Reviewerly/exHarmony huggingface dataset:
Enhancing STEM Education through Interactive Metaverses: A Case Study and Methodological Framework

In recent years, the rapid growth of virtual reality has opened up new possibilities for creating immersive virtual worlds known as metaverses. With advancements in technology, such as virtual reality headsets, and the emergence of applications with social interaction, metaverses offer exciting opportunities for STEM (Science, Technology, Engineering, and Mathematics) education. This article proposes the integration of metaverses into STEM courses, enabling students to forge connections between different subjects within their curriculum, to leverage current technological advancements, and to revolutionize project presentations and audience interaction. Drawing upon a case study conducted with students enrolled in the “Proyectos RII 1: Organización y Escenarios” course at the Escuela Politécnica Superior de Alcoy of the Technical University of Valencia (Spain), this article provides a comprehensive description of the methodology employed by the students and professors. It outlines the process followed, shedding light on the innovative use of metaverses in their projects. Moreover, the article shares valuable insights obtained through surveys and personal opinions gathered from students throughout the course and upon its completion. By exploring the intersection of metaverses and STEM education, this study showcases the transformative potential of integrating interactive virtual environments. Furthermore, this work highlights the benefits of increased student engagement, motivation, and collaboration, as well as the novel ways in which projects can be presented and interactions with audiences can be redefined.
Green knowledge management and technology for organizational sustainability: The mediating role of knowledge-based leadership

In today’s business landscape, the integration of green knowledge management and technology has become a crucial element for organizations to meet the emerging expectations of environmentally-conscious consumers. This study aims at examining the impact of green knowledge management, including green knowledge acquisition, storage, sharing, application, creation, as well as technology in organizational sustainability across its economic, social, and environmental dimensions. The data for this research was collected from different industries of IT companies in Jordan operating in IT solutions and services such as the financial services with a sample size of 288 companies. The Structural equation modeling SEM was used to analyze the data. The results indicated a positive effect of green knowledge management on organizational sustainability with an estimate of 0.253 for this relationship. Furthermore, the findings suggest that knowledge-based leadership mediates the relationship between green knowledge management, technology, and organizational sustainability. The research provides useful insights and perspectives for a better understanding of this topic and offers suggestions and implications to cover the gaps in existing research. Overall, this study highlights the importance of implementing green knowledge management and technology in promoting organizational sustainability, which can lead to a competitive advantage, enhanced organizational performance, and increased stakeholder satisfaction.
Infrastructural or organizational decentralization? Developing a typology of blockchain ventures

Blockchain technology, an institutional innovation, has been a growing research area in recent years, especially within the information system literature. If it brings along opportunities for entrepreneurs, there is no such thing as a theory of blockchain ventures yet. Blockchain technology drives the emergence of unprecedented types of ventures, with varying degrees of decentralization, both from an infrastructural and organizational perspective. However, there is no consistency regarding the definitions and labels used to refer to blockchain ventures. This paper proposes a definition of blockchain ventures and provides a typology distinguishing four ideal types of blockchain ventures. Our findings reveal that blockchain ventures do not inherently entail either organizational or infrastructural decentralization. They contribute to a better understanding of the diverse nature of blockchain ventures and their implications.
Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms

Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensive surveys have been presented to briefly analyze methods for detecting breast cancer with mammograms. In this article, our objective is to give an overview of recent advancements in machine learning (ML) and deep learning (DL)-based breast cancer detection systems.We give a structured framework to categorize mammogram-based breast cancer detection techniques. Several publicly available mammogram databases and different performance measures are also mentioned.After deliberate investigation, we find most of the works classify breast tumors either as normal-abnormal or malignant-benign rather than classifying them into three classes. Furthermore, DL-based features are more significant than hand-crafted features. However, transfer learning is preferred over others as it yields better performance in small datasets, unlike classical DL techniques.In this article, we have made an attempt to give recent advancements in artificial intelligence (AI)-based breast cancer detection systems. Furthermore, a number of challenging issues and possible research directions are mentioned, which will help researchers in further scopes of research in this field.
Management Practices for Mitigating Cybersecurity Threats to Biotechnology Companies, Laboratories, and Healthcare Research Organizations

