Reviewerly/exHarmony huggingface dataset:
Sensors, Students, and Self: Exploring Knowledge, Self-Efficacy, and Interest Impact of Ocean Sensor Learning on High School Marine Science Students

This study examined the effect of online technical lessons of how ocean sensors function on student interest in ocean science technology, as well as knowledge gain outcomes. Additionally, the study contributes novel findings to sensor-based learning literature by measuring changes to self-efficacy and confidence gains stemming from sensor-based learning, as well as changes in interest in ocean careers. The area of educational focus was also novel-focusing on how the sensors themselves function, not just what they do. Precipitated by COVID-19 pandemic constraints, the team used a remote learning approach to provide lessons on sensors at a distance, providing an additional opportunity to contrast this approach with previously studied hands-on learning modes. A sample of students from four high school marine science classes completed two assessments both before and after a series of lessons on ocean sensors. This included a self-reported survey (N = 48), and an open-ended knowledge assessment (N = 40). Results showed modest gains in knowledge assessments, and students experienced statistically significant gains in confidence in their ability to explain what sensors are, confidence in their ability to use sensors and understand resulting data, and confidence in accuracy of sensor data (p < 0.05). No changes were observed for several measures of interest in ocean technology, nor were there changes in an already high belief that understanding these sensors is important to marine science careers. Notably, these findings measure a positive shift in several measures of self-efficacy and confidence, which is a new finding for sensor-based learning. The findings also contrast with prior related work that included hands-on activities with sensors, which reported an increase in interest after working with sensors, whereas this intervention did not. This suggests a hands-on component is key to increasing interest in ocean technology.
Mobile application for the dissemination, learning and revitalisation of the native Muniche language in the Peruvian Amazon, in the context of Covid-19

The present work comprises the conceptualisation, design, development and validation of the software called “Jiaminchií”, created to support educational processes in times of COVID-19 and the recovery of the Muniche language. The Muniches are an endangered indigenous Peruvian nation located in the province of Alto Amazonas in the Loreto region. The research group proposes as educational tools the use of mobile software applications to continue with the educational service in indigenous communities, which is a complex problem in Peru, considering the limitations and conditions generated by the Covid-19 pandemic. The application has been conceptualised based on the basic contents for learning the Muniche language (review of the dictionary and other documentation), the community of Villa Muniche has been visited and the participation of one of the 3 speakers has been achieved through the generation of a sound bank based on the prioritised contents. The validation included a test group with children from indigenous communities. Two groups were formed, the first used the software and the second received the same contents using the traditional method. Among the most important results, it can be mentioned that the use of the “Jiaminchií” application in the education of the indigenous communities of Alto Amazonas overcomes a series of limitations typical of schools in the indigenous communities of the Peruvian Amazon.
A Course Teacher Recommendation Method Based on Hypergraph Model

Recommending suitable teachers for university courses is a personalized recommendation problem with a very practical value. However, because the recommendation task involves multiple objects and the complex cross-relationship between them, the commonly used collaborative filtering algorithm makes it difficult to recommend this data set accurately with a complex structure. The task also has the problems of cold start and data sparsity. Although the recommendation model based on an ordinary graph can use a bipartite graph to establish a simple relationship between teachers and courses, it does not reflect the complex structural relationship between courses and teachers. We propose a personalized teacher recommendation algorithm based on the hypergraph (TRH) model. The basic idea of TRH is to use the feature of hypergraph that does not lose high-dimensional information to more accurately model the complex relationship data among courses, teachers, students, supervisors, and ratings and use the manifold ranking algorithm to achieve accurate recommendations. The experimental results show that the personalized teacher recommendation algorithm based on hypergraph has higher accuracy than the user-based collaborative filtering and the recommendation algorithm based on the ordinary graph.
3D optical flow for large CT data of materials microstructures

Abstract We compute three‐dimensional displacement vector fields to estimate the deformation of microstructural data sets in mechanical tests. For this, we extend the well‐known optical flow by Brox et al. to three dimensions, with special focus on the discretization of nonlinear terms. We evaluate our method first by synthetically deforming foams and comparing against this ground truth and second with data sets of samples that underwent real mechanical tests. Our results are compared to those from state‐of‐the‐art algorithms in materials science and medical image registration. By a thorough evaluation, we show that our proposed method is able to resolve the displacement best among all chosen comparison methods.
Requirements for Automating Moderation in Community Question-Answering Websites

