Reviewerly/exHarmony huggingface dataset:
On The Relevance of Multi-Graph Matching for Sulcal Graphs

Fine-scale characterization of the geometry of the folding patterns of the brain is a key processing step in neuroscience, with high impact applications such as for uncovering biomarkers indicative of a neurological pathology. Sulcal graphs constitute relevant representations of the complex and variable geometry of the cortex of individual brains. Comparing sulcal graphs is challenging due to variations across subjects in the number of nodes, graph topology and attributes (on both nodes and edges). Graph matching experiments on real data are limited by the absence of ground truth. In this paper we propose to generate synthetic graphs to benchmark graph matching methods and assess their robustness to noise on attributes and to the presence of un-matchable nodes. Three multi-graph matching methods are compared to one pairwise approach in various simulation settings, showing that good matching performances can be achieved even with highly perturbed sulcal graphs. An experiment on real data from a population of 134 subjects further unveil large performance differences across matching methods.
Fixed Point Logics and Definable Topological Properties

Modal logic enjoys topological semantics that may be traced back to McKinsey and Tarski, and the classification of topological spaces via modal axioms is a lively area of research. In the past two decades, there has been interest in extending topological modal logic to the language of the $$\mu $$ -calculus, but previously no class of topological spaces was known to be $$\mu $$ -calculus definable that was not already modally definable. In this paper we show that the full $$\mu $$ -calculus is indeed more expressive than standard modal logic, in the sense that there are classes of topological spaces (and weakly transitive Kripke frames) which are $$\mu $$ -definable, but not modally definable. The classes we exhibit satisfy a modally definable property outside of their perfect core, and thus we dub them imperfect spaces. We show that the $$\mu $$ -calculus is sound and complete for these classes. Our examples are minimal in the sense that they use a single instance of a greatest fixed point.
Tense Logics over Lattices

Lattice theory has close connections with modal logic via algebraic semantics and lattices of modal logics. However, one less explored direction is to view lattices as relational structures based on partial orders, and study the modal logic over them. In this paper, following the earlier steps of Burgess and van Benthem in the 1980s, we use the basic tense logic and its nominal extensions with binary modalities of infimum and supremum to talk about lattices via standard Kripke semantics. As the main results, we obtain a series of complete axiomatizations of lattices, (un)bounded lattices over partial orders or strict preorders. In particular, we solve an axiomatization problem left open by Burgess (1984).
Almost Certain Termination for $$\mathcal {ALC}$$ Weakening

Concept refinement operators have been introduced to describe and compute generalisations and specialisations of concepts, with, amongst others, applications in concept learning and ontology repair through axiom weakening. We here provide a probabilistic proof of almost-certain termination for iterated refinements, thus for an axiom weakening procedure for the fine-grained repair of $$\mathcal {ALC}$$ ontologies. We determine the computational complexity of refinement membership, and discuss performance aspects of a prototypical implementation, verifying that almost-certain termination means actual termination in practice.
Cherry-Picking Meta-heuristic Algorithms and Parameters for Real Optimization Problems

We present an approach that is able to automatically choose the best meta-heuristic and configuration for solving a real optimization problem. Our approach allows the researcher to indicate which meta-heuristics to choose from and, for each meta-heuristic, which parameters should be automatically configured to find good solutions for the optimization problem. We show that our approach is sound using ten well know real optimization problems and five meta-heuristics. As a side effect, we were also able to provide an unbiased way of assessing meta-heuristics concerning their performance to address one or more classes of real optimization problems. Our approach improved the results found for all the meta-heuristics in all problems and was also able to find very competitive results for all optimization problems when given the liberty to choose which meta-heuristic to use.
An Active Learning-Based Medical Diagnosis System

Every year thousands of people get their diagnoses wrongly, and several patients have their health conditions aggravated due to misdiagnosis. This problem is even more challenging when the list of possible diseases is long, as in a general medicine speciality. The development of Artificial Intelligence (AI) medical diagnosis systems could prevent misdiagnosis when clinicians are in doubt. We developed an AI system to help clinicians in their daily practice. They could consult the system to get an immediate opinion and diminish waiting times in triage services since this task could be carried out with minimal human interaction. Our method relies on Machine Learning techniques, more precisely on Active Learning and Neural Networks classifiers. To train this model, we used a data set that relates symptoms to several diseases. We compared our models with other models from the literature, and our results show that it is possible to achieve even better performance with much less data, mainly because of the contribution of the Active Learning component.
The Joint Role of Batch Size and Query Strategy in Active Learning-Based Prediction - A Case Study in the Heart Attack Domain

