Reviewerly/exHarmony huggingface dataset:
THE WEBSITE OF THE HIGHER EDUCATION INSTITUTION AND ITS IMPORTANCE IN THE INFORMATION AND EDUCATIONAL ENVIRONMENT

The article examines the importance of creating an informational and educational environment of a higher education institution as a condition for quality provision of educational services for its applicants and informing potential users. The role of the informational competence of the educational worker in the innovative educational environment for orientation in today's variety of information sources is revealed. The theoretical and practical aspects of the functioning of the information and educational environment and the essence of the concept itself are considered. The functions and tasks of the educational website as an element of the informational and educational environment, which occupies the key place of an effective organizational lever, are analyzed. The structure of the University website was studied and the principles of filling it with public access information depending on the category of users were highlighted. A scheme for filling the official website of the Higher Education Institution with data is proposed. It has been established that in order to effectively solve the tasks that rely on the website of the Higher Education Institution, its structure must be competent and clear, as optimized and logical as possible, which affects its perception by users and ranking in the global Internet. The information content should take into account the category of users, the navigation should be as clear as possible, and the visual component should be attractive and recognizable.
Leveraging Group Contrastive Explanations for Handling Fairness

With the increasing adoption of Artificial Intelligence (AI) for decision-making processes by companies, developing systems that behave fairly and do not discriminate against specific groups of people becomes crucial. Reaching this objective requires a multidisciplinary approach that includes domain experts, data scientists, philosophers, and legal experts, to ensure complete accountability for algorithmic decisions. In such a context, Explainable AI (XAI) plays a key role in enabling professionals from different backgrounds to comprehend the functioning of automatized decision-making processes and, consequently, being able to identify the presence of fairness issues. This paper presents FairX, an innovative approach that uses Group-Contrastive (G-contrast) explanations to estimate whether different decision criteria apply among distinct population subgroups. FairX provides actionable insights through a comprehensive explanation of the decision-making process, enabling businesses to: detect the presence of direct discrimination on the target variable and choose the most appropriate fairness framework.
PERFEX: Classifier Performance Explanations for Trustworthy AI Systems

Explainability of a classification model is crucial when deployed in real-world decision support systems. Explanations make predictions actionable to the user and should inform about the capabilities and limitations of the system. Existing explanation methods, however, typically only provide explanations for individual predictions. Information about conditions under which the classifier is able to support the decision maker is not available, while for instance information about when the system is not able to differentiate classes can be very helpful. In the development phase it can support the search for new features or combining models, and in the operational phase it supports decision makers in deciding e.g. not to use the system. This paper presents a method to explain the qualities of a trained base classifier, called PERFormance EXplainer (PERFEX). Our method consists of a meta tree learning algorithm that is able to predict and explain under which conditions the base classifier has a high or low error or any other classification performance metric. We evaluate PERFEX using several classifiers and datasets, including a case study with urban mobility data. It turns out that PERFEX typically has high meta prediction performance even if the base classifier is hardly able to differentiate classes, while giving compact performance explanations.
Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features

Deep learning (DL) models achieve remarkable performance in classification tasks. However, models with high complexity can not be used in many risk-sensitive applications unless a comprehensible explanation is presented. Explainable artificial intelligence (xAI) focuses on the research to explain the decision-making of AI systems like DL. We extend a recent method of Class Activation Maps (CAMs) which visualizes the importance of each feature of a data sample contributing to the classification. In this paper, we aggregate CAMs from multiple samples to show a global explanation of the classification for semantically structured data. The aggregation allows the analyst to make sophisticated assumptions and analyze them with further drill-down visualizations. Our visual representation for the global CAM illustrates the impact of each feature with a square glyph containing two indicators. The color of the square indicates the classification impact of this feature. The size of the filled square describes the variability of the impact between single samples. For interesting features that require further analysis, a detailed view is necessary that provides the distribution of these values. We propose an interactive histogram to filter samples and refine the CAM to show relevant samples only. Our approach allows an analyst to detect important features of high-dimensional data and derive adjustments to the AI model based on our global explanation visualization.
The Duet of Representations and How Explanations Exacerbate It

