Reviewerly/exHarmony huggingface dataset:
Segmenting medical images via explicit–implicit attention aggregation

Medical image segmentation methods using the attention mechanism have achieved great results. However, these attention mechanisms primarily focus on the evaluation of explicit response values, ignoring the important information provided by the implicit energy, i.e., the grayscale change frequency contained in the data. The deficiency in the attention mechanism mentioned above limits its ability to aggregate valid features in medical image segmentation tasks. We propose an efficient network for medical image segmentation, named TransMIS. This network innovatively designs an implicit attention mechanism to interoperate with the explicit attention mechanism and fuses multi-level features with long-range and local dependencies to construct a robust feature representation, effectively improving its segmentation performance. Specifically, TransMIS introduces two new components: (1) A Multi-Scale Compression Self-Attention Mechanism (MSCS) that uses the sparse representation to replace complex feature tensors for matrix multiplication, which can effectively enhance feature representation while reducing the amount of computation. (2) An Explicit–Implicit Channel Mixer Module (CMM) that combines implicit energy quantification and explicit response value evaluation methods, which can bridge the discrepancies between different semantic information and fuse multi-level features effectively. With extensive experiments on multiple benchmark datasets, it shows that TransMIS outperforms current state-of-the-art methods for medical image segmentation. We also conduct rigorous ablation experiments to verify the effectiveness of the proposed components.
Causal lifting and link prediction

Existing causal models for link prediction assume an underlying set of inherent node factors—an innate characteristic defined at the node’s birth—that governs the causal evolution of links in the graph. In some causal tasks, however, link formation is path-dependent : the outcome of link interventions depends on existing links. Unfortunately, these existing causal methods are not designed for path-dependent link formation, as the cascading functional dependencies between links (arising from path dependence ) are either unidentifiable or require an impractical number of control variables. To overcome this, we develop the first causal model capable of dealing with path dependencies in link prediction. In this work, we introduce the concept of causal lifting, an invariance in causal models of independent interest that, on graphs, allows the identification of causal link prediction queries using limited interventional data. Further, we show how structural pairwise embeddings exhibit lower bias and correctly represent the task’s causal structure, as opposed to existing node embeddings, e.g. graph neural network node embeddings and matrix factorization. Finally, we validate our theoretical findings on three scenarios for causal link prediction tasks: knowledge base completion, covariance matrix estimation and consumer-product recommendations.
Multiple-Inheritance Hazards in Dependently-Typed Algebraic Hierarchies

Abstract algebra provides a large hierarchy of properties that a collection of objects can satisfy, such as forming an abelian group or a semiring. These classifications can arranged into a broad and typically acyclic directed graph. This graph perspective encodes naturally in the typeclass system of theorem provers such as Lean, where nodes can be represented as structures (or records) containing the requisite axioms. This design inevitably needs some form of multiple inheritance; a ring is both a semiring and an abelian group. In the presence of dependently-typed typeclasses that themselves consume typeclasses as type-parameters, such as a vector space typeclass which assumes the presence of an existing additive structure, the implementation details of structure multiple inheritance matter. The type of the outer typeclass is influenced by the path taken to resolve the typeclasses it consumes. Unless all possible paths are considered judgmentally equal, this is a recipe for disaster. This paper provides a concrete explanation of how these situations arise (reduced from real examples in mathlib), compares implementation approaches for multiple inheritance by whether judgmental equality is preserved, and outlines solutions (notably: kernel support for $$\eta $$ -reduction of structures) to the problems discovered.
Nominal AC-Matching

The nominal syntax is an extension of the first-order syntax that smoothly represents languages with variable bindings. Nominal matching is first-order matching modulo alpha-equivalence. This work extends a certified first-order AC-unification algorithm to solve nominal AC-matching problems. To our knowledge, this is the first mechanically-verified nominal AC-matching algorithm. Its soundness and completeness were verified using the proof assistant PVS. The formalisation enriches the first-order AC-unification algorithm providing structures and mechanisms to deal with the combinatorial aspects of nominal atoms, permutations and abstractions. Furthermore, by adding a parameter for “protected variables” that cannot be instantiated during the execution, it enables nominal matching. Such a general treatment of protected variables also gives rise to a verified nominal AC-equality checker as a byproduct.
Explainability Based on Feature Importance for Better Comprehension of Machine Learning in Healthcare

