Reviewerly/exHarmony huggingface dataset:
Semantic Enrichment of Explanations of AI Models for Healthcare

Explaining AI-based clinical decision support systems is crucial to enhancing clinician trust in those powerful systems. Unfortunately, current explanations provided by eXplainable Artificial Intelligence techniques are not easily understandable by experts outside of AI. As a consequence, the enrichment of explanations with relevant clinical information concerning the health status of a patient is fundamental to increasing human experts' ability to assess the reliability of AI decisions. Therefore, in this paper, we propose a methodology to enable clinical reasoning by semantically enriching AI explanations. Starting with a medical AI explanation based only on the input features provided to the algorithm, our methodology leverages medical ontologies and NLP embedding techniques to link relevant information present in the patient's clinical notes to the original explanation. Our experiments, involving a human expert, highlight promising performance in correctly identifying relevant information about the diseases of the patients.
Exploring Label Correlations for Quantification of ICD Codes

The International Classification of Diseases (ICD) has been adopted worldwide in the healthcare domain, e.g. to summarize the key information in clinical documents. Since manual ICD coding is very expensive, time-consuming, and error-prone, deep learning algorithms have been proposed to automate this task. However, the final goal of ICD coding often lays not in determining the codes associated with individual documents, but instead in quantifying the prevalence of each code within sets of documents. In this work, we experimentally assess different quantification methods in connection to ICD coding, including a simple learning-based approach that leverages associations between the codes, in order to predict their relative frequencies more accurately. Experiments show that the proposed approach can effectively explore existing associations between ICD codes, improving the quantification performance over baseline methods that deal with each code independently.
Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy

Active learning aims to reduce the amount of labeled data while maximizing machine learning models' performances. Currently, there is sparse research on the potential of an optimal active learning strategy. Therefore, we propose a non-myopic oracle policy that accesses the true labels of the data pool to approximate an optimal active learning strategy. We evaluate how the hyperparameters of this oracle policy influence its performance and empirically demonstrate that it is an upper baseline for common active learning strategies while being faster than a state-of-the-art oracle policy. For the sake of reproducibility, all the code related to our research is publicly available on our GitHub repository at https://github.com/ies-research/non-myopic-oracle-policy .
Self-Supervised Learning of Gait-Based Biomarkers

Markerless motion capture (MMC) is revolutionizing gait analysis in clinical settings by making it more accessible, raising the question of how to extract the most clinically meaningful information from gait data. In multiple fields ranging from image processing to natural language processing, self-supervised learning (SSL) from large amounts of unannotated data produces very effective representations for downstream tasks. However, there has only been limited use of SSL to learn effective representations of gait and movement, and it has not been applied to gait analysis with MMC. One SSL objective that has not been applied to gait is contrastive learning, which finds representations that place similar samples closer together in the learned space. If the learned similarity metric captures clinically meaningful differences, this could produce a useful representation for many downstream clinical tasks. Contrastive learning can also be combined with causal masking to predict future timesteps, which is an appealing SSL objective given the dynamical nature of gait. We applied these techniques to gait analyses performed with MMC in a rehabilitation hospital from a diverse clinical population. We find that contrastive learning on unannotated gait data learns a representation that captures clinically meaningful information. We probe this learned representation using the framework of biomarkers and show it holds promise as both a diagnostic and response biomarker, by showing it can accurately classify diagnosis from gait and is responsive to inpatient therapy, respectively. We ultimately hope these learned representations will enable predictive and prognostic gait-based biomarkers that can facilitate precision rehabilitation through greater use of MMC to quantify movement in rehabilitation.
Exploring the Intricacies of Neural Network Optimization

Recent machine learning breakthroughs in computer vision and natural language processing were possible due to Deep Neural Networks (DNNs) learning capabilities. Even so, applying DNNs is quite challenging, as they usually have more hyperparameters than shallow models. The higher number of hyperparameters leads to allocating more time for model optimization and training to achieve optimal results. However, if there is a better understanding of the impact of each hyperparameter on the model performance, then one can decide which hyperparameters to optimize according to the available optimization budget or desired performance. This work analyzes the impact of the different hyperparameters when applying dense DNNs to tabular datasets. This is achieved by optimizing each hyperparameter individually and comparing their influence on the model performance. The results show that the batch size usually only affects training time, reducing it by up to 80% or increasing it by 200%. In contrast, the hidden layer size does not consistently affect the considered performance metrics. The optimizer can significantly affect the model's overall performance while also varying the training time, with Adam being the generally the better optimizer. Overall, we show that the hyperparameters do not equally affect the DNN and that some can be discarded if there is a constrained search budget.
From Tweets to Stance: An Unsupervised Framework for User Stance Detection on Twitter

