Reviewerly/exHarmony huggingface dataset:
Bridging the Transparency Gap: What Can Explainable AI Learn from the AI Act?

The European Union has proposed the Artificial Intelligence Act which introduces detailed requirements of transparency for AI systems. Many of these requirements can be addressed by the field of explainable AI (XAI), however, there is a fundamental difference between XAI and the Act regarding what transparency is. The Act views transparency as a means that supports wider values, such as accountability, human rights, and sustainable innovation. In contrast, XAI views transparency narrowly as an end in itself, focusing on explaining complex algorithmic properties without considering the socio-technical context. We call this difference the “transparency gap”. Failing to address the transparency gap, XAI risks leaving a range of transparency issues unaddressed. To begin to bridge this gap, we overview and clarify the terminology of how XAI and European regulation – the Act and the related General Data Protection Regulation (GDPR) – view basic definitions of transparency. By comparing the disparate views of XAI and regulation, we arrive at four axes where practical work could bridge the transparency gap: defining the scope of transparency, clarifying the legal status of XAI, addressing issues with conformity assessment, and building explainability for datasets.
Finite Sample Guarantees of Differentially Private Expectation Maximization Algorithm

(Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating the maximum likelihood of mixture models or incomplete data problems. A major challenge facing this popular technique is how to effectively preserve the privacy of sensitive data. Previous research on this problem has already lead to the discovery of some Differentially Private (DP) algorithms for (Gradient) EM. However, unlike in the non-private case, existing techniques are not yet able to provide finite sample statistical guarantees. To address this issue, we propose in this paper the first DP version of Gradient EM algorithm with statistical guarantees. Specifically, we first propose a new mechanism for privately estimating the mean of a heavy-tailed distribution, which significantly improves a previous result in [25], and it could be extended to the local DP model, which has not been studied before. Next, we apply our general framework to three canonical models: Gaussian Mixture Model (GMM), Mixture of Regressions Model (MRM) and Linear Regression with Missing Covariates (RMC). Specifically, for GMM in the DP model, our estimation error is near optimal in some cases. For the other two models, we provide the first result on finite sample statistical guarantees. Our theory is supported by thorough numerical experiments on both real-world data and synthetic data.
Multi-Modal Fusion with Semantic Supervision for Radiology Report Generation

Radiology report generation, one way of analyzing radiology images, is to generate a textual report automatically for the given image, and it is of great significance to assist diagnosis and alleviate the workload of radiologists. Some report generation methods have been therefore proposed. However, these methods suffer from the problem of low-quality generation, because of the visual and textual bias and training with text similarity oriented objective. To solve this problem, we propose a novel radiology report generation model with multi-modal fusion and semantic supervision, namely MS-Gen. MS-Gen consists of two main components, i.e., the semantic-visual fusion module and the semantic weighted contrastive loss. Specifically, the main idea of the semantic-visual fusion module is to make use of the domain-specific prior knowledge contained in a large pre-trained visual-language model and also the complementary nature between the image and text modalities. Moreover, a novel optimization term, i.e., the semantic weighted contrastive loss, is proposed to guide the optimization process with semantic similarity objective, and further enforce the generated reports with higher clinical accuracy. Extensive experiments conducted on two real datasets of IU X-Ray and MIMIC-CXR demonstrate the effectiveness of MS-Gen.
What Wikipedia Misses About Yuriko Nakamura? Predicting Missing Biography Content by Learning Latent Life Patterns

Action-related KnowledGe (AKG) is important for facilitating deeper understanding of people’s life patterns, objectives and motivations. In this study, we present a novel framework for automatically predicting missing human biography records in Wikipedia by generating such knowledge. The generation method, which is based on a neural network matrix factorization model, is capable of encoding action semantics from diverse perspectives and discovering latent inter-action relations. By correctly predicting missing information and correcting errors, our work can effectively improve the quality of data about the behavioral records of historical figures in the knowledge base (e.g., biographies in Wikipedia), thus contributing to the understanding and study of human actions by the general public on the one hand, and can be considered as a new paradigm for managing action-related knowledge in digital libraries on the other. Extensive experiments demonstrate that the AKG we generate can capture well missing or “forgotten” human biography related information in Wikipedia.
Causally Disentangled Generative Variational AutoEncoder

