Reviewerly/exHarmony huggingface dataset:
Digital Competencies among Higher Education Professors and High-School Teachers: Does Teaching Experience matter?

Abstract Background: To provide high-quality education and remain innovative, thus contributing to sustainable development goals, educational institutions use digital tools and implement ICT in the teaching process. In addition to providing technical resources, it requires the appropriate education of teachers who should have the appropriate knowledge and skills to take full advantage of the opportunities provided by ICT. Objectives: The main objective of this article is to identify the current state of ICT knowledge and skills of university professors and high school teachers and to establish if there exists a relationship between their digital competencies and teaching experience. We strive to discover areas where digital competencies are already relatively high and ICT knowledge and skills gaps. Methods/Approach: Survey was conducted on a sample of university and secondary school professors who were asked to estimate their perceived level of knowledge and skills in various ICT domains. Results: The results of our research show that the total self-assessed level of competence is intermediate, with slightly higher values for ICT knowledge than for ICT skills. The results vary depending on the different subcategories of competencies and the years of respondents’ teaching experience. Conclusions: Our research findings, which revealed variations and gaps in digital knowledge and skills among professors and teachers, may have significant policy implications for policymakers and educators committed to ensuring quality education.
A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions

Color QR Codes are often generated to encode digital information, but one also could use colors or to allocate colors in a QR Code to act as a color calibration chart. In this dataset, we present several thousand QR Codes images generated with two different colorization algorithms (random and back-compatible) and several tuning variables in these color encoding. The QR Codes were also exposed to three different channel conditions (empty, augmentation and real-life). Also, we derive the SNR and BER computations for these QR Code in comparison with their black and white versions. Finally, we also show if ZBar, a commercial QR Code scanner, is able to read them.
Attention-Based Recurrent Autoencoder for Motion Capture Denoising

&lt;p&gt;To resolve the problem of massive loss of MoCap data from optical motion capture, we propose a novel network architecture based on attention mechanism and recurrent network. Its advantage is that the use of encoder-decoder enables automatic human motion manifold learning, capturing the hidden spatial-temporal relationships in motion sequences. In addition, by using the multi-head attention mechanism, it is possible to identify the most relevant corrupted frames with specific position information to recovery the missing markers, which can lead to more accurate motion reconstruction. Simulation experiments demonstrate that the network model we proposed can effectively handle the large-scale missing markers problem with better robustness, smaller errors and more natural recovered motion sequence compared to the reference method.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Use of Conceptual Graph-based Reasoning and Modeling to Support the Forensic Analysis of Bridges Failure

Despite the development of mathematical models, procedures, decision support systems, and databases, bridges are still today confronted with failures that can lead to dramatic situations. The lack of tools allowing to learn from the past failures associated with the uncertainties in its environment makes difficult the decision making in the bridge maintenance. The difficulty of maintaining the functions of bridges comes from the complexity of relationships between random hazards, the limit states of the bridge structure, and the human decisions and activities. To date, there is no generic and holistic model capable of modeling all types of bridges failure situations and capitalizing on their knowledge. This paper proposes a knowledge model of bridge failures, based on the formalism of conceptual graphs, capable of representing and capitalizing the failures of all types of bridges in a structured common vocabulary and to conduct automatic reasoning. The aim is to be able to provide explanations of failures and alerts on future projects.
Tilt Angle and Orientation Assessment of Photovoltaic Thermal (PVT) System for Sub-Saharan Tropical Regions: Case Study Douala, Cameroon

Photovoltaic systems when integrated into a building structure can satisfy the world’s energy requirements at a competitive cost by providing onsite electrical and thermal energies for domestic appliances. The energy yield of the photovoltaic system is affected by the intensity of the solar radiation, wind speed, tilt angle, orientation, geographical location, etc. This paper presents an optimisation model of the tilt angle and orientation of a photovoltaic thermal system. A methodology has been developed to optimize the tilted angle of the roof for higher exergy output taking into account the intensity of solar radiation, wind speed, geographical location, cost of cleaning dust, etc. For a system installed in the city of Douala, Cameroon, it is recommended that the PV should be inclined at an angle between 10° and 20° south-facing for economical output. The cost per unit of electricity between the tilt inclination angles from 0° to 20° with south orientation is USD 0.04 per kWh. The cost of electricity loss due to a 20° tilt inclination angle can be compensated with the labour cost and work required for cleaning the photovoltaic thermal (PVT) system of the horizontal roof. The system installed over an effective area of 8 m2 is capable of producing annual net exergy of 2195.81 kWh/year at an efficiency of 11.8%.
Influence of Digital Fluctuations on Behavior of Neural Networks

