Reviewerly/exHarmony huggingface dataset:
Hate speech and social acceptance of migrants in Europe: Analysis of tweets with geolocation

Hate speech against vulnerable groups is acknowledged as a serious problem for integration and respect for the social diversity existing within the territory of the European Union. The growth of this type of discourse has been supported by the expansion of social media, which have been proven to act as a mechanism for the propagation of crimes against targets such as migrants and refugees, one of the main affected groups. That is why we have conducted the first European study of the social acceptance of migrants and refugees by studying the presence of hate speech. The research is based on the perspective of the theories of intergroup contact and mediated intergroup contact. The methodology includes large-scale longitudinal analysis (2015-2020) of online hate speech on Twitter (N=847,978) and its contrast with existing official indicators. The results suggest that personal intergroup contact is positively corretaled with the support of the local population towards migrants and refugees but mediated intergroup contact is not correlated with hate speech on Twitter. We found evidence that those regions where the support for foreigners was higher, there was a lower level of hate speech on Twitter. This is an advance in the study of hate speech by territories and can help in the formulation of action strategies. El discurso de odio contra públicos vulnerables es reconocido como un grave problema para la integración y el respeto a la diversidad social dentro de la Unión Europea. El aumento de este tipo de discurso se ha visto reforzado con la expansión de las redes sociales, donde se ha demostrado que actúan como mecanismo de propagación de delitos contra colectivos como los migrantes y refugiados, uno de los principales afectados. Por ello se aborda el desarrollo del primer estudio europeo de la aceptación social de migrantes y refugiados mediante el estudio de la presencia de discurso de odio. La investigación se basa en la perspectiva de la teoría del contacto intergrupal y el contacto intergrupal mediado. La metodología incluye el análisis longitudinal (2015-2020) a gran escala del discurso de odio en línea en Twitter (N=847.978) y el contraste con indicadores oficiales existentes. Los resultados apuntan a que el contacto intergrupal personal está correlacionado positivamente con el apoyo de la población hacia migrantes y refugiados, pero el contacto intergrupal mediado no está correlacionado con la disminución del discurso de odio. Encontramos evidencia que muestra que en aquellas regiones en las que el apoyo al colectivo era mayor existía un menor nivel de discurso de odio en Twitter. Esto supone un avance en el estudio del discurso de odio por territorios y puede ayudar en el planteamiento de estrategias de actuación.
A quantitative review of LIS programs accredited by ALA and CILIP under contemporary technology advancement

Purpose Advancements in technology have led to many changes in the field of Library and Information Science (LIS). As global communications and technology continue to become more available and sophisticated, LIS programs need to prepare students for employment in rapidly changing and globalized LIS professions. Design/methodology/approach Data from a total of 63 programs from the American Library Association (ALA) and 32 programs from the Chartered Institute of Library and Information Professionals (CILIP) was collected through openly accessible websites of these programs. Areas explored include program name, name and level of the academic unit offering the program, credit hours, required courses, percentage of required courses and capstone measurements used within the different LIS programs. Findings A majority of programs still preserve the keyword “Library” in their name, but not the academic units offering them. Most programs in ALA and CILIP follow a semester-based program. Research methods, internships, practical experience, combined with traditional library core and information technology requirements were found to constitute the major subjects in general. Comprehensive exams were replaced by e-portfolios among ALA programs while a dissertation remains the preferred choice of capstone requirement for CILIP. Originality/value Scant studies compare accredited LIS programs worldwide, motivating the study of the similarities, differences and trends of LIS programs under the current globalized technology-driven knowledge economy. This paper seeks to fill the literature gap and promote a global discussion and understanding of LIS curricula in different regions of the world, guiding potential students to select their suitable LIS programs.
Decoding the rise of Central Bank Digital Currency in China: designs, problems, and prospects

