Reviewerly/exHarmony huggingface dataset:
Prompt-Independent Automated Scoring of L2 Oral Fluency by Capturing Prompt Effects

We propose a prompt-independent automated scoring method of second language (L2) oral fluency, which is robust to different cognitive demands of speaking prompts. When human examiners assess L2 learners' oral fluency, they can consider the effects of different task prompts on speaking performance, systematically adjusting their evaluation criteria across prompts. However, conventional automated scoring methods tend to ignore such variability in speaking performance caused by prompt design and use prompt-specific features of speech. Their robustness is thus arguably limited to a specific prompt used in model training. To address this challenge, we operationalize prompt effects in terms of conceptual, linguistic and phonological features of speech and embed them, as well as a set of temporal features of speech, into a scoring model. We examined the agreement between true and predicted fluency scores in four different L2 English monologue prompts. The proposed method outperformed a conventional method which used only temporal features ( $$\kappa = 0.863 \text { vs. } 0.797$$ ). The detailed analysis showed that the conceptual and phonological features improved the performance of automated scoring. Meanwhile, the effectiveness of the linguistic features was not confirmed possibly because it may largely reflect redundant information to capture the prompt demands. These results suggest that the robustness of the automated fluency scoring should be achieved by careful consideration of what characteristics of L2 speech reflect the prompt effects.
Exploration of Annotation Strategies for Automatic Short Answer Grading

Automatic Short Answer Grading aims to automatically grade short answers authored by students. Recent work has shown that this task can be effectively reformulated as a Natural Language Inference problem. State-of-the-art is defined by the use of large pretrained language models fine-tuned in the domain dataset. But how to quantify the effectiveness of the models in small data regimes still remains an open issue. In this work we present a set of experiments to analyse the impact of different annotation strategies when not enough training examples for fine-tuning the model are available. We find that when annotating few examples, it is preferable to have more question variability than more answers per question. With this annotation strategy, our model outperforms state-of-the-art systems utilizing only 10% of the full-training set. Finally, experiments show that the use of out-of-domain annotated question-answer examples can be harmful when fine-tuning the models.
Visual Positioning of a Moving Object Using Multi-objective Control Algorithm

The paper is devoted to the problem of multi-objective control design for visual positioning of moving objects. This research area incorporates the methods of control theory and computer vision, and has a special importance for autonomous vehicles, where visual information from on-board camera gives a rich data about the surrounding world. Visual information can be effectively used in feedback control, for example, in such applications as visual positioning, tracking a visually given line, moving in a changing environment with visual obstacles. The main objective of this work is to develop the results obtained earlier and design a feedback control algorithm for visual positioning problem based on the multi-objective approach. This approach allows to take into account a set of requirements for closed-loop system performance in different regimes. These regimes, in particular, include the object motion under constant or random external disturbances. The multi-objective structure of the control law has adjustable elements that must be selected in accordance with the imposed requirements. It is convenient to formulate the problems of searching these elements as optimization tasks on the corresponding admissible sets. The nonlinear mathematical model of object dynamics is considered. In addition, the nonlinear model of the dynamics in image plane of the camera is introduced. The control objective in visual positioning problem is to provide the desired projection of some three-dimensional object of the scene to the image plane. This projection is described by features vector. The main result of the work is the developed feedback control law, which is based on multi-objective structure and computer vision algorithms. Methods for searching adjustable elements of multi-objective structure are proposed. The efficiency of the approach is illustrated by a computational experiment in MATLAB environment.
Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis

This paper evaluates an automatically extracted domain model from textbooks and applies learning curve analysis to assess its ability to represent students' knowledge and learning. Results show that extracted concepts are meaningful knowledge components with varying granularity, depending on textbook authors' perspectives. The evaluation demonstrates the acceptable quality of the extracted domain model in knowledge modeling.
Automatic Analysis of Student Drawings in Chemistry Classes

Automatic analyses of student drawings in chemistry education have the potential to support classroom teaching. To date, related work on handwritten chemical structures or formulas is limited to well-defined presentation formats, e.g., Lewis structures. However, the large variety of possible illustrations in student drawings in chemical education has not been addressed yet. In this paper, we present a novel approach to identify visual primitives in student drawings from chemistry classes. Since the field lacks suitable datasets for the given task, we introduce a method to synthetically create a dataset for visual primitives. We demonstrate how detected visual primitives can be used to automatically classify drawings according to a taxonomy of drawing characteristics in chemistry and physics. Our experiments show that (1) the detection of visual primitives in student drawings, and (2) the subsequent classification of chemistry- and physics-specific drawing characteristics is possible.
Trustworthy Academic Risk Prediction with Explainable Boosting Machines