This paper deals with effect of digital noise to numerical stability of neural networks. Digital noise arises from the inexactness of floating point values operations. Accumulated errors finally lead to the loss of significance. Experiments show that more redundant networks have higher noise influence. This effect is tested in both model and real world samples. As a result, one should exclude all the networks results from the beginning of fluctuations. Results of experiments allow us to hypothesize that minimal values of loss function preserving significance were achieved for the networks of size close to the complexity of the dataset. So, it is a reason to choose sizes of network layers in accordance with complexity of particular datasets and not universally for an architecture and general problem statement without relation to data. In the case of fine tuning this suggests that pruning of network layers can improve result accuracy and reliability of prediction due to decrease of numerical noise influence. Results of this article are based on analysis of numerical experiments with train of more than 50000 neural networks for thousands epochs for each network. Almost all the networks begin to fluctuate.
Pose matters: Pose guided graph attention network for person re-identification

Person re-Identification (reID), aiming at retrieving a person across different cameras, has been playing a more and more important role in the construction of smart city and social security. For deep-learning-based reID methods, it has been proved that using local feature together with global feature could help to give robust representation for person retrieval. Human pose information can provide the locations of human skeleton to effectively guide the network to pay more attention to these key areas, and can also help to reduce the noise distractions from background or occlusions. Based on human pose, a Pose Guided Graph Attention (PGGA) network is proposed in this paper, which is a multi-branch architecture consisting of one branch for global feature and two branches for local key-point features. A graph attention convolution layer is carefully designed to re-assign the contribution weight of each extracted local feature by modeling the similarity relations. The experimental results demonstrate the effectiveness of our approach on discriminative feature learning. Our model achieves the state-of-the-art performance on several mainstream evaluation datasets. A plenty of ablation studies and different kinds of comparison experiments are conducted to prove the effectiveness of this work, including the tests on occluded datasets and cross-domain datasets. Moreover, we further design supplementary tests in practical scenario to indicate the advantage of our work in real-word applications.
Barriers to Digital Higher Education Teaching and How to Overcome Them—Lessons Learned during the COVID-19 Pandemic

The COVID-19 pandemic forced a transition to digital teaching in higher education institutions (HEIs) as itwas the only safe method for higher education (HE) teaching during the pandemic. However, this crisis emphasized the barriers students face worldwide. For digital HE teaching to survive in the future, these barriers should be overcome. The present paper aimed to systematically identify these barriers and present recommendations to overcome them. For this purpose, a quantitative survey (n = 369) was conducted with students in three countries, and qualitative student statements were analyzed. Possible countermeasures for corresponding barriers are described, and related stakeholders are identified. Thus, the study provided an overview of recommendations for stakeholders to overcome the barriers. The recommendations to resolve most barriers entail offering hybrid formats, adjusting lecture design, and ensuring proper communication.
Interactive Hybrid Recommendation of Pedagogical Resources

Recommender systems provide a solution to the information overload problem and can estimate user preferences in a given resource from certain information about other similar users and the resource's features.This research presented a recommender system for pedagogical resources.The built system adapts a hybrid method of two principal approaches: content-based and collaborative filtering.We have used the term frequencyinverse document frequency (TF-IDF) technique and singular value decomposition (SVD) to let the system better understand the semantics of the pedagogical resources in a content-based approach and prevent some shortcomings of the existing collaborative filtering approach.The hybridization of those techniques and methods helps the system improve the accuracy of recommendations and better respond to user requirements.The first tests of our system give encouraging results.
Natural Language Processing Resources for the Kashmiri Language

