Reviewerly/exHarmony huggingface dataset:
Sequential interactive image segmentation

Abstract Interactive image segmentation (IIS) is an important technique for obtaining pixel-level annotations. In many cases, target objects share similar semantics. However, IIS methods neglect this connection and in particular the cues provided by representations of previously segmented objects, previous user interaction, and previous prediction masks, which can all provide suitable priors for the current annotation. In this paper, we formulate a sequential interactive image segmentation (SIIS) task for minimizing user interaction when segmenting sequences of related images, and we provide a practical approach to this task using two pertinent designs. The first is a novel interaction mode. When annotating a new sample, our method can automatically propose an initial click proposal based on previous annotation. This dramatically helps to reduce the interaction burden on the user. The second is an online optimization strategy, with the goal of providing semantic information when annotating specific targets, optimizing the model with dense supervision from previously labeled samples. Experiments demonstrate the effectiveness of regarding SIIS as a particular task, and our methods for addressing it.
Current studies and applications of Krill Herd and Gravitational Search Algorithms in healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.
The notion of Abstraction in Ontology-based Data Management

We study a novel reasoning task in Ontology-based Data Management (OBDM), called Abstraction, which aims at associating formal semantic descriptions to data services. In OBDM a domain ontology is used to provide a semantic layer mapped to the data sources of an organization. The basic idea of the work presented in this paper is to explain the semantics of a data service in terms of a query over the ontology. We illustrate a formal framework for this problem, based on three different notions of abstraction, called sound, complete, and perfect, respectively. We present a thorough complexity analysis of two computational problems, namely verification (checking whether a query is an abstraction of a given data service), and computation (computing an abstraction of a given data service).
Evolutionary Computation and Explainable AI Towards ECXAI 2023: A Year in Review

At GECCO 2022, we organized the first Workshop on Evolutionary Computation and Explainable AI (ECXAI). With no pretence at completeness, this paper briefly comments on its outcome, what has happened recently in the field, and our expectations for the near future and, in particular, for the upcoming second edition of EXCAI, at GECCO 2023.
ContourNet: Research on Contour Based Nighttime Semantic Segmentation

Due to the scarcity of nighttime semantic segmentation datasets and the high demand for network models, the development of semantic segmentation of nighttime scenes is still very slow. This paper proposes a new network model, ContourNet, which can model multi-level features. In addition, a separate contour network module is designed to accurately predict object contours, improving performance for objects far away, small, or with high contour continuity. A large number of experiments demonstrate that the ContourNet proposed in this paper can significantly improve the semantic segmentation ability of existing models for nighttime images, and can also improve the semantic segmentation accuracy of daytime images to a certain extent, with good generalization abilities. Specifically, after adding the contour module in this article, MIoU has increased by 5.1% on the night dataset Rebecca; MIoU has increased by 2.5% on the daytime dataset CamVid.
Enhancing adversarial robustness of quantum neural networks by adding noise layers

Abstract The rapid advancements in machine learning and quantum computing have given rise to a new research frontier: quantum machine learning. Quantum models designed for tackling classification problems possess the potential to deliver speed enhancements and superior predictive accuracy compared to their classical counterparts. However, recent research has revealed that quantum neural networks (QNNs), akin to their classical deep neural network-based classifier counterparts, are vulnerable to adversarial attacks. In these attacks, meticulously designed perturbations added to clean input data can result in QNNs producing incorrect predictions with high confidence. To mitigate this issue, we suggest enhancing the adversarial robustness of quantum machine learning systems by incorporating noise layers into QNNs. This is accomplished by solving a Min-Max optimization problem to control the magnitude of the noise, thereby increasing the QNN’s resilience against adversarial attacks. Extensive numerical experiments illustrate that our proposed method outperforms state-of-the-art defense techniques in terms of both clean and robust accuracy.
Comparation of Rotational Deep Learning based Methods