An algorithm effects a causal representation of relations between features and labels in the human's perception. Such a representation might conflict with the human's prior belief. Explanations can direct the human's attention to the conflicting feature and away from other relevant features. This leads to causal overattribution and may adversely affect the human's information processing. In a field experiment we implemented an XGBoost-trained model as a decision-making aid for counselors at a public employment service to predict candidates' risk of long-term unemployment. The treatment group of counselors was also provided with SHAP. The results show that the quality of the human's decision-making is worse when a feature on which the human holds a conflicting prior belief is displayed as part of the explanation.
Contrastive Visual Explanations for Reinforcement Learning via Counterfactual Rewards

Causal attribution aided by counterfactual reasoning is recognised as a key feature of human explanation. In this paper we propose a post-hoc contrastive explanation framework for reinforcement learning (RL) based on comparing learned policies under actual environmental rewards vs. hypothetical (counterfactual) rewards. The framework provides policy-level explanations by accessing learned Q-functions and identifying intersecting critical states. Global explanations are generated to summarise policy behaviour through the visualisation of sub-trajectories based on these states, while local explanations are based on the action-values in states. We conduct experiments on several grid-world examples. Our results show that it is possible to explain the difference between learned policies based on Q-functions. This demonstrates the potential for more informed human decision-making when deploying policies and highlights the possibility of developing further XAI techniques in RL.
A Novel Architecture for Robust Explainable AI Approaches in Critical Object Detection Scenarios Based on Bayesian Neural Networks

Precise object detection algorithms are a mandatory requirement in safety critical applications such as autonomous driving or the analysis of safety critical situations. We provide a novel approach focusing on potential edge cases in order to address those specific situations which are often most the hardest part in bringing machine learning models into production within the aforementioned scenarios. We improve upon existing explainable artificial intelligence (XAI) methods by exploring both qualitative and quantitative approaches in the test setting of marker detection and develop novel techniques for quantifying the localization of explanations of the object detection models building upon class activation maps (CAMs). More specifically, we quantify CAMs via the shape of the pixel value sums across the CAMs axes. Building upon established object detection models our novel architecture is followed by the subsequent marker classification task via a reimplemented LeNet-like Bayesian CNN. Our presented results demonstrate that this approach to quantify the XAI techniques can improve the interpretability of both object detection and marker classification explanations by reducing their dimensionality. Moreover, the proposed method provides more clarity through increasingly interpretable explanations for the model’s decisions, helping domain experts to better understand the model’s behavior and improve the decision-making process. Our proposed approach can robustly be re-applied to other computer vision tasks focused on object detection and classification.
A Modified Artificial Potential Field Method Based on Subgoal Points for Mobile Robot

In this paper, a modified artificial potential field (MAPF) method was proposed for general mobile robot navigation system. This MAPF method can effectively solve the unreachable goal problem and the local minima problem of traditional APF method. The first key idea of MAPF method is to modify the repulsive force function and thus optimize the direction of total repulsive force; the second key idea is to generate a serials of subgoal points around obstacles with specific method, so as to help the robot escape from or keep away from local minima area. By comparing with other similar algorithms in simulation environment, the MAPF algorithm can generate shorter paths efficiently. More importantly, it also can generate effective paths in multi-obstacle environment where other algorithms cannot. Finally the simulation results verified the reasonability and practicability of the proposed MAPF method.
Human-Computer Interaction and Explainability: Intersection and Terminology

Human-computer interaction (HCI) is generally considered the broader domain encompassing the study of the relationships between humans and types of technological artifacts or systems. Explainable AI (xAI) is involved in HCI to have humans better understand computers or AI systems which fosters, as a consequence, better interaction. The term “explainability” is sometimes used interchangeably with other closely related terms such as interpretability or understandability. The same can be said for the term “interaction”. It is a very broad way to describe the relationship between humans and technologies, which is why it is often replaced or completed by more precise terms like cooperation, collaboration, teaming, symbiosis, and integration. In the same vein, the technologies are represented by several terms like computer, machine, AI, agent, and robot. However, each of these terms (technologies and relationships) has its specificity and properties which need to be clearly defined. Currently, the definitions of these various terms are not well established in the literature, and their usage in various contexts is ambiguous. The goals of this paper are threefold: First, clarify the terminology in the HCI domain representing the technologies and their relationships with humans. A few concepts specific to xAI are also clarified. Second, highlight the role that xAI plays or can play in the HCI domain. Third, study the evolution and tendency of the usage of explainability and interpretability with the HCI terminology throughout the years and highlight the observations in the last three years.
XInsight: Revealing Model Insights for GNNs with Flow-Based Explanations

