Reviewerly/exHarmony huggingface dataset:
Architectural Models Created with Mixed Reality Technologies Towards a New STEAM Practice

Architecture can be used as real-world examples in the classroom by analyzing them and understanding them from many dimensions to develop STEAM (Science, Technology, Engineering, Arts and Mathematics) skills. Furthermore, these practices can allow participants to explore architecture, cultural and historical aspects and use their mathematical knowledge while modelling, thus contributing to mathematics education. The proposed architectural modelling is modelled in various ways using a broad spectrum of technologies. GeoGebra and GeoGebra AR features allow participants to explore architectural models’ components from a mathematical point of view through the modelling process and support of mixed-reality technologies. This chapter will illustrate and discuss a case study using mixed-reality technologies that took place in a hybrid learning setting in Upper Austria for architectural modelling.
A New Framework for Learning Patterns and Social Presence in Virtual Reality for Learning

In this chapter, we will combine existing concepts and frameworks from both research on virtual reality as a media and research on teaching methodology. We will combine our findings to present a new framework for planning and organizing learning activities in virtual reality (VR). There is a wide variety of pedagogical theories and frameworks available, but they are quite seldom thoroughly utilized in work on virtual reality for learning. VR has now been evolving technically and creatively for many decades. Even though it has been slow in widespread adoption, we believe that the area of VR for learning is now mature enough to be properly supported by contemporary pedagogical frameworks. After going through first the basics of VR, as well as Laurillard’s learning patterns, we will present our suggested framework and use it on two case studies.
Transforming Learning Experiences Through Affordances of Virtual and Augmented Reality

Educational reforms across the globe in recent years have focused on increasing the quality of learning through innovation. Immersive Virtual and Augmented Reality (VR/AR) technologies have enabled schools and universities to raise the quality of learning by transforming learning experiences from passive to active, and empowering educators to leverage on four key affordances—Multimedia Augmentation, Enhanced Visualization, Experiential Learning and Learning Motivation. A review of studies has shown that using VR/AR can positively affect students’ learning experiences and students are generally favorable towards the use of these technologies. Experiments show that there is a significant increase in students’ engagement in learning and improvement in conceptual understanding through enhanced visualization. These findings have implications on the pedagogical design of lessons that use VR/AR.
Amazing Walk Through Mathematics

Labyrinths and mazes, formations with either unicursal or multicursal pathways, are based on mathematical concepts as well as design features. Both have been part of human culture for thousands of years. In recent experiments, we have observed positive stimulation and high participants’ engagement in solving mazes and labyrinths in an interdisciplinary workshop that teaches 3D modelling, augmented reality and 3D printing skills. These technologies require skills in science, technology, engineering, art and mathematics (STEAM) which can be trained by this activity. The workshop was tested both online and offline at ArsElectronica at Kepler's grounds and in further workshops. It was used in courses for pre-service mathematics teachers aiming to teach them about 3D modelling and 3D printing. Participants aged five to the age of retirement experienced the geometrical attributes of mazes to develop an understanding of mathematical concepts such as mirroring, rotation, translation and algorithm basics. The created mazes from this workshop are solvable, shareable and experienceable using mobile devices.
A Comparative Analysis of Regression Models for Software Effort Estimation

Software Effort Estimation is the utmost task in software engineering and project management. This is important to estimate cost properly and the number of people required for a project to be developed. Many techniques have been used to estimate cost, time, schedule and required manpower for software development industries. Nowadays software is developed in a more complex way and its success depends on efficient estimation techniques. In this research, we have compared five regression algorithms on different projects to estimate software effort. The main advantage of these models is they can be used in the early stages of the software life cycle and that can be helpful to project managers to conduct effort estimation efficiently before starting the project. It avoids project overestimation and late delivery. Software size, productivity, complexity and requirement stability are the input vectors for these regression models. The estimated efforts have been calculated using Ridge Regression, Lasso Regression, Elastic Net, Random Forest and Support Vector Regression. We have compared unitedly these models for the first time as software effort estimators. R-squared Score, Mean Squared Error (MSE) and Mean Absolute Error (MAE) are calculated for these regression models. Ridge, Lasso and Elastic Net show comparatively better results among others.
The Gestural Construction of Musical Time

This chapter presents a mathematically conceived method of gestural time construction in music. In particular we adhere to the prominent thesis that in music, time is not only inherited from the physical reality, but it is essentially the result of the composers’ and musicians’ constructive activity. This approach is in particular forwarded by Jonathan Kramer’s concept of a “vertical time”.
Providing a Desired Compression Ratio for Better Portable Graphics Encoder of Color Images

