Reviewerly/exHarmony huggingface dataset:
Digital competence development in a few countries of the European Union

Abstract The first computers were made as early as in the 20 th century, but the rapid development of digitalisation took place in the 21 st century. Nowadays, Industry 4.0 has created a network connection between man and machines. Without computers and the Internet, the world is unthinkable. Digitalisation is pervading every part of our lives. Whether at work or in our leisure activities, digitalisation and the use of digital tools are present. The digital revolution was started by computers and is now an indispensable our lives, for example with the internet, mobile phones, cloud solutions and self-driving cars. It's no coincidence that digital competence is no longer an expectation, but an obligation for employers and employees alike. Several studies have undertaken to define digital skills; however, we can talk about a rather complex competence. The current study examines the question in the dimension of the European Union from a multitude of aspects. The authors use the DESI index and EU statistics on this topic to find correlations between digital potential, use of tools and the exploitation of opportunities offered by digitalisation in thirteen countries. The authors primarily examined the impact of the level of digital infrastructure and the frequency of Internet use on digital skills. It is outlined from statistics that Scandinavian countries have a leading role in the digital competition. Nevertheless, our results throw light on the individual level that digital skills forcefully improve during browsing on the internet, and practical activities have a significant role in development, which the authors named PMP.
Spatio-temporal heuristic method: a trajectory planning for automatic parking considering obstacle behavior

Purpose The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking. Design/methodology/approach To deal with dynamic obstacles for autonomous vehicles during parking, a long- and short-term mixed trajectory planning algorithm is proposed in this paper. In long term, considering obstacle behavior, A-star algorithm was improved by RS curve and potential function via spatio-temporal map to obtain a safe and efficient initial trajectory. In short term, this paper proposes a nonlinear model predictive control trajectory optimizer to smooth and adjust the trajectory online based on the vehicle kinematic model. Moreover, the proposed method is simulated and verified in four common dynamic parking scenarios by ACADO Toolkit and QPOASE solver. Findings Compared with the spline optimization method, the results show that the proposed method can generate efficient obstacle avoidance strategies, safe parking trajectories and control parameters such as the front wheel angle and velocity in high-efficient central processing units. Originality/value It is aimed at improving the robustness of automatic parking system and providing a reference for decision-making in a dynamic environment.
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.94 for ε=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.
Immersive Applications in Museums: An Analysis of the Use of XR Technologies and the Provided Functionality Based on Systematic Literature Review

Immersive technologies (Virtual, Augmented, and Mixed Reality) are widely used in cultural heritage for communication, enhancing the visiting experience, and improving learning and understanding. Immersive technologies have found their way into museums and other cultural spaces in various forms and shapes. This work aims to recognize the main forms of immersive technologies and applications in museums and other cultural spaces and provide information on the employed methods, technologies, equipment, and software solutions by conducting a systematic literature review aligned with the PRISMA guidelines. The analyzed literature was collected through a focused search in scientific databases (Scopus, ACM, and IEEE). The relevance to the subject was assessed based on the main technological focus (VR/AR/MR or XR) and the employed technologies. Methods and approaches for realizing their applications were studied and discussed. Thirteen articles were found to meet the selection criteria, of which two focus on VR, six are on AR, two are on Audio-AR, and three are on MR. The results showed that Augmented Reality solutions are preferred for on-site use; Mixed Reality applications started to emerge as Mixed Reality hardware technology became available and Virtual Reality despite being criticized for isolating visitors. The findings cover the existing gap in recent literature and can reveal a set of good practices and innovative ideas for future applications.
Socializing in the Metaverse: The Innovation and Challenge of Interpersonal Communication