In the rapidly advancing landscape of biotechnology, laboratories, and healthcare research, the criticality of robust cybersecurity risk management techniques cannot be overstated. As these industries increasingly rely on interconnected technologies and digitized data for innovation and patient care, they become attractive targets for malicious cyber threats. This chapter underscores the paramount importance for biotechnology companies, laboratories, and healthcare research organizations to develop real-world and practical cybersecurity risk management techniques. By proactively addressing cybersecurity vulnerabilities, these entities can safeguard their sensitive data, protect intellectual property, ensure patient privacy, and maintain the trust of stakeholders. This chapter emphasizes the necessity of adopting effective cybersecurity strategies tailored to the specific needs of each sector, taking into account the dynamic threat landscape and the potential consequences of cyber incidents.
Research on an Intelligent Agricultural Machinery Unmanned Driving System

Intelligent agricultural machinery refers to machinery that can independently complete tasks in the field, which has great significance for the transformation of agricultural modernization. However, most of the existing research on intelligent agricultural machinery is limited to unilateral research on positioning, planning, and control, and has not organically combined the three to form a fully functional intelligent agricultural machinery system. Based on this, this article has developed an intelligent agricultural machinery system that integrates positioning, planning, and control. In response to the problem of large positioning errors in the large range of plane anchoring longitude and latitude, this article integrates geographic factors such as ellipsoid ratio, long and short axis radius, and altitude into coordinate transformation, and combines RTK/INS integrated inertial navigation to achieve precise positioning of the entire vehicle over a large range. In response to the problem that existing full-coverage path planning algorithms only focus on job coverage as the optimization objective and cannot achieve path optimization, this paper proposes a multi-objective function-coupled full-coverage path planning algorithm that integrates three optimization objectives: job coverage, job path length, and job path quantity. This algorithm achieves optimal path planning while ensuring job coverage. As the existing pure pursuit algorithm is not suitable for the motion control of tracked mobile machinery, this paper reconstructs the existing pure pursuit algorithm based on the Kinematics characteristics of tracked mobile machinery, and adds a linear interpolation module, so that the actual tracking path points of motion control are always ideal tracking path points, effectively improving the motion control accuracy and control stability. Finally, the feasibility of the intelligent agricultural machinery system was demonstrated through corresponding simulation and actual vehicle experiments. This intelligent agricultural machinery system can cooperate with various operating tools and independently complete the vast majority of agricultural production activities.
Retrieval of sub-kilometer resolution solar irradiance from Fengyun-4A satellite using a region-adapted Heliosat-2 method

It is, now, universally accepted that modern solar resource assessment should proceed with acquiring satellite-derived irradiance; the underlying problem is one of retrieval. The Advanced Geostationary Radiation Imager (AGRI), which is one of the main payloads of China’s latest-generation geostationary satellite series, namely, Fengyun-4 (FY-4), offers such an opportunity, albeit its full potential remains largely unexplored. On this account, a region-adapted semi-empirical model based on Heliosat-2 is herein developed to estimate global horizontal irradiance (GHI) at the native resolution of FY-4 satellite (0.5 km, 15 min), using AGRI reflectance measurement at 640 nm. The adaptation of Heliosat-2 parameters to China’s climatology and geography is based on a research-grade ground-based radiometry network with 38 stations evenly distributed across the country. Model validation is conducted against ground-based observations made at 34 unseen stations, over the course of one year (2018). As compared to the 5-km Himawari-8 radiation product, the developed product not only has a 10-fold refinement in resolution, which can clearly depict those small-scale variations in solar resources that are not observable from the Himawari-8 product, but also attains a substantially lower bias (1.4% versus 7.6% under clear skies and –0.3% versus 9.5% under cloudy skies). This ultra-high-resolution product is of vital importance to solar resource assessment on a scale consistent with the size of typical utility-scale photovoltaic plants. The product is also of great value for agricultural and ecological studies, which often depends upon high-quality solar radiation at a high spatio-temporal resolution.
Multimodal Brain Age Estimation Using Interpretable Adaptive Population-Graph Learning

Brain age estimation is clinically important as it can provide valuable information in the context of neurodegenerative diseases such as Alzheimer's. Population graphs, which include multimodal imaging information of the subjects along with the relationships among the population, have been used in literature along with Graph Convolutional Networks (GCNs) and have proved beneficial for a variety of medical imaging tasks. A population graph is usually static and constructed manually using non-imaging information. However, graph construction is not a trivial task and might significantly affect the performance of the GCN, which is inherently very sensitive to the graph structure. In this work, we propose a framework that learns a population graph structure optimized for the downstream task. An attention mechanism assigns weights to a set of imaging and non-imaging features (phenotypes), which are then used for edge extraction. The resulting graph is used to train the GCN. The entire pipeline can be trained end-to-end. Additionally, by visualizing the attention weights that were the most important for the graph construction, we increase the interpretability of the graph. We use the UK Biobank, which provides a large variety of neuroimaging and non-imaging phenotypes, to evaluate our method on brain age regression and classification. The proposed method outperforms competing static graph approaches and other state-of-the-art adaptive methods. We further show that the assigned attention scores indicate that there are both imaging and non-imaging phenotypes that are informative for brain age estimation and are in agreement with the relevant literature.
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models