This paper proposes an Active Learning algorithm that could detect heart attacks based on different body measures, which requires much less data than the passive learning counterpart while maintaining similar accuracy. To that end, different parameters were tested, namely the batch size and the query strategy used. The initial tests on batch size consisted of varying its value until 50. From these experiments, the conclusion was that the best results were obtained with lower values, which led to the second set of experiments, varying the batch size between 1 and 5 to understand in which value the accuracy was higher. Four query strategies were tested: random sampling, least confident sampling, margin sampling and entropy sampling. The results of each approach were similar, reducing by 57% to 60% the amount of data required to obtain the same results of the passive learning approach.
Region of Interest Identification in the Cervical Digital Histology Images

The region of interest (RoI) identification has a significant potential for yielding information about relevant histological features and is imperative to improve the effectiveness of digital pathology in clinical practice. The typical RoI is the stratified squamous epithelium (SSE) that appears on relatively small image areas. Hence, taking the entire image for classification adds noise caused by irrelevant background, making classification networks biased towards the background fragments. This paper proposes a novel approach for epithelium RoI identification based on automatic bounding boxes (bb) construction and SSE extraction and compares it with state-of-the-art histology RoI localization and detection techniques. Further classification of the extracted epithelial fragments based on DenseNet made it possible to effectively identify the SSE RoI in cervical histology images (CHI). The design brings significant improvement to the identification of diagnostically significant regions. For this research, we created two CHI datasets, the CHI-I containing 171 color images of the cervical histology microscopy and CHI-II containing 1049 extracted fragments of microscopy, which are the most considerable publicly available SSE datasets.
REEdI-Rethinking Engineering Education in Ireland

The pressure on Higher Educational Institutions (HEIs—which include universities, colleges, teacher-training schools, junior colleges, institutes of technology, and professional schools) to produce engineering graduates capable of meeting the needs of Industry is not solely an Irish problem. HEIs need to re-think the way that engineering is taught to students, with the ultimate aim of producing engineering graduates that are capable of meeting the skills needs of the countries’ industries now and into the future. Many institutions across the globe have developed different frameworks and initiatives to tackle the issue. These changing demands come from a variety of different cohorts: students, society, and science. Furthermore, there is an increasing argument that engineering education in Ireland is risking being a barrier to economic growth. A recent report by Engineers Ireland, the representative body for engineering professionals has outlined that 91% of engineering employers identified skills shortages as a significant barrier to growth within the industry [1]. The skills shortages identified are not only in the technical areas of engineering, but also in the areas of transversal skills—skills not only considered specifically related to a particular job, task, academic discipline, or area of knowledge but can be used in a wide variety of situations and work settings, for example, organizational skills. The report found that engineering employers were struggling to fill roles in the mechanical and manufacturing engineering professions, which has an annual employment growth rate of 16.6%.
The Impact of COVID-19 on Higher Education: A Systematic Literature Review of Pedagogical Approaches and Challenges

By March 2020 most Higher Education institutions across the world had been forced to move all learning and teaching activities onto digital platforms, abandoning face-to-face classroom practices as a result of the global COVID-19 pandemic. How did the response to this emergency manifest itself in changes to pedagogy and practice for staff and students in Higher Education? The authors completed a systematic literature review to gain insight into the challenges and pedagogic practices that emerged at this time. To represent the complexities of the ‘activity’, the writers sought a theoretical framework that would help identify the key themes and tensions that the empirical studies had identified. Drawing on the work of Vygotsky, Leont’ev and Engeström, the authors created a theoretical framework to analyse how learning and teaching activities within Higher Education were practised because of COVID-19. In total, 206 papers were reviewed under four categories; theoretical, narrative, empirical and blended (which consists of two or more categories). For example, some articles provided an empirical study alongside their authors’ narrative reflections about their own experiences of handling the challenges that were created by Covid -19 pandemic. Then a more detailed review of 23 papers that met the empirical research criteria were explored for their data with coding categories drawn from the theoretical framework. These papers, in varying degrees, illuminated the tensions and the complexities of how those involved in Higher Education globally, were affected by COVID-19. Confined by the digital knowledge capabilities of both staff and students, universities were challenged to find ways to maintain their programme provision, manage expectations, support students’ wellbeing, and enable staff to balance the challenges as they directly interfaced with students. It is clear that while this emergency offered possibilities for staff and students to harness the productive capabilities of adaptive teaching by moving beyond fixed pedagogical frameworks, many struggled and felt constrained by the complexities of the activities required during the pandemic.
Analyzing the Network Evaluation Model in Mobile Learning Environment to Monitor Different Capabilities of Students