Current stance inference methods use topic-aligned training data, leaving many unexplored topics due to the lack of training data. Zero-shot approaches utilizing advanced pre-trained Natural Language Inference (NLI) models offer a viable solution when training data is unavailable. This work introduces the Tweets2Stance - T2S framework, an unsupervised stance detection framework based on Zero-Shot Learning. It detects a five-valued user's stance on social-political statements by analyzing their Twitter timeline. The ground-of-truth user's stance is obtained from Voting Advice Applications (VAAs), online tools that compare political preferences with party political stances. The T2S framework's generalization potential is demonstrated by measuring its performance (F1 and MAE scores) across nine datasets. These datasets were built by collecting tweets from competing parties' Twitter accounts in nine political elections held in different countries from 2019 to 2021. Through comprehensive experiments, an optimal setting was identified for each election. The results, in terms of F1 and MAE scores, outperformed all baselines and approached the best scores for each election. This showcases the ability of T2S to generalize across different cultural-political contexts.
Refining Temporal Visualizations Using the Directional Coherence Loss

Abstract Many real-world data sets contain a temporal component or include transitions from state to state. For exploratory data analysis, we can present these high-dimensional data sets in two-dimensional maps, using embeddings of data objects under exploration and representing their temporal relations with directed edges. Most existing dimensionality reduction techniques, such as t-SNE and UMAP, disregard the temporal or relational nature of the data during embedding construction, leading to cluttered visualizations obscuring potentially interesting temporal patterns. To address this issue, we introduce Directional Coherence Loss (DCL), a differentiable loss function that we can incorporate into existing dimensionality reduction techniques. We have designed DCL to highlight the temporal aspects of the data, revealing temporal patterns that might otherwise remain unnoticed. By encouraging local directional coherence of the directed edges, the DCL produces more temporally-meaningful and less-cluttered visualizations. We demonstrate the effectiveness of our approach on a real-world multivariate time-series data set tracking the progression of the COVID-19 pandemic in Slovenia. We show that incorporating the DCL into the t-SNE algorithm elucidates the time progression of the pandemic in the embedding and reveals interesting cyclical patterns otherwise hidden in standard embeddings.
Ensembles of Classifiers and Quantifiers with Data Fusion for Quantification Learning

Quantification is a supervised Machine Learning task that estimates the class distribution in an unlabeled test set. Quantification has practical applications in various fields, including medical research, environmental monitoring, and quality control. For instance, medical research often estimates the prevalence of a particular disease in a population. Despite being a thriving research area, most existing quantification methods are limited to binary-class problems. Moreover, recent experimental evidence suggests that modern state-of-the-art quantifiers do not perform well for multi-class problems, which are prevalent in quantification. This paper proposes two novel multi-class ensemble quantifiers, FMC-SQ and FMC-MQ, that use data fusion methods at the classifier and quantifier levels. We conducted experiments with 12 state-of-the-art (single and ensemble) quantifiers to evaluate our models on 31 multi-class datasets. Our experimental results indicate that FMC-MQ is the best-performing quantifier outperforming other single and ensemble methods. Also, aggregating quantifier outputs seem to be a more promising research direction than aggregating classification scores for quantification.
Explainable Spatio-Temporal Graph Modeling

Explainable AI (XAI) focuses on designing inference explanation methods and tools to complement machine learning and black-box deep learning models. Such capabilities are crucially important with the rising adoption of AI models in real-world applications, which require domain experts to understand how model predictions are extracted in order to make informed decisions. Despite the increasing number of XAI approaches for tabular, image, and graph data, their effectiveness in contexts with a spatial and temporal dimension is rather limited. As a result, available methods do not properly explain predictive models' inferences when dealing with spatio-temporal data. In this paper, we fill this gap proposing a XAI method that focuses on spatio-temporal geo-distributed sensor network data, where observations are collected at regular time intervals and at different locations. Our model-agnostic method performs perturbations on the feature space of the data to uncover relevant factors that influence model predictions, and generates explanations for multiple analytical views, such as features, timesteps, and node location. Our qualitative and quantitative experiments with real-world forecasting datasets show the effectiveness of the proposed method in providing valuable explanations of model predictions.
Explaining the Chronological Attribution of Greek Papyri Images

