Reviewerly/exHarmony huggingface dataset:
The Use of Digital Technologies in Teaching and Assessment

Nowadays modern technologies can be found everywhere in our digital era leading to and requiring new competencies to be able to fully participate in society; consequently, digital competencies are indispensable for students. However, the development of students’ competencies requires teachers’ competencies, in particular knowledge and abilities to initiate processes for developing learners’ competencies in learning environments which can be both technologically rich and adaptive to new technologies. The new mathematical competencies required by students are becoming reflected in national and international policy documents and address numerous aims of globally stated directions. This chapter elaborates two areas, Teaching and Learning and Assessment, from the European Framework for the Digital Competence of Educators (DigCompEdu), through six examples that highlight possible uses of digital technologies in mathematics and illustrate how technologies can enhance the development of students’ competencies. These examples attempt to illuminate learners’ and teachers’ experience (and understanding) of mathematics and assessment (and how they might change) through integration of innovative, as well as commonly used, technologies. We summarize and generalize some main results in a way that could be taken as recommendations for teachers, teacher educators, and policymakers in relation to the implementation of digital technologies in the upcoming digital era.
Detecting outliers in crowdsourced bathymetric data based on intelligently optimized 3D-DBSCAN algorithm

To address the problem of low quality of the outlier detection results caused by the irregular spatial distribution of crowdsourced bathymetric data, an intelligently optimized 3D-DBSCAN method is hereby proposed for detecting outliers in crowdsourced bathymetric data. Firstly, the potential of the classic DBSCAN algorithm in clustering irregularly distributed data has been explored for three-dimensional space and upgraded to the 3D-DBSCAN algorithm. Meanwhile, three key parameters affecting the 3D-DBSCAN algorithm results have been determined, namely, the minimum number of objects in the neighborhood, the horizontal neighborhood radius and the vertical neighborhood radius. Then, the abilities of the K-nearest neighbor method and the genetic algorithm in setting the initial range of parameters and searching for optimal values have been comprehensively utilized. The optimal combination solution vector of the key parameters of 3D-DBSCAN under different distribution conditions has been adaptively acquired by defining and calculating the silhouette coefficient index. Finally, spatial clustering analysis on crowdsourced bathymetric data and intelligently detect outliers has been conducted by using the optimal combination solution vector as the input of the 3D-DBSCAN model parameters. The experimental results show that the proposed method can not only detect outliers in crowdsourced bathymetric data after adaptively optimizing the parameters, but also detect outliers by analyzing the distance relationship between points in the 3D space, thereby overcoming the limitations of traditional methods in detecting water depth anomalies at specific positions and irregularly distributed anomalies.
Face Recognition Bias Assessment through Quality Estimation Models

Recent advances in facial recognition technology have achieved outstanding performance, but unconstrained face recognition remains an ongoing issue. Facial-image-quality-evaluation algorithms evaluate the quality of the input samples, providing crucial information about the accuracy of recognition decisions. By doing so, this can lead to improved results in challenging scenarios. In recent years, significant progress has been made in assessing the quality of facial images. The computation of quality scores has become highly precise and closely correlated with the model results. In this paper, we reviewed and analyzed the existing biases of cutting-edge quality-estimation techniques for face recognition. Our experimentation focused on the quality estimators developed by MagFace, FaceQNet, and SER-FIQ and were evaluated on the CelebA reference dataset. A study of bias in the face-recognition model was conducted by analyzing the quality scores presented in each article. This allowed for an examination of existing biases within both the quality estimators and the face-recognition models.
A Four-Stage Mahalanobis-Distance-Based Method for Hand Posture Recognition

Automatic recognition of hand postures is an important research topic with many applications, e.g., communication support for deaf people. In this paper, we present a novel four-stage, Mahalanobis-distance-based method for hand posture recognition using skeletal data. The proposed method is based on a two-stage classification algorithm with two additional stages related to joint preprocessing (normalization) and a rule-based system, specific to hand shapes that the algorithm is meant to classify. The method achieves superior effectiveness on two benchmark datasets, the first of which was created by us for the purpose of this work, while the second is a well-known and publicly available dataset. The method’s recognition rate measured by leave-one-subject-out cross-validation tests is 94.69% on the first dataset and 97.44% on the second. Experiments, including comparison with other state-of-the-art methods and ablation studies related to classification accuracy and time, confirm the effectiveness of our approach.
Consistent Weighted Correlation-Based Attention for Transformer Tracking