In recent years, community Q&A websites have attracted many users and have become reliable sources among experts from various fields. These platforms have specific rules to maintain their content quality in addition to general user agreements. Due to the vast expanse of these systems in terms of the number of users and posts, manual checking and verification of new contents by the administrators and official moderators are not feasible, and these systems require scalable solutions. In major Q&A networks, the current strategy is to use crowdsourcing with reliance on reporting systems. This strategy has serious problems, including the slow handling of violations, the loss of new and experienced users’ time, the low quality of user reports, and discouraging feedback to new users.
RAP: RAnsomware Protection Scheme Based on Blockchain

Ransomware attacks have caused serious data loss, and a frequent/good backup is the only solution to deal with such risks. Unfortunately, current research of data backup mainly focuses on improving data recovery efficiency, and rarely takes backups confidentiality and service DDoS attacks resistance into consideration, which greatly limits their usability in practice. In this work, we propose a novel data backup scheme called RAP (RAnsomware Protection scheme based on blockchain) to make up for the above deficiencies. Based on the principle of data isolation, we first formally discuss the role of gateways in data backup systems and deploy mechanism on gateways to resist DDoS attacks. RAP employs an optimized all-or-nothing transform (AONT) and offers the scheme for setup, backups upload and data recovery through secure channel. We define and analyze the security of RAP for data confidentiality and DDoS attacks resistance. We instantiate RAP by a consortium blockchain based on Ethereum and evaluate its performance. The running time of each phase in RAP is less than 1 millisecond excluding the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> ead/ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w$</tex> rite delay of typic data size and our scheme would be compatible with most existing blockchains, which further demonstrate its practicality and scalability.
Online Streaming Features Causal Discovery Algorithm Based on Partial Rank Correlation

Aimedat the problem of dynamic causal discovery in the era of artificial intelligence, this article combines partial rank correlation coefficients and streaming features in the field of Bayesian network structure learning and proposes a new online streaming feature causal discovery algorithm based on partial rank correlation named the partial rank casual discovery streaming feature based algorithm. This algorithm is not only suitable for Bayesian causal structure learning in dynamic feature spaces generated by sequential streams of features but can also effectively process multivariate linear Gaussian and nonlinear non-Gaussian data. We present three main contributions. First, for arbitrarily distributed datasets, which can be generated by the additive noise model, we proved that the partial rank correlation coefficient can be used as the criterion for the conditional independence test and explored the distribution of corresponding statistics. Second, the PCSDSF algorithm redefined the relevance based on partial rank statistic prospects and then redefined conditional dependence or independence. This method can significantly reduce the number of conditional independence tests and achieves a good time performance. Finally, theoretical analysis and several experiments proved the reliability of the algorithm. A simulation showed that on average, the PCSDSF algorithm outperforms existing algorithms in terms of both the accuracy and time performance.
Hong-Ou-Mandel interference of entangled photons generated under pump-tight-focusing condition

Hong-Ou-Mandel (HOM) interference is a non-classical effect of photons and plays an important role in quantum optics. The &lt;i&gt;β&lt;/i&gt;-barium borate (BBO) has a high nonlinear efficiency, and is commonly used to generate biphoton states, thereby exhibiting HOM interference. However, in previous experiments, researchers often used band-pass filters, so the resulting spectrum was directly determined by the band-pass filter. As a result, the original spectrum of the BBO crystal, especially the spectrum under tight focusing, was lack of systematic research. In this paper, the biphoton spectral distribution and HOM interference generated by the BBO crystal under the condition of tight focusing are systematically studied for the first time. Theoretical calculations show that using a lens with 50-mm focusing length, the spectral width of the down-converted photons is increased by 7.9 times that of the non-focused case; the width of the HOM interference fringe is reduced to 1/8, and the visibility of the interference fringe increases from 53.0% to 98.7%. We experimentally prepare the energy-time entanglement state by using type-II BBO crystal and perform HOM interference, thereby obtaining the interference visibility of &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$(86.6 \pm 1.0)$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20211783_M2.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20211783_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;%. The increasing of the HOM visibility is due to the improvement of biphoton's spectral symmetry. In addition, the proposed technique by which different spectral distributions are obtained at different incident angles is expected to be applied to the preparation of high-dimensional qudits in the future.
Expressive power and succinctness of the positive calculus of binary relations

In this paper, we study the expressive power and succinctness of the positive calculus of (binary) relations . We show that, for binary relations, (1) the calculus has the same expressive power as that of three-variable existential positive (first-order) logic, and (2) the calculus is exponentially less succinct than three-variable existential positive logic, namely, there is no subexponential-size translation from three-variable existential positive logic to the calculus. Additionally, we give a more fine-grained expressive power equivalence between the (full) calculus of relations and three-variable first-order logic in terms of the quantifier alternation hierarchy. It remains open whether the calculus of relations is also exponentially less succinct than three-variable first-order logic.
COVID-19 Digital Technology Response in Sub-Saharan African Countries

