Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Medical Data Generation Using Adversarial Learning

Outstanding performance has been observed in a number of real-world applications such as speech processing and image classification using deep learning models. However, developing these kinds of models in sensitive domains such as healthcare usually necessitates dealing with a specific level of privacy challenges which provide unique concerns. For managing such privacy concerns, a practical method might involve generating feasible synthetic data that not only provides acceptable data quality but also helps to improve the efficiency of the model. Synthetic Data Generation (SDG) innately includes Generative Adversarial Networks (GANs) that have drawn significant interest in this field as a result of their achievement in various other research areas. In the study, a framework safeguarding privacy, which employs Rényi Differential Privacy along with Generative Adversarial Networks and a Variational Autoencoder (RDP-VAEGAN), is introduced. This approach is evaluated and contrasted with other top-tier models having identical privacy constraints, utilizing both unsupervised and supervised methods on two medical datasets that are publicly accessible.
Validation of Federated Unlearning on Collaborative Prostate Segmentation

Machine Unlearning (MU) is an emerging discipline studying methods to remove the effect of a data instance on the parameters of a trained model. Federated Unlearning (FU) extends MU to unlearn the contribution of a dataset provided by a client wishing to drop from a federated learning study. Due to the emerging nature of FU, a practical assessment of the effectiveness of the currently available approaches in complex medical imaging tasks has not been studied so far. In this work, we propose the first in-depth study of FU in medical imaging, with a focus on collaborative prostate segmentation from multi-centric MRI dataset. We first verify the unlearning capabilities of a panel of FU methods from the state-of-the-art, including approaches based on model adaptation, differential privacy, and adaptive retraining. For each method, we quantify their unlearning effectiveness and computational cost as compared to the baseline retraining of a model from scratch after client dropout. Our work highlights a new perspective for the practical implementation of data regulations in collaborative medical imaging applications.
EXPLORING STUDENTS’ PERCEPTIONS OF PEER FEEDBACK PROCESSES IN PHARMACY EDUCATION

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 7623-7627ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.1909Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
FEASIBILITY OF IMMERSIVE ENVIRONMENTS IN THE METAVERSE FOR REMOTE PRACTICAL EDUCATION IN COMPUTER NETWORKS

FEASIBILITY OF IMMERSIVE ENVIRONMENTS IN THE METAVERSE FOR REMOTE PRACTICAL EDUCATION IN COMPUTER NETWORKS
THE CHALLENGES AND POSSIBILITIES OF COMPUTATIONAL THINKING FOR THE INITIAL TRAINING OF TEACHERS

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 4621-4625ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.1149Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
INTERDISCIPLINARY CO-CREATION OF A MULTIPLAYER GAMIFIED MOBILE APP TO ADDRESS HERITAGE PRESERVATION CONSCIOUSNESS AMONG MUSEUM VISITORS: THE CASE OF THE MILITARY MUSEUM OF PORTO

INTERDISCIPLINARY CO-CREATION OF A MULTIPLAYER GAMIFIED MOBILE APP TO ADDRESS HERITAGE PRESERVATION CONSCIOUSNESS AMONG MUSEUM VISITORS: THE CASE OF THE MILITARY MUSEUM OF PORTO
FORMATIVE EVALUATION USING ICTS IN TECHNICAL STUDIES AND ITS INFLUENCE ON THE FINAL COURSE SCORE

A. Meana-Fernández 1R. Martínez-Pérez1M.J. Suárez-López1D. García-Menéndez1A. Ramos-Martín2A.M. Blanco-Marigorta2A.J. Gutiérrez-Trashorras1
Manufacturing Companies Financial Fraud Detection Based on Interpretable Machine Learning

Because of the industrial characteristics like complex purchasing and selling links in manufacturing companies, it is relatively easy for them to conduct financial fraud. Taking A-share manufacturing companies from 2009 to 2021 as an observation sample, this paper constructed a financial fraud detec
Ways of Governance of Internet Financial Crimes Based on Blockchain Perspective -Taking Virtual Currency Money Laundering Crimes as an Example