In light of the current ideological and political teaching quality’s poor impact, this paper proposes the construction and research method of a network ideological and political teaching quality evaluation model from the perspective of school-enterprise cooperation, optimises the network ideological and political teaching model using the Internet technology, constructs the teaching quality evaluation index and algorithm, and realizes the optimization of the ideopolitical teaching model. Finally, it is confirmed by experiments. According to the viewpoint of school endeavor collaboration, the organization’s philosophical and political quality evaluation model has higher practicability during the time spent in commonsense application and completely meets the examination prerequisites.
Meta-ED: Cross-lingual Event Detection Using Meta-learning for Indian Languages

Lack of annotated data is a major concern in Event Detection (ED) tasks for low-resource languages. Cross-lingual ED seeks to address this issue by transferring information across various languages to improve overall performance. In this article, we propose a method for cross-lingual ED with a few training instances. We present a model agnostic meta-learning approach for few-shot cross-lingual ED that is able to find good parameter initialization and enables fast adaptation to new low-resource languages. We evaluate our model on four Indian languages. The results show that our approach significantly outperforms the base model.
Classification of Layout vs. Relational Tables on the Web: Machine Learning with Rendered Pages

Table mining on the web is an open problem, and none of the previously proposed techniques provides a complete solution. Most research focuses on the structure of the HTML document, but because of the nature and structure of the web, it is still a challenging problem to detect relational tables. Web Content Accessibility Guidelines (WCAG) also cover a wide range of recommendations for making tables accessible, but our previous work shows that these recommendations are also not followed; therefore, tables are still inaccessible to disabled people and automated processing. We propose a new approach to table mining by not looking at the HTML structure, but rather, the rendered pages by the browser. The first task in table mining on the web is to classify relational vs. layout tables, and here, we propose two alternative approaches for that task. We first introduce our dataset, which includes 725 web pages with 9,957 extracted tables. Our first approach extracts features from a page after being rendered by the browser, then applies several machine learning algorithms in classifying the layout vs. relational tables. The best result is with Random Forest with the accuracy of 97.2% (F1-score: 0.955) with 10-fold cross-validation. Our second approach classifies tables using images taken from the same sources using Convolutional Neural Network (CNN), which gives an accuracy of 95% (F1-score: 0.95). Our work here shows that the web’s true essence comes after it goes through a browser and using the rendered pages and tables, the classification is more accurate compared to literature and paves the way in making the tables more accessible.
Visual Marine Debris Detection using Yolov5s for Autonomous Underwater Vehicle

The trash in the ocean is causing harm to the marine environment. The current most used removal technique is the use of trawlers. It is a highly laborious job and requires high costs as well. With the help of Autonomous Underwater Vehicles (AUVs), removing marine debris could be one of the best and cheapest solutions available. This paper evaluates the use of the You Only Live Once Version 5 Small (YOLOv5s) to compare with the other networks used to identify marine debris. Without fine-tuning the YOLOv5s model in this study, it can achieve a Mean Average Precision (mAP) of 0.681 and an inference speed of 153.9 frames per second. It shows an improvement in mAP compared to YOLOv2, Tiny-YOLO, and Single Shot Multibox Detector.
Computer aided diagnosis system for breast density classification in mammograms

Abstract Breast cancer is a deadly disease affecting women around the globe. Mass detection in the breast tissue at an early stage can lessen the mortality rate occurring due to breast cancer. Through mammograms, the presence of masses can be detected at an early stage, however, it’s sensitivity and specificity are limited in the case of dense tissues. Identification of the breast density type prior to the detection of mass can lessen the chance of misclassifying a breast tissue as normal or abnormal, which eventually decreases the false negative and false positive rate. The proposed system classifies breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS). The proposed method has explored the effect of local descriptors on breast density classification and various feature-classifier combinations have also been explored for the classification. The proposed method validated on 624 mammograms from the Image Retrieval in Medical Applications (IRMA) version of the Digital Database for Screening Mammography (DDSM) database has produced an accuracy of 73% for multi-class breast density classification using the speeded-up robust features (SURF) and support vector machine (SVM) classifier.
Automatic classification of histology images into normal/cancer class with pre-trained CNN

Abstract Deep-Learning-Scheme (DLS) based medical data assessment has been widely employed in recent years due to its improved accuracy. Our goal is to study the performance of the pre-trained DLS on RGB-scale breast-histology images. The implemented idea holds these phases; (i) Data collection, pre-processing and resizing, (ii) Training the DLS with chosen test-pictures, (iii) Testing and validating the performance of the DLS with 5-fold cross-validation. This investigation considered the breast-histology pictures for the study and binary classification is employed to achieve Normal/Cancer class grouping of images. The proposed work compared the classification performance of AlexNet, VGG16 and VGG19.The experimental outcome of this study authenticates that the AlexNet with the Random-Forest (RF) classifier helps to get a higher classification accuracy (&gt;87%) compared to VGG16 and VGG19.
Transfer learning approaches in deep learning for Indian sign language classification

