Reviewerly/exHarmony huggingface dataset:
Self-Adaptive Graph With Nonlocal Attention Network for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) have achieved encouraging progress in modeling human body skeletons as spatial-temporal graphs. However, existing methods still suffer from two inherent drawbacks. Firstly, these models process the input data based on the physical structure of the human body, which leads to some latent correlations among joints being ignored. Furthermore, the key temporal relationships between nonadjacent frames are overlooked, preventing to fully learn the changes of the body joints along the temporal dimension. To address these issues, we propose an innovative spatial-temporal model by introducing a self-adaptive GCN (SAGCN) with global attention network, collectively termed SAGGAN. Specifically, the SAGCN module is proposed to construct two additional dynamic topological graphs to learn the common characteristics of all data and represent a unique pattern for each sample, respectively. Meanwhile, the global attention module (spatial attention (SA) and temporal attention (TA) modules) is designed to extract the global connections between different joints in a single frame and model temporal relationships between adjacent and nonadjacent frames in temporal sequences. In this manner, our network can capture richer features of actions for accurate action recognition and overcome the defect of the standard graph convolution. Extensive experiments on three benchmark datasets (NTU-60, NTU-120, and Kinetics) have demonstrated the superiority of our proposed method.
The Impact of Digitalization of Education on the Development of Key Teacher Competencies

The aim of the study is to identify the relationship between the digital competence of a higher school teacher and creativity, critical thinking, communicative, subject methodical, and communicative competence. The main research methods included testing to identify the level of digital competence, creativity, and critical thinking, as well as the interview to determine the level of subject methodological competence. The results of the study indicate that the medium level of the teacher competencies dominates. The level of teachers’ digital, subject methodological, communicative competencies, creativity, and critical thinking decrease in persons with 16-25 years of work experience. Digitalization of education most significantly affects the development of communicative and subject methodological competence. The relationship between digitalization and professional competencies is more significant among teachers with 1-15 years of work experience. The obtained results can be used to improve existing educational programmes for teacher training. The prospects for further research include the creation and verification of programmes of formative experiments aimed at clarifying the causal relationship between the digitalization of education and the development of the professional competence of higher school teachers
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning

The Rashomon Effect describes the following phenomenon: for a given dataset there may exist many models with equally good performance but with different solution strategies. The Rashomon Effect has implications for Explainable Machine Learning, especially for the comparability of explanations. We provide a unified view on three different comparison scenarios and conduct a quantitative evaluation across different datasets, models, attribution methods, and metrics. We find that hyperparameter-tuning plays a role and that metric selection matters. Our results provide empirical support for previously anecdotal evidence and exhibit challenges for both scientists and practitioners.
SegTransConv: Transformer and CNN Hybrid Method for Real-Time Semantic Segmentation of Autonomous Vehicles

Real-time and high-performance semantic segmentation is a crucial task in the scene understanding of autonomous vehicles. This paper focuses on this issue and proposes a transformer and convolutional neural networks (CNN) hybrid encoder-decoder structure SegTransConv. Firstly, we present a four-stage hierarchical encoder, and the feature extractor in each stage is composed of two transformer layers and CNN modules in series. In this way, the encoder better exploits the global contexts of the input and expands the receptive fields. In the U-shape decoder, the feature maps are upsampled through the proposed feature enhancement upsampling module (FE_Up). Then the knowledge distillation strategy is leveraged to improve the model performance under the guidance of the teacher network STDCNet. Finally, a novel evaluation metric is designed to comprehensively assess the accuracy, speed, floating-point operations (FLOPs), and parameters of real-time segmentation methods. Extensive experiments on two public datasets and self-collected images have evaluated the effectiveness of our method. SegTransConv-A and SegTransConv-B obtain 72.8% and 73.0% mIoU, respectively, at the inference speed of 68.0 FPS with an input resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\times 512$</tex-math> </inline-formula> .
Optimally Computing Compressed Indexing Arrays Based on the Compact Directed Acyclic Word Graph

In this paper, we present the first study of the computational complexity of converting an automata-based text index structure, called the Compact Directed Acyclic Word Graph (CDAWG), of size e for a text T of length n into other text indexing structures for the same text, suitable for highly repetitive texts: the run-length BWT of size r, the irreducible PLCP array of size r, and the quasi-irreducible LPF array of size e, as well as the lex-parse of size O(r) and the LZ77-parse of size z, where $$r, z \leqslant e$$ . As main results, we showed that the above structures can be optimally computed from either the CDAWG for T stored in read-only memory or its self-index version of size e without a text in O(e) worst-case time and words of working space. To obtain the above results, we devised techniques for enumerating a particular subset of suffixes in the lexicographic and text orders using the forward and backward search on the CDAWG by extending the result by Belazzougui et al. in 2015.
Possible Ways to Search for Scientific Information in New Realities