Combined with the 47th Statistical Report on the Development Status of the Internet in China issued by the China Internet Information Center, it can be found that by the end of December 2020, the number of Internet users in China has exceeded 989 million, and there are 986 million mobile phone Internet users, and the mobile phone Internet access rate of the nationals has reached 99.7%. The demand for mobile internet applications has led to the development of a large number of internet industries. In the financial sector, for better financial business, many customers use financial trading platforms, but illegal criminals use blockchain technology in the name of virtual currency to carry out illegal fund raising, pyramid selling, money laundering and other criminal activities, involving a large amount of money and a relatively wide audience, which seriously affects China's Internet financial security. Therefore, the only way to ensure the safety of Internet finance is to strengthen the governance of such crimes. In this paper, based on the existing research results on blockchain technology and the governance of virtual currency money laundering crimes in Internet finance crimes, we use interdisciplinary research methods to determine the ways of governance that combine the pre-existing, ex-post and post-existing governance, taking into account the current status of legislation, existing cases, the dilemma of governance, and the existing governance achievements.
Effect of Digital Learning Media Using Scratch Game Based Learning on Student Problem Solving Skills

In the current digital era, education has undergone a significant transformation, providing students with rapid and convenient access to a vast pool of knowledge. Through a Research and Development (R&amp;D) approach and the ADDIE model, Scratch-based digital game-based learning media is created and evaluated, demonstrating its effectiveness in significantly improving students' problem-solving abilities, motivating learners, and providing a positive learning experience. In this digital age, educators are encouraged to embrace technology, particularly game-based learning, as it holds the potential to enhance student engagement and cultivate vital 21st-century skills. The research findings conclude that the use of digital game-based learning media using Scratch is effective in enhancing students' problem-solving skills. This learning media offers an engaging and effective learning experience, boosts student motivation, and cultivates critical, creative, and problem-solving skills relevant to the demands of the era. Therefore, Scratch can serve as an effective and innovative alternative to address educational challenges in the digital age.
Implementation of the discovery learning model with engaged writing strategy and image media in improving fantasy story writing skil

Fantasy story writing requires imagination, reasoning, sensitive feelings, and self-motivation to discover and process narrative ideas. Thus, this study describes how the discovery learning model with engaged writing strategies and image media improves fantasy narrative writing. This research employed a Quasi-Experimental Design. Data was collected using writing tasks, tests, and semi-structured interviews. Data analysis was conducted using SPSS Statistics and Microsoft Excel software. The research sample was class VII students at SMP Muhammadiyah 15 Brondong, Lamongan Regency. Research shows that the discovery learning model with engaged writing strategies and picture media improves fantasy narrative writing skills by integrating students' thoughts, feelings, and intrinsic motivation. The findings were evident from the Mann-Whitney U test, which showed a significant influence of this innovative learning model with a post-test value (0.0000005) and writing task value (0.00000001). Selain itu, hasil N-Gain value kelas eksperimen lebih tinggi (71%) dibandingkan kelas kontrol (5.4851%). By integrating engaged writing and image media, the discovery learning model significantly enhanced the experimental group's capacity to compose fantasy stories. However, the expository model was less effective in the control group. Thus, discovery learning models with engaged writing strategy and image media may improve fantasy story writing ability.
SmartCart Project: Adding Intelligence to Shopping Carts

The retail sector has been experiencing a transformation in which the incorporation of technologies into the shopping process is becoming increasingly common, aiming to provide a better experience for consumers. In this context, this paper seeks to propose a solution that uses embedded hardware in shopping carts to enhance the shopping experience, primarily speeding up the payment process by allowing customers to scan the barcodes of desired products without having to face long queues. It also aims to provide a supermarket map, indicating the current position of the cart. To achieve these objectives, computer vision was used to recognize QR codes attached to the ceiling. Following this methodology, satisfactory results were obtained, with paths for improvement and possible changes to be made in the project to achieve greater accuracy in estimating the cart’s location.
THEORETICAL FOUNDATIONS OF THE EMERGENCE AND DEVELOPMENT OF BLENDED LEARNING IN VOCATIONAL EDUCATION IN UKRAINE