Attention mechanism takes a crucial role among the key technologies in transformer-based visual tracking. However, the current methods for attention computing neglect the correlation between the query and the key, which results in erroneous correlations. To address this issue, a CWCTrack framework is proposed in this study for transformer visual tracking. To balance the weights of the attention module and enhance the feature extraction of the search region and template region, a consistent weighted correlation (CWC) module is introduced into the cross-attention block. The CWC module computes the correlation score between each query and all keys. Then, the correlation multiplies the consistent weights of the other query–key pairs to acquire the final attention weights. The weights of consistency are computed by the relevance of the query–key pairs. The correlation is enhanced for the relevant query–key pair and suppressed for the irrelevant query–key pair. Experimental results conducted on four prevalent benchmarks demonstrate that the proposed CWCTrack yields preferable performances.
Exploring the English Teaching Model Based on College Students' Participation in Natural Environment Integration

In ELT, how to assess and evaluate students' natural factors by labeling and classifying them has become a key issue. Based on this, this paper makes an in-depth study on the application of natural environment incorporation algorithm based on deep learning in English teaching. First, this paper briefly explains the background and development direction of the current applications in ELT, and categorizes, summarizes, and analyzes them, focusing on the characteristics, problems, and issues of each study itself, and dissecting their limitations. Secondly, the environmental integration technologies in English teaching are categorized, and an intelligent and smart teaching assessment scheme is proposed, combined with the assessment of English teaching mode. Finally, the paper also conducts an experimental validation. The results of the study show that applying English assessment methods to learners of different English levels to their categorized English teaching can improve the quality of English teaching.
How to Reduce Online Hate Speech Among Adolescents? A Pilot Study on the Effects of a Teaching Unit on Social Norms, Self-efficacy and Knowledge About Hate Speech

A quasi-experimental study with ninth graders evaluated a 1.5-hour hate speech teaching unit in an intervention vs. control group (N = 82) before (T1) and after the intervention (T2). Participants reported frequency of witnessing hate speech (T1), hate speech norm and self-efficacy countering hate speech (T1 and T2), and knowledge concerning hate speech (T2). Repeated ANOVAs showed a significant three-way interaction for hate speech norm: Especially among those who witnessed hate speech more often, the program diminished the agreement to hate speech norm. Self-efficacy did not change significantly, but knowledge was slightly higher in the intervention group, particularly among students with a migration background. In sum, the intervention showed effects on norms and knowledge, and longer programs with more interactive elements for coping with hate speech seem recommendable. The current research is a first step and evidence-based practice for prevention of hate speech like the current evaluation study is desperately needed.
Toward Design of Synthetic Active Inference Agents by Mere Mortals

The theoretical properties of active inference agents are impressive, but how do we realize effective agents in working hardware and software on edge devices? This is an interesting problem because the computational load for policy exploration explodes exponentially, while the computational resources are very limited for edge devices. In this paper, we discuss the necessary features for a software toolbox that supports a competent non-expert engineer to develop working active inference agents. We introduce a toolbox-in-progress that aims to accelerate the democratization of active inference agents in a similar way as TensorFlow propelled applications of deep learning technology.
Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-k high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance.
Geochemical Overprinting and Secondary Placer Crystal Formation in the La Cholla District, Quartzsite, Arizona, USA: Evidence from Copper Isotopes, Morphology, and Trace Elements