Unmanned Aerial Vehicles (UAV) imagery-based object detection has become more and more widespread, especially in fields like agriculture, city planning, construction oversight or even military reconnaissance. This research focused on a review of the state-of-the-art object detection algorithms which use oriented bounding boxes. The images were collected using DJI Tello and Zll SG906 Pro 2. The images were annotated using Computer Vision Annotation Tool (CVAT). The different detectors were trained using MMDetection and MMRotate modules from MMLab framework. The advantages and disadvantages of different deep neural network approaches are aimed to be compared: Rotated Faster R-CNN (two-stage anchor based detector), Single Shot Alignment Network (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ANet) (one-stage anchor detector) and FCOSR (one-stage anchor-free detector). On neural network models two series of measurements were performed. In the first version, all the image samples were trained during the epochs. In the second version the images were grouped in tasks, and in each epoch a new tasks were load to train. Based on the measurement results, it can be concluded that better results were achieved with the Rotated Faster R-CNN model and the S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ANet model, the FCOSR performance lagged behind the previous models.
Deep joint learning of pathological region localization and Alzheimer’s disease diagnosis

The identification of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) has been studied based on the subtle morphological changes in the brain. One of the typical approaches is a deep learning-based patch-level feature representation. For this approach, however, the predetermined patches before learning the diagnostic model can limit classification performance. To mitigate this problem, we propose the BrainBagNet with a position-based gate (PG), which applies position information of brain images represented through the 3D coordinates. Our proposed method represents the patch-level class evidence based on both MR scan and position information for image-level prediction. To validate the effectiveness of our proposed framework, we conducted comprehensive experiments comparing it with state-of-the-art methods, utilizing two publicly available datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle (AIBL) dataset. Furthermore, our experimental results demonstrate that our proposed method outperforms the existing competing methods in terms of classification performance for both AD diagnosis and mild cognitive impairment conversion prediction tasks. In addition, we performed various analyses of the results from diverse perspectives to obtain further insights into the underlying mechanisms and strengths of our proposed framework. Based on the results of our experiments, we demonstrate that our proposed framework has the potential to advance deep-learning-based patch-level feature representation studies for AD diagnosis and MCI conversion prediction. In addition, our method provides valuable insights, such as interpretability, and the ability to capture subtle changes, into the underlying pathological processes of AD and MCI, benefiting both researchers and clinicians.
Cultural and natural environment in education as support for the development of active life from early childhood

The article focuses on the issue of motivation provided by school and extracurricular education of children for their active interest in the outside world. It is based on the educational concept of regional education which is present in the curriculum of primary education in Slovakia as a cross-sectional topic of educational content and is integrated in all educational areas of its curriculum in pre-primary education.
A Clustering Algorithm for Polygonal Data Applied to Scientific Journal Profiles

Millions of papers are submitted and published every year, but researchers often do not have much information about the journals that interest them. In this paper, we introduced the first dynamical clustering algorithm for symbolic polygonal data and this was applied to build scientific journals profiles. Dynamic clustering algorithms are a family of iterative two-step relocation algorithms involving the construction of clusters at each iteration and the identification of a suitable representation or prototype (means, axes, probability laws, groups of elements, etc.) for each cluster by locally optimizing an adequacy criterion that measures the fitting between clusters and their corresponding prototypes The application gives a powerful vision to understand the main variables that describe journals. Symbolic polygonal data can represent summarized extensive datasets taking into account variability. In addition, we developed cluster and partition interpretation indices for polygonal data that have the ability to extract insights about clustering results. From these indices, we discovered, e.g., that the number of difficult words in abstract is fundamental to building journal profiles.
Direct estimation of volume under the ROC surface with verification bias

In practice, the receiver operating characteristic (ROC) curve of a diagnostic test is widely used to show the performance of the test for discriminating two-class events. The area under the ROC curve (AUC) is proposed as an index for the assessment of the diagnostic accuracy of the test under consideration. Due to ethical and cost considerations associated with application of gold standard (GS) tests, only a subset of the patients initially tested have verified disease status. Statistical evaluation of the test performance based only on test results from subjects with verified disease status are typically biased. Various AUC estimation methods for tests with verification biased data have been developed over the last few decades. In this article, we develop new direct estimation methods for the volume under the ROC surface (VUS) by extending the AUC estimation methods for two-class diagnostic tests to three-class diagnostic tests in the presence of verification bias. The proposed methods will provide a comprehensive guide to deal with the verification bias in three-class diagnostic test accuracy studies and lead to a better choice of diagnostic tests.
The communication path and improvement strategy of symbolic culture of sneaker consumption culture using the big data analysis