Progress in graph neural networks has grown rapidly in recent years, with many new developments in drug discovery, medical diagnosis, and recommender systems. While this progress is significant, many networks are ‘black boxes’ with little understanding of the ‘what’ exactly the network is learning. Many high-stakes applications, such as drug discovery, require human-intelligible explanations from the models so that users can recognize errors and discover new knowledge. Therefore, the development of explainable AI algorithms is essential for us to reap the benefits of AI. We propose an explainability algorithm for GNNs called eXplainable Insight (XInsight) that generates a distribution of model explanations using GFlowNets. Since GFlowNets generate objects with probabilities proportional to a reward, XInsight can generate a diverse set of explanations, compared to previous methods that only learn the maximum reward sample. We demonstrate XInsight by generating explanations for GNNs trained on two graph classification tasks: classifying mutagenic compounds with the MUTAG dataset and classifying acyclic graphs with a synthetic dataset that we have open-sourced. We show the utility of XInsight’s explanations by analyzing the generated compounds using QSAR modeling, and we find that XInsight generates compounds that cluster by lipophilicity, a known correlate of mutagenicity. Our results show that XInsight generates a distribution of explanations that uncovers the underlying relationships demonstrated by the model. They also highlight the importance of generating a diverse set of explanations, as it enables us to discover hidden relationships in the model and provides valuable guidance for further analysis.
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal

Explainable artificial intelligence (XAI) provides explanations for not interpretable machine learning (ML) models. While many technical approaches exist, there is a lack of validation of these techniques on real-world datasets. In this work, we present a use-case of XAI: an ML model which is trained to estimate electrification rates based on mobile phone data in Senegal. The data originate from the Data for Development challenge by Orange in 2014/15. We apply two model-agnostic, local explanation techniques and find that while the model can be verified, it is biased with respect to the population density. We conclude our paper by pointing to the two main challenges we encountered during our work: data processing and model design that might be restricted by currently available XAI methods, and the importance of domain knowledge to interpret explanations.
Necessary and Sufficient Explanations of Multi-Criteria Decision Aiding Models, with and Without Interacting Criteria

We are interested in explaining models from Multi-Criteria Decision Aiding. These can be used to perform pairwise comparisons between two options, or classify an instance in an ordered list of categories, on the basis of multiple and conflicting criteria. Several models can be used to achieve such goals ranging from the simplest one assuming independence among criteria - namely the weighted sum model - to complex models able to represent complex interaction among criteria, such as the Hierarchical Choquet Integral (HCI). We consider two complementary explanations of these two models under these two goals: sufficient explanation (a.k.a. Prime Implicants) and necessary explanations (a.k.a. Counterfactual explanations). The idea of prime implicants is to identify the parts of the instance that need to be kept unchanged so that the decision remains the same, while the other parts are replaced by any value. We generalize the notion of information that needs to be kept not only on the values of the criteria (values of the instance of the criteria) but also on the weights of criteria (parameters of the model). For the HCI model, we propose a Mixed-Integer Linear Program (MILP) formulation to compute the prime implicants. We also propose a weak version of prime implicants to account for the case where the requirements of changing the other criteria in any possible way is too strong. Finally, we also propose a MILP formulation for computing counterfactual explanations of the HCI model.
Improving Local Fidelity of LIME by CVAE

Knowing the basis of decisions is essential for using Machine Learning (ML) in various regions, such as medical diagnosis, automated driving, and organizational decision-making. The LIME algorithm is an XAI method that can be applied to many black-box models. However, there is a problem in that the local fidelity of the interpretable model decreases. The problem is due to the sampling and weighting step of LIME; using an autoencoder is an effective way to improve this. In this study, we aim to simultaneously improve the sampling and weighting of LIME in a classification task by using a conditional variational autoencoder and filtering samples by classes. Experiments were conducted to compare the local fidelity of the proposed and existing methods for neural network classifiers trained on three medical diagnostic data sets. The results show that the proposed method improves the local fidelity compared to the existing methods. In addition, we visualized the distribution of samples in the autoencoder latent space and conducted comparative experiments by attaching and detaching components of the existing and proposed methods to analyze the factors that improve fidelity.
Explainable Artificial Intelligence in Education: A Comprehensive Review