2021 is called the first year of the "metaverse", and the "metaverse" has quickly become a hot topic of discussion and has attracted the attention of the academic community.Social is one of the main innovation channels of the metaverse.However, what kind of future, what kind of challenges, and what kind of social impacts will social interaction in the metaverse form need to be considered.In order to provide thinking for social development under the metaverse, this research analyzes the concept of the metaverse, the era background and existing cases of social interaction in the metaverse.The study found that social interaction is also entering the fourth era-metaverse social interaction, which is gradually revealed to people with the development of Internet technology.But metaverse social interaction still faces technical and ethical challenges, and there is still a long way to go to achieve a true metaverse.
Adoption enablers of big data analytics in supply chain management practices: the moderating role of innovation culture

The enablers of Big Data Analytics (BDA) on the BDA adoption intention of consumer goods’ retailing firms were measured in this study along with innovation culture as a moderator. Based on a literature review, six BDA adoption intention enablers: financial readiness, perceived advantages, top management support, IT infrastructure, technology sophistication, and data quality were identified. The study collected data from different levels of managers in the consumer goods’ retailing sector in Jordan to test the proposed study framework. To obtain primary data, a quantitative method was used, and a survey (structured questionnaire) was conducted. SmartPLS version 3.3 was used to analyze and test the proposed study model, which included 211 respondents. Three BDA enablers, including perceived advantages, top management support, and IT infrastructure, were found to have a statistically significant effect on BDA adoption intention in their supply chain operations. Furthermore, the relationship between financial readiness and BDA adoption intention was significantly moderated by innovation culture. This research model can be used to determine the challenges and enablers to BDA adoption in supply chain operations for both developed and developing countries. Future research may replicate the model in various sectors or the same sector in different countries.
IPFS and Blockchain Based Reliability and Availability Improvement for Integrated Rivers’ Streamflow Data

The streamflow data acquisition with various techniques and dispersing of River&#x2019;s locations demand improvement of reliability and frequency aspects. One of the reliability measurement characteristics is data ownership. The authority sharing data authorizes its quality and is also responsible for the wrong decision triggered by incorrect data. The consensus-based crowdsourcing data contribute to aggregated streamflow records&#x2019; generation. The aggregated streamflow records are stored on a streamflow ledger, a Hyperledger fabric-based ledger for rivers&#x2019; streamflow data. In contrast, InterPlanery File System(IPFS) distributed file storage system is helpful for policy documents and distribution scheme storage. Blockchain-based techniques for improvement of data-intensive decision support systems. The distributed river streamflow data measured and shared by distributed gauging officials and stored on the blockchain-based distributed storage system contributes to scalability, transparency, availability, and accessibility of shareable data. All stakeholders require quality data with trust and provenance management through a persistent, linked, and immutable copy of data. This technique resolves the conflict or disagreement on streamflow optimization and flood mitigation decisions. In a nutshell, the blockchain technology with IPFS for off-chain large files storage would contribute twofold to irrigation systems and flood mitigation domains. On one side, the streamflow data aggregation has consensus from streamflow assessment for software agents and stakeholders. Secondly, the persistent data copy is shared among distributed stakeholders using IPFS-based content addressed file-sharing protocol for a common operating picture to improve effective collaboration and coordination among managers of irrigation systems and flood mitigation activities.
Image Classification With Small Datasets: Overview and Benchmark

Image classification with small datasets has been an active research area in the recent past.However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods.This article addresses both issues.First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered.Second, we propose a common benchmark that allows for an objective comparison of approaches.It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral).We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues.Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years.Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier.
Fault-Tolerance in the Scope of Cloud Computing

Fault-tolerance methods are required to ensure high availability and high reliability in cloud computing environments. In this survey, we address fault-tolerance in the scope of cloud computing. Recently, cloud computing-based environments have presented new challenges to support fault-tolerance and opened new paths to develop novel strategies, architectures, and standards. We provide a detailed background of cloud computing to establish a comprehensive understanding of the subject, from basic to advanced. We then highlight fault-tolerance components and system-level metrics and identify the needs and applications of fault-tolerance in cloud computing. Furthermore, we discuss state-of-the-art proactive and reactive approaches to cloud computing fault-tolerance. We further structure and discuss current research efforts on cloud computing fault-tolerance architectures and frameworks. Finally, we conclude by enumerating future research directions specific to cloud computing fault-tolerance development.
Toward Automated Security Analysis and Enforcement for Cloud Computing Using Graphical Models for Security