State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applications like skin cancer detection. To tackle this problem, we propose Reveal to Revise (R2R), a framework entailing the entire eXplainable Artificial Intelligence (XAI) life cycle, enabling practitioners to iteratively identify, mitigate, and (re-)evaluate spurious model behavior with a minimal amount of human interaction. In the first step (1), R2R reveals model weaknesses by finding outliers in attributions or through inspection of latent concepts learned by the model. Secondly (2), the responsible artifacts are detected and spatially localized in the input data, which is then leveraged to (3) revise the model behavior. Concretely, we apply the methods of RRR, CDEP and ClArC for model correction, and (4) (re-)evaluate the model's performance and remaining sensitivity towards the artifact. Using two medical benchmark datasets for Melanoma detection and bone age estimation, we apply our R2R framework to VGG, ResNet and EfficientNet architectures and thereby reveal and correct real dataset-intrinsic artifacts, as well as synthetic variants in a controlled setting. Completing the XAI life cycle, we demonstrate multiple R2R iterations to mitigate different biases. Code is available on https://github.com/maxdreyer/Reveal2Revise .
Cognitive complexity measures for educational texts: Empirical validation of linguistic parameters

The article presents a study conducted within the framework of discourse complexology - an integral scientific domain that has united linguists, cognitive scientists, psychologists and programmers dealing with the problems of discourse complexity. The issue of cognitive complexity of texts is one of the central issues in discourse complexology. The paper presents the results of the study aimed to identify and empirically validate a list of educational texts’ complexity predictors. The study aims to identify discriminant linguistic parameters sufficient to assess cognitive complexity of educational texts. We view text cognitive complexity as a construct, based on the amount of presented information and the success of reader-text interactions. The idea behind the research is that text cognitive complexity notably increases across middle and high schools. The research dataset comprises eight biology textbooks with the total size of 219,319 tokens. Metrics of text linguistic features were estimated with the help of automatic analyzer RuLingva (rulingva.kpfu.ru). Linguistic and statistical analysis confirmed the hypothesis that text syntactic and lexical parameters are discriminative enough to classify different levels of cognitive complexity of educational texts used in middle and high schools. Text parameters that manifest variance in cognitive complexity include lexical diversity (TTR); local argument overlap; abstractness index; number of polysyllabic words, Flesch-Kincaid Grade Level; number of nouns and number of adjectives per sentence. Empirical evidence indicates that the proposed approach outperforms existing methods of text complexity assessment. The research results can be implemented in the system of scientific and educational content expertise for Russian school textbooks. They can also be of some use in the development of educational resources and further research in the field of text complexity.
Strengthening Cloud Security: An Innovative Multi-Factor Multi-Layer Authentication Framework for Cloud User Authentication

Cloud multi-factor authentication is a critical security measure that helps strengthen cloud security from unauthorized access and data breaches. Multi-factor authentication verifies that authentic cloud users are only authorized to access cloud apps, data, services, and resources, making it more secure for enterprises and less inconvenient for users. The number of authentication factors varies based on the security framework’s architecture and the required security level. Therefore, implementing a secured multi-factor authentication framework in a cloud platform is a challenging process. In this paper, we developed an adaptive multi-factor multi-layer authentication framework that embeds an access control and intrusion detection mechanisms with an automated selection of authentication methods. The core objective is to enhance a secured cloud platform with low false positive alarms that makes it more difficult for intruders to access the cloud system. To enhance the authentication mechanism and reduce false alarms, multiple authentication factors that include the length, validity, and value of the user factor is implemented with a user’s geolocation and user’s browser confirmation method that increase the identity verification of cloud users. An additional AES-based encryption component is applied to data, which are protected from being disclosed. The AES encryption mechanism is implemented to conceal the login information on the directory provider of the cloud. The proposed framework demonstrated excellent performance in identifying potentially malicious users and intruders, thereby effectively preventing any intentional attacks on the cloud services and data.
Efficient and persistent backdoor attack by boundary trigger set constructing against federated learning