In this chapter, the task of providing a desired compression ratio (CR) is considered for the lossy compression of color images by better portable graphics (BPG) encoder. This encoder is characterized by many benefits compared to other modern encoders according to rate-distortion performance and some other important facilities. However, the produced CR for a given value of a parameter that controls compression (PCC) for the considered encoder varies in very wide limits depending on image content and the format the original image is presented. In practice, it can be desired to provide a desired CR for color images compressed by BPG. We show that this can be done using a recently proposed two-step method based on average rate-distortion curves and PCC refining at the second step. The applicability of the two-step approach to providing a desired CR is analyzed and proven for the formats 4:2:0, 4:2:2, and 4:4:4. It is demonstrated that, due to the second step, the accuracy of providing a desired CR radically improves. The variance of the residual errors of the provided CR is decreased tens of times. It is also shown that accuracy depends on a desired CR and the variance of residual errors is larger for the larger desired CR. Some other properties of the residual errors are studied. Two sets of test color images are exploited in our studies. The first set is used to obtain the average dependence of CR on PCC and get an idea of the accuracy of the proposed method. The second set is employed to verify the method's performance. It is shown that the results for both sets are in good agreement.
Towards accurate recommendations of merge conflicts resolution strategies

in software engineering, developers working concurrently on a project frequently need to merge changes in the source code. The manual resolution of merge conflicts is a laborious and time-consuming task. Some studies have investigated the nature of merge conflicts and proposed methods to predict, mitigate, and resolve conflicts. However, the automatic resolution of conflicts is still an open problem. in this paper, we design and evaluate MESTRE (MErge STrategy REcommender), a conflict resolution strategy recommender that predicts the merge resolution strategy among version 1, version 2, concatenation of version 1 and 2, concatenation of version 2 and 1, combination of lines from version 1 and 2, and manually writing new code. For the first four strategies, MESTRE is able to not only recommend the strategy but also automatically resolve the conflict. we collected data from 20 open-source projects with more than 1000 merge conflicts each. Using this data, we trained and evaluated a separate classifier for each project to predict the conflict resolution strategy for a conflicting chunk. MESTRE achieved an overall average accuracy of 80.8% among all projects. It represents a normalized improvement of 54.8% over the majority class baseline. Furthermore, since MESTRE can provide the exact conflict resolution for the most frequent conflict resolution strategies, it could automatically resolve 70.5% of the conflicts. We also found that attributes related to the conflicting chunk notably impact the classification accuracy more than those related to the merge and the file. This paper makes the following contributions: (1) MESTRE, a tool to predict merge conflict resolutions based on attributes of a Git repository; (2) an analysis of the relevance of attributes to the prediction of resolution strategies; and (3) an ablation study to find the contribution of each group of attributes to MESTRE’s performance.
Self-supervised efficient sample weighting for multi-exit networks

Dynamic sample weighting is an effective approach for improving the inference accuracy and efficiency of different classifiers in multi-exit networks. By bringing the early-exit behavior from testing to training, such a method allows input samples to contribute differently at each sub-network, reducing the gap between training and inference. Recent methods mainly rely on learning a weighting mechanism through reinforcement learning and meta-learning based frameworks jointly with the multi-exit models. However, additional data are usually needed and essential for these approaches, which results in more computation consumption and limits their general applicability. To address these two problems, we propose a Self-supervised Efficient Sample Weighting (SESW) method to predict weights for input samples based on their losses at each exit. We pose the problem as a self-supervised multi-class classification problem. The SESW module is trained to predict if the log-likelihood for the loss of each sample in the current training stage belongs to the most confident classifier. The results of our experiments demonstrate that the proposed method performs comparably to state-of-the-art methods in terms of prediction accuracy and dynamic inference efficiency while achieving a significant improvement in training time.
A contrastive framework for enhancing Knowledge Graph Question Answering: Alleviating exposure bias

Current encoder–decoders for Knowledge Graph Question Answering (KGQA) commonly utilize teacher-forcing training to accelerate convergence. However, this training approach limits the model’s exposure to ground truths, resulting in exposure bias that hampers generalization performance during autoregressive inference. To alleviate the issue, we propose a contrastive framework that enables the model to access a variety of positive and negative examples, thereby enhancing generalization. Firstly, we introduce a sampling augmentation strategy to construct contrastive samples, which can ensure explicit semantic consistency of positive pairs and inconsistency of negative pairs. Secondly, we augment the training process by incorporating “hard” negatives to enhance the contrastive objective, along with augmented positives to improve the generation objective. Finally, we also sample multiple logical forms for each question during the inference to reduce the bias potential and train a contrastive ranking model to obtain the target logical form. We achieve improvements of 1.95% and 1% over the previous state-of-the-art methods on the KQA Pro and OVERNIGHT benchmarks, respectively. Furthermore, our approach obtains competitive results on the WebQSP dataset. These findings validate the efficacy of our contrastive framework for advancing KGQA performance.
Adding a Novel Italian Treebank of Marked Constructions to Universal Dependencies