The use of Artificial Intelligence (AI) in healthcare is getting more prevalent, encompassing responsibilities like intelligent medical diagnoses and operative robots. The accuracy and performance of AI systems are prioritized by Machine Learning (ML) engineers while medical professionals are more interested in their applicability and usefulness in clinical settings. Unfortunately, medical practitioners often lack the necessary skills to interpret AI-based systems, limiting the usage of the tools that enhance healthcare solutions, automating routine analysis tasks and limiting expertise available for validation. Explainable Artificial Intelligence(XAI) is a field that focuses on methods to help understand and interpret ML models. However, most XAI research has been from a viewpoint of Computer Science (CS), with little focus on supporting other domains like healthcare. In this work, a straightforward solution is presented to increase the explainability of ML models to professionals from non-CS domains like healthcare experts. The suggested method integrates feature importance that assesses the influence of distinct features on AI-based system outcomes into standard ML workflows. This could permit medical experts to better understand AI-based systems, improving their ability to comprehend the usefulness and applicability of ML models.
Tackling the RecSys Side Effects via Deep Learning Approaches

Digital platforms, such as social media and e-commerce websites, widely make use of Recommender Systems to provide value to users. However, social consequences of such uses and their long-term effects are still unclear and controversial, spanning from influencing users' preferences to perpetrating bias embedded in the data. In this work, we present our current research activities on these phenomena, based in particular on a proper use of ML/DL techniques: we overview the state of the art and then highlight our contribution, focusing on the study of opinions drift and unfairness in recommendations.
Mobile Feature-Oriented Knowledge Base Generation Using Knowledge Graphs

Knowledge bases are centralized repositories used for developing knowledge-oriented information systems. They are essential for adaptive, specialized knowledge in dialogue systems, supporting up-to-date domain-specific discussions with users. However, designing large-scale knowledge bases presents multiple challenges in data collection and knowledge exploitation. Knowledge graphs provide various research opportunities by integrating decentralized data and generating advanced knowledge. Our contribution presents a knowledge base in the form of a knowledge graph for extended knowledge generation for mobile apps and features extracted from related natural language documents. Our work encompasses the knowledge graph completion and deductive and inductive knowledge requests. We evaluated the effectiveness and performance of these knowledge strategies, which can be used as on-demand knowledge requests used by third-party software systems.
Ensemble Learning Based Quantum Text Classifiers

Quantum Natural Language Processing (QNLP) is a very young area of research, aimed at the design and implementation of NLP models that exploit certain quantum phenomena such as superposition, entanglement, and interference to perform language-related tasks on quantum hardware. To explore the structural relationships between quantum theory and natural languages Lambeq toolkit was created. This first high-level open-source Python toolkit for quantum natural language processing offers a fully automated quantum machine learning pipeline. Lambeq currently includes five compositional models that use varying degrees of syntactic information. While the previous studies focus on Discocat model, the aim of this study is to conducte an extensive evaluation of various classifiers based on different compositional models for text classification applications, focusing on the potential advantages of ensemble learning. The classifiers, including Spider, Cups, Stairs, Tree, and DisCoCat, were examined on two datasets: MC and RP. Performance evaluation was carried out using accuracy, precision, recall, and F1-score as the metrics. The results revealed notable variations in the performance of the classifiers across the datasets. Spider emerged as the top-performing classifier on the MC dataset, achieving remarkable scores of 100% in accuracy, precision, recall, and F1-score. However, on the RP dataset, Stairs outperformed the other classifiers with an accuracy of 68%, precision of 71%, recall of 68%, and F1-score of 69%. Furthermore, ensemble models using hard voting and soft voting techniques were constructed and evaluated. The ensemble models showcased improved performance compared to individual classifiers, with the soft voting ensemble achieving a score of 97% for all metrics on the MC dataset, and a precision of 72% and F1-score of 68% on the RP dataset. These findings highlight the potential benefits of ensemble learning in enhancing the overall performance of text classification tasks on both the MC and RP datasets using soft voting method, allowing for more accurate and reliable predictions.
Digital CSR and Corporate Sustainability Moderated by Perceived Technological Environmental Uncertainty: Empirical Evidence

This study investigates the nexus among digital corporate social responsibility (CSR) practices and corporate sustainability as well as the moderating impact of perceived technological environmental uncertainty in this context. It provides empirical evidence from the Jordanian market. To achieve the study’s purpose, 170 e-survey questionnaires were emailed to all Jordanian corporations listed on the Amman Stock Exchange. In the research letter, we invited the respondent to be the chief executive officer (CEO). Using a partial least squares structural equation modeling (PLS-SEM), the results show that corporate sustainability is positively affected by digital CSR practices and that the relationship between digital CSR practices and corporate sustainability is positively moderated by perceived technological environmental uncertainty. Finally, this study has immediate impact for a wide range of managers, decision-makers, consultants, and policymakers.
Performance and Reproducibility of BERT4Rec