During the Covid-19 pandemic, there has been a rapid shift in global transaction patterns from offline to online digital payment models, along with a growing interest in the development of Central Bank Digital Currencies (CBDCs) in various countries. This article spotlights the unexamined issue of digital currency regulation by examining the practice and related regulatory rules of the pilot CBDC in China. Beginning with the global design choices of digital currencies, the article comparatively examines the technical design of China’s CBDC, known as e-CNY. It further triggers a rethinking of conventional regulations for the protection of digital currency information by investigating the gap between the actual operation and design of e-CNY, as well as the gap between pilot policies and legal provisions such as the Cybersecurity Law, the Data Security Law, and the Personal Information Protection Law. This article argues that, on the one hand, the legislative balance between the protection of personal information and the regulation of illicit financial activities involved in the “loosely coupled account link” system of e-CNY should be reconsidered. On the other hand, the delineation of rights and responsibilities between dissemination institutions, payment service providers, and end-users needs to be further redefined and clarified.
Resolving correlated states of benzyne with an error-mitigated contracted quantum eigensolver

The simulation of strongly correlated many-electron systems is one of the most promising applications for near-term quantum devices. Here we use a class of eigenvalue solvers [presented in Smart and Mazziotti, Phys. Rev. Lett. 126, 070504 (2021)] in which a contraction of the Schr\"odinger equation is solved for the two-electron reduced density matrix (2-RDM) to resolve the energy splittings of the ortho-, meta-, and para-isomers of benzyne ${\text{C}}_{6}{\text{H}}_{4}$. In contrast to the traditional variational quantum eigensolver, the contracted quantum eigensolver can solve an integration (or contraction) of the many-electron Schr\"odinger equation onto the two-electron space. The quantum solution of the anti-Hermitian part of the contracted Schr\"odinger equation provides a scalable approach with few variational parameters that has its foundations in 2-RDM theory. Experimentally, a variety of error-mitigation strategies enable the calculation, including a linear shift in the 2-RDM targeting the iterative nature of the algorithm as well as a projection of the 2-RDM onto the convex set of approximately $N$-representable 2-RDMs defined by the 2-positive $N$-representability conditions. The relative energies exhibit single-digit millihartree errors, capturing a large part of the electron correlation energy, and the computed natural orbital occupations reflect the significant differences in the electron correlation of the isomers.
Comparative analysis of explainable machine learning prediction models for hospital mortality

Machine learning (ML) holds the promise of becoming an essential tool for utilising the increasing amount of clinical data available for analysis and clinical decision support. However, the lack of trust in the models has limited the acceptance of this technology in healthcare. This mistrust is often credited to the shortage of model explainability and interpretability, where the relationship between the input and output of the models is unclear. Improving trust requires the development of more transparent ML methods.In this paper, we use the publicly available eICU database to construct a number of ML models before examining their internal behaviour with SHapley Additive exPlanations (SHAP) values. Our four models predicted hospital mortality in ICU patients using a selection of the same features used to calculate the APACHE IV score and were based on random forest, logistic regression, naive Bayes, and adaptive boosting algorithms.The results showed the models had similar discriminative abilities and mostly agreed on feature importance while calibration and impact of individual features differed considerably and did in multiple cases not correspond to common medical theory.We already know that ML models treat data differently depending on the underlying algorithm. Our comparative analysis visualises implications of these differences and their importance in a healthcare setting. SHAP value analysis is a promising method for incorporating explainability in model development and usage and might yield better and more trustworthy ML models in the future.
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark

Deep neural networks have achieved great success in many important remote sensing tasks. Nevertheless, their vulnerability to adversarial examples should not be neglected. In this study, we systematically analyze the universal adversarial examples in remote sensing data for the first time, without any knowledge from the victim model. Specifically, we propose a novel black-box adversarial attack method, namely Mixup-Attack, and its simple variant Mixcut-Attack, for remote sensing data. The key idea of the proposed methods is to find common vulnerabilities among different networks by attacking the features in the shallow layer of a given surrogate model. Despite their simplicity, the proposed methods can generate transferable adversarial examples that deceive most of the state-of-the-art deep neural networks in both scene classification and semantic segmentation tasks with high success rates. We further provide the generated universal adversarial examples in the dataset named UAE-RS, which is the first dataset that provides black-box adversarial samples in the remote sensing field. We hope UAE-RS may serve as a benchmark that helps researchers to design deep neural networks with strong resistance toward adversarial attacks in the remote sensing field. Codes and the UAE-RS dataset are available online (https://github.com/YonghaoXu/UAE-RS).
Scenario-Aware Recurrent Transformer for Goal-Directed Video Captioning