Abstract Speech is the major way of human communication, but when it is limited, humans move to tactile kinaesthetic communication. People with speech-hearing impairments use sign language as an example of such adaptations. The deaf community uses Indian sign language (ISL) throughout India. In India, 250 licensed sign language interpreters are serving a deaf population of 1.8 to 7 million individuals. ISL interpreters are badly needed at institutes and places where persons with hearing impairments communicate. An Indian sign language picture database for English alphabets is established in this project. To prepare it for training, several pre-processing techniques were used. The effectiveness of deep learning neural networks is frequently influenced by the quantity of data available. As a result, data augmentation, a strategy for adding more and diverse samples to train datasets, was used to boost the effectiveness and outcomes of machine learning models. Our model is trained in CNN models utilizing transfer learning methodologies, with an accuracy of 95% for vgg16 and an accuracy of 92% for the inception model. More study on this research, as well as real-time implementation, has the potential to better connect people with hearing loss to society.
Graph Representation Learning for Gate Arrival Time Prediction

An accurate estimate of the timing profile at different stages of the physical design flow allows for pre-emptive changes to the circuit, significantly reducing the design time and effort. In this work, a graph based deep regression model is utilized to predict the gate level arrival time of the timing paths of a circuit. Three scenarios for post routing prediction are considered: prediction after completing floorplanning, prediction after completing placement, and prediction after completing clock tree synthesis (CTS). A commercial static timing analysis (STA) tool is utilized to determine the mean absolute percentage error (MAPE) and the mean absolute error (MAE) for each scenario. Results obtained across all models trained on the complete dataset indicate that the proposed methodology outperforms the baseline errors produced by the commercial physical design tools with an average improvement of 61.58 in the MAPE score when predicting the post-routing arrival time after completing floorplanning and 13.53 improvement when predicting the post-routing arrival time after completing placement. Additional prediction scenarios are analyzed, where the complete dataset is further sub-divided based on the size of the circuits, which leads to an average improvement of 34.83 in the MAPE score as compared to the commercial tool for post-floorplanning prediction of the post-routing arrival time and 22.71 improvement for post-placement prediction of the post-routing arrival time.
Multi-Finger-Based Arbitrary Region-of-Interest Selection in Virtual Reality

We present a new 3D user interface for selecting an arbitrarily shaped region of interest (ROI) in virtual reality. The interface’s primary objective is the selection of a surface geometry’s boundaries having various shapes (e.g., arch- or concave-shaped boundaries) for 3D model editing or annotation. Compared with previous convex ROI shape selection, we explore a more challenging arbitrarily shaped selection. We address this issue by generating a flexible selection space based on a user’s multiple finger position mapping into the shaped ROI on a 3D surface geometry and providing visual feedback to assist the user in progressively refining the selection shape. We employ a leap motion controller for free-hand finger position input and gesture-based navigation to construct a flexible and natural user interface. To demonstrate the efficiency of our method, we evaluate the system usability by conducting a user study and compare the results with those of the state-of-the-art manual-input ROI selection technique.
Listening to the Post-script: Intersections Between Improvisation and Indeterminacy in Music and Education

Abstract This article begins by exploring how current educational research describes the role of improvisation in education. Drawing on Biesta’s (2017) discussion of the purpose of the educational task, it then argues for a broader consideration of the role of improvisation in education and its potential to positively disrupt traditional linear timescapes. While discussions of improvisation in education often reference jazz music, this study explores a broader range of indeterminate musical forms to gain different insights. Specifically, it translates Stern’s (1988) Table of Transformational and Musical Hierarchies into a framework for education to support educators in considering the role of improvisation in their design and teaching practice. The adapted conceptual model incorporates the dimensions of complex learning environments described by the Activity-Centred Analysis and Design (ACAD) framework as discussed by Goodyear and Carvalho (2014), and in the process, surfaces the temporal aspects of the ACAD framework for closer examination. The model highlights several factors that take on a more significant role in highly indeterminate educational forms such as silence, absence, openings, and rupture. It also provides an opportunity to reflect on ways in which technology contributes to enabling or constraining improvisational processes in postdigital education. The study contributes to research on the intersection between sound and education, and to making the sonic dimension of education one of more ‘intentional concern’ as described by Ford and Sasaki (2021).