Geochemical overprinting on placer gold deposits produces complex deposits with apparently paradoxical geochemical and morphological attributes. The La Cholla placer gold deposits near Quartzsite, Arizona, USA, contain sharp and often undamaged gold crystals within high-energy gravels as a result of postdepositional recrystallization by localized hot springs. Geothermal fluids emanated from a basin-bounding fault and left a distinct signature that includes recrystallized gold with low Pb and δ65Cu &gt; 1.14‰, secondary uraninite and carnotite, travertine deposits, and mounds of highly silicified placer gravels. Surrounding these deposits are placer gravels with angular gold morphology and geochemistry indicative of flash-flood pulse placers with a lode gold source &lt; 5 km and gold particles with low Pb and δ65Cu &lt; 0.77‰. The multitude of small local lode gold source veins is likely related to specialized fluid migration along mid-Tertiary detachment faults. The unroofing of these veins at 5 to 17 Ma by high-angle Basin and Range faulting led to extensive placer development, with subsequent localized modification by geothermal fluid migration along the same faults. This study documents geochemical overprinting and modification of placer deposits and bears upon the study of other placers containing crystalline gold and uranium mineralization. Similar deposits may document past geothermal activity and identify structural fluid conduits and steep paleo-topographic gradients.
Innovative Dual-Stage Blind Noise Reduction in Real-World Images Using Multi-Scale Convolutions and Dual Attention Mechanisms

The distribution of real noise in images can disrupt the inherent symmetry present in many natural visuals, thus making its effective removal a paramount challenge. However, traditional denoising methods often require tedious manual parameter tuning, and a significant portion of deep learning-driven techniques have proven inadequate for real noise. Moreover, the efficiency of end-to-end algorithms in restoring symmetrical patterns in noisy images remains questionable. To harness the principles of symmetry for improved denoising, we introduce a dual deep learning model with a focus on preserving and leveraging symmetrical patterns in real images. Our methodology operates in two stages. In the first, we estimate the noise level using a four-layer neural network, thereby aiming to capture the underlying symmetrical structures of the original image. To enhance the extraction of symmetrical features and overall network performance, a dual attention mechanism is employed before the final convolutional layer. This innovative module adaptively assigns weights to features across different channels, thus emphasizing symmetry-preserving elements. The subsequent phase is devoted to non-blind denoising. It integrates the estimated noise level and the original image, thus targeting the challenge of denoising while preserving symmetrical patterns. Here, a multi-scale architecture is used, thereby amalgamating image features into two branches. The first branch taps into dilation convolution, thus amplifying the receptive field without introducing new parameters and making it particularly adept at capturing broad symmetrical structures. In contrast, the second branch employs a standard convolutional layer to focus on finer symmetrical details. By harnessing varied receptive fields, our method can recognize and restore image symmetries across different scales. Crucial skip connections are embedded within this multi-scale setup, thus ensuring that symmetrical image data is retained as the network deepens. Experimental evaluations, conducted on four benchmark training sets and 12 test datasets, juxtaposed with over 20 contemporary models based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, underscore our model’s prowess in not only denoising but also in preserving and accentuating symmetrical elements, thereby setting a new gold standard in the field.
Probabilistic Majorization of Partially Observable Markov Decision Processes

Markov Decision Processes (MDPs) are wielded by the Reinforcement Learning and control community as a framework to bestow artificial agents with the ability to make autonomous decisions. Control as Inference (CaI) is a tangent research direction that aims to recast optimal decision making as an instance of probabilistic inference, with the dual hope to incite exploration and simplify calculations. Active Inference (AIF) is a sibling theory conforming to similar directives. Notably, AIF also entertains a procedure for per- and proprio-ception, which is currently lacking from the CaI theory. Recent work has established an explicit connection between CaI and Markov Decision Processes (MDPs). In particular, it was shown that the CaI policy can be iterated recursively, ultimately retrieving the associated MDP policy. In this work, such results are generalized to Partially Observable Markov Decision Processes, that – apart from a procedure to make optimal decisions – now also entertains a procedure for model based per- and proprio-ception. By extending the theory of CaI to the context of optimal decision making under partial observability, we mean to further our understanding of and illuminate the relationship between these different frameworks.
Bank Digital Transformation and Enterprise Innovation—Evidence from China