Sequential Recommendation with Bidirectional Encoder Representations from Transformer, BERT4Rec, is an efficient and effective model for sequential recommendation, regarded as a state-of-the-art in this field. However, studies of said architecture achieve varying results, causing doubts whether it is possible to consistently reproduce the result with reported training parameters. This study aims to test the performance and reproducibility of a BERT4Rec implementation on MovieLens1M and Netflix Prize datasets. Overall findings suggest that while using a proper implementation, BERT4Rec can still be called a state-of-the-art solution, additional work is needed to increase reproducibility of the original model. Moreover, possible avenues for further improvement are discussed.
Evaluating the Robustness of ML Models to Out-of-Distribution Data Through Similarity Analysis

In Machine Learning systems, several factors impact the performance of a trained model. The most important ones include model architecture, the amount of training time, the dataset size and diversity. We present a method for analyzing datasets from a use-case scenario perspective, detecting and quantifying out-of-distribution (OOD) data on dataset level. Our main contribution is the novel use of similarity metrics for the evaluation of the robustness of a model by introducing relative Fréchet Inception Distance (FID) and relative Kernel Inception Distance (KID) measures. These relative measures are relative to a baseline in-distribution dataset and are used to estimate how the model will perform on OOD data (i.e. estimate the model accuracy drop). We find a correlation between our proposed relative FID/relative KID measure and the drop in Average Precision (AP) accuracy on unseen data.
Exploring the Capabilities and Limitations of VQC and QSVC for Sentiment Analysis on Real-World and Synthetic Datasets

This study conducts a comparative analysis of two quantum classifiers, namely the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC), within the framework of sentiment analysis on both real-world and synthetic datasets. The primary aim is to assess their performance and delineate the current limitations and challenges associated with the application of these classifiers to complex tasks. The IMDB movie review dataset serves as a real-world example, while a generated dataset is employed for a simplified benchmark. The findings indicate that, although both classifiers exhibit potential in sentiment analysis, their performance on real-world datasets is impeded by factors such as limited qubit numbers, noise, and error rates in contemporary quantum hardware. This study underscores the necessity for advancements in quantum hardware and algorithms to enhance performance in sentiment analysis tasks and offers insights into potential avenues for future research.
Neural Graph Collaborative Filtering: Analysis of Possibilities on Diverse Datasets

This paper continues the work by Wang et al. [17]. Its goal is to verify the robustness of the NGCF (Neural Graph Collaborative Filtering) technique by assessing its ability to generalize across different datasets. To achieve this, we first replicated the experiments conducted by Wang et al. [17] to ensure that their replication package is functional. We received sligthly better results for ndcg@20 and somewhat poorer results for recall@20, which may be due to the randomness. Afterward, we applied their framework to four additional datasets (NYC2014, TOKYO2014, Yelp2022, and MovieLens1M) and compared NGCF with HOP-Rec [18] and MF-BPR [14] as in the original study. Our results confirm that NGCF outperforms other models in terms of ndcg@20. However, when considering recall@20, either HOP-Rec or MF-BPR performed better on the new datasets. This finding suggests that NGCF may have been optimized for the datasets used in the original paper. Furthermore, we analyzed the models' performance using recall@K and ndcg@K, where K was set to 1, 5, 10, and 40. The obtained results support our previous finding. The replication package for this paper can be found in our GitHub repository [1].
Intelligent Index Tuning Using Reinforcement Learning

Index tuning in databases is a critical task that can significantly impact database performance. However, the process of manually configuring indexes is often time-consuming and can be inefficient. In this study, we investigate the process of creating indexes in a database using reinforcement learning. Our research aims to develop an agent that can learn to make optimal decisions for configuring indexes in a chosen database. The paper also discusses an evaluation method to measure database performance. The adopted performance test provides necessary documentation, database schema (on which experiments will be performed) and auxiliary tools such as data generator. This benchmark evaluates a selected database management system in terms of loading, querying and processing power of multiple query streams at once. It is a comprehensive test which results, calculated on measured queries time, will be used in the reinforcement learning algorithm. Our results demonstrate that used index technique requires repeatable benchmark with stable environment and high compute power, which cause cost and time demand. The replication package for this paper is available at GitHub: https://github.com/Chotom/rl-db-indexing .
Decentralised Solutions for Preserving Privacy in Group Recommender Systems