The use of predictive models in education promises individual support and personalization for students. To develop trustworthy models, we need to understand what factors and causes contribute to a prediction. Thus, it is necessary to develop models that are not only accurate but also explainable. Moreover, we need to conduct holistic model evaluations that also quantify explainability or other metrics next to established performance metrics. This paper explores the use of Explainable Boosting Machines (EBMs) for the task of academic risk prediction. EBMs are an extension of Generative Additive Models and promise a state-of-the-art performance on tabular datasets while being inherently interpretable. We demonstrate the benefits of using EBMs in the context of academic risk prediction trained on online learning behavior data and show the explainability of the model. Our study shows that EBMs are equally accurate as other state-of-the-art approaches while being competitive on relevant metrics for trustworthy academic risk prediction such as earliness, stability, fairness, and faithfulness of explanations. The results encourage the broader use of EBMs for other Artificial Intelligence in education tasks.
Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets

Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues is significant to the design of dialogue-based intelligent tutoring systems. Many prior studies employ machine learning models to classify DAs in tutoring dialogues and invest much effort to optimize the classification accuracy by using limited amounts of training data (i.e., low-resource data scenario). However, beyond the classification accuracy, the robustness of the classifier is also important, which can reflect the capability of the classifier on learning the patterns from different class distributions. We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution. The DA classifiers in these studies tend to prioritize accuracy on the majority class at the expense of the minority class which might not be robust to the data with imbalanced ratios of different DA classes. To optimize the robustness of classifiers on imbalanced class distributions, we propose to optimize the performance of the DA classifier by maximizing the area under the ROC curve (AUC) score (i.e., AUC maximization). Through extensive experiments, our study provides evidence that (i) by maximizing AUC in the training process, the DA classifier achieves significant performance improvement compared to the CE approach under low-resource data, and (ii) AUC maximization approaches can improve the robustness of the DA classifier under different class imbalance ratios.
Involving Teachers in the Data-Driven Improvement of Intelligent Tutors: A Prototyping Study

Several studies show that log data analysis can lead to effective redesign of intelligent tutoring systems (ITSs). However, teachers are seldom included in the data-driven redesign of ITS, despite their pedagogical content knowledge. Examining teachers' possible contributions is valuable. To investigate what contributions teachers might make and whether (and how) data would be useful, we first built an interactive prototype tool for visualizing student log data, SolutionVis, based on needs identified in interviews with tutor authors. SolutionVis presents students' problem-solving processes with an intelligent tutor, including meta-cognitive aspects (e.g., hint requests). We then conducted a within-subjects user study with eight teachers to compare teachers' redesign suggestions obtained in three conditions: a baseline "no data" condition (where teachers examined just the tutor itself) and two "with data" conditions in which teachers worked with SolutionVis and with a list representation of student solutions, respectively. The results showed that teachers generated useful redesign ideas in all three conditions, that they viewed the availability of data (in both formats) as helpful and enabled them to generate a wider range of redesign suggestions, specifically with respect to hint design and feedback on gaming-the-system behaviors and struggle. The current work suggests potential benefits and ways of involving teachers in the data-driven improvement of ITSs.
Neural Automated Essay Scoring Considering Logical Structure

Automated essay scoring (AES) models based on deep neural networks (DNN) have recently achieved high accuracy. However, conventional neural AES models cannot explicitly consider the logical structure of each essay. Explicitly considering the logical structure in neural AES models is expected to improve scoring accuracy because logical structure is an important factor affecting essay quality. Accordingly, this study proposes a neural AES method that incorporates information about logical structure. First, the proposed method estimates the logical structure of each essay using the argument mining method, which is a machine learning method for extracting the logical structure from texts. Then, the logical structure is processed using a newly developed neural architecture, which we formulate as a transformer-based DNN model with modified self-attention, and a distributed representation of the logical structure is output. Finally, the proposed method integrates that distributed representation into conventional neural AES models to predict the essay score. We demonstrate the effectiveness of the proposed method through experiments using benchmark data for AES.
FM-Unet: Biomedical image segmentation based on feedback mechanism Unet