The article analyses various approaches to the interpretation of the concept of ‘blended learning’, which is often also called ‘hybrid’, ‘combined’, ‘integrative’ or ‘multimethod’ by researchers. It is found that the regulatory documents most often use the concept of ‘blended learning’, which is defined as an approach or model of interaction between teachers and students, which takes place both during the direct work of participants in the educational process in the classroom and with the use of online resources. However, the authors note that the current legislation of Ukraine does not provide for a blended form of education as such, but educational institutions are forced to organise full-time education using distance technologies due to the epidemiological situation (2020–2022) and military operations in Ukraine. The article also analyses four levels of implementation of blended learning (at the level of activity, at the level of discipline/course, at the level of educational programme, at the level of educational institution). It is found that one of the most common practices of using blended learning technologies is at the level of discipline/course, which involves a combination of face-to-face and distance learning. The article analyses the advantages of different models of organising the educational process (Supplemental Model, Replacement Model, Emporium Model, Rotation Model, Flex Model, Self-Blend Model, and Enriched Virtual Model). The choice of the most optimal model of blended learning for use in the educational process of an educational institution lies with its administration, which develops and approves the relevant regulatory document that defines the main components and principles of blended learning, regulates all organisational procedures of this form of education, as well as the rights and obligations of participants in the educational process. In addition, the educational manager should monitor the effectiveness of the educational process at all levels and take prompt corrective measures.
Design of an optical linear-discriminant filter: optimization for enhancement of filter transmittance and discrimination accuracy

To discriminate fine concave and convex defects on a dielectric substrate, an optical machine learning system is proposed. This system comprises an optical linear-discriminant filter (OLDF) that performs linear discriminant analysis (LDA) of the scattered-wave distribution from target samples. However, the filter output from the OLDF is considerably weak and cannot be measured experimentally. Therefore, an algorithm is also proposed to improve the discrimination accuracy and filter transmittance. The designed filter is validated using a rigorous optical simulator based on vector diffraction theory. We also analyze and discuss a mechanism that provides high transmittance with high discrimination accuracy.
Early childhood mathematics routines empowering young minds to think <b>Early childhood mathematics routines empowering young minds to think</b> , by Antonia Cameron, Patricia Gallahue, Danielle Iacoviello, Routledge, 2023, 196 pp, £25.19 (ebook), ISBN 9781625311832

Click to increase image sizeClick to decrease image size AcknowledgementsWe would like to thank the Lembaga Pengelola Dana Pendidikan (LPDP) for funding the study and our publications as well as supervisors who have guided and evaluated these publications.
Based on Improved YOLOv7 Small Target Detection

In recent years, deep learning-based object detection has developed rapidly, but its performance in the small object detection field is not ideal compared to the natural scene image domain. Hence, this paper proposes an improved small object detection algorithm based on YOLOV7 algorithm. Firstly, based on Ghost model, the ELAN module in backbone structure is improved to Gmodel which effectively reduces computation and improves accuracy. Secondly, this paper introduces a Triplet Attention-improved small object attention module Amodel in YOLOV7’s head structure; through Amodel’s cross-latitude interaction function, it enhances the feature detection performance for small objects. Experiments were conducted on RSOD dataset and our method increased yolov7’s AP50 by 1.65mAP and AP50-95 by 1.88mAP while also reducing FLOPs by 0.2G, making it more suitable for dense small target scenes for object detection.
SiamPKHT: Hyperspectral Siamese Tracking Based on Pyramid Shuffle Attention and Knowledge Distillation