In this paper we present a novel treebank developed to analyse marked constructions in Italian called MarkIT. The resource contains almost 1,300 sentences manually annotated with dependency relations following the Universal Dependencies paradigm. The sentences have been extracted from essays written by high-school students along several years, which accounts for the structure and the topic variability of the sentences. In this work, we detail the process to select the sentences, parse them automatically and then manually correct them. The resource covers seven types of marked constructions (839 sentences overall) plus some sentences, whose syntax can be wrongly classified as marked and which can serve as negative examples of markedness (453 sentences). We also present an evaluation of parsing performance, comparing a model trained on existing Italian treebanks with the model obtained by adding MarkIT to the training set.
SI-M/O: Swarm Intelligence-based Modeling and Optimization of complex synthesis reaction processes

Processes with unknown reactions and limited data are commonplace in the chemical industry. However, modeling and optimizing these processes are challenging tasks. In this paper, we propose the Swarm Intelligence-based Modeling and Optimization (SI-M/O) algorithm to address these challenges. The SI-M/O algorithm integrates swarm intelligence with chemical process fundamentals. This fusion empowers SI-M/O to navigate complex chemical landscapes effectively. Swarm intelligence excels at exploring vast solution spaces and adapting dynamically. When combined with first-principle knowledge of chemical reactions and thermodynamics, SI-M/O not only finds optimal solutions but also considers chemical feasibility and physical constraints. To validate its effectiveness, we applied SI-M/O to optimize a production plant, achieving a substantial 5.3 % productivity increase during preliminary testing. We also designed a user-friendly graphical interface for SI-M/O, enhancing accessibility for researchers and practitioners.
A 4D discrete Hopfield neural network-based image encryption scheme with multiple diffusion modes

A novel 4D chaotic map is proposed in this paper using the model of a self-feedback Discrete Hopfield Neural Network (DHNN) with nonlinear synaptic ICMIC (4D-IDHNN). Based on an analysis of chaos characteristics, including attractors, Lyapunov exponents, Bifurcation, Sample Entropy and CO algorithms, the 4D-IDHNN system demonstrates reasonable stochasticity and ergodicity, alongside a broad spectrum of hyperchaotic parameters. Based on the 4D-IDHNN system, we design a new Pixel-dependent bit-plane Chaotic random Diffusion (PbCrD) algorithm with high diffusion capability and security. To begin, the 4D-IDHNN system generates pseudo-random sequences to confuse the plaintext image pixel positions with Chaotic Magic Random Scramble (CMRS). Subsequently, the PbCrD algorithm can be employed to diffuse pixel values. With the initial value of 4D-IDHNN generated using the SHA-256 hash of plaintext image, the ability to resist plaintext attacks can be enhanced. The algorithm includes 24 diffusion modes, which is three times greater than the number of DNA rules, thereby enhancing the complexity of decryption. The proposed encryption algorithm has good effectiveness, robustness, and resistance to differential attacks based on simulations and security analyses.
The Good, The Bad, and The Average: Benchmarking of Reconstruction Based Multivariate Time Series Anomaly Detection

Reconstruction-based algorithms offer state-of-the-art performance in multivariate time series anomaly detection. But as always: there is no single best algorithm. To find the optimal solution, one has to compare different methods and tune their hyperparameters. This paper introduces a lightweight modular benchmarking framework for data scientists and researchers in the field. The framework can be easily set up and automatically create a visual summary of the relevant performance indicators and automatically selected examples to give insight into the behavior of the model and aid during the development.
Inclusively: An AI-Based Assistant for Inclusive Writing

Inclusive writing is compulsory in formal communications. However, employees in private organizations, universities, and ministries often lack inclusive writing skills. For example, despite Italian grammar having masculine and feminine declensions of words, many official documents have a disrespectful prevalence of the masculine form. To promote inclusive writing practices, we present Inclusively, a language support tool that leverages natural language processing techniques to automatically identify instances of non-inclusive language and suggest more inclusive alternatives. The tool can be used as a text proofreader and, at the same time, fosters self-learning of inclusive writing forms. The recorded demo of the tool, available at https://youtu.be/3uiW_ti8wmY , shows how end-users can interact with Inclusively to feed new data, visualize the non-inclusive pieces of text, explore the list of alternative forms, and provide feedback or human annotations for system fine-tuning.
PIQARD System for Experimenting and Testing Language Models with Prompting Strategies