&lt;abstract&gt;&lt;p&gt;With the development of deep learning, medical image segmentation technology has made significant progress in the field of computer vision. The Unet is a pioneering work, and many researchers have conducted further research based on this architecture. However, we found that most of these architectures are improvements in the backward propagation and integration of the network, and few changes are made to the forward propagation and information integration of the network. Therefore, we propose a feedback mechanism Unet (FM-Unet) model, which adds feedback paths to the encoder and decoder paths of the network, respectively, to help the network fuse the information of the next step in the current encoder and decoder. The problem of encoder information loss and decoder information shortage can be well solved. The proposed model has more moderate network parameters, and the simultaneous multi-node information fusion can alleviate the gradient disappearance. We have conducted experiments on two public datasets, and the results show that FM-Unet achieves satisfactory results.&lt;/p&gt;&lt;/abstract&gt;
Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentation

Unsupervised domain adaptation (UDA) aims to adapt a model learned from the source domain to the target domain. Thus, the model can obtain transferable knowledge even in target domain that does not have ground truth in this way. In medical image segmentation scenarios, there exist diverse data distributions caused by intensity in homogeneities and shape variabilities. But multi source data may not be freely accessible, especially medical images with patient identity information.To tackle this issue, we propose a new multi-source and source-free (MSSF) application scenario and a novel domain adaptation framework where in the training stage, we only get access to the well-trained source domain segmentation models without source data. First, we propose a new dual consistency constraint which uses domain-intra and domain-inter consistency to filter those predictions agreed by each individual domain expert and all domain experts. It can serve as a high-quality pseudo label generation method and produce correct supervised signals for target domain supervised learning. Next, we design a progressive entropy loss minimization method to minimize the class-inter distance of features, which is beneficial to enhance domain-intra and domain-inter consistency in turn.Extensive experiments are performed for retinal vessel segmentation under MSSF condition and our approach produces impressive performance. The sensitivity metric of our approach is highest and it surpasses other methods with a large margin.It is the first attempt to conduct researches on the retinal vessel segmentation task under multi-source and source-free scenarios. In medical applications, such adaptation method can avoid the privacy issue. Furthermore, how to balance the high sensitivity and high accuracy need to be further considered.
Explainable Artificial Intelligence for Ancient Architecture and Lacquer Art

This research investigates the use of explainable artificial intelligence (XAI) in ancient architecture and lacquer art. The aim is to create accurate and interpretable models to reveal these cultural artefacts’ underlying design principles and techniques. To achieve this, machine learning and data-driven techniques are employed, which provide new insights into their construction and preservation. The study emphasises the importance of transparent and trustworthy AI systems, which can enhance the reliability and credibility of the results. The developed model outperforms CNN-based emotion recognition and random forest models in all four evaluation metrics, achieving an impressive accuracy of 92%. This research demonstrates the potential of XAI to support the study and conservation of ancient architecture and lacquer art, opening up new avenues for interdisciplinary research and collaboration.
Research and analysis of interaction in virtual reality films in the context of new media

With the continuous progress of computer network technology and the burst of new digital media art, virtual reality technology, a revolution in transmitting information, has come into being. As a brand new combination of computer technology and film art, virtual reality films have an interactive experience that no other art genre can achieve. During the movie watching process, the audience can walk into the movie scene, panoramic arbitrary observation of any angle around, and active interaction with people or objects in the scene, creating a personal memory palace, creating a unique storyline, creating a virtual dream space, and re-creating personal life and personal experience in the process of virtual experience. This paper firstly, through understanding the origin and background of virtual reality, analyzing and comparing the development status of virtual reality movies at home and abroad and looking forward to its prospect, so as to arrive at the possible value of virtual reality movies realized under the future digital technology media; secondly, combining the characteristics of virtual reality interactive technology, through analyzing the development process of traditional movie experience, comparing and arriving at the difference between traditional movies and virtual reality movies in Secondly, by analyzing the development process of traditional movie experience with the characteristics of virtual reality interactive technology, we compare and contrast the experience difference between traditional movies and virtual reality movies in four aspects: time and space construction, screen boundary, subjective experience, and interaction mode; secondly, we analyze the experience process from shallow to deep in virtual reality movies, and explore the psychological demand value rooted in the spirit after the intermingling of technology and movie content; finally, we elaborate on the experience value feedback brought to the audience after the practical application of virtual reality technology, and at the same time, we think deeply about this new digital technology medium. Lastly, it is the feedback of the experience value brought by the practical application of virtual reality technology to the audience and the reflection and critique of this new digital technology medium.
A Comparison of Biology Pre-service Teacher Learning Achievement Using Offline and Online Method in Biology Learning Evaluation Course

