Reviewerly/exHarmony huggingface dataset:
Online Diagnostic Assessment in Support of Personalized Teaching and Learning: The eDia System

The aims of this paper are: to provide a comprehensive introduction to eDia, an online diagnostic assessment system; to show how the use of technology can contribute to solving certain crucial problems in education by supporting the personalization of learning; and to offer a general reference for further eDia-based studies. The primary function for which the system is designed is to provide regular diagnostic feedback in three main domains of education, reading, mathematics and science, from the beginning of schooling to the end of the six years of primary education. The cognitive foundations of the system, the assessment frameworks, are based on a three-dimensional approach in each domain, distinguishing the psychological (reasoning), the application and the disciplinary (curricular content) dimensions of learning. The frameworks have been carefully mapped into item banks containing over a thousand innovative (multimedia-supported) items in each dimension. The online assessments were piloted, and the system has been operating in experimental mode in over 1000 schools for several years. This paper outlines the theoretical foundations of the eDia system and summarizes how results from research on the cognitive sciences, learning and instruction, and technology-based assessment have been integrated into a working system designed to assess a large population of students. The paper describes the main functions of eDia and discusses how it supports item writing, constructing tests, online test delivery, automated scoring, data processing, scaling and the provision of feedback both for students and teachers. It shows how diagnostic assessments can be implemented in school practice to facilitate differentiated instruction through regular measurements and to provide instruments for teachers to make formative assessments. Beyond its main function (supporting development towards personalizing education), the eDia platform has been used for assessments in a number of areas from pre-school to higher education both in Hungary and in a number of other countries as well. The paper also reviews results from eDia-based studies and highlights how technology-based assessment extends the possibilities of educational research by making more constructs measurable.
A Diagnostic Study of Explainability Techniques for Text Classification

Recent developments in machine learning have introduced models that approach human performance at the cost of increased architectural complexity.Efforts to make the rationales behind the models' predictions transparent have inspired an abundance of new explainability techniques.Provided with an already trained model, they compute saliency scores for the words of an input instance.However, there exists no definitive guide on (i) how to choose such a technique given a particular application task and model architecture, and (ii) the benefits and drawbacks of using each such technique.In this paper, we develop a comprehensive list of diagnostic properties for evaluating existing explainability techniques.We then employ the proposed list to compare a set of diverse explainability techniques on downstream text classification tasks and neural network architectures.We also compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones.Overall, we find that the gradient-based explanations perform best across tasks and model architectures, and we present further insights into the properties of the reviewed explainability techniques.
Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction

Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting aspect terms and opinion terms from review in the form of opinion pairs or additionally extracting sentiment polarity of aspect term to form opinion triplet.Because of containing several opinion factors, the complete AFOE task is usually divided into multiple subtasks and achieved in the pipeline.However, pipeline approaches easily suffer from error propagation and inconvenience in real-world scenarios.To this end, we propose a novel tagging scheme, Grid Tagging Scheme (GTS), to address the AFOE task in an end-to-end fashion only with one unified grid tagging task.Additionally, we design an effective inference strategy on GTS to exploit mutual indication between different opinion factors for more accurate extractions.To validate the feasibility and compatibility of GTS, we implement three different GTS models respectively based on CNN, BiL-STM, and BERT, and conduct experiments on the aspect-oriented opinion pair extraction and opinion triplet extraction datasets.Extensive experimental results indicate that GTS models outperform strong baselines significantly and achieve state-of-the-art performance.
Avatar Embodiment. A Standardized Questionnaire