The paper reviewed the opportunities of various online products and services for searching for scientific information in the changed modern reality caused by the sanctions policy of a number of countries to Russia. Proprietary systems that stay on in Russia are characterized, as well as open access resources that allow receiving scientific, bibliometric, bibliographic and factual information. In addition, a list of Russian information products and scientific information search systems is presented. The resource data is supported by real examples describing the results obtained. All system and product names supported by URLs. The article is informational and does not aim to present the full radius of open and proprietary systems currently present online. The article shows various ways of information search by both ordinary users and specialists in the scientific and technical information sphere.
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-contrast MRI Super-Resolution

Multi-contrast magnetic resonance imaging (MRI) is the most common management tool used to characterize neurological disorders based on brain tissue contrasts. However, acquiring high-resolution MRI scans is time-consuming and infeasible under specific conditions. Hence, multi-contrast super-resolution methods have been developed to improve the quality of low-resolution contrasts by leveraging complementary information from multi-contrast MRI. Current deep learning-based super-resolution methods have limitations in estimating restoration uncertainty and avoiding mode collapse. Although the diffusion model has emerged as a promising approach for image enhancement, capturing complex interactions between multiple conditions introduced by multi-contrast MRI super-resolution remains a challenge for clinical applications. In this paper, we propose a disentangled conditional diffusion model, DisC-Diff, for multi-contrast brain MRI super-resolution. It utilizes the sampling-based generation and simple objective function of diffusion models to estimate uncertainty in restorations effectively and ensure a stable optimization process. Moreover, DisC-Diff leverages a disentangled multi-stream network to fully exploit complementary information from multi-contrast MRI, improving model interpretation under multiple conditions of multi-contrast inputs. We validated the effectiveness of DisC-Diff on two datasets: the IXI dataset, which contains 578 normal brains, and a clinical dataset with 316 pathological brains. Our experimental results demonstrate that DisC-Diff outperforms other state-of-the-art methods both quantitatively and visually.
Privacy-Enhanced Pneumonia Diagnosis: IoT-Enabled Federated Multi-Party Computation in Industry 5.0

Pneumonia is a significant global health concern that can lead to severe and sometimes fatal consequences. Timely identification and classification of pneumonia can substantially improve patient outcomes. However, the disclosure of sensitive medical data for diagnostic purposes raises important issues regarding patient privacy and data security. The Federated Multi-Party Computing (FMPC) technique has emerged as a promising solution to these challenges, enabling multiple parties to collaborate and compute a function over their private data while maintaining data privacy. This paper presents a novel Internet of Things (IoT)-enabled FMPC framework that significantly enhances the accuracy of pneumonia categorization while preserving patient privacy and data security. Using publicly available Kaggle datasets, the efficacy of the proposed strategy is assessed, and a comparison with contemporary deep learning models is made. The study demonstrates the remarkable efficacy of the IoT-enabled FMPC approach in pneumonia recognition within the industry 5.0 landscape. With 96.67% accuracy achieved for the unbalanced dataset and 97.84% accuracy for the balanced dataset, the results of the proposed algorithm demonstrate the potential for improvement. This approach ensures the privacy of sensitive medical information, aligning with the core principles of Industry 5.0 that emphasize the harmonious integration of advanced technologies and human-centric values.
Blockchain managed federated learning for a secure IoT framework

Abstract In this work, we present a blockchain-based federated learning (FL) framework that aims achieving high system efficiency while simultaneously addressing issues relating to data sparsity and the disclosure of private information. It is more efficient to build a number of smaller clusters rather than one big cluster for multiple networks. Blockchain-based FL is carried out in each cluster, with the model changes being compiled at the end of the process. Following that, the accumulated updates are swapped across the clusters, which, in practise, improves the updates that are accessible for each cluster. When compared to the extensive interactions that take place in blockchain-based FL, cluster-based FL only sends a limited number of aggregated updates across a substantial distance. This is in contrast to the extensive interactions that take place in blockchain-based FL. In order to conduct an analysis of our system, we have implemented the prototypes of both cluster and blockchain-based FL models. The findings of the experiments show that cluster-based FL model raise the accuracy goes upto 72.6%, and goes down to 11%. The loss goes upto 3.6 and goes down to 0.8. In addition, cluster-based FL model has the potential to hasten the convergence of the model, provided that the same quantity of data is input into it. The reason for this is due to the fact that during a training cycle, cluster-based FL model combines the computational resources of many different clusters.
Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant

