Reviewerly/exHarmony huggingface dataset:
Development of Central Bank Digital Currency as a Payment Method for the SARS-CoV-2 Virus Pandemic

The SARS-CoV-2 virus pandemic forced a change in the traditional model of the activities of people, states, and institutions. Many of the daily activities have been transferred to the online realm. One of the visible effects is the digitisation of payment processing and the development of the Central Bank Digital Currency concept, i.e., blockchain-based digital currencies to be issued by the central bank. In the first part, the chapter will present the most important theoretical findings on the very concept of a central bank’s digital currency, as well as an outline of the most important problems and challenges related to this method of payment execution. In the second part, the author, using the case study method, will indicate the intensification of the development of subsequent projects in the face of the pandemic. Based on a literature study on the subject, numerous pilot projects have been identified, which are carried out, for example, by central banks of Sweden and China, or a report prepared at the European Union level indicating the potential benefits resulting from the implementation of solutions.
Safety and Robustness for Deep Neural Networks: An Automotive Use Case

Current automotive safety standards are cautious when it comes to utilizing deep neural networks in safety-critical scenarios due to concerns regarding robustness to noise, domain drift, and uncertainty quantification. In this paper, we propose a scenario where a neural network adjusts the automated driving style to reduce user stress. In this scenario, only certain actions are safety-critical, allowing for greater control over the model’s behavior. To demonstrate how safety can be addressed, we propose a mechanism based on robustness quantification and a fallback plan. This approach enables the model to minimize user stress in safe conditions while avoiding unsafe actions in uncertain scenarios. By exploring this use case, we hope to inspire discussions around identifying safety-critical scenarios and approaches where neural networks can be safely utilized. We see this also as a potential contribution to the development of new standards and best practices for the usage of AI in safety-critical scenarios. The work done here is a result of the TEACHING project, an European research project around the safe, secure and trustworthy usage of AI.
Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments

In order to reach the highest level of automation, autonomous vehicles (AVs) are required to be aware of surrounding objects and detect them even in adverse weather. Detecting objects is very challenging in sandy weather due to characteristics of the environment, such as low visibility, occlusion, and changes in lighting. In this paper, we considered the You Only Look Once (YOLO) version 5 and version 7 architectures to evaluate the performance of different activation functions in sandy weather. In our experiments, we targeted three activation functions: Sigmoid Linear Unit (SiLU), Rectified Linear Unit (ReLU), and Leaky Rectified Linear Unit (LeakyReLU). The metrics used to evaluate their performance were precision, recall, and mean average precision (mAP). We used the Detection in Adverse Weather Nature (DAWN) dataset which contains various weather conditions, though we selected sandy images only. Moreover, we extended the DAWN dataset and created an augmented version of the dataset using several augmentation techniques, such as blur, saturation, brightness, darkness, noise, exposer, hue, and grayscale. Our results show that in the original DAWN dataset, YOLOv5 with the LeakyReLU activation function surpassed other architectures with respect to the reported research results in sandy weather and achieved 88% mAP. For the augmented DAWN dataset that we developed, YOLOv7 with SiLU achieved 94% mAP.
The use of extended reality (XR) in patient education: A critical perspective

Objective: Extended reality (XR) has emerged as an innovative educational modality that offers the potential for the creation of more interactive and engaging forms of patient education experiences and products. The purpose of this article is to describe the field of XR technologies and review its potential through a critical lens as well as its possible adoption as a mainstream technology for providing patient education in the future. Method: A review of the literature was undertaken to summarise the emerging evidence concerning the effectiveness of XR as a patient education modality. The findings of several reviews are summarised and a critical discussion of potential issues and challenges in the adoption and use of XR among particular marginalised populations are explored. Results: The emerging evidence suggests that different forms of XR technology applications have the potential to create immersive and engaging patient education experiences that can lead to enhanced patient satisfaction, positive educational outcomes and reduced patient anxiety. Nonetheless, there have been calls for greater consideration of how patient characteristics, including socioeconomic status, gender, cultural and generational differences, influence the learning effects of virtual reality educational applications, as well as its adoption and implementation for patient education purposes. Conclusion: The evidence surrounding the effectiveness of XR in patient education is growing; however, various factors could influence the successful adoption and implementation of XR in different patient populations who have traditionally experienced challenges with digital health literacy. The paper offers some recommendations for enhancing the evidence base and potential approaches to advance the design and evaluation of XR applications in patient education.
Digital competence of youth in the context of global changes