Hyperspectral images provide a wealth of spectral and spatial information, offering significant advantages for the purpose of tracking objects. However, Siamese trackers are unable to fully exploit spectral features due to the limited number of hyperspectral videos. The high-dimensional nature of hyperspectral images complicates the model training process. In order to address the aforementioned issues, this article proposes a hyperspectral object tracking (HOT) algorithm callled SiamPKHT, which leverages the SiamCAR model by incorporating pyramid shuffle attention (PSA) and knowledge distillation (KD). First, the PSA module employs pyramid convolutions to extract multiscale features. In addition, shuffle attention is adopted to capture relationships between different channels and spatial positions, thereby obtaining good features with a stronger classification performance. Second, KD is introduced under the guidance of a pre-trained RGB tracking model, which deals with the problem of overfitting in HOT. Experiments using HOT2022 data indicate that the designed SiamPKHT achieves better performance compared to the baseline method (SiamCAR) and other state-of-the-art HOT algorithms. It also achieves real-time requirements at 43 frames per second.
Decentralized Coordination of a Multi-UAV System for Spatial Planar Shape Formations

Motivated by feedback from firefighters in Normandy, this work aims to provide a simple technique for a set of identical drones to collectively describe an arbitrary planar virtual shape in a 3D space in a decentralized manner. The original problem involved surrounding a toxic cloud to monitor its composition and short-term evolution. In the present work, the pattern is described using Fourier descriptors, a convenient mathematical formulation for that purpose. Starting from a reference point, which can be the center of a fire, Fourier descriptors allow for more precise description of a shape as the number of harmonics increases. This pattern needs to be evenly occupied by the fleet of drones under consideration. To optimize the overall view, the drones must be evenly distributed angularly along the shape. The proposed method enables virtual planar shape description, decentralized bearing angle assignment, drone movement from takeoff positions to locations along the shape, and collision avoidance. Furthermore, the method allows for the number of drones to change during the mission. The method has been tested both in simulation, through emulation, and in outdoor experiments with real drones. The obtained results demonstrate that the method is applicable in real-world contexts.
FuzzyCSampling: A Hybrid fuzzy c-means clustering sampling strategy for imbalanced datasets

Classification model with imbalanced datasets is recently one of the most researched areas in machine learning applications since they induce to the emergence of low-performing machine learning models. The imbalanced datasets occur if target variables have an uneven number of examples in a dataset. The most prevalent solutions to imbalanced datasets can be categorized as data preprocessing, ensemble techniques, and cost-sensitive learning. In this article, we propose a new hybrid approach for binary classification, named FuzzyCSampling, which aims to increase model performance by ensembling fuzzy c-means clustering and data sampling solutions. This article compares the proposed approaches' results not only to the base model built on an imbalanced dataset but also to the previously presented state-of-the-art solutions undersampling, SMOTE oversampling, and Borderline Smote Oversampling. The model evaluation metrics for the comparison are accuracy, roc_auc score, precision, recall and F1-score. We evaluated the success of the brand-new proposed method on three different datasets having different imbalanced ratios and for three different machine learning algorithms (k-nearest neighbors algorithm, support vector machines and random forest). According to the experiments, FuzzyCSampling is an effective way to improve the model performance in the case of imbalanced datasets.
Exploring the impact of training datasets on Turkish stance detection