Biology learning evaluation course is a compulsory subject for undergraduate biology education students because it prepares students to design learning assessments when they become teachers later.Regularly, offline learning used discussion and problem-solving methods face-to-face.However, with the pandemic of Covid-19 since 2020, learning was to be online; began after the midterm exam.This study aims to compare the learning outcome of prospective biology teacher students when participating in offline and online learning.This research is quantitative-comparative research which a population is 164 students.Data were analyzed using SPSS with paired sample t-test technique, and used Wilcoxon test (non-parametric test), because the normality test was not met.The results showed a significance value of 0.328 > 0.050 which means that H0 is accepted.To be concluded, there is no difference in the learning achievement of biology pre-service teachers in this course using offline and online methods.
Quantum Circuit-Width Reduction through Parameterisation and Specialisation

As quantum computing technology continues to develop, the need for research into novel quantum algorithms is growing. However, such algorithms cannot yet be reliably tested on actual quantum hardware, which is still limited in several ways, including qubit coherence times, connectivity, and available qubits. To facilitate the development of novel algorithms despite this, simulators on classical computing systems are used to verify the correctness of an algorithm, and study its behaviour under different error models. In general, this involves operating on a memory space that grows exponentially with the number of qubits. In this work, we introduce quantum circuit transformations that allow for the construction of parameterised circuits for quantum algorithms. The parameterised circuits are in an ideal form to be processed by quantum compilation tools, such that the circuit can be partially evaluated prior to simulation, and a smaller specialised circuit can be constructed by eliminating fixed input qubits. We show significant reduction in the number of qubits for various quantum arithmetic circuits. Divide-by-n-bits quantum integer dividers are used as an example demonstration. It is shown that the complexity reduces from 4n+2 to 3n+2 qubits in the specialised versions. For quantum algorithms involving divide-by-8 arithmetic operations, a reduction by 28=256 in required memory is achieved for classical simulation, reducing the memory required from 137 GB to 0.53 GB.
Increasing Indonesian Students’ Writing Ability Through Make A Match Learning Model

Nowdays in elementary and middle school, there has been a noticeable rise in expectations for children' writing abilities. This study aimed to analyze the application of make and match model on the writing skills of Indonesian students. The type of research used was experimental research, with a one group pretest-posttest design. The population of this study was fourth grade students, totaling 27 students, consisting of 13 students from class A and 14 students from class B. Data collection techniques in conducting this research are observation, tests, and documentation. Data analysis techniques use descriptive analysis and inferential analysis. Based on the results of the study obtained an average pretest score of 68.21 and posttest 78.85. The results obtained from the t-test, the t-count value is 8.034 and the t-table is 1.782 with a significant level of 0.05 turns out to meet the criteria, t-count t table. The data obtained by using the –t test formula. Based on the results of the study, it was obtained that the calculated data value was &gt; from t table. Therefore, it can be concluded that the use of the make a match learning method is effective in learning Indonesian for improvement of the writing ability of the fourth grade students.
System-Level Dependability Analysis of Bitcoin under Eclipse and 51% Attacks

Bitcoin is an electronic cryptocurrency developed based on Blockchain technology. With its decentralized feature, it has become incredibly popular since its invention. However, the Bitcoin network suffers from 51% attacks, where if malicious attackers’ control over half of the computing power, they are able to rewrite the network. The attackers are capable of doing so by initiating the Eclipse attack first, which aims to monopolize all communications from and to a controlled Bitcoin node. In this paper, we model and analyze the dependability of the Bitcoin network subject to the Eclipse and 51% attacks. We propose a hierarchical model that encompasses a continuous-time Markov chain method for the node-level dependability analysis and a multi-valued decision diagram method for the system-level dependability analysis. Detailed case studies on Bitcoin systems with homogeneous and heterogeneous nodes are conducted to demonstrate the proposed model and investigate the impacts of several critical parameters on Bitcoin network dependability.
EPISTEMOLOGICAL COMPETENCE AS SCIENCE IMPERATIVE IN THE ERA OF GLOBAL TURBULENCE

The principal thematic, conceptual and argumentation approaches to the problem of epistemological competency in the era of information society and global turbulence have been considered.