The article analyzes the results of a study conducted in March-April 2023 on the nature of digital competencies of Russian youth in the subjects of the Russian Federation. 2206 respondents aged 18-35 took part in the survey. The study revealed the level of digital competence of the younger generation. Digital competence refers to the possession of the skills of using the digital environment: using a computer, mobile phone, Internet, digital technology, etc. at a confident user level. The survey revealed a contradiction between objective and subjective assessments of the level of digital competence of young people.
Quantum authentication method based on key-controlled maximally mixed quantum state encryption

Abstract Quantum authentication is a fundamental first step that ensures secure quantum communication. Although various quantum authentication methods have been proposed recently, their implementation efficiency is limited. This paper proposes a key-controlled maximally mixed quantum state encryption (MMQSE) method using only a single qubit, unitary operation, minimized quantum transmissions, and a single qubit measurement, which improves implementation feasibility and operation efficiency. We applied it to representative quantum authentication applications, namely, quantum identity and message authentication. The security of our authentication schemes was verified by analyzing the relationship between the integral ratio of Uhlmann’s fidelity and probability of successful eavesdropping. Moreover, we demonstrate the higher authentication efficiency of the proposed scheme in a real quantum-channel noise environment. The upper bound of the valid noise rate was quantified using the integral ratio of Uhlmann’s fidelity in a noise environment. Finally, the optimal number of authentication sequences was estimated.
VNIR-SWIR Spectroscopy, XRD and Traditional Analyses for Pedomorphogeological Assessment in a Tropical Toposequence

Tropical climate conditions favor landscape evolution and the formation of highly weathered soils under different pedogenic processes due to certain differential properties. Traditional analysis coupled with VNIR-SWIR reflectance spectroscopy and X-ray diffractometry (XRD) analyses can reveal such characteristics. Several researchers cited throughout this study already discussed the possible applications of analyses in this field. All agree that integrated knowledge (holistic) can drive the future of the soil sciences. However, few refer to the potential of soil spectroscopy in deriving pedogenetic information. Thus, this paper aimed to assess pedomorphogeological relationships in a representative toposequence of the Brazilian Midwest using traditional analyses and geotechnologies. We performed landscape observations and soil sampling in the field. The laboratory’s physical, chemical, spectral, and mineralogical determinations supported the soil classification according to the World Reference Basis (WRB/FAO) system. Based on the analysis results, we divided five profiles into two soil groups (highly and slightly weathered soils) using Pearson’s correlation and hierarchical clustering analysis (HCA). Traditional analyses determined the diagnostic attributes. Spectroscopic readings from 0.35 to 2.5 µm wavelengths and XRD supported identifying soil attributes and properties. Finally, all soil classes were correlated according to correspondent reflectance spectra and primary pedological attributes. There was a strong correlation between spectral oxide features and X-ray diffraction peaks. The HCA based on oxide content and mineral composition validated the previous soil grouping. Thus, we could assess the pedomorphogeological relationships through VNIR-SWIR spectroscopy, XRD, and traditional analyses concerning pedogenic processes through their correlation with soil properties resulting from these processes. However, periodic measurements are required, making orbital sensing a continuous data source for soil monitoring.
A New Generation? A Discussion on Deep Generative Models in Supply Chains

With the advent of Chat-GPT, Artificial Intelligence (AI) became one of the most discussed technological developments of today. Although there are not yet many studies discussing the application of Chat-GPT and other Large Language Models (LLM) to the Supply Chain (SC), several works have used a larger family of techniques called Deep Generative Models (DGM). DGM are deep learning networks that aim to discover complex and high-dimensional probability distributions from, instead of just discriminating between the samples. In this paper, we briefly explain the recent developments in DGM (especially Energy-based models, Variational Autoencoders, Generative Adversarial Networks, and Autoregressive models), review previous applications of these techniques to the SC, and discuss research and application opportunities.
Child Tracking and Prediction of Violence on Children In Social Media Using Natural Language Processing and Machine Learning

Crimes against children are a direct threat to the world of tomorrow. The future of the world is in the hands of children who are the real wealth of mankind. The challenge for society is to address and handle challenges against children such as sexual abuse, trafficking, child labour, and harassment. Even though measures are taken by several governments globally, violence against children is an evident day-to-day process. This becomes the most challenging issue in the post-Internet era, where organized crime against children takes place around the world through a targeted group of people through virtual private networks and the dark web. What comes next is the emergence of social media, which provides an opportunity for these criminals to harm children globally through powerful social networks [15]. The existing methods focus on classifiers that do not work across multiple keywords [6] to look for sentiment analysis regarding a single classifier. There are only a few works that address the issue and these methods do not employ the multi-classification model [2]. In this paper, we propose a model which includes the Twitter API, the APIFY web scraper framework, as well as the VADER semantic analyzer in Python which possesses Decision tree analysis of sentiment prediction. The proposed work provides 99.6904% accuracy and predicts the classification of sentiment analysis. Our work has novelty in attribute selection, child trade, and violence analysis in social media and prediction of classification accuracy using the Decision Tree algorithm.
Synthetic Data for Feature Selection

