allowing them to conduct safe communications. We discuss these concepts in detail, and explain how this is effective to cope with some of the most relevant security challenges.
Dynamic group optimization algorithm with a mean–variance search framework

Dynamic group optimization has recently appeared as a novel algorithm developed to mimic animal and human socialising behaviours. Although the algorithm strongly lends itself to exploration and exploitation, it has two main drawbacks. The first is that the greedy strategy, used in the dynamic group optimization algorithm, guarantees to evolve a generation of solutions without deteriorating than the previous generation but decreases population diversity and limit searching ability. The second is that most information for updating populations is obtained from companions within each group, which leads to premature convergence and deteriorated mutation operators. The dynamic group optimization with a mean–variance search framework is proposed to overcome these two drawbacks, an improved algorithm with a proportioned mean solution generator and a mean–variance Gaussian mutation. The new proportioned mean solution generator solutions do not only consider their group but also are affected by the current solution and global situation. The mean–variance Gaussian mutation takes advantage of information from all group heads, not solely concentrating on information from the best solution or one group. The experimental results on public benchmark test suites show that the proposed algorithm is effective and efficient. In addition, comparative results of engineering problems in welded beam design show the promise of our algorithms for real-world applications.
Deep Learning Based Low-light Enhancement and Noise Suppression in USV Imaging System

With the rapid development of artificial intelligence technology and autonomous navigation technology, the unmanned surface vessel (USV) industry has developed accordingly, and it has played an important role in the fields of water quality monitoring, maritime inspection, and maritime safety assurance. However, USV is easily affected by the external lighting environment. In the case of insufficient lighting, the collected images have the characteristics of low brightness, low contrast and low resolution, and are extremely susceptible to external noise interference, making USV difficult obtain input requirements that meet the visual tasks such as target recognition and semantic segmentation. In this paper, we propose a deep learning-based low-light image enhancement and noise suppression method (LENet). Specifically, LENet is used to map the low-light image to the normal-light image through a deep Unet network, and CBM3D further suppresses the interference noise in the image to achieve the enhancement of the low-light image. We enhance the generalization ability and robustness of the deep network by embedding dilated convolutions and dense blocks in the deep Unet network. Structural similarity (SSIM) and norm are used as the loss function to further improve the quality of the enhanced image. The experimental results show that the deep network proposed in this paper improves the brightness and contrast of the images collected by the USV under insufficient lighting conditions, which can meet the input requirements of the USV visual task.
Social Workers’ Role Perception in Culture and Arts Education for the Disabled: Focusing on Service Mediation

This study categorizes the subjective role recognition of social workers in culture and arts education for the disabled by applying Q-methodology. Thirty social workers who oversaw or had experience in cultural arts education for the disabled were sampled. The finalized Q-statements were composed of 61 items, which included (among others) the overall recognition of social workers’ culture and arts education, job awareness, service mediation, and improvement plan. Following a factor analysis using the PQmethod program, Q-sorted data were named according to the revealed types and were then analyzed by their characteristics. The results of the analysis extracted three types. The first type, which was “Social Worker-Centered, Lead a Program,” examined culture and arts education in the social welfare area and recognized the professionalism of social workers as important. The second type, “The Disabled Centered-Cultural Arts Expansion,” centered on participants with disabilities in operating culture and arts education and recognized the scope of culture and arts education more widely. The third type was “Culture and Arts Centered-Cooperation” and appreciated the role of recognizing and mediating the professionalism of art instructors. The results showed that the role of social workers in culture and arts education, which requires cooperation with various areas, is not shrinking, and the job leadership and activeness of social workers are confirmed. Additionally, the study identified the role awareness in the relationship between art instructors and social welfare, focusing on service mediation, and suggested improvement plans by type.
Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings

Static word embeddings encode word associations, extensively utilized in downstream NLP tasks. Although prior studies have discussed the nature of such word associations in terms of biases and lexical regularities captured, the variation in word associations based on the embedding training procedure remains in obscurity. This work aims to address this gap by assessing attributive word associations across five different static word embedding architectures, analyzing the impact of the choice of the model architecture, context learning flavor and training corpora. Our approach utilizes a semi-supervised clustering method to cluster annotated proper nouns and adjectives, based on their word embedding features, revealing underlying attributive word associations formed in the embedding space, without introducing any confirmation bias. Our results reveal that the choice of the context learning flavor during embedding training (CBOW vs skip-gram) impacts the word association distinguishability and word embeddings' sensitivity to deviations in the training corpora. Moreover, it is empirically shown that even when trained over the same corpora, there is significant inter-model disparity and intra-model similarity in the encoded word associations across different word embedding models, portraying specific patterns in the way the embedding space is created for each embedding architecture.
Oral English Evaluation Algorithm Based on Fuzzy Measures and Speech Recognition Technology

As an important part of English teaching, oral English evaluation plays an important role in promoting students to learn English. The establishment of a diversified oral college English evaluation system is conducive to changing the traditional summative evaluation model, promoting the smooth progress of college English reform, and facilitating the in-depth development of the overall education reform. Fuzzy measure theory abandons the requirement of additivity in classical measure theory. On the basis of the concept of generalized additivity, the condition of additivity is weakened to make it additive in the new sense. With the development of deep learning, speech recognition technology has undergone tremendous technological changes, in which the acoustic model has gradually developed from the traditional Gaussian mixture model to the neural network model. In this paper, the speech recognition technology and fuzzy measure rules are analyzed, and the evaluation system of spoken English is constructed.
Evaluation of Two-Step Spectral Clustering Algorithm for Large Untypical Data Sets