Cloud computing has become widely adopted by businesses for hosting applications with improved performance at a fraction of the operational costs and complexity.The rise of cloud applications has been coupled with an increase in security threat vectors and vulnerabilities.In this paper, we propose a new security assessment and enforcement tool for the cloud named CloudSafe, which provides an automated security assessment and enforce best security control for the cloud by collating various security tools.To demonstrate the applicability and usability of CloudSafe, we implemented CloudSafe and conducted security assessment in Amazon AWS.Also, we analyzed four different security countermeasure options in depth; Vulnerability Patching, Virtual Patching, Network Hardening and Moving Target Defence.Virtual Patching, Network Hardening and Moving Target Defence were determined to be feasible with regards to deployment implementation for the project.Proof of concepts were developed demonstrating the effectiveness of each feasible countermeasure option.These results indicate that the proposed tool CloudSafe is effective and efficient in helping security administrators to select optimal countermeasures to secure their cloud by conducting an in-depth security assessment.
Cross-domain Named Entity Recognition via Graph Matching

Cross-domain NER is a practical yet challenging problem since the data scarcity in the realworld scenario.A common practice is first to learn a NER model in a rich-resource general domain and then adapt the model to specific domains.Due to the mismatch problem between entity types across domains, the wide knowledge in the general domain can not effectively transfer to the target domain NER model.To this end, we model the label relationship as a probability distribution and construct label graphs in both source and target label spaces.To enhance the contextual representation with label structures, we fuse the label graph into the word embedding output by BERT.By representing label relationships as graphs, we formulate cross-domain NER as a graph matching problem.Furthermore, the proposed method has good applicability with pre-training methods and is potentially capable of other crossdomain prediction tasks.Empirical results on four datasets show that our method outperforms a series of transfer learning, multi-task learning, and few-shot learning methods.
Audio Tampering Forensics Based on Representation Learning of ENF Phase Sequence

This paper proposes an audio tampering detection method based on the ENF phase and BI-LSTM network from the perspective of temporal feature representation learning. First, the ENF phase is obtained by discrete Fourier transform of ENF component in audio. Second, the ENF phase is divided into frames to obtain ENF phase sequence characterization, and each frame is represented as the change information of the ENF phase in a period. Then, the BI-LSTM neural network is used to train and output the state of each time step, and the difference information between real audio and tampered audio is obtained. Finally, these differences were fitted and dimensionally reduced by the fully connected network and classified by the Softmax classifier. Experimental results show that the performance of this method is better than the state-of-the-art approaches.
A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge

Medical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes.Rare code problem, the medical codes with low occurrences, is prominent in medical code prediction.Recent studies employ deep neural networks and the external knowledge to tackle it.However, such approaches lack interpretability which is a vital issue in medical application.Moreover, due to the lengthy and noisy clinical notes, such approaches fail to achieve satisfactory results.Therefore, in this paper, we propose a novel framework based on medical concept driven attention to incorporate external knowledge for explainable medical code prediction.In specific, both the clinical notes and Wikipedia documents are aligned into topic space to extract medical concepts using topic modeling.Then, the medical concept-driven attention mechanism is applied to uncover the medical code related concepts which provide explanations for medical code prediction.Experimental results on the benchmark dataset show the superiority of the proposed framework over several state-of-the-art baselines.
Arabic Sign Language Recognition using Lightweight CNN-based Architecture