Feature selection is an important and active field of research in machine learning and data science. Our goal in this paper is to propose a collection of synthetic datasets that can be used as a common reference point for feature selection algorithms. Synthetic datasets allow for precise evaluation of selected features and control of the data parameters for comprehensive assessment. The proposed datasets are based on applications from electronics in order to mimic real life scenarios. To illustrate the utility of the proposed data we employ one of the datasets to test several popular feature selection algorithms. The datasets are made publicly available on GitHub and can be used by researchers to evaluate feature selection algorithms.
Towards Dependable Integration Concepts for AI-Based Systems

AI-based methods are currently on the rise for multiple applications, therefore also their application for autonomous or trustworthy embedded systems is discussed more frequently. There are various ways that can leverage AI-based technologies, but since AI has the potential to affect the key system properties of trustworthy embedded systems, a far higher level of maturity and dependability is required for the implementation of emerging AI-based technologies in safety-critical domains (such as an autonomous vehicle). The TEACHING project focuses on mission-critical, energy-sensitive autonomous systems and the development of technology bricks for humanistic AI concepts. To enhance the development of the technology bricks, the building of a dependable engineering environment to support the development of a self-adaptive artificial humanistic intelligence in a dependable manner is intended. The paper establishes the body of knowledge and fundamental ground for a workshop discussion on engineering methods and design patterns that can be used to develop dependable and AI-based autonomous system development. The assurance of dependability continues to be an open issue with no common solution yet. Therefore, the expert discussion on multiple perspectives related to all factors of the PESTEL analysis (political, environmental, social, technological, economic, and legal) should provide an updated common view of the diverse fields of expertise and exchange between domain experts during the workshop.
Fine-Grained Face Editing via Personalized Spatial-Aware Affine Modulation

Fine-grained face editing, as a special case of image translation task, aims at modifying face attributes according to users' preference. Although generative adversarial networks (GANs) have achieved great success in general image translation tasks, these models cannot be directly applied in the face editing problem. Ideal face editing is challenging as it has two special requirements -- personalization and spatial-awareness. To address these issues, we propose a novel Personalized Spatial-aware Affine Modulation (PSAM) method based on a general GAN structure. The key idea is to modulate the intermediate features in a personalized and spatial-aware manner, which corresponds to the face editing procedure. Specifically, for personalization, we adopt both the face image and the desired attribute as input to generate the modulation tensors. For spatial-aware, we set these tensors to be of the same size as the input image, allowing pixel-wise modulation. Extensive experiments in four fine-grained face editing tasks, i.e., makeup, expression, illumination and aging, demonstrate the effectiveness of the proposed PSAM method. The synthesis results of PSAM can be further boosted by a new transferable training strategy. To facilitate research of face editing, we also construct a new large-scale makeup dataset. The code and dataset are available at http://colalab.org/projects/PSAM.
Digital sovereignty, digital infrastructures, and quantum horizons

Abstract This article holds that governmental investments in quantum technologies speak to the imaginable futures of digital sovereignty and digital infrastructures, two major areas of change driven by related technologies like AI and Big Data, among other things, in international law today. Under intense development today for future interpolation into digital systems that they may alter, quantum technologies occupy a sort of liminal position, rooted in existing assemblages of computational technologies while pointing to new horizons for them. The possibilities they raise are neither certain nor determinate, but active investments in them (legal, political and material investments) offer perspective on digital technology-driven influences on an international legal imagination. In contributing to visions of the future that are guiding ambitions for digital sovereignty and digital infrastructures, quantum technologies condition digital technology-driven changes to international law and legal imagination in the present. Privileging observation and description, I adapt and utilize a diffractive method with the aim to discern what emerges out of the interference among the several related things assembled for this article, including material technologies and legal institutions. In conclusion, I observe ambivalent changes to an international legal imagination, changes which promise transformation but appear nonetheless to reproduce current distributions of power and resources.
Lemmas: Generation, Selection, Application