Stance detection has garnered considerable attention from researchers due to its broad range of applications, including fact-checking and social computing. While state-of-the-art stance detection models are usually based on supervised machine learning methods, their effectiveness is heavily reliant on the quality of training data. This problem is more prevalent in stance detection task because the stance of a text is intimately tied to the target under consideration. While numerous datasets exist for stance detection, determining their suitability for a specific target can be challenging. In this work, we focus on Turkish stance detection and explore the impact of training data on the model performance. In particular, we fine-tune BERT model with various datasets and assess their performance when the test data is the same/different compared to the training data in terms of target and domain. In addition, given the scarcity of resources for Turkish stance detection, we investigate i) whether we can use existing datasets in other languages in a cross-lingual setup, and ii) the effectiveness of data augmentation with simple automatic labeling methods. In order to conduct our experiments, we also create new Turkish stance detection datasets for various targets in different domains. In our comprehensive experiments, our findings are as follows. 1) Using training data with multiple targets in the same domain yields high performance as the model is able to learn more characteristics of expressing stance with additional data. 2) The domain of the training data plays a crucial role in achieving high performance. 3) Automatically generated data enhances performance when combined with manually annotated data. 4) Training solely on Turkish data outperforms training with the combination of Turkish and English data. Overall, our study points out the importance of creating Turkish annotated datasets for different domains to achieve high performance in stance detection.
THE US “METABIRKINS” CASE IN THE LIGHT OF EU IP AND CONSUMER PROTECTION LAW

In early 2023 a United States (US) court ruled that a crypto art collection named “Metabirkins”, depictingthe famous “Birkin” bag of Hermès, infringedtrademark rights. This ruling conferred Hermès the power to ban the commercial exploitation of Metabirkins by their designer, through a permanent injunction order. By the time that order was issued, however, several Metabirkins had already been sold to third parties. Taking this case as a point of reference, thispaper examinescrypto art transactions from the perspective of EU intellectual property (IP) and consumer protection law. First, it clarifies the conditions under which the purchasers and licensees of Non-Fungible Tokens (NFTs) fall under the consumer concept. Then, it examines whether the critical facts would constitute a trademark infringement in the EU, and what would have been the impact of such an infringement on the rightful use of the NFTs by their right-holders. Finally, the paper discussesDirective 2019/770 in protectingconsumersand itsapplicability in the blockchain ecosystem.
Characterizing Requirements Smells

Context: Software specifications are usually written in natural language and may suffer from imprecision, ambiguity, and other quality issues, called thereafter, requirement smells. Requirement smells can hinder the development of a project in many aspects, such as delays, reworks, and low customer satisfaction. From an industrial perspective, we want to focus our time and effort on identifying and preventing the requirement smells that are of high interest. Aim: This paper aims to characterise 12 requirements smells in terms of frequency, severity, and effects. Method: We interviewed ten experienced practitioners from different divisions of a large international company in the safety-critical domain called MBDA Italy Spa. Results: Our interview shows that the smell types perceived as most severe are Ambiguity and Verifiability, while as most frequent are Ambiguity and Complexity. We also provide a set of six lessons learnt about requirements smells, such as that effects of smells are expected to differ across smell types. Conclusions: Our results help to increase awareness about the importance of requirement smells. Our results pave the way for future empirical investigations, ranging from a survey confirming our findings to controlled experiments measuring the effect size of specific requirement smells.
Double quantum images encryption scheme based on chaotic system

Abstract This paper explores a double quantum images representation (DNEQR) model that allows for simultaneous storage of two digital images in a quantum superposition state. Additionally, a new type of two-dimensional hyperchaotic system based on Sine and Logistic maps is investigated, offering a wider parameter space and better chaotic behavior compared to the Sine and Logistic maps. Based on the DNEQR model and the hyperchaotic system, a double quantum images encryption algorithm is proposed. Firstly, two classical plaintext images are transformed into quantum states using the DNEQR model. Then, the proposed hyperchaotic system is employed to iteratively generate pseudo-random sequences. These chaotic sequences are utilized to perform pixel value and position operations on the quantum image, resulting in changes to both pixel values and positions. Finally, the ciphertext image can be obtained by qubit-level diffusion using two XOR operations between the position permutated image and the pseudo-random sequences. The corresponding quantum circuits are also given. Experimental results demonstrate that the proposed scheme ensures the security of the images during transmission, improves the encryption efficiency, and enhances anti-interference and anti-attack capabilities.