Researchers analyzing large (>100,000 objects) data sets with the methods of cluster analysis often face the problem of computational complexity of algorithms that sometimes makes it impossible to analyze in an acceptable time. Common solution of this problem is to use less computationally complex algorithms (like k-means), which in turn can in many cases give much worse results than for example algorithms using eigenvalues decomposition. In the article, the new algorithm from spectral clustering family is proposed and compared with other approaches.
Incremental Anonymous Privacy-Protecting Data Mining Method Based on Feature Correlation Algorithm

With the arrival of the information age, privacy leaks have been getting increasingly serious. The commonly-used method for privacy protection is to introduce k-anonymity when data publishing. In this paper, how to employ k-anonymity in stream data sets is studied, and the relationship and the sensitivity matrix of the basic knowledge about Quasi identifier (QI) and sensitive attributes are established. RSLK-anonymity algorithm is proposed for solving the private information leakage during the streaming data publishing. The main idea is to anonymize the streaming data set on the basis of sliding window as well as relation and sensitive matrix, which can make anonymous streaming data effectively defense background knowledge attack and homogeneity attack while solving sensitive attribute diversity. Experimental results indicate that RSLK-anonymity algorithm is practical, effective and highly efficient.
Design of a Real Time Path of Motion Control for Manufacturing Applications

Due to an ever-increasing variety of parts in production, the programming of robots becomes more and more complex. For the optical quality control of contours with image processing systems, these robots must be individually programmed. The goal of this work is therefore to simplify the programming of the robot. The work has shown that this is possible through discrete real-time and model independent control. The contour is measured by a 3D triangulation sensor whose data is evaluated by an image processing system. Communication with the robot's system takes place via a Profinet interface to ensure real-time capability. The development of the controller has shown that a classical PID-Controller does not provide the desired control quality in particular in the presence of disturbances such as roughness of the surface to be inspected. Instead, a better result could be achieved with a particular switching Sliding Mode Control (SMC) approach that switches back and forth between two surfaces depending on the distance to the surface.
Design of Distributed Network Mass Data Processing System based on Cloud Computing Technology

Due to the rapid increase in the amount of data that people need to deal with computing tasks, such as distributed computing, cloud computing and other technologies are more and more concerned by most people. In recent years, with people's attention to information security and personal privacy protection, cloud computing security has gradually become the focus of researchers. In the cloud computing environment, massive distributed data storage and management and data centralization autonomy can control the data mechanism, and distribute management of redundant data and transaction processing. These management and application are generally through the distributed database for actual operation, from the user's point of view, the access process to the distributed database is transparent. In fact, from the user's point of view, a single distributed database is essentially a combination of a group of databases stored on multiple computers. This paper studies the distributed data processing system based on cloud computing technology.
Formation of the goals of general education as part of the social system

The subject of this research is formation of the goals of general education. The majority of modern authors correlate formation of the goals of education with the traditions and trends existing in pedagogical practice; some authors formulate the goals of education based on the expert opinion; others rely on the futuristic forecasts of social development; and only few of them obtain educational goals from the science-based theories dedicated to personality development of a child. Such divergence of opinions allow speculating the goals of education, since the concepts of &amp;ldquo;social procurement&amp;rdquo;, &amp;ldquo;world traditions&amp;rdquo;, &amp;ldquo;harmonious personality development&amp;rdquo;, and &amp;ldquo;expert opinion&amp;rdquo;, although being virtually indeterminate and susceptible to personal fluctuations and political trends, are ultimately enshrined normative documents. This defines the relevance of the topic of formation of the goals of education regardless of personal, political, or other attitudes. The research problem is formulated in the following way: whether there is an invariant basis in the concept of the &amp;ldquo;goals of education&amp;rdquo;. This article explores the question of formation of the goals of general education from the perspective of systemology. Leaning on the theory of complex systems and the theory of signal transmission, the author outlines the goals of general education, which are interconnected by society and the state. The author describes how these goals are reflected in pedagogical practice and the Russian law &amp;ldquo;On Education&amp;rdquo;. The acquired results testify to the fact that the goals of general education are focused on the three aspects: inclusion of a child into the &amp;ldquo;social norms&amp;rdquo; subsystem; child&amp;rsquo;s development for effective resistance to the &amp;ldquo;external environment&amp;rdquo;; child&amp;rsquo;s development for effective interaction within the &amp;ldquo;educational structure&amp;rdquo; subsystem. All the identified goals are crucial, as they resemble the structure of relations between society, the state, and external environment. All other goals of education either correlated with the three aforementioned goals, or are not related to education. A hypothesis is advances on the hierarchy of goals: 1) preservation of society; 2) relationship with the external environment; efficiency of such relationship in the society. All these goals are reflected in the modern pedagogical practice, which may suggest the triviality of the formulated conclusions. In the scheme described by the author, they are not obtained empirically, but rather indicate systemic relationship with society and the state. The novelty of this research lies in justification of reasons for forming the goals of education. The provided formulizations allow to accurately and logically outline the strategy and structure of educational policy, both on the federal level and on the level each individual pedagogue.
The Effect Of Youtube On High School Studentsâ€™ Second Language Acquisition

The research investigates the effect of watching Youtube videos on studentsâ€™ second language acquisition based on Krashen (2002). To achieve this aim, this research employs descriptive qualitative method. The data was taken from Junior High School Students in East Barito regency. In collecting data, the researchers used observational method. Student`s utterances were observed after watching Youtube`s videos. The result of the analysis shows that Youtube`s video blogging has effect upon students in SLA. The students get new English vocabulary from various genres of videos. They also improve their English sentence structure found on the video to sentence structure with other words. Video blogging is part of informal environment English as second language acquisition given the benefit in this research so that the student can learn English from different objects, apart from school.