With the emergence of Artificial Intelligence technology and the advancement of science and technology, the current mainstream path of social development is continuously updating and improving various industries using technology. Therefore, in order to promote the development of sneaker consumer culture, this study explores the use of technological means to improve the dissemination effect of symbolic culture in sneaker consumer culture. Firstly, the development concept and mainstream direction of sneaker consumer culture in the era of big data are discussed, and the application principle of big data technology is introduced. Then, a sneaker culture dissemination model based on big data technology is designed. Finally, the model is optimized using a Convolutional Neural Network (CNN), and its effectiveness is evaluated. The results show that the Convolutional Neural Network-Big Data (CNN-BD) model designed in this study has the highest fitting degree of 93% and a lowest fitting degree of 78% in the UT-Zap50K dataset. In the Ai2 dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 76%. In the Kaggle Women's Shoe dataset, the highest fitting degree of the big data classification model is 92%, and the lowest is 77%. In the Kaggle Men's Shoe dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 79%. The designed model has the highest accuracy rate of 93% in sneaker classification, while other models have the highest accuracy rate of around 82% in sneaker classification. Compared with traditional big data technology, the designed model has greatly improved and can adapt to more working environments. This study not only provides technical support for the application of big data technology but also contributes to improving the dissemination effect and promoting the comprehensive development of sneaker consumer culture.
A high-resolution large-scale dataset of pathological and normal white blood cells

Microscopic examination plays a significant role in the initial screening for a variety of hematological, as well as non-hematological, diagnoses. Microscopic blood smear examination that is considered a key diagnostic technique, is in recent clinical practice still performed manually, which is not only time consuming, but can lead to human errors. Although automated and semi-automated systems have been developed in recent years, their high purchasing and maintenance costs make them unaffordable for many medical institutions. Even though much research has been conducted lately to explore more accurate and feasible solutions, most researchers had to deal with a lack of medical data. To address the lack of large-scale databases in this field, we created a high-resolution dataset containing a total of 16027 annotated white blood cells. Moreover, the dataset covers overall 9 types of white blood cells, including clinically significant pathological findings. Since we used high-quality acquisition equipment, the dataset provides one of the highest quality images of blood cells, achieving an approximate resolution of 42 pixels per 1 μm.
Fraud detection with natural language processing

Abstract Automated fraud detection can assist organisations to safeguard user accounts, a task that is very challenging due to the great sparsity of known fraud transactions. Many approaches in the literature focus on credit card fraud and ignore the growing field of online banking. However, there is a lack of publicly available data for both. The lack of publicly available data hinders the progress of the field and limits the investigation of potential solutions. With this work, we: (a) introduce FraudNLP , the first anonymised, publicly available dataset for online fraud detection, (b) benchmark machine and deep learning methods with multiple evaluation measures, (c) argue that online actions do follow rules similar to natural language and hence can be approached successfully by natural language processing methods.
A simulation methodology for quantum photonic integrated circuits in the presence of fabrication imperfections, loss, and partially distinguishable photons

Discrete variable quantum photonic circuits rely on the interference between indistinguishable photons to produce non-classical results. However, indistinguishability between photons is often spoiled due to timing delays, different spectral profiles, or the presence of unwanted spectral correlations. Additionally, variability in circuit components can introduce further errors. Here we present a method for simulating the frequency domain response of quantum photonic integrated circuits (PICs), allowing the fidelity and probability of success of realistic quantum circuits to be characterized. As an example, we first model the biphoton wavefunction produced by spontaneous four-wave mixing in a silicon nitride microring resonator, then use our methodology to simulate the interference between heralded signal photons from two such sources in the presence of spectral correlations and circuit component variability due to manufacturing imperfections.
Multipartite entanglement and geometry