Greek literary papyri, which are unique witnesses of antique literature, do not usually bear a date. They are thus currently dated based on palaeographical methods, with broad approximations which often span more than a century. We created a dataset of 242 images of papyri written in "bookhand" scripts whose date can be securely assigned, and we used it to train machine and deep learning algorithms for the task of dating, showing its challenging nature. To address the data scarcity problem, we extended our dataset by segmenting each image to the respective text lines. By using the line-based version of our dataset, we trained a Convolutional Neural Network, equipped with a fragmentation-based augmentation strategy, and we achieved a mean absolute error of 54 years. The results improve further when the task is cast as a multiclass classification problem, predicting the century. Using our network, we computed and provided precise date estimations for papyri whose date is disputed or vaguely defined and we undertake an explainability-based analysis to facilitate future attribution.
Performance Analysis on an Arduino-Based Low-Cost Active Dual-Axis Solar Tracking System

The common term used to describe devices that orient solar panels towards the sun is a solar tracking system. Trackers are used to minimise the angle of incidence between the incoming light and a photovoltaic panel. An overview of the low-cost active dual-axis solar tracking system based on an Arduino is provided in this paper. Improving the solar tracker efficiency and reducing its development costs are the catalysts to the implementation of this study. This solar tracking device is intended to optimise the power generation compared to a fixed solar panel installation. This study aimed to design and developed a low-cost dual-axis solar tracking system and evaluate its performance. The hardware designs are solar panels, light dependence resistance as sensors, Arduino as the main controller, servo motors, solar chargers, and the battery. The performance of the dual-axis tracker is compared to a fixed solar panel to analyse the panel efficiency. An analysis of power, current and voltage is then carried out. The study shows that the solar tracker can be built with a minimal cost, and it significantly works efficiently for about 4.45% better than the fixed solar panel.
Privacy-Preserving Learning of Random Forests Without Revealing the Trees

The paper presents a method for the privacy-preserving learning of random forests from private data of three parties, where not even the decision trees, i.e., neither the tree structures nor their parameters (the annotations of attributes and attribute values), are disclosed to any of the parties. To make this practical for realistically size data, a custom protocol is needed for the private comparison of two numbers, such that the numbers themselves are only available in shares and are not known to either party. Experiments with five datasets indicate that the overall protocol matches classical random forests in accuracy and can handle datasets of realistic size.
Photovoltaic Prediction and Virtual Collection

The output of photovoltaic (PV) power generation is random and intermittent, which has a strong impact on the safe and stable operation of power grids.
Error Analysis on Industry Data: Using Weak Segment Detection for Local Model Agnostic Prediction Intervals

Machine Learning has been overtaken by a growing necessity to explain and understand decisions made by trained models as regulation and consumer awareness have increased. Alongside understanding the inner workings of a model comes the task of verifying how adequately we can model a problem with the learned functions. Traditional global assessment functions lack the granularity required to understand local differences in performance in different regions of the feature space, where the model can have problems adapting. Residual Analysis adds a layer of model understanding by interpreting prediction residuals in an exploratory manner. However, this task can be unfeasible for high-dimensionality datasets through hypotheses and visualizations alone. In this work, we use weak interpretable learners to identify regions of high prediction error in the feature space. We achieve this by examining the absolute residuals of predictions made by trained regressors. This methodology retains the interpretability of the identified regions. It allows practitioners to have tools to formulate hypotheses surrounding model failure on particular regions for future model tunning, data collection, or data augmentation on critical cohorts of data. We present a way of including information on different levels of model uncertainty in the feature space through the use of locally fitted Model Agnostic Prediction Intervals (MAPIE) in the identified regions, comparing this approach with other common forms of conformal predictions which do not take into account findings from weak segment identification, by assessing local and global coverage of the prediction intervals. To demonstrate the practical application of our approach, we present a real-world industry use case in the context of inbound retention call-centre operations for a Telecom Provider to determine optimal pairing between a customer and an available assistant through the prediction of contracted revenue.
Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features

Replacing missing features in data streams is an important task in order to enable many machine learning algorithms that require feature-complete instances for training and prediction. Two popular methods for dealing with missing features are imputation and active feature acquisition, where in the former missing values are approximated, whereas in the latter, missing features are provided by an expert for a cost and within a limited budget. In this work, we present a hybridized approach, where we employ an active feature acquisition method in the first stage to pick candidate features on which we would require a costly expert and then check in a second stage how well we could impute these candidate features. If the imputation is expected to be of a certain quality, we skip the purchase and impute instead. We provide a framework for such a scenario and used it to run extensive experiments. Our results on 6 data sets show that our proposed method can achieve a similar classification performance while spending 1% to 27% less budget.
Fairness-Aware Mixture of Experts with Interpretability Budgets