Communication is a critical skill for humans.People who have been deprived from communicating through words like the rest of humans, usually use sign language.For sign language, the main signs features are the handshape, the location, the movement, the orientation and the non-manual component.The vast spread of mobile phones presents an opportunity for hearing-disabled people to engage more into their communities.Designing and implementing a novel Arabic Sign Language (ArSL) recognition system would significantly affect their quality of life.Deep learning models are usually heavy for mobile phones.The more layers a neural network has, the heavier it is.However, typical deep neural network necessitates a large number of layers to attain adequate classification performance.This project aims at addressing the Arabic Sign Language recognition problem and ensuring a trade-off between optimizing the classification performance and scaling down the architecture of the deep network to reduce the computational cost.Specifically, we adapted Efficient Network (EfficientNet) models and generated lightweight deep learning models to classify Arabic Sign Language gestures.Furthermore, a real dataset collected by many different signers to perform hand gestures for thirty different Arabic alphabets.Then, an appropriate performance metrics used in order to assess the classification outcomes obtained by the proposed lightweight models.Besides, preprocessing and data augmentation techniques were investigated to enhance the models generalization.The best results were obtained using the EfficientNet-Lite 0 architecture and the Label smooth as loss function.Our model achieved 94% and proved to be effective against background variations.
Insights and Example Use Cases on Industrial Transfer Learning

Despite the high solution potential of machine learning for common problems in automation technology, there are only few examples of its application in real-world manufacturing practice. In order to find the reason for this phenomenon, the authors identify the hurdles for conventional machine learning using four exemplary use cases namely self-learning robots, wear prediction, visual object detection, and predictive quality in manufacturing. While these use-cases differ in principle, the problems engineers face when using conventional machine learning approaches to solve them are related, such as the lack of manifold training data or high dynamics of industrial processes. The authors showcase that utilizing deep transfer learning and continual learning approaches in the industrial context – subsumed under the term industrial transfer learning – can overcome these hurdles. Even for industrial transfer learning, there is a deficiency regarding preconditions for the large-scale deployment of such approaches, but unlike in conventional machine learning, it is principally possible to establish those. The article concludes with a discussion of these prerequisites and makes suggestions as to how they could be fulfilled.
Dynamic Target Search Using Multi-UAVs Based on Motion-Encoded Genetic Algorithm With Multiple Parents

In this paper, a new optimization algorithm called Motion-Encoded Genetic Algorithm with Multiple Parents (MEGA-MPC) is developed to locate moving targets using multiple Unmanned Aerial Vehicles (UAVs).Bayesian theory is used to formulate the moving target tracking as an optimization problem where target detection probability defines the objective function as the probability of detecting the target.In the developed MEGA-MPC algorithm, a series of UAV motion paths encodes the search trajectory.In every iteration of the MEGA-MPC algorithm, UAV motion paths undergo evolution.The proposed approach for dynamic target search using multi-UAVs uses parallel computations to solve the optimization problem based on the MEGA-MPC algorithm where Each UAV can communicate with other UAVs if requested.The algorithm's performance is tested with various characteristics under six distinct scenarios using a different number of UAVs and targets.The statistical analysis of the results obtained using MEGA-MPC compared with other well-known metaheuristics shows that MEGA-MPC offers better solutions to find dynamic targets since it outperforms all the compared algorithms.
KinyaBERT: a Morphology-aware Kinyarwanda Language Model

Pre-trained language models such as BERT have been successful at tackling many natural language processing tasks.However, the unsupervised sub-word tokenization methods commonly used in these models (e.g., byte-pair encoding -BPE) are sub-optimal at handling morphologically rich languages.Even given a morphological analyzer, naive sequencing of morphemes into a standard BERT architecture is inefficient at capturing morphological compositionality and expressing word-relative syntactic regularities.We address these challenges by proposing a simple yet effective twotier BERT architecture that leverages a morphological analyzer and explicitly represents morphological compositionality.Despite the success of BERT, most of its evaluations have been conducted on high-resource languages, obscuring its applicability on low-resource languages.We evaluate our proposed method on the low-resource morphologically rich Kinyarwanda language, naming the proposed model architecture KinyaBERT.A robust set of experimental results reveal that KinyaBERT outperforms solid baselines by 2% in F1 score on a named entity recognition task and by 4.3% in average score of a machine-translated GLUE benchmark.KinyaBERT fine-tuning has better convergence and achieves more robust results on multiple tasks even in the presence of translation noise. 1