Genuine multipartite entanglement is crucial for quantum information and quantum technologies but quantifying it has been a long-standing challenge. Most existing measures do not meet a “genuine” requirement, making them unsuitable for many applications. Here, we present a surprising triangle measure for tripartite entanglement, and introduce the extension to four-qubit systems. We discuss potential avenues for further generalizations along this geometric path. This indicates that our work may pave the way for further advancements.
Flash-profilometry: fullfield lensless acquisition of spectral holograms for coherence scanning profilometry

Flash-profilometry is a novel measurement approach based on the fullfield lensless acquisition of spectral holograms. It is based on spectral sampling of the mutual coherence function and the subsequent calculation of its propagation along the optical axis several times the depth-of-field. Numerical propagation of the entire coherence function, rather than solely the complex amplitude, allows to digitally reproduce a complete scanning white-light interferometric (WLI) measurement. Hence, the corresponding 3D surface profiling system presented here achieves precision in the low nanometer range along an axial measurement range of several hundred micrometers. Due to the lensless setup, it is compact, immune against dispersion effects and lightweight. Additionally, because of the spectral sampling approach, it is faster than conventional coherence scanning WLI and robust against mechanical distortions, such as vibrations and rigid body movements. Flash-profilometry is therefore suitable for a wide range of applications, such as surface metrology, optical inspection, and material science and appears to be particularly suitable for a direct integration into production environments.
OMGD-StarGAN: improvements to boost StarGAN v2 performance

A good image editing model should learn to map the relationships between styles from different domains, cater to the high quality and diversity of the generated images, and be highly scalable across different domains. At the same time, given the importance of multi-device deployments, especially of models on lightweight devices, lightweight optimization of models is an essential and critical task. Based on these key points, a new approach to optimizing and improving existing base models, namely OMGD-StarGAN, is proposed by combining PatchGAN discriminators and DynamicD dynamic training strategies, ResNet style generators, and modulated convolution, based on online multi-granularity knowledge distillation algorithms and StarGAN v2. A comparison of various experiments is conducted, and the experimental results show that the proposed model reduces the computational cost while improving the quality and diversity of the generated images.
Chapter 3. Lexical reduplication in Spanish and Italian

The aim of the article is to analyse the mechanism of lexical reduplication in two Romance languages: Spanish and Italian. Since the study is based on a constructional approach, the repetition of a lexical unit within one phrase is considered to form a construction that presents some specific semantic and pragmatic meanings. Based on the examples from oral corpora of Spanish and Italian, the article presents a pragmatic interpretation of reduplicative constructions. Although Spanish and Italian lexical reduplications seem to share a range of characteristics, the study also focuses on meanings and interactive functions that differentiate these two languages.
Inflight demonstrator of quantum key distribution between CubeSats of Q-ANSER program

In Quantum Key Distribution (QKD), the emitter and receiver need to share an optical quantum channel - which can be optical fibre, terrestrial free-space or space-based links- to exchange the quantum states. However, with the future aim to achieve a quantum global communication network, communications links between small satellites in constellations will be required. In this context, the experience of INTA in the ANSER (Advanced Nanosatellite Systems for Earth Observation Research) small satellite constellation program will be exploited. This program develops a set of missions that will include groupings of a minimum of three CubeSats (a leader and two or more followers) flying in formation and in coordinated operation for a common mission. Therefore, the only difference between ANSER and Q-ANSER program will be the payload of the satellite. In Q-ANSER, in which a prepare-and-measure B92 QKD protocol will be used to generate the secret key, two optical systems will be introduced. In the emitter this system will be capable of sending polarized weak coherent laser pulses, attenuated to single-photon level, to the receiver, which will also be an optical system capable of receiving and detecting these single photons. Prepare-and-measure QKD schemes with polarization encoding require the minimization of polarization degradation both in the transmitter and receiver designs. In particular, the polarization extinction ratio (PER) should be maintained as high as possible to reduce the quantum bit error rate (QBER) . This polarization control will be done with the polarization modulators based on liquid crystals developed by INTA.