Group Recommender Systems (GRS) combine large amounts of data from various user behaviour signals (likes, views, purchases) and contextual information to provide groups of users with accurate suggestions (e.g. rating prediction, rankings). To handle those large amounts of data, GRS can be extended to use distributed processing and storage solutions (e.g. MapReduce-like algorithms and NoSQL databases). As such, privacy has always been a core issue since most recommendation algorithms rely on user behaviour signals and contextual information that may contain sensitive information. However, existing work in this domain mostly distributes data processing tasks without addressing privacy, and the solutions that address privacy for GRS (e.g. k-anonymisation and local differential privacy) remain centralised. In this paper, we identify and analyse privacy concerns in GRS and provide guidelines on how decentralised techniques can be used to address them.
Lightweight Aspect-Oriented Software Product Lines with Automated Product Derivation

Aspect-oriented software product lines are not a new idea, but their application is facing two obstacles: establishing software product lines is challenging and aspect-oriented programming is not that widely accepted. In this paper, we address exactly these two obstacles by an approach to establishing lightweight aspect-oriented software product lines with automated product derivation. This is particularly relevant for data preprocessing systems, which are typically custom-built with respect to the data and its structure. They may involve data cleaning, reduction, profiling, validation, etc., which may have variant implementations and may be composed in different settings. The approach is simple and accessible because developers decide about variation points directly in the code without any assumption on development process and applied management. Also, it allows for variability management by making the code readable, configurable, and adaptable mainly to scripts and code fragments in a modular and concise way. The use of annotations helps preserve feature models in code. We presented the approach on the battleship game and data preprocessing pipeline product lines, which include the configuration of features, product derivation mechanism based on annotations applied by the user on certain classes and methods, implementation of features according to the feature model, and the possibility to generate all given software product derivations.
Overcoming the Cold-Start Problem in Recommendation Systems with Ontologies and Knowledge Graphs

Many recommendation systems struggle with the cold-start problem, especially in the early stages of a new application, when there are few active users and limited interactions. Traditional approaches like Collaborative Filtering cannot provide enough recommendations, and text-based methods, while helpful, do not offer sufficient context. This paper argues against the idea that the cold-start phase will eventually disappear and proposes a solution to enhance recommendation performance from the start. We propose using Ontologies and Knowledge Graphs to add a semantic layer to text-based methods and improve the recommendation performance in cold-start scenarios. Our approach uses ontologies to generate a knowledge graph that captures item text attributes' implicit and explicit characteristics, extending the item profile with similar semantic keywords. We evaluate our method against state-of-the-art text feature extraction techniques and present the results of our experiments.
Knowledge Graphs Embeddings for Link Prediction in the Context of Sustainability

The use of a large amount of heterogeneous, interconnected and quality data is nowadays essential to address challenges related to the development of solutions for "complex systems". In this paper, we propose a framework for building a collaborative platform that can support the System Dynamics experts in identifying new partnerships and innovative solutions. To this end, we use a Knowledge Graph approach that can track the links among stakeholders and store their metadata. It is charge of leveraging ML methodologies and models for data analysis as well as the prediction of connections among stakeholders and potential work tables to facilitate the achievement of the SDGs in specific contexts.
Text Classification for Automatic Distribution of Review Notes in Movie Production

During movie production many review sessions take place in which many text notes on the film are generated. These notes are manually distributed among the studio departments by the production team. This is a time-consuming and error prone process and many notes are not addressed because they do not reach the appropriate departments. In this paper, a multi-label text classification system is presented to automatically distribute these notes. By combining a DistilBERT transformer model and a Logistic Regression classifier, a mean accuracy of 0.68 is achieved when assigning the review notes to the appropriate departments. The resulting deep learning approach is compared against a standard classifier showing an improvement in the results. The outcome shows that it is possible to automatically distribute movie review notes to the interested departments in an automatic way, to improve the movie production process.
Reinforcement learning with multimodal advantage function for accurate advantage estimation in robot learning

In this paper, we propose a reinforcement learning (RL) framework that uses a multimodal advantage function (MAF) to come close to the true advantage function, thereby achieving high returns. The MAF, which is constructed as a logarithm of a mixture of Gaussians policy (MoG-P) and trained by globally collected past experiences, directly assesses the complex true advantage function with its multi-modality and is expected to enhance the sample-efficiency of RL. To realize the expected enhanced learning performance with the proposed RL framework, two practical techniques are developed that include mode selection and rounding off of actions during the policy update process. Mode selection is conducted to sample the action around the most influential or weighted mode for efficient environment exploration. For fast policy updates, past actions are rounded off to discretized action values when calculating the multimodal advantage function. The proposed RL framework was validated using simulation environments and a real inverted pendulum system. The findings showed that the proposed framework can achieve a more sample-efficient performance or higher returns than other advantage-based RL benchmarks.