Explainable artificial intelligence (AI) has drawn a lot of attention recently since AI systems are being employed more often across a variety of industries, including education. Building trust and increasing the efficacy of AI systems in educational settings requires the capacity to explain how they make decisions. This article provides a comprehensive review of the current level of explainable AI (XAI) research and its application to education. We begin with the challenges of XAI in education, the complexity of AI algorithms, and the necessity for transparency and interpretability. Furthermore, we discuss the obstacles involved with using AI in education, and explore several solutions, including human-AI collaboration, explainability techniques, and ethical and legal frameworks. Subsequently, we debate about the importance of developing new competencies and skills among students and educators to interact with AI effectively, as well as how XAI impacts politics and government. Finally, we provide recommendations for additional research in this field and suggest potential future directions for XAI in educational research and practice.
MEGAN: Multi-explanation Graph Attention Network

We propose a multi-explanation graph attention network (MEGAN). Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications. This proves crucial to improve the interpretability of graph regression predictions, as explanations can be split into positive and negative evidence w.r.t to a reference value. Additionally, our attention-based network is fully differentiable and explanations can actively be trained in an explanation-supervised manner. We first validate our model on a synthetic graph regression dataset with known ground-truth explanations. Our network outperforms existing baseline explainability methods for the single- as well as the multi-explanation case, achieving near-perfect explanation accuracy during explanation supervision. Finally, we demonstrate our model’s capabilities on multiple real-world datasets. We find that our model produces sparse high-fidelity explanations consistent with human intuition about those tasks.
THE METHODOLOGY OF USE OF OPEN SCIENCE BY TEACHERS OF NATURAL AND MATHEMATICAL SUBJECTS OF THE UKRAINIAN SCHOOL

The statement of basic materials. Examples of open science environments are considered in the following areas: platforms for placing and searching for data, scientific publications with substantiation of scientific results, platforms that include drawings, data sets, images and videos, platforms with tools for collecting data, search engines and educational platforms on topics that being investigated. The directions of using open science by teachers of institutions of general secondary education are substantiated; forms, methods and approaches to teaching teachers to use open science environments. Examples of the use of open science approaches by teachers in the lessons of physics and mathematics are given.
A Novel Fast and Robust Multimodal Images Matching Method Based on Primary Structure-Weighted Orientation Consistency

The matching of multimodal remote sensing images, especially high-resolution images, is a challenging task due to the nonlinear radiation distortions (NRD), the noise distribution, and the differences in structural texture differences between them. In this paper, we proposed a novel fast, robust, and extensible matching method based on the primary structure-weighted orientation consistency (PSOC), which aims to extract relatively consistent primary structures and suppress texture details effectively. To construct the PSOC, we first presented a fast multiscale sigmoid Gabor filter (Fast-MSG) that employs angle interpolation instead of using angle space construction. Then we enhanced feature representation by using the Local Primary Structure (LPS) strategy and constructed the PSOC descriptor using an orientation look-up table (OLUT). Finally, we used the non-maximum suppressed 3D normalized cross-correlation (NMS-3DNCC) fast template matching method for the feature descriptor matching, which improved the matching success rate and reduced the matching complexity under large search radius. In experiments conducted with eight pairs of high-resolution multimodal images, the PSOC descriptor outperformed other state-of-the-art descriptors with an average improvement of 36.6% in correct match rate (CMR) and an improvement of 22.7% in root mean square error (RMSE) (eliminating the results that these algorithms failed to match). Additionally, PSOC achieves efficient matching under large search radius, and the average time complexity is about 1/3 of the other descriptors, which is important for the matching with large offsets in practical applications.
CoI<sup>2</sup>A: Collaborative Inter-domain and Intra-domain Alignments for Multisource Domain Adaptation

In the remote sensing information interpretation tasks, compared with collecting lots of high-quality image labels for the target domain, a large amount of labeled remote sensing data from multiple source domains are generally available without any extra cost. In this paper, our work focuses on how to exploit the rich knowledge obtained from multiple source domains to guide the interpretation of the target scene, and we propose a novel framework called Collaborative Inter-domain and Intra-domain Alignments for multi-source domain adaptation, namely CoI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A, in which inter-domain and intra-domain alignments are well collaborated to reduce the distribution divergence across sources and target. To reduce the discrepancy across sources, the inter-source alignment is proposed to map multiple sources into a unified representation space. In addition, the cross-domain attention is introduced to enforce the intra-class compactness of the target. Inter-domain alignment aligns each source with target domain separately with the help of cross-domain attention. As for the intra-domain alignment, the multi-head attentive representations of the target obtained by cross-domain attention are correlated into a unified one. The experimental results obtained from different scene classification tasks demonstrate the superiority of our model.