As artificial intelligence becomes more pervasive, explainability and the need to interpret machine learning models' behavior emerge as critical issues. Discussions are usually bounded by those who defend that interpretable models must be the rule or that non-interpretable models' ability to capture more complex patterns warrants their use. In this paper, we argue that interpretability should not be viewed as a binary aspect and that, instead, it should be viewed as a continuous domain-informed notion. With this aim, we leverage the well-known Mixture of Experts architecture with user-defined budgets for the controlled use of non-interpretable models. We extend this idea with a counterfactual fairness module to ensure the selection of consistently fair experts: FairMOE . We compare our proposal to contemporary approaches in fairness-related data sets and demonstrate that FairMOE is competitive with the state-of-the-art methods when considering the trade-off between predictive performance and fairness while providing competitive scalability and, most importantly, greater interpretability .
Pseudo Session-Based Recommendation with Hierarchical Embedding and Session Attributes

Recently, electronic commerce (EC) websites have been unable to provide an identification number (user ID) for each transaction data entry because of privacy issues. Because most recommendation methods assume that all data are assigned a user ID, they cannot be applied to the data without user IDs. Recently, session-based recommendation (SBR) based on session information, which is short-term behavioral information of users, has been studied. A general SBR uses only information about the item of interest to make a recommendation (e.g., item ID for an EC site). Particularly in the case of EC sites, the data recorded include the name of the item being purchased, the price of the item, the category hierarchy, and the gender and region of the user. In this study, we define a pseudo-session for the purchase history data of an EC site without user IDs and session IDs. Finally, we propose an SBR with a co-guided heterogeneous hypergraph and globalgraph network plus, called CoHHGN+. The results show that our CoHHGN+ can recommend items with higher performance than other methods.
Interpretable Data Partitioning Through Tree-Based Clustering Methods

The growing interpretable machine learning research field is mainly focusing on the explanation of supervised approaches. However, also unsupervised approaches might benefit from considering interpretability aspects. While existing clustering methods only provide the assignment of records to clusters without justifying the partitioning, we propose tree-based clustering methods that offer interpretable data partitioning through a shallow decision tree. These decision trees enable easy-to-understand explanations of cluster assignments through short and understandable split conditions. The proposed methods are evaluated through experiments on synthetic and real datasets and proved to be more effective than traditional clustering approaches and interpretable ones in terms of standard evaluation measures and runtime. Finally, a case study involving human participation demonstrates the effectiveness of the interpretable clustering trees returned by the proposed method.
Unsupervised Graph Neural Networks for Source Code Similarity Detection

In this paper, we propose a novel unsupervised approach for code similarity and clone detection that is based on Graph Neural Networks. We propose a hybrid approach to detect similarities within source code, using centroid distances and a Graph Auto-Encoder that uses a raw abstract syntax trees as input. When compared to $$\mathrm {R_{TV}NN}$$ [33], the state-of-the-art unsupervised approach for code similarity and clone detection, our method improves significantly training and inference time efficiency, while preserving or improving precision. In our experiments, our algorithm is on average 77 times faster during training and 21 times faster during inference. This shows that using Graph Auto-Encoders in the domain of source code similarity analysis is the better option in an industrial context or in a production environment. We illustrate this by using our approach to compute source code similarity within a large dataset of phishing kits written in PHP provided by our industry partner.
Enhancing Intra-modal Similarity in a Cross-Modal Triplet Loss

Cross-modal retrieval requires building a common latent space that captures and correlates information from different data modalities, usually images and texts. Cross-modal training based on the triplet loss with hard negative mining is a state-of-the-art technique to address this problem. This paper shows that such approach is not always effective in handling intra-modal similarities. Specifically, we found that this method can lead to inconsistent similarity orderings in the latent space, where intra-modal pairs with unknown ground-truth similarity are ranked higher than cross-modal pairs representing the same concept. To address this problem, we propose two novel loss functions that leverage intra-modal similarity constraints available in a training triplet but not used by the original formulation. Additionally, this paper explores the application of this framework to unsupervised image retrieval problems, where cross-modal training can provide the supervisory signals that are otherwise missing in the absence of category labels. Up to our knowledge, we are the first to evaluate cross-modal training for intra-modal retrieval without labels. We present comprehensive experiments on MS-COCO and Flickr30K, demonstrating the advantages and limitations of the proposed methods in cross-modal and intra-modal retrieval tasks in terms of performance and novelty measures. Our code is publicly available on GitHub https://github.com/MariodotR/FullHN.git .