This chapter investigated the use of digital technologies as a response to the COVID-19 pandemic in Sub-Saharan African countries. The study explored possible ways towards building an open, inclusive, and secure digital ecosystem using digital technologies to create awareness of the coronavirus pandemic. The qualitative approach and critical discourse analysis were adopted to critically analyze the effects of digital technologies in the healthcare sector of the Sub-Saharan African region, while the technology determinism theory was used as the theoretical lens of the study. This study advocated the use of digital technologies as an effective tool to disseminate information about infectious diseases to the citizens of the Sub-Saharan African region.
Modelling the Process of Production of Automotive Gasoline by the Use of Generalized Nets

A generalized net model of the process of production of different grades of automotive gasoline is constructed. The processes in Lukoil Neftohim Burgas, Bulgaria refinery are considered as an example. The model can be used for synchronization and optimization of the process. It is simulated in GN IDE (Generalized Net Integrated Development Environment) – a platform independent software for constructing and simulating generalized nets models.
The Application of Internet Technology in Vocational Education Informatization

Under the background of the new era, higher vocational colleges should improve education in line with the characteristics of social development, so as to improve education quality and promote the development of education informatization. In regard to this, teachers and teaching managers should strengthen the research on internet technology, improve the application level of internet technology, and appropriately modify the mode of teaching, so as to create favorable conditions for the application of internet technology in higher vocational education.
GAN-based Intrinsic Exploration for Sample Efficient Reinforcement Learning

In this study, we address the problem of efficient exploration in reinforcement learning. Most common exploration approaches depend on random action selection, however these approaches do not work well in environments with sparse or no rewards. We propose Generative Adversarial Network-based Intrinsic Reward Module that learns the distribution of the observed states and sends an intrinsic reward that is computed as high for states that are out of distribution, in order to lead agent to unexplored states. We evaluate our approach in Super Mario Bros for a no reward setting and in Montezuma's Revenge for a sparse reward setting and show that our approach is indeed capable of exploring efficiently. We discuss a few weaknesses and conclude by discussing future works.
Security Analysis of Poisoning Attacks Against Multi-agent Reinforcement Learning

As the closest machine learning method to general artificial intelligence, multi-agent reinforcement learning (MARL) has shown great potential. However, there are few security studies on MARL, and related security problems also appear, especially the serious misleading caused by the poisoning attack on the model. The current research on poisoning attacks for reinforcement learning mainly focuses on single-agent setting, while there are few such studies for multi-agent RL. Hence, we propose an analysis framework for the poisoning attack in the MARL system, taking the multi-agent soft actor-critic algorithm, which has the best performance at present, as the target of the poisoning attack. In the framework, we conduct extensive poisoning attacks on the agent's state signal and reward signal from three different aspects: the modes of poisoning attacks, the impact of the timing of poisoning, and the mitigation ability of the MARL system. Experiment results in our framework indicate that 1) compared to the baseline, the random poisoning against state signal reduces the average reward by as high as -65.73%; 2) the timing of poisoning has completely opposite effects on reward-based and state-based attacks; and 3) the agent can completely alleviate the toxicity when the attack interval is 10000 episodes.
Design and Implementation of Online Intelligent Mental Health Testing Platform

In order to solve the problems of high misevaluation rate and low work efficiency in the process of mental health intelligent evaluation, a method of mental health intelligent evaluation system oriented to the decision tree algorithm is proposed. First, the current research status of mental health intelligent evaluation was analyzed and the framework of mental health intelligent evaluation system was constructed. Then, the mental health intelligent evaluation data were collected and the decision tree algorithm was used to analyze and classify the mental health intelligent evaluation data to obtain the mental health intelligent evaluation results. Finally, specific simulation experiments are used to analyze the feasibility and superiority of the mental health intelligent evaluation system. The experimental results show that the recall rate of each system increases with the increasing number of iterations, and the system has the highest recall rate. Also, it is stable after the number of iterations reaches 20, with good recall and adaptive scheduling performance. The recall rate of comparison system 1 and comparison system 2 fluctuates greatly, and the recall rate is lower than that of the system in this paper. It is proved that the method of the mental health intelligent evaluation system of the decision tree algorithm can effectively solve the problem and improve the accuracy of the mental health intelligent evaluation. The efficiency of mental health intelligent evaluation is improved, and the system stability is better, which can meet the actual requirements of current mental health intelligent evaluation.
Framework for Recommending Data Residency Compliant Application Architecture