Fully mining visual cues to aid in content understanding is crucial for video captioning. However, most state-of-the-art video captioning methods are limited to generating captions purely based on straightforward information while ignoring the scenario and context information. To fill the gap, we propose a novel, simple but effective scenario-aware recurrent transformer (SART) model to execute video captioning. Our model contains a “scenario understanding” module to obtain a global perspective across multiple frames, providing a specific scenario to guarantee a goal-directed description. Moreover, for the sake of achieving narrative continuity in the generated paragraph, a unified recurrent transformer is adopted. To demonstrate the effectiveness of our proposed SART, we have conducted comprehensive experiments on various large-scale video description datasets, including ActivityNet, YouCookII, and VideoStory. Additionally, we extend a story-oriented evaluation framework for assessing the quality of the generated caption more precisely. The superior performance has shown that SART has a strong ability to generate correct, deliberative, and narrative coherent video descriptions.
Dynamic gesture recognition based on 2D convolutional neural network and feature fusion

Gesture recognition is one of the most popular techniques in the field of computer vision today. In recent years, many algorithms for gesture recognition have been proposed, but most of them do not have a good balance between recognition efficiency and accuracy. Therefore, proposing a dynamic gesture recognition algorithm that balances efficiency and accuracy is still a meaningful work. Currently, most of the commonly used dynamic gesture recognition algorithms are based on 3D convolutional neural networks. Although 3D convolutional neural networks consider both spatial and temporal features, the networks are too complex, which is the main reason for the low efficiency of the algorithms. To improve this problem, we propose a recognition method based on a strategy combining 2D convolutional neural networks with feature fusion. The original keyframes and optical flow keyframes are used to represent spatial and temporal features respectively, which are then sent to the 2D convolutional neural network for feature fusion and final recognition. To ensure the quality of the extracted optical flow graph without increasing the complexity of the network, we use the fractional-order method to extract the optical flow graph, creatively combine fractional calculus and deep learning. Finally, we use Cambridge Hand Gesture dataset and Northwestern University Hand Gesture dataset to verify the effectiveness of our algorithm. The experimental results show that our algorithm has a high accuracy while ensuring low network complexity.
TranSalNet: Towards perceptually relevant visual saliency prediction

Convolutional neural networks (CNNs) have significantly advanced computational modelling for saliency prediction. However, accurately simulating the mechanisms of visual attention in the human cortex remains an academic challenge. It is critical to integrate properties of human vision into the design of CNN architectures, leading to perceptually more relevant saliency prediction. Due to the inherent inductive biases of CNN architectures, there is a lack of sufficient long-range contextual encoding capacity. This hinders CNN-based saliency models from capturing properties that emulate viewing behaviour of humans. Transformers have shown great potential in encoding long-range information by leveraging the self-attention mechanism. In this paper, we propose a novel saliency model that integrates transformer components to CNNs to capture the long-range contextual visual information. Experimental results show that the transformers provide added value to saliency prediction, enhancing its perceptual relevance in the performance. Our proposed saliency model using transformers has achieved superior results on public benchmarks and competitions for saliency prediction models. The source code of our proposed saliency model TranSalNet is available at: https://github.com/LJOVO/TranSalNet.
Fintech, Risk-Based Thinking and Cyber Risk

Abstract Financial technology innovations (Fintech) are changing the provision of traditional financial services. Although they bring with them various benefits and opportunities, they also have weaknesses and pose potential threats to financial systems. The paper examines the latest developments in the area of Fintech and outlines the potential benefits and associated risks. It highlights the vital role of the monetary authorities in the context of the policies and initiatives required in order to modernize the financial system, including research and the potential issuance of central bank digital currency, while simultaneously fulfilling their core objectives of preserving monetary and financial stability. The authors highlight the importance of artificial intelligence in Fintech development. They create a Fintech SWOT to support and analyse the above. It goes on further to explain the new management concept of “Risk-based thinking” as a way to approach these potential opportunities and threats of Fintech. Finally, the paper looks at cyber risk in the Fintech landscape as the latest and potentially greatest threat springing from these turbulent and uncertain times.
Blockchain-Enabled Privacy-Preserving Authentication Mechanism for Transportation CPS With Cloud-Edge Computing