Large Language Models (LLMs) have seen a surge in popularity due to their impressive results in natural language processing tasks, but there are still challenges to be addressed. Prompting in the question is a solution for some of them. In this paper, we present PIQARD, an open-source Python library that allows researchers to experiment with prompting techniques and information retrieval, and combine them with LLMs. This library includes pre-implemented components and also allows users to integrate their own methods.
Predictive Maintenance, Adversarial Autoencoders and Explainability

The transition to Industry 4.0 provoked a transformation of industrial manufacturing with a significant leap in automation and intelligent systems. This paradigm shift has brought about a mindset that emphasizes predictive maintenance: detecting future failures when current behaviour of industrial processes and machines is thought to be normal. The constant monitoring of industrial equipment produces massive quantities of data that enables the application of machine learning approaches to this task. This study uses deep learning-based models to build a data-driven predictive maintenance framework for the air production unit (APU), a crucial system for the proper functioning of a Metro do Porto train. This public transport system moves thousands of people every day and train failures lead to delays and loss of trust by clients. Therefore, it is essential not only to detect APU failures before they occur to minimize negative impacts, but also to provide explanations for the failure warnings that can aid in decision-making processes. We propose an autoencoder architecture trained with an adversarial loss, known as the Wasserstein Autoencoder with Generative Adversarial Network (WAE-GAN), designed to detect sensor failures in systems connected to the APU. Our model can detect APU failures up to two hours before they occur, allowing timely intervention of the maintenance teams. We further augment our model with an explainability layer, by providing explanations generated by a rule-based model that focuses on rare events. Results show that our model is able to detect APU failures without any false alarms, fulfilling the requisites of Metro do Porto for early detection of the failures.
Refinement of Systems with an Attacker Focus

Tools and techniques for assessing the possibilities and impacts of attacks on IT systems are necessary to ensure the IT systems upon which society depends on continue to operate despite targeted attacks. This reality compels the development of intuitive brainstorming formalisms like attack-defense trees. With an attack-defense tree and a suitable system description, one can validate if a system succumbs to or withstands a described attack. Yet having established a secure system, it is still necessary to understand if and how system security may or may not be compromised or improved when the system requires modifications. Our research describes how we develop and implement a modeling methodology to resolve attacker-oriented refinement between systems.
Applying Rely-Guarantee Reasoning on Concurrent Memory Management and Mailbox in $$\mu $$C/OS-II: A Case Study

Real-time operating systems (RTOSs) such as $$\mu $$ C/OS-II are critical components of many industrial systems, which makes it of vital importance to verify their correctness. However, earlier specifications for verification of RTOSs often do not explicitly specify the behavior of possible unbounded kernel service invocations. To address the problem, a new event-based modelling approach is recently proposed to treat the operating system as a concurrent reactive system (CRS). Besides, a respective parametric rely-guarantee style reasoning framework called PiCore is developed to verify such systems effectively. Witnessing the advancement, we conduct a case study to investigate the use of PiCore to compositionally verify two important entangled modules of a practical RTOS $$\mu $$ C/OS-II, i.e., the memory management module and the mailbox module. Several desirable safety properties regarding the memory pools and mailboxes are formally defined and proved with PiCore ( $$\approx $$ 2500 lines of specifications and proof scripts in Isabelle/HOL) based on a formal execution model considering the two modules simultaneously. We also discuss the shortcomings of PiCore for our case study and present possible improvement directions.
Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping

The maritime industry is under pressure to increase energy efficiency for climate change mitigation. Navigational data, combining vessel operational and environmental measurements from onboard instruments and external sources, are critical for achieving this goal. Short-sea shipping presents a unique challenge due to the significant influence of surrounding landscape characteristics. With high-resolution onboard data increasingly accessible through IoT devices, appropriate data representations and AI/ML analytical tools are needed for effective decision support. The aim of this study is to investigate the fuel consumption estimation model’s role in developing an energy efficiency decision support tool. ML models that lacking explainability may neglect important factors and essential constraints, such as the need to meet arrival time requirements. Onboard weather measurements are compared to external forecasts, and our findings demonstrate the necessity of eXplainable Artificial Intelligence (XAI) techniques for effective decision support. Real-world data from a short-sea passenger vessel in southern Sweden, consisting of 1754 voyages over 15 months (More of data description and code sources of this study can be found in the GitHub repository at https://github.com/MohamedAbuella/ST4EESSS ), are used to support our conclusions.