Data is a critical asset for organizations. It helps them generate business insights, improves decision making and creates a competitive advantage. Typically, organizations want exclusive control over data for their own advantage. To protect individual and national rights, governments frame data residency regulations. These laws govern the geographical constraints where storage, transmission and processing of data are allowed. Non-compliance to data regulations often lead to serious reper-cussions for organizations, ranging from hefty penalties to loss of brand value. The different variants of data residency constraints such as first copy within country storage poses challenges in designing a regulation-compliant application deployment architecture. In this paper, we propose a framework and multi-criteria decision technique for determining an optimal single cloud or multi cloud architecture. The framework is based on several criteria including permitted data flows as per regulations, data sensitivity and type, availability of cloud providers etc. The framework helps Cloud architects rapidly arrive at a set of deployment architecture options, which can further optimize by the architects.
Deep Learning-based Anomaly Detection on X-Ray Images of Fuel Cell Electrodes

Anomaly detection in X-ray images has been an active and lasting research area in the last decades, especially in the domain of medical X-ray images. For this work, we created a real-world labeled anomaly dataset, consisting of 16-bit X-ray image data of fuel cell electrodes coated with a platinum catalyst solution and perform anomaly detection on the dataset using a deep learning approach. The dataset contains a diverse set of anomalies with 11 identified common anomalies where the electrodes contain e.g. scratches, bubbles, smudges etc. We experiment with 16-bit image to 8-bit image conversion methods to utilize pre-trained Convolutional Neural Networks as feature extractors (transfer learning) and find that we achieve the best performance by maximizing the contrasts globally across the dataset during the 16-bit to 8-bit conversion, through histogram equalization. We group the fuel cell electrodes with anomalies into a single class called abnormal and the normal fuel cell electrodes into a class called normal, thereby abstracting the anomaly detection problem into a binary classification problem. We achieve a balanced accuracy of 85.18\%. The anomaly detection is used by the company, Serenergy, for optimizing the time spend on the quality control of the fuel cell electrodes
Feasible–Infeasible Two-Population Genetic Algorithm to evolve dungeon levels with dependencies in barrier mechanics

This paper presents a search-based solution for the generation of dungeon levels with barrier mechanics and the placement of challenges and rewards in the levels’ rooms. The barrier is a feature that temporarily blocks the player’s progression, where one or more keys will unblock the way. The placement of barriers and keys must satisfy some constraints since the player cannot be stuck during the gameplay. Feasible–Infeasible Two-Population Genetic Algorithm (FI2Pop GA) evolves a grid representation that handles the level dependencies of barrier mechanics. We propose the concept of ordered regions to control the availability of keys better in the levels and procedures to create levels with more diversity in their contents. Data to measure the variety of the generated content is collected based on map linearity, mission linearity, leniency, and path redundancy. We analyzed our results through expressive range analysis, and it shows that our approach can generate a wide variety of playable levels.
LifeLens: Deep Learning Based Application for the Visually Impaired

The swift rise in the field of Deep Learning and Computer Vision is transforming how we approach important problems. This paper aims at aiding the visually impaired with environment navigation using a novel prototype which utilizes prevalent solutions to the Image Captioning and Visual Question Answering problems. The IC model was implemented using two approaches - an encoder-decoder framework that incorporates a visual attention mechanism and a Reinforcement Learning (RL) Based Policy-Value Network. The performance of the two models were evaluated using the BLEU score metric. The visual attention model performed relatively better and attained an average score of 0.47. This model was combined with a Text-to-speech mechanism and was deployed on the Raspberry Pi thus facilitating the visually impaired with environment navigation. Further, a Long Short Term Memory (LSTM) network was implemented for Visual Question Answering applications.
Mutual Recognition Mechanism Based on DVCS Oracle in the Blockchain Platform

The main purpose of this chapter is to present the need to use the mutual recognition mechanism (MRM) of electronic signatures based on the DVCS oracle in the blockchain platform for the global supply chain. The authors begin their research by comparing a single-domain traditional supply chain with a multi-domain global supply chain. In the second case, the necessity of using an MRM electronic signature based on the DVCS oracle is justified. Various options for constructing MRM are discussed. The chapter provides a comparative assessment of the electronic signature validation protocols and the rationale for using the DVCS protocol to implement the blockchain oracle. As a result, the authors propose to use a well-tested software and hardware complex of the Litoria DVCS as a DVCS oracle and illustrate its use with practical examples.