We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangled outcomes simultaneously. We call this approach Causally Disentangled Generation (CDG). CDG is a generative model that accurately decodes an output based on a causally disentangled representation. Our research demonstrates that adding supervised regularization to the encoder alone is insufficient for achieving a generative model with CDG, even for a simple task. Therefore, we explore the necessary and sufficient conditions for achieving CDG within a specific model. Additionally, we introduce a universal metric for evaluating the causal disentanglement of a generative model. Empirical results from both image and tabular datasets support our findings.
ADVANTAGES AND DISADVANTAGES OF ONLINE LEARNING

The article reveals some features of online learning, examines its advantages and disadvantages, traces its relevance to the modern education system, largely based on digital technologies. Attention is drawn to the innovative nature of this educational model. The nature of synchronous and asynchronous learning formats is investigated, the need for a rational combination of mixed (hybrid) and traditional educational practices in the educational process is emphasized, including from the point of view of increasing cooperation between educational institutions and the real sector of the economy. The role of online learning in the formation of scientific and educational competencies and practical skills is shown.
New Paths to Enlighten Passion for Education in Challenging Times: DigiSEL and Creative Education

RESEARCH OBJECTIVE: This article examines teachers’ attitudes towards socioemotional learning (SEL) and digital technology in the post-pandemic era, focusing on the DigiSEL project. This project aimed to integrate digital tools and SEL to support students’ well-being and academic success, enhancing primary school teachers’ skills in these areas.&#x0D; THE RESEARCH PROBLEM AND METHODS: The study involved partner countries conducting qualitative and quantitative analyses to determine the needs of in-service and preservice teachers. The Italian team’s needs analysis, conducted through a Focus Group and a creativity survey, revealed insights into teachers’ attitudes and experiences with digital teaching and creativity skills development.&#x0D; THE PROCESS OF ARGUMENTATION: The authors introduce the DigiSEL project, detailing how it aligned with their research for the online course modules.&#x0D; RESEARCH RESULTS: The Italian team’s focus group outcomes highlighted innovative solutions for educational challenges. Initial frustration with technology evolved into a rewarding transformation as educators mastered its use. Online courses were identified as motivating and providing novel approaches to address students’ diverse issues, such as concentration and emotional expression difficulties.&#x0D; CONCLUSIONS, INNOVATIONS AND RECOMMENDATIONS: Despite challenges, the project effectively supported educators in their roles and maintained their enthusiasm for teaching. Drawing inspiration from earlier European research on creativity and ICT in teaching, this study aligned with teachers’ perceptions of creativity as vital for students’ development and demonstrated how technology could enhance teaching methods. In conclusion, the DigiSEL project showcased the potential of merging SEL and digital tools to empower educators and foster student growth amidst evolving educational landscapes. More research should take in consideration the importance of SEL and creativity to enlighten passion in teaching with new and inspiring methods.
Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems

Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking algorithm that addresses the limitations of existing models. TKGRS uniquely integrates graph convolutional networks (GCNs), matrix factorization, and temporal decay factors to offer a robust and dynamic recommendation mechanism. The algorithm’s architecture comprises an initial embedding layer for identifying the user and item, followed by a GCN layer for a nuanced understanding of the relationships and fully connected layers for prediction. A temporal decay factor is also used to give weightage to recent user–item interactions. Empirical validation using the MovieLens 100K, 1M, and Douban datasets showed that TKGRS outperformed the state-of-the-art models according to the evaluation metrics, i.e., RMSE and MAE. This innovative approach sets a new standard in movie recommendation systems and opens avenues for future research in advanced graph algorithms and machine learning techniques.
CONCEPTUAL APPROACHES TO THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE AUDIT OF STATE ASSETS

In order to understand the impact of digital and artificial intelligence on auditing, it is important for professionals to understand the forces driving the Fourth Industrial Revolution. One of these forces is the explosive growth of data that fuels digital technology. Another force is the acceleration of the pace of change, which brings additional complexity in managing trust in an environment that is becoming increasingly insecure. For example, cyberattacks are becoming more complex and large-scale. Social media also creates new risks.&#x0D; In the field of auditing, first of all we need to understand the ongoing transformations, the types of risks and controls that need to be audited. On the other hand, these trends also open up new opportunities for accounting and auditing professionals.&#x0D; There is an active platform for audit professionals to understand these changes. For example, two years ago, an Ernst &amp; Young survey showed that 74% of executives said they had no strategic plans for artificial intelligence. A year later, exactly the same survey results showed that 73% of managers are already ready to implement artificial intelligence (AI) or plan to implement it in the next 2 years. The second statistic provided by the World Economic Forum shows that 30% of every corporate audit will be performed by artificial intelligence by 2025. Finally, the OECD has counted more than 200 ongoing public sector audit blockchain initiatives in 46 countries.&#x0D; They show the importance of being prepared for this changing environment. To deal with these challenges, audit and finance professionals need to understand (i) how “what” they have to analyse is changing, (ii) the new risks that are emerging, (iii) the new opportunities offered by these disruptive technologies.
A Local Non-Additive Framework for Explaining Black-Box Predictive Models