Federated learning systems encounter various security risks, including backdoor, inference and adversarial attacks. Backdoor attacks within this context generally require careful trigger sample design involving candidate selection and automated optimization. Previous methods randomly selected trigger candidates from training dataset, disrupting sample distribution and blurring boundaries among them, which adversely affected the main task accuracy. Moreover, these methods employed non-optimized handcrafted triggers, resulting in a weakened backdoor mapping relationship and lower attack success rates. In this work, we propose a flexible backdoor attack approach, Trigger Sample Selection and Optimization (TSSO), motivated by neural network classification patterns. TSSO employs autoencoders and locality-sensitive hashing to select trigger candidates at class boundaries for precise injection. Furthermore, it iteratively refines trigger representations via the global model and historical outcomes, establishing a robust mapping relationship. TSSO is evaluated on four classical datasets with non-IID settings, outperforming state-of-the-art methods by achieving higher attack success rate in fewer rounds, prolonging the backdoor effect. In scalability tests, even with the defense deployed, TSSO achieved the attack success rate of over 80% with only 4% malicious clients (a poisoning rate of 1/640).
Energy–Aware security protocol for IoT devices

Internet of Things (IoT) has permeated various aspects of modern life, from smart homes to factories and even gardens. In the coming years, number of IoT devices is expected to surpass that of computers, laptops, mobile phones, and tablets. However, many of these devices are small and operate on batteries, making energy efficiency a significant challenge. This challenge affects all aspects of IoT, including security. To address this issue, we present an adaptive security approach in this paper. Adaptive security involves adjusting the security level based on the level of threats and data context, rather than always assuming the worst-case scenario. This approach reduces energy consumption and is implemented in three parts: 1) Adapting the length of RSA public and private keys, where longer keys provide more security but consume more power. 2) Adapting the trust level between nodes based on the history of the transmitting node, where the receiving node decides whether to verify the correctness of the received messages or not. 3) Utilizing TrustChain, which is transactional verification method inspired by the blockchain concept. We evaluated the performance of our proposed model through exhaustive simulation scenarios and experiments. Our approach outperforms state-of-the-art methods, with the variable key length approach reducing energy consumption by 50%, the trust level approach reducing energy consumption by approximately 50%, and the TrustChain approach reducing energy consumption to 0.771 J, while the blockchain-based method consumed 2.955 J to verify transactions.
Complete conversion of KLM entanglement to GHZ entanglement with error-detected quantum devices

Abstract Quantum entanglement plays a crucial role in quantum information technologies. In the paper, we propose two schemes to convert from two-photon Knill–Laflamme–Milburn (KLM) entangled states to Bell states and three-photon KLM state to Greenberger–Horne–Zeilinger states based on error-detected quantum devices (EDQDs), which employ the interaction between a quantum-dot-cavity system and a photon. Moreover, the quantum circuits of EDQDs applied in the conversion processes make our schemes carry out faithfully, as the practical photon-scattering deviations are changed into heralded-failure detections. Analyses show that conversion cases have unity fidelities and high efficiencies, which encourage us to appreciate deeply fundamental properties of entanglement.
Partial Siamese With Multiscale Bi-Codec Networks for Remote Sensing Image Haze Removal

Recently, the U-Shaped networks has been widely explored in remote sensing image dehazing and obtained promising performance. However, most of the existing dehazing methods based on U-Shaped framework lack the reconstruction constraints of haze areas, which is particularly important to restore haze-free images. Moreover, their encoding and decoding layers cannot effectively fuse multi-scale features, resulting in deviations in the color and texture of the dehazing image. To address these issues, in this paper, we propose a Partial Siamese with Multiscale Bi-codec Dehazing Network (PSMB-Net) which is mainly composed of a Partial Siamese Framework (PSF) and a Multiscale Bi-codec Information Fusion (MBIF) module. Specifically, the PSF is proposed to create dehazing prior information to guide the network to build Siamese constraints and achieve improved dehazing results. Furthermore, we design a MBIF module which can enhance feature extraction, and the multi-scale information is used to improve the reconstruction ability of the network for the color and texture of the dehazing image. Experimental results on challenging benchmark datasets demonstrate the superiority of our PSMB-Net over state-of-the-art image dehazing methods. The source code is available at https://github.com/thislzm/PSMB-Net.