Objectives:The main objective of this paper as a maiden attempt is to identify the basic resources necessary for undertaking Natural Language Processing (NLP) specific research activities pertaining to Kashmiri language.The paper also deliberates on key issues related to Natural Language Processing of Kashmiri language such as complex linguistic phenomena, the lack of standard linguistic tools, documented as well as standardized resources and the influence of some dominant languages mostly Urdu and English on Kashmiri language.Methods: As there is no substantial work reported in literature specific to NLP of Kashmiri language, a holistic research strategy was adopted to explore the possible sources as potential means for creation of basic resources to undertake the NLP research for Kashmiri language.Findings: After thorough investigation, it was observed that there has been some trivial work reported in the literature related to Machine Translation of Kashmiri language.Further there are few newspapers published in Kashmiri language which can be used as a means for creation of Kashmiri corpus.Moreover crowdsourcing could be used a potential means for development of digital linguistic resources for Kashmiri language.Novelty: The present study is a maiden attempt towards identification of NLP resources for Kashmiri language and will be of immense importance for the research community interested to work for development of Kashmiri language in digital domain.
Underwater Image Classification Algorithm Based on Convolutional Neural Network and Optimized Extreme Learning Machine

In order to deal with the target recognition in the complex underwater environment, we carried out experimental research. This includes filtering noise in the feature extraction stage of underwater images rich in noise, or with complex backgrounds, and improving the accuracy of target classification in the recognition process. This paper discusses our contribution to improving the accuracy of underwater target classification. This paper proposes an underwater target classification algorithm based on the improved flow direction algorithm (FDA) and search agent strategy, which can simultaneously optimize the weight parameters, bias parameters, and super parameters of the extreme learning machine (ELM). As a new underwater target classifier, it replaces the full connection layer in the traditional classification network to build a classification network. In the first stage of the network, the DenseNet201 network pre-trained by ImageNet is used to extract features and reduce dimensions of underwater images. In the second stage, the optimized ELM classifier is trained and predicted. In order to weaken the uncertainty caused by the random input weight and offset of the introduced ELM, the fuzzy logic, chaos initialization, and multi population strategy-based flow direction algorithm (FCMFDA) is used to adjust the input weight and offset of the ELM and optimize the super parameters with the search agent strategy at the same time. We tested and verified the FCMFDA-ELM classifier on Fish4Knowledge and underwater robot professional competition 2018 (URPC 2018) datasets, and achieved 99.4% and 97.5% accuracy, respectively. The experimental analysis shows that the FCMFDA-ELM underwater image classifier proposed in this paper has a greater improvement in classification accuracy, stronger stability, and faster convergence. Finally, it can be embedded in the recognition process of underwater targets to improve the recognition performance and efficiency.
Acoustic Sensing Based on Online Handwritten Signature Verification

Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, exploring a way to balance the security and user experiment of handwritten signatures is critical. In this paper, we propose a handheld signature verification scheme called SilentSign, which leverages acoustic sensors (i.e., microphone and speaker) in mobile devices. Compared to the previous online signature verification system, it provides handy and safe paper-based signature verification services. The prime notion is to utilize the acoustic signals that are bounced back via a pen tip to depict a user's signing pattern. We designed the signal modulation stratagem carefully to guarantee high performance, developed a distance measurement algorithm based on phase shift, and trained a verification model. In comparison with the traditional signature verification scheme, SilentSign allows users to sign more conveniently as well as invisibly. To evaluate SilentSign in various settings, we conducted comprehensive experiments with 35 participants. Our results reveal that SilentSign can attain 98.2% AUC and 1.25% EER. We note that a shorter conference version of this paper was presented in Percom (2019). Our initial conference paper did not finish the complete experiment. This manuscript has been revised and provided additional experiments to the conference proceedings; for example, by including System Robustness, Computational Overhead, etc.
Physical-model guided self-distillation network for single image dehazing

Motivation Image dehazing, as a key prerequisite of high-level computer vision tasks, has gained extensive attention in recent years. Traditional model-based methods acquire dehazed images via the atmospheric scattering model, which dehazed favorably but often causes artifacts due to the error of parameter estimation. By contrast, recent model-free methods directly restore dehazed images by building an end-to-end network, which achieves better color fidelity. To improve the dehazing effect, we combine the complementary merits of these two categories and propose a physical-model guided self-distillation network for single image dehazing named PMGSDN. Proposed method First, we propose a novel attention guided feature extraction block (AGFEB) and build a deep feature extraction network by it. Second, we propose three early-exit branches and embed the dark channel prior information to the network to merge the merits of model-based methods and model-free methods, and then we adopt self-distillation to transfer the features from the deeper layers (perform as teacher) to shallow early-exit branches (perform as student) to improve the dehazing effect. Results For I-HAZE and O-HAZE datasets, better than the other methods, the proposed method achieves the best values of PSNR and SSIM being 17.41dB, 0.813, 18.48dB, and 0.802. Moreover, for real-world images, the proposed method also obtains high quality dehazed results. Conclusion Experimental results on both synthetic and real-world images demonstrate that the proposed PMGSDN can effectively dehaze images, resulting in dehazed results with clear textures and good color fidelity.
Length-Four Pattern Avoidance in Inversion Sequences