The aim of this paper is to further the understanding of embodiment by 1) analytically determining the components defining embodiment, 2) increasing comparability and standardization of the measurement of embodiment across experiments by providing a universal embodiment questionnaire that is validated and reliable, and 3) motivating researchers to use a standardized questionnaire. In this paper we validate numerically and refine our previously proposed Embodiment Questionnaire. We collected data from nine experiments, with over 400 questionnaires, that used all or part of the original embodiment 25-item questionnaire. Analysis was performed to eliminate non-universal questions, redundant questions, and questions that were not strongly correlated with other questions. We further numerically categorized and weighted sub-scales and determined that embodiment is comprised of interrelated categories of Appearance, Response, Ownership, and Multi-Sensory. The final questionnaire consists of 16 questions and four interrelated sub-scales with high reliability within each sub-scale, Chronbach’s α ranged from 0.72 to 0.82. Results of the original and refined questionnaire are compared over all nine experiments and in detail for three of the experiments. The updated questionnaire produced a wider range of embodiment scores compared to the original questionnaire, was able to detect the presence of a self-avatar, and was able to discern that participants over 30 years of age have significantly lower embodiment scores compared to participants under 30 years of age. Removed questions and further research of interest to the community are discussed.
Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (Re-ID) aims to match the pedestrian images of the same identity from different modalities. Existing works mainly focus on alleviating the modality discrepancy by aligning the distributions of features from different modalities. However, nuanced but discriminative information, such as glasses, shoes, and the length of clothes, has not been fully explored, especially in the infrared modality. Without discovering nuances, it is challenging to match pedestrians across modalities using modality alignment solely, which inevitably reduces feature distinctiveness. In this paper, we propose a joint Modality and Pattern Alignment Network (MPANet) to discover cross-modality nuances in different patterns for visible-infrared person Re-ID, which introduces a modality alleviation module and a pattern alignment module to jointly extract discriminative features. Specifically, we first propose a modality alleviation module to dislodge the modality information from the extracted feature maps. Then, We devise a pattern alignment module, which generates multiple pattern maps for the diverse patterns of a person, to discover nuances. Finally, we introduce a mutual mean learning fashion to alleviate the modality discrepancy and propose a center cluster loss to guide both identity learning and nuances discovering. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the superiority of MPANet over state-of-the-arts.
Neural Multimodal Cooperative Learning Toward Micro-Video Understanding

The prevailing characteristics of micro-videos result in the less descriptive power of each modality. The micro-video representations, several pioneer efforts proposed, are limited in implicitly exploring the consistency between different modality information but ignore the complementarity. In this paper, we focus on how to explicitly separate the consistent features and the complementary features from the mixed information and harness their combination to improve the expressiveness of each modality. Toward this end, we present a neural multimodal cooperative learning (NMCL) model to split the consistent component and the complementary component by a novel relation-aware attention mechanism. Specifically, the computed attention score can be used to measure the correlation between the features extracted from different modalities. Then, a threshold is learned for each modality to distinguish the consistent and complementary features according to the score. Thereafter, we integrate the consistent parts to enhance the representations and supplement the complementary ones to reinforce the information in each modality. As to the problem of redundant information, which may cause overfitting and is hard to distinguish, we devise an attention network to dynamically capture the features which closely related the category and output a discriminative representation for prediction. The experimental results on a real-world micro-video dataset show that the NMCL outperforms the state-of-the-art methods. Further studies verify the effectiveness and cooperative effects brought by the attentive mechanism.
Object Detection through Modified YOLO Neural Network

In the field of object detection, recently, tremendous success is achieved, but still it is a very challenging task to detect and identify objects accurately with fast speed. Human beings can detect and recognize multiple objects in images or videos with ease regardless of the object’s appearance, but for computers it is challenging to identify and distinguish between things. In this paper, a modified YOLOv1 based neural network is proposed for object detection. The new neural network model has been improved in the following ways. Firstly, modification is made to the loss function of the YOLOv1 network. The improved model replaces the margin style with proportion style. Compared to the old loss function, the new is more flexible and more reasonable in optimizing the network error. Secondly, a spatial pyramid pooling layer is added; thirdly, an inception model with a convolution kernel of 1 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>∗</mml:mi></mml:math> 1 is added, which reduced the number of weight parameters of the layers. Extensive experiments on Pascal VOC datasets 2007/2012 showed that the proposed method achieved better performance.
An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.
Teachers’ digital competencies in higher education: a systematic literature review