Abstract Noting that lemmas are a key feature of mathematics, we engage in an investigation of the role of lemmas in automated theorem proving. The paper describes experiments with a combined system involving learning technology that generates useful lemmas for automated theorem provers, demonstrating improvement for several representative systems and solving a hard problem not solved by any system for twenty years. By focusing on condensed detachment problems we simplify the setting considerably, allowing us to get at the essence of lemmas and their role in proof search.
Pauli-based model of quantum computation with higher-dimensional systems

Pauli-based computation (PBC) is a universal model for quantum computation with qubits where the input state is a magic (resource) state and the computation is driven by a sequence of adaptively chosen and compatible multiqubit Pauli measurements. Here we generalize PBC for odd-prime-dimensional systems and demonstrate its universality. Additionally, we discuss how any qudit-based PBC can be implemented on actual, circuit-based quantum hardware. Our results show that we can translate a PBC on $n\phantom{\rule{4pt}{0ex}}p$-dimensional qudits to adaptive circuits on $n+1$ qudits with $O\left(p{n}^{2}/2\right)$ sum gates and depth. Alternatively, we can carry out the same computation with $O\left(pn/2\right)$ depth at the expense of an increased circuit width. Finally, we show that the sampling complexity associated with simulating a number $k$ of virtual qudits is related to the robustness of magic of the input states. Computation of this magic monotone for qutrit and ququint states leads to sampling complexity upper bounds of, respectively, $O({3}^{1.0848k}{\ensuremath{\epsilon}}^{\ensuremath{-}2})$ and $O({5}^{1.4022k}{\ensuremath{\epsilon}}^{\ensuremath{-}2})$, for a desired precision $\ensuremath{\epsilon}$. We further establish lower bounds to this sampling complexity for qubits, qutrits, and ququints: $\mathrm{\ensuremath{\Omega}}({2}^{0.5431k}{\ensuremath{\epsilon}}^{\ensuremath{-}2})$, $\mathrm{\ensuremath{\Omega}}({3}^{0.7236k}{\ensuremath{\epsilon}}^{\ensuremath{-}2})$, and $\mathrm{\ensuremath{\Omega}}({5}^{0.8544k}{\ensuremath{\epsilon}}^{\ensuremath{-}2})$, respectively.
FinChain-BERT: A High-Accuracy Automatic Fraud Detection Model Based on NLP Methods for Financial Scenarios

This research primarily explores the application of Natural Language Processing (NLP) technology in precision financial fraud detection, with a particular focus on the implementation and optimization of the FinChain-BERT model. Firstly, the FinChain-BERT model has been successfully employed for financial fraud detection tasks, improving the capability of handling complex financial text information through deep learning techniques. Secondly, novel attempts have been made in the selection of loss functions, with a comparison conducted between negative log-likelihood function and Keywords Loss Function. The results indicated that the Keywords Loss Function outperforms the negative log-likelihood function when applied to the FinChain-BERT model. Experimental results validated the efficacy of the FinChain-BERT model and its optimization measures. Whether in the selection of loss functions or the application of lightweight technology, the FinChain-BERT model demonstrated superior performance. The utilization of Keywords Loss Function resulted in a model achieving 0.97 in terms of accuracy, recall, and precision. Simultaneously, the model size was successfully reduced to 43 MB through the application of integer distillation technology, which holds significant importance for environments with limited computational resources. In conclusion, this research makes a crucial contribution to the application of NLP in financial fraud detection and provides a useful reference for future studies.
Active learning for medical image segmentation with stochastic batches

The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image segmentation tasks because of the limited expert availability and intensive manual effort required. To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate and add to the labelled training set. On the one hand, most active learning works have focused on the classification or limited segmentation of natural images, despite active learning being highly desirable in the difficult task of medical image segmentation. On the other hand, uncertainty-based AL approaches notoriously offer sub-optimal batch-query strategies, while diversity-based methods tend to be computationally expensive. Over and above methodological hurdles, random sampling has proven an extremely difficult baseline to outperform when varying learning and sampling conditions. This work aims to take advantage of the diversity and speed offered by random sampling to improve the selection of uncertainty-based AL methods for segmenting medical images. More specifically, we propose to compute uncertainty at the level of batches instead of samples through an original use of stochastic batches (SB) during sampling in AL. Stochastic batch querying is a simple and effective add-on that can be used on top of any uncertainty-based metric. Extensive experiments on two medical image segmentation datasets show that our strategy consistently improves conventional uncertainty-based sampling methods. Our method can hence act as a strong baseline for medical image segmentation. The code is available on: https://github.com/Minimel/StochasticBatchAL.git.