Understanding the rationale behind the predictions made by machine learning models holds paramount importance across numerous applications. Various explainable models have been developed to shed light on these predictions by assessing the individual contributions of features to the outcome of black-box models. However, existing methods often overlook the crucial aspect of interactions among features, restricting the explanation to isolated feature attributions. In this paper, we introduce a novel Choquet integral-based explainable method, termed ChoquEx, which not only considers the interactions among features but also enables the computation of contributions for any subset of features. To achieve this, we propose an innovative algorithm based on support vector regression that efficiently estimates the contributions of all feature subsets. Intriguingly, we leverage game-theoretic concepts, including Shapley values and interaction index, to calculate both the feature importance and interaction strength. This approach adds further interpretability and insight into the model’s decision-making process. To evaluate the effectiveness of ChoquEx, we conduct extensive experiments on diverse real-world scenarios. Our results convincingly demonstrate the superiority of the proposed model over existing explainable techniques. With its ability to unravel feature interactions and furnish comprehensive explanations, ChoquEx significantly enhances our understanding of predictive models, opening new avenues for applying machine learning in critical domains that require transparent decision-making.
Uncertainty-Encoded Multi-Modal Fusion for Robust Object Detection in Autonomous Driving

Multi-modal fusion has shown initial promising results for object detection of autonomous driving perception. However, many existing fusion schemes do not consider the quality of each fusion input and may suffer from adverse conditions on one or more sensors. While predictive uncertainty has been applied to characterize single-modal object detection performance at run time, incorporating uncertainties into the multi-modal fusion still lacks effective solutions due primarily to the uncertainty’s cross-modal incomparability and distinct sensitivities to various adverse conditions. To fill this gap, this paper proposes Uncertainty-Encoded Mixture-of-Experts (UMoE) that explicitly incorporates single-modal uncertainties into LiDAR-camera fusion. UMoE uses individual expert network to process each sensor’s detection result together with encoded uncertainty. Then, the expert networks’ outputs are analyzed by a gating network to determine the fusion weights. The proposed UMoE module can be integrated into any proposal fusion pipeline. Evaluation shows that UMoE achieves a maximum of 10.67%, 3.17%, and 5.40% performance gain compared with the state-of-the-art proposal-level multi-modal object detectors under extreme weather, adversarial, and blinding attack scenarios.
Leveraging Error Patterns to Correct Prediction Intervals

When assessing uncertainty in model predictions, it is key to consider potential error patterns in some regions of the feature space. In this paper, we build on quantile regression to propose a new method to produce prediction intervals in regression tasks. It estimates a conditional quantile function of the residual variable given a specific representation. The method then adjusts the regressor’s prediction with an upper and lower conditional quantile prediction in order to produce an adaptive prediction interval for any new input. Further, we suggest an additional layer based on conformal prediction in order to provide coverage guarantees. Lastly, as distribution-free conditional coverage is impossible to achieve, we suggest a tree-based representation which displays patterns of undercoverage. This diagnostic tool aims to reveal which regions of the feature space are significantly less likely to have trustworthy prediction intervals. In order to prove their efficacy, our techniques are tested over various use cases and compared against four main baselines. Our methods empirically achieve the expected coverage and tend to produce shorter intervals.
Complexity of Verification and Existence Problems in Epistemic Argumentation Framework

Dung’s Argumentation Framework (AF) has been extended in several directions. An interesting extension, among others, is the Epistemic AF (EAF) which allows representing the agent’s belief by means of epistemic constraints. In particular, an epistemic constraint is a propositional formula over labeled arguments (e.g. in(a), out(c)) extended with the modal operators K and M that intuitively state that the agent believes that a given formula is certainly or possibly true, respectively. In this paper, focusing on EAF, we investigate the complexity of the possible and necessary variants of three canonical problems in abstract argumentation: verification, existence, and non-empty existence. Moreover, we explore the relationship between EAF and incomplete AF (iAF), an extension of AF where arguments and attacks may be uncertain. Our complexity analysis shows that the verification problem in iAF can be naturally reduced to the verification in EAF, while it turns out that a similar result cannot hold for the necessary (non-empty) existence problem.
A Siamese Based System for City Verification