Malaysia's most plentiful renewable energy (RE) source, solar, only generate power during the day. High penetration of intermittent PV generation, especially from Large-Scale Solar Photovoltaic (LSSPV) plants, may complicate power system planning and operation. Hence, the need to accurately forecast solar generation for sustaining grid stability. This study identified the meteorological variables that significantly impact the power generation of an LSSPV plant using the Pearson Correlation Coefficient (PCC). The LSSPV plant, located in the central area of Malaysia, is used as a case study. To this end, a regression analysis model was developed using Artificial Neural Network (ANN) to predict solar generation with changing meteorological conditions. From August 1, 2020, to July 1, 202,1 and September 1, 2022, to November 1, 2022, a 5-minute dataset is collected. The meteorological variables with the strongest correlation on solar generation were the total global horizontal irradiance and global irradiance on the module plane selected by PCC, followed by PV module temperature, ambient temperature, wind speed, total slope and total horizontal irradiation. The final input to the forecasting model consists of four primary features that indicate a significant correlation with solar PV generation, which are total global horizontal irradiance (W/m²), PV module temperature (°C), ambient temperature (°C) and wind speed (m/s). The simulation findings reveal that the accuracy evaluation metrics are higher, with R² metrics improved by 45% when employing feature selection for prediction. While ANN has been commonly used for solar generation prediction, this study uniquely combines ANN with PCC as a feature selection technique. This method has not been widely explored in the literature and has resulted in significant improvements in the accuracy and robustness of the prediction model. Thus, our study provides a valuable contribution to solar energy prediction and can potentially enhance solar energy utilisation efficiency in various applications.
NimbRo Wins ANA Avatar XPRIZE Immersive Telepresence Competition: Human-Centric Evaluation and Lessons Learned

Abstract Robotic avatar systems can enable immersive telepresence with locomotion, manipulation, and communication capabilities. We present such an avatar system, based on the key components of immersive 3D visualization and transparent force-feedback telemanipulation. Our avatar robot features an anthropomorphic upper body with dexterous hands. The remote human operator drives the arms and fingers through an exoskeleton-based operator station, which provides force feedback both at the wrist and for each finger. The robot torso is mounted on a holonomic base, providing omnidirectional locomotion on flat floors, controlled using a 3D rudder device. Finally, the robot features a 6D movable head with stereo cameras, which stream images to a VR display worn by the operator. Movement latency is hidden using spherical rendering. The head also carries a telepresence screen displaying an animated image of the operator’s face, enabling direct interaction with remote persons. Our system won the $10 M ANA Avatar XPRIZE competition, which challenged teams to develop intuitive and immersive avatar systems that could be operated by briefly trained judges. We analyze our successful participation in the semifinals and finals and provide insight into our operator training and lessons learned. In addition, we evaluate our system in a user study that demonstrates its intuitive and easy usability.
A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

The enormous volume of heterogeneous data from various smart device-based applications has growingly increased a deeply interlaced cyber-physical system. In order to deliver smart cloud services that require low latency with strong computational processing capabilities, the Edge Intelligence System (EIS) idea is now being employed, which takes advantage of Artificial Intelligence (AI) and Edge Computing Technology (ECT). Thus, EIS presents a potential approach to enforcing future Intelligent Transportation Systems (ITS), particularly within a context of a Vehicular Network (VNets). However, the current EIS framework meets some issues and is conceivably vulnerable to multiple adversarial attacks because the central aggregator server handles the entire system orchestration. Hence, this paper introduces the concept of distributed edge intelligence, combining the advantages of Federated Learning (FL), Differential Privacy (DP), and blockchain to address the issues raised earlier. By performing decentralized data management and storing transactions in immutable distributed ledger networks, the blockchain-assisted FL method improves user privacy and boosts traffic prediction accuracy. Additionally, DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing transactions. Our model has been deployed in two strategies: First, DP-based FL to strengthen user privacy by masking the intermediate data during model uploading. Second, blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular networks. The simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget (ε) of 4.03, 1.18, and 0.522, achieving model accuracy of 95.8%, 93.78%, and 89.31%, respectively.
OSM: Leveraging model checking for observing dynamic behaviors in aspect-oriented applications