Abstract Digital competence has gained a strong prominence in the educational context, being one of the key competencies that teachers must master in today's society. Although most models and frameworks focus on the pre-university level, there is a growing interest in knowing the state of digital competencies of university teachers, that is, the set of knowledge, skills and attitudes necessary for a teacher to make effective use of technologies. The aim of this research is to present a systematic review of the literature in the Web of Science and Scopus, to identify, analyze and classify the published articles between 2000 and 2021 on digital competences, and thus find and improve the research being done on digital skills and future avenues of teachers in the university context. The SciMAT software is used in the analysis. The initial search reveals more than 343 articles in English, of which 152 are duplicates and 135 are not related to the topic of study. After this filtering, 56 articles are obtained and analyzed in depth. The results reveal a predominance of research that focuses on analyzing teachers' self-assessment and reflection of their digital competencies. Teachers recognize that they have a low or medium–low digital competence, as well as the absence of certain competencies, especially those related to the evaluation of educational practice. Despite the multiple studies that address this issue, it is necessary to continue improving research in this area, deepening the assessment of teachers' digital competencies and design, on this basis, more practical and personalized training programs that respond to the needs of teachers in the digital era.
SeaSign: Compact Isogeny Signatures from Class Group Actions

We give a new signature scheme for isogenies that combines the class group actions of CSIDH with the notion of Fiat-Shamir with aborts. Our techniques allow to have signatures of size less than one kilobyte at the 128-bit security level, even with tight security reduction (to a non-standard problem) in the quantum random oracle model. Hence our signatures are potentially shorter than lattice signatures, but signing and verification are currently very expensive.
Earthquake early warning: Recent advances and perspectives

Earthquake early warning (EEW) is a relatively new strategy for reducing disaster risk and increasing resilience to seismic hazard in urban settings. EEW systems provide real-time information about ongoing earthquakes, enabling individuals, communities, governments, businesses and others located at distance to take timely action to reduce the probability of harm or loss before the earthquake-induced ground shaking reaches them. Examples of potential losses mitigated by EEW systems include injuries and infrastructure downtime. These systems are currently operating in nine countries, and are being/have been tested for implementation in 13 more. This paper reviews state-of-the-art approaches to EEW around the world. We specifically focus on the various algorithms that have been developed for the rapid calculation of seismic-source parameters, ground shaking, and potential consequences in the wake of an event. We also discuss limitations of the existing applied methodologies, with a particular emphasis on the lack of engineering-related (i.e., risk and resilience) metrics currently used to support decision-making related to the triggering of alerts by various end users. Finally, we provide a number of suggestions for future end-user-orientated advances in the field of EEW. For example, we propose that next-generation EEW systems should incorporate engineering-based, application-specific models/tools for more effective risk communication. They should operate within robust probabilistic frameworks that explicitly quantify uncertainties at each stage of the analysis, for more informed stakeholder decision-making. These types of advancements in EEW systems would represent an important paradigm shift in current approaches to issuing early warnings for natural hazards.
Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation

In many practical transfer learning scenarios, the feature distribution is different across the source and target domains (i.e., nonindependent identical distribution). Maximum mean discrepancy (MMD), as a domain discrepancy metric, has achieved promising performance in unsupervised domain adaptation (DA). We argue that the MMD-based DA methods ignore the data locality structure, which, up to some extent, would cause the negative transfer effect. The locality plays an important role in minimizing the nonlinear local domain discrepancy underlying the marginal distributions. For better exploiting the domain locality, a novel local generative discrepancy metric-based intermediate domain generation learning called Manifold Criterion guided Transfer Learning (MCTL) is proposed in this paper. The merits of the proposed MCTL are fourfold: 1) the concept of manifold criterion (MC) is first proposed as a measure validating the distribution matching across domains, and DA is achieved if the MC is satisfied; 2) the proposed MC can well guide the generation of the intermediate domain sharing similar distribution with the target domain, by minimizing the local domain discrepancy; 3) a global generative discrepancy metric is presented, such that both the global and local discrepancies can be effectively and positively reduced; and 4) a simplified version of MCTL called MCTL-S is presented under a perfect domain generation assumption for more generic learning scenario. Experiments on a number of benchmark visual transfer tasks demonstrate the superiority of the proposed MC guided generative transfer method, by comparing with the other state-of-the-art methods. The source code is available in https://github.com/wangshanshanCQU/MCTL.
Predicting Intentions of Students for Master Programs Using a Chaos-Induced Sine Cosine-Based Fuzzy K-Nearest Neighbor Classifier