Image geolocalization is receiving increasing attention due to its importance in several applications, such as image retrieval, criminal investigations and fact-checking. Previous works focused on several instances of image geolocalization including place recognition, GPS coordinates estimation and country recognition. In this paper, we tackle an even more challenging problem, which is recognizing the city where an image has been taken. Due to the vast number of cities in the world, we cast the problem as a verification problem, whereby the system has to decide whether a certain image has been taken in a given city or not. In particular, we present a system that given a query image and a small set of images taken in a target city, decides if the query image has been shot in the target city or not. To allow the system to handle the case of images, taken in cities that have not been used during training, we use a Siamese network based on Vision Transformer as a backbone. The experiments we run prove the validity of the proposed system which outperforms solutions based on state-of-the-art techniques, even in the challenging case of images shot in different cities of the same country.
High Dynamic Range Image Reconstruction via Deep Explicit Polynomial Curve Estimation

Due to limited camera capacities, digital images usually have a narrower dynamic illumination range than real-world scene radiance. To resolve this problem, High Dynamic Range (HDR) reconstruction is proposed to recover the dynamic range to better represent real-world scenes. However, due to different physical imaging parameters, the tone-mapping functions between images and real radiance are highly diverse, which makes HDR reconstruction extremely challenging. Existing solutions can not explicitly clarify a corresponding relationship between the tone-mapping function and the generated HDR image, but this relationship is vital when guiding the reconstruction of HDR images. To address this problem, we propose a method to explicitly estimate the tone mapping function and its corresponding HDR image in one network. Firstly, based on the characteristics of the tone mapping function, we construct a model by a polynomial to describe the trend of the tone curve. To fit this curve, we use a learnable network to estimate the coefficients of the polynomial. This curve will be automatically adjusted according to the tone space of the Low Dynamic Range (LDR) image, and reconstruct the real HDR image. Besides, since all current datasets do not provide the corresponding relationship between the tone mapping function and the LDR image, we construct a new dataset with both synthetic and real images. Extensive experiments show that our method generalizes well under different tone-mapping functions and achieves SOTA performance. The code/dataset is available at https://github.com/jqtangust/EPCE-HDR.git.
Anticipating Responsibility in Multiagent Planning

Responsibility anticipation is the process of determining if the actions of an individual agent may cause it to be responsible for a particular outcome. This can be used in a multi-agent planning setting to allow agents to anticipate responsibility in the plans they consider. The planning setting in this paper includes partial information regarding the initial state and considers formulas in linear temporal logic as positive or negative outcomes to be attained or avoided. We firstly define attribution for notions of active, passive and contributive responsibility, and consider their agentive variants. We then use these to define the notion of responsibility anticipation. We prove that our notions of anticipated responsibility can be used to coordinate agents in a planning setting and give complexity results for our model, discussing equivalence with classical planning. We also present an outline for solving some of our attribution and anticipation problems using PDDL solvers.
Reinforcement Learning by Guided Safe Exploration

Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them in the real world. However, the real-world target task might be unknown prior to deployment. Reward-free RL trains an agent without the reward to adapt quickly once the reward is revealed. We consider the constrained reward-free setting, where an agent (the guide) learns to explore safely without the reward signal. This agent is trained in a controlled environment, which allows unsafe interactions and still provides the safety signal. After the target task is revealed, safety violations are not allowed anymore. Thus, the guide is leveraged to compose a safe behaviour policy. Drawing from transfer learning, we also regularize a target policy (the student) towards the guide while the student is unreliable and gradually eliminate the influence of the guide as training progresses. The empirical analysis shows that this method can achieve safe transfer learning and helps the student solve the target task faster.
Shrink-Perturb Improves Architecture Mixing During Population Based Training for Neural Architecture Search

In this work, we show that simultaneously training and mixing neural networks is a promising way to conduct Neural Architecture Search (NAS). For hyperparameter optimization, reusing the partially trained weights allows for efficient search, as was previously demonstrated by the Population Based Training (PBT) algorithm. We propose PBT-NAS, an adaptation of PBT to NAS where architectures are improved during training by replacing poorly-performing networks in a population with the result of mixing well-performing ones and inheriting the weights using the shrink-perturb technique. After PBT-NAS terminates, the created networks can be directly used without retraining. PBT-NAS is highly parallelizable and effective: on challenging tasks (image generation and reinforcement learning) PBT-NAS achieves superior performance compared to baselines (random search and mutation-based PBT).