In the intricate domain of software systems verification, dynamically model checking multifaceted system characteristics remains paramount, yet challenging. This research proposes the advanced observe-based statistical model-checking (OSM) framework, devised to craft executable formal models directly from foundational system code. Leveraging model checking predicates, the framework melds seamlessly with aspect-oriented programming paradigms, yielding a potent method for the analytical verification of varied behavioral attributes. Exploiting the transformative capacity of OSM framework, primary system code undergoes a systematic metamorphosis into multifaceted analysis constructs. This not only simplifies the model verification process but also orchestrates feature interactions using an innovative observing join point abstraction mechanism. Within this framework, components encompassing parsing, formal verification, computational analytics, and rigorous validation are intrinsically interwoven. Marrying the principles of model checking with aspect-oriented (AO) modularization, OSM framework stands as a paragon, proficiently scrutinizing and affirming system specifications. This ensures the unyielding performance of electronic health record systems amidst shifting preconditions. OSM framework offers runtime verification of both object-oriented and AO deployments, positioning itself as an indispensable open-source resource, poised to automate the enhancement of system performance and scalability.
Role of E-Governance amid the Pandemic in Providing Healthcare, Education, and Social Services (HESS) in Bangladesh

COVID-19 has imposed a severe impact on health, economics, society, trade &amp; commerce, administration, and all aspects of human existence. In this regard, digital governance, i.e., electronic governance (e-governance), is now playing a significant role in tackling the drastic effects of COVID-19 in Bangladesh. E-governance refers to the process of using information and communication technology in the field of delivering public services. The principal aim of this study was to figure out what role e-governance plays in health, education, and social services (HESS) in Bangladesh during the pandemic. This research, in precise, focused on the impact of e-governance on the government's decision-making, emergency health care management, education management, and social welfare in Bangladesh during the COVID-19 pandemic. The study was done using a qualitative method. The findings of the study reveal the significant implications of e-governance in the management of health issues, education issues, and social issues during the pandemic. In particular, the findings of the study demonstrate the contribution of electronic learning, telemedicine, one-stop service centers, and emergency helpline services (999) in terms of addressing the devastating effects of the pandemic in Bangladesh. Moreover, the study incorporated both practical and managerial contributions. As a result, the authors believe that this study's contribution is crucial and practical for determining what the actual effects of e-governance were in Bangladesh during the pandemic.
Migration from VTLS to KOHA Library Application Software in Ahmadu Bello University Library Complex: Reasons, Challenges and the Success Story

Abstract Providing an overview of automation project and ILS migrations in Ahmadu Bello University Library, the paper highlights reasons and challenges faced during the various switch over processes. Although the VTLS served the library very well, the rising cost of the software associated with the increase in exchange rate necessitated the library to immigrate from the software. The paper also discusses the critical challenge of data/record retrieval from VTLS to KOHA and how the university library system librarians were able to achieve that without losing any data/record. The paper concludes that the exercise have not only been worthwhile but successful. Apart from empowering library staff with additional skills and knowledge, the migration led to increase in the number of records in the library management system and provides remote access to library holdings via the Web leading to resources sharing and collaboration with other university libraries using KOHA nationally and globally. Keywords: Migration, VTLS, KOHA, Library Application Software, Ahmadu Bello University Library, ILS.
Immersive Learning Environments: Designing XR into Higher Education

The body of research supporting the inclusion of extended reality (XR) into higher education is substantial. However, due to the pandemic and the need to increase virtual presence with remote students and workers, the incorporation of diverse XR options into education is catching serious attention of university administrators. Instructional designers (IDs) are well trained in the analysis, design, implementation, and evaluation skills needed to select appropriate platforms and uses of XR. This chapter illustrates how IDs can assist in high-level design decisions regarding these resources. Familiar models and design approaches are recommended along with templates for working with leadership regarding research and funding and evaluating XR for best use for the higher education applications.
Disentangling human–AI divide: complementarity in the age of artificial intelligence

Journal Article Disentangling human–AI divide: complementarity in the age of artificial intelligence Get access Jeff Clyde G Corpuz, PhD Jeff Clyde G Corpuz, PhD Department of Theology and Religious Education, De La Salle University, Manila 1004, Philippines Address correspondence to Jeff Clyde G. Corpuz, E-mail: jeff.corpuz@dlsu.edu.ph https://orcid.org/0000-0003-1517-4866 Search for other works by this author on: Oxford Academic PubMed Google Scholar Journal of Public Health, fdad201, https://doi.org/10.1093/pubmed/fdad201 Published: 19 October 2023 Article history Received: 22 August 2023 Revision received: 22 August 2023 Accepted: 05 October 2023 Published: 19 October 2023