In this article, we propose a secure and effective blockchain-enabled privacy-preserving authentication scheme for the transportation cyber-physical system (CPS) with the cloud-edge computing environment, which supports unconditional anonymity and data batch integrity verification while greatly simplifying key management issues. The proposed privacy-preserving authentication scheme employs an elliptic curve to construct a pairing-free ring signature scheme, which greatly reduces the resources overhead in the transportation CPS with cloud-edge computing. Moreover, the authentication process is performed on the blockchain to provide more reliable service information for vehicular communication. Furthermore, the security proof of the scheme is demonstrated based on the elliptic curve discrete logarithm problem under the random oracle model, and gives the corresponding security analysis. Finally, a simulation experiment demonstrates that the proposed scheme is feasible compared with the existing schemes.
OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts

OpenAlex is a new, fully-open scientific knowledge graph (SKG), launched to replace the discontinued Microsoft Academic Graph (MAG). It contains metadata for 209M works (journal articles, books, etc); 2013M disambiguated authors; 124k venues (places that host works, such as journals and online repositories); 109k institutions; and 65k Wikidata concepts (linked to works via an automated hierarchical multi-tag classifier). The dataset is fully and freely available via a web-based GUI, a full data dump, and high-volume REST API. The resource is under active development and future work will improve accuracy and coverage of citation information and author/institution parsing and deduplication.
Brain Tumor and Augmented Reality: New Technologies for the Future

In recent years, huge progress has been made in the management of brain tumors, due to the availability of imaging devices, which provide fundamental anatomical and pathological information not only for diagnostic purposes [...].
THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE EFFECTIVE APPLYING OF CYBER GOVERNANCE IN JORDANIAN COMMERCIAL BANKS

This study aims to examine the impact of artificial intelligence on the effective applying of cyber governance in Jordanian commercial banks. The objectives are divided into identifying the impact of expert systems on cyber governance in Jordanian commercial banks, identifying the impact of neural networks on cyber governance in Jordanian commercial banks, identifying the impact of genetic algorithms on cyber governance in Jordanian commercial banks, and identifying the impact of smart agents on governance cyber ​​in Jordanian commercial banks, The current study population includes (13) Jordanian commercial banks listed on the Amman Stock Exchange and the study sample includes all employees in the accounting departments, internal auditors, and programmers. The researchers distributed (208) questionnaires to the study sample members electronically, at a rate of (16) questionnaires in each bank and retrieved them in the same way. Then, 208 questionnaires were retrieved, including (7) questionnaires that are not valid for analysis, due to following the pattern method in answering all paragraphs, so that the researcher has (201) questionnaires valid for statistical analysis, This indicates the interdependence between artificial intelligence techniques and applications (expert systems, neural networks, genetic algorithms, and smart agents) on the effective application of cyber governance in Jordanian commercial banks, as these technologies and applications contribute to enhancing the effectiveness of cyber governance. Thus, it is necessary for commercial banks in Jordan to rely more on advanced systems, so that banks can use modern computer equipment and software, and thus increase the dependence of these banks on the effective application of cyber governance to follow up the progress of business and tasks in accordance with its strategies.
The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges—A Systematic Literature Review (SLR)

Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly, an evidence-based approach is used in this study. Preliminary results are based on answers to four research questions. Out of 493 research articles, 52 studies were selected. 52 papers use different data protection techniques, which can be divided into two main categories, namely noncryptographic techniques and cryptographic techniques. Noncryptographic techniques consist of data splitting, data anonymization, and steganographic techniques, whereas cryptographic techniques consist of encryption, searchable encryption, homomorphic encryption, and signcryption. In this work, we compare all of these techniques in terms of data protection accuracy, overhead, and operations on masked data. Finally, we discuss the future research challenges facing the implementation of these techniques.