Inversion sequences of length $n$ are integer sequences $e_1,\ldots ,e_n$ with $0\le e_i&lt;i$ for all $i$, which are in bijection with the permutations of length $n$. In this paper, we classify all Wilf equivalence classes of pattern-avoiding inversion sequences of length-4 patterns except for one case (whether 3012 $\equiv$ 3201) and enumerate some of the length-4 pattern-avoiding inversion sequences that are in the OEIS.
Attributed Graph Embedding Based on Attention with Cluster

Graph embedding is of great significance for the research and analysis of graphs. Graph embedding aims to map nodes in the network to low-dimensional vectors while preserving information in the original graph of nodes. In recent years, the appearance of graph neural networks has significantly improved the accuracy of graph embedding. However, the influence of clusters was not considered in existing graph neural network (GNN)-based methods, so this paper proposes a new method to incorporate the influence of clusters into the generation of graph embedding. We use the attention mechanism to pass the message of the cluster pooled result and integrate the whole process into the graph autoencoder as the third layer of the encoder. The experimental results show that our model has made great improvement over the baseline methods in the node clustering and link prediction tasks, demonstrating that the embeddings generated by our model have excellent expressiveness.
Deep Learning for Predicting Congestive Heart Failure

Congestive heart failure (CHF) is one of the most debilitating cardiac disorders. It is a costly disease in terms of both lives and financial outlays, given the high rate of hospital re-admissions and mortality. Heart failure (HF) is notoriously difficult to identify on time, and is frequently accompanied by additional comorbidities that further complicate diagnosis. Many decision support systems (DSS) have been developed to facilitate diagnosis and to raise the standard of screening and monitoring operations, even for non-expert staff. This is confirmed in the literature by records of highly performing diagnosis-aid systems, which are unfortunately not very relevant to expert cardiologists. In order to assist cardiologists in predicting the trajectory of HF, we propose a deep learning-based system which predicts severity of disease progression by employing medical patient history. We tested the accuracy of four models on a labeled dataset, composed of 1037 records, to predict CHF severity and progression, achieving results comparable to studies based on much larger datasets, none of which used longitudinal multi-class prediction. The main contribution of this work is that it demonstrates that a fairly complicated approach can achieve good results on a medium size dataset, providing a reasonably accurate means of determining the evolution of CHF well in advance. This potentially constitutes a significant aid for healthcare managers and expert cardiologists in designing different therapies for medication, healthy lifestyle changes and quality of life (QoL) management, while also promoting allocation of resources with an evidence-based approach.
Overcoming Remote Leadership Challenges: Lessons Learned from Covid-19

The purpose of this study is to determine how leaders are overcoming remote work and team management challenges while navigating the pandemic context that we are now facing. The relevance of this research is based on the approach to leadership theory, articulating them with the remote work and teams management theories. To formulate the research problem, we considered two analytical dimensions, remote leadership and the competencies anchored, which help to understand how nowadays leaders adapt to remote-work throughout the Covid-19 pandemic. A qualitative method was applied, and interviews by questionnaire were conducted to obtain 40 responses. As teams suddenly were pushed into remote work settings, valuable opportunities have arisen to learn more about: (1) which are the most used and intuitive digital tools, (2) which are the main challenges faced by leaders, (3) what their teams expect from them when facing a crisis. The results show that remote leadership is crucial while moving forward to Society 5.0 as technology becomes more and more present in our lives (Fukuda, 2020), the existence of three dimensions that need work while promoting work-life balance, and developing the right soft skills to help teams. The main goal of this study is centered on the research of relevant leadership competencies, practices, and techniques that can contribute to overcoming the challenges that may arise in remote team settings and contribute to the body of knowledge on the subject of Covid-19. The findings point out the belief or disbelieve of remote work efficacy, the new role of managers and the continuous use of technology for the development of their daily work, and the renovation of skills to have better performance.