With the rapid advancement of digital technology, the banking industry has embarked on a journey of digital transformation. While existing literature primarily examines how these changes impact the banks themselves, our study focuses on a relatively unexplored aspect: the direct influence of bank digital transformation on the performance and behavior of borrowing enterprises. The research objective of this study is to explore the influence of bank digital transformation on the innovation performance of borrowing enterprises and the underlying mechanisms. Leveraging data from Chinese listed companies and commercial banks, we find a positive effect of bank digital transformation on enterprise innovation output as measured by firms’ patent applications. The findings remain robust across alternative model specifications, controls for regional digital economy development levels, and bank financial performance, as well as alternative measures of bank digital transformation. Mechanism tests show that bank digital transformation contributes to corporate innovation by alleviating corporate financial constraints and improving corporate governance. Further research demonstrates that bank digital transformation also helps promote corporate innovation efficiency as measured by the proportion of patent output to total R&amp;D input and corporate innovation output as measured by firms’ invention patent applications and patent grants. Additionally, borrowing firms’ own digital transformations may substitute for bank digital transformation in their effect on innovations.
Enhancing Web Application Security through Automated Penetration Testing with Multiple Vulnerability Scanners

Penetration testers have increasingly adopted multiple penetration testing scanners to ensure the robustness of web applications. However, a notable limitation of many scanning techniques is their susceptibility to producing false positives. This paper presents a novel framework designed to automate the operation of multiple Web Application Vulnerability Scanners (WAVS) within a single platform. The framework generates a combined vulnerabilities report using two algorithms: an automation algorithm and a novel combination algorithm that produces comprehensive lists of detected vulnerabilities. The framework leverages the capabilities of two web vulnerability scanners, Arachni and OWASP ZAP. The study begins with an extensive review of the existing scientific literature, focusing on open-source WAVS and exploring the OWASP 2021 guidelines. Following this, the framework development phase addresses the challenge of varying results obtained from different WAVS. This framework’s core objective is to combine the results of multiple WAVS into a consolidated vulnerability report, ultimately improving detection rates and overall security. The study demonstrates that the combined outcomes produced by the proposed framework exhibit greater accuracy compared to individual scanning results obtained from Arachni and OWASP ZAP. In summary, the study reveals that the Union List outperforms individual scanners, particularly regarding recall and F-measure. Consequently, adopting multiple vulnerability scanners is recommended as an effective strategy to bolster vulnerability detection in web applications.
Design Recommendations for Immersive Virtual Reality Application for English Learning: A Systematic Review

The growing popularity of immersive virtual reality (iVR) technologies has opened up new possibilities for learning English. In the literature, it is possible to find several studies focused on the design, development, and evaluation of immersive virtual reality applications. However, there are no studies that systematize design recommendations for immersive virtual reality applications for English learning. To fill this gap, we present a systematic review that aims to identify design recommendations for immersive virtual reality English learning applications. We searched the ACM Digital Library, ERIC, IEEE Xplore, Scopus, and Web of Science (1 January 2010 to April 2023) and found that 24 out of 847 articles met the inclusion criteria. We identified 18 categories of design considerations related to design and learning and a design process used to create iVR applications. We also identified existing trends related to universities, publications, devices, human senses, and development platforms. Finally, we addressed study limitations and future directions for designing iVR applications for English learning.
Sequential Joint State Estimation and Track Extraction Algorithm Based on Improved Backward Smoothing

In order to further promote the accuracy of state estimation and track extraction capabilities for multi-target tracking, a sequential joint state estimation and track extraction algorithm is proposed in this article. This algorithm is based on backward smoothing under the framework of Labeled Random Finite Set and utilizes label iterative processing to perform outlier removal, invalid short-lived track removal, and track continuity processing in a sequential manner in order to achieve the goal of improving the multi-target tracking performance of the algorithm. Finally, this paper verifies through experimental simulation and measured data analysis that the proposed algorithm has improved the performance of radar multi-target tracking to a certain extent.
Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence

Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data. Some relational learning methods have been proposed, but those with discriminative relationship analysis are lacking yet, as important supervisory information is usually ignored. In this paper, we propose a novel and general framework, called relational Fisher analysis (RFA), which successfully integrates relational information into the dimensionality reduction model. For nonlinear data representation learning, we adopt the kernel trick to RFA and propose the kernelized RFA (KRFA). In addition, the convergence of the RFA optimization algorithm is proved theoretically. By leveraging suitable strategies to construct the relational matrix, we conduct extensive experiments to demonstrate the superiority of our RFA and KRFA methods over related approaches.