One of the essential tasks for the planning and development of talents training programs in different colleges of universities is to find how we can reasonably guide students to pursue a master's degree concerning their comprehensive situations.The purpose of this study is to develop a modified fuzzy k-Nearest Neighbor (FKNN) framework to predict the college students' intentions for master programs in advance, that is, students choose to attend the postgraduate exam or find a job after graduation.The proposed integrated framework combines the random forest (RF), FKNN, and a new chaos-enhanced sine cosineinspired algorithm (CESCA).In this model, RF is employed to evaluate the importance of features in the dataset, while the FKNN is utilized to establish the relationship framework between the features and the college students' decisions to earn a master's degree or not.The proposed CESCA is utilized to tune the key parameters of the FKNN automatically.All eight variants of SCA have been rigorously compared based on 13 benchmark problems to validate the effectiveness of the proposed CESCA.Then, the CESCA-based FKNN (CESCA-FKNN) has been further compared against the other three classical classifiers in terms of four common performance metrics.The experimental results indicate that the proposed CESCA-FKNN can obtain the best classification accuracy.The results indicate that the established adaptive FKNN framework can be served as a powerful tool for college students' intention before pursuing a master's degree.
Predicting sepsis with a recurrent neural network using the MIMIC III database

Predicting sepsis onset with a recurrent neural network and performance comparison with InSight - a previously proposed algorithm for the prediction of sepsis onset. A retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC III database) who did not fall under the definition of sepsis at the time of admission. The area under the receiver operating characteristic (AUROC) measures the performance of the prediction task. We examine the sequence length given to the machine learning algorithms for different points in time before sepsis onset concerning the prediction performance. Additionally, the impact of sepsis onset's definition is investigated. We evaluate the model with a relatively large and thus more representative patient population compared to related works in the field. For a prediction 3 h prior to sepsis onset, our network achieves an AUROC of 0.81 (95% CI: 0.78–0.84). The InSight algorithm achieves an AUROC of 0.72 (95% CI: 0.69–0.75). For a fixed sensitivity of 90% our network reaches a specificity of 47.0% (95% CI: 43.1%–50.8%) compared to 31.1% (95% CI: 24.8%–37.5%) for InSight. In addition, we compare the performance for 6 and 12 h prediction time for both approaches. Our findings demonstrate that a recurrent neural network is superior to InSight considering the prediction performance. Most probably, the improvement results from the network's ability of revealing time dependencies. We show that the length of the look back has a significant impact on the performance of the classifier. We also demonstrate that for the correct detection of sepsis onset for a retrospective analysis, further research is necessary.
An Iterative Mean Filter for Image Denoising

We propose an Iterative Mean Filter (IMF) to eliminate the salt-and-pepper noise. IMF uses the mean of gray values of noise-free pixels in a fixed-size window. Unlike other nonlinear filters, IMF does not enlarge the window size. A large size reduces the accuracy of noise removal. Therefore, IMF only uses a window with a size of $3\times3$ . This feature is helpful for IMF to be able to more precisely evaluate a new gray value for the center pixel. To process high-density noise effectively, we propose an iterative procedure for IMF. In the experiments, we operationalize Peak Signal-to-Noise Ratio (PSNR), Visual Information Fidelity, Image Enhancement Factor, Structural Similarity (SSIM), and Multiscale Structure Similarity to assess image quality. Furthermore, we compare denoising results of IMF with ones of the other state-of-the-art methods. A comprehensive comparison of execution time is also provided. The qualitative results by PSNR and SSIM showed that IMF outperforms the other methods such as Based-on Pixel Density Filter (BPDF), Decision-Based Algorithm (DBA), Modified Decision-Based Untrimmed Median Filter (MDBUTMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Adaptive Type-2 Fuzzy Filter (FDS): for the IMAGESTEST dataset - BPDF (25.36/0.756), DBA (28.72/0.8426), MDBUTMF (25.93/0.8426), NAFSMF (29.32/0.8735), AWMF (32.25/0.9177), DAMF (31.65/0.9154), FDS (27.98/0.8338), and IMF (33.67/0.9252); and for the BSDS dataset - BPDF (24.95/0.7469), DBA (26.84/0.8061), MDBUTMF (26.25/0.7732), NAFSMF (27.26/0.8191), AWMF (28.89/0.8672), DAMF (29.11/0.8667), FDS (26.85/0.8095), and IMF (30.04/0.8753).