Reviewerly/exHarmony huggingface dataset:
Barcode-based Navigation Concept for Autonomous Wheelchairs and Walking Frames

After a knee or hip surgery, a fast mobilization of the patient is highly recommended. The best therapy would be individual physiotherapy or guided walks with personal assistance, but unfortunately, this is very quite expensive and clinics are suffering from a lack of well-educated personnel. With RoRo, an autonomous walking frame (rollator), the patient receives walking support, reminders for exercises, and a measurement of the gait and walking parameters. For the navigation purposes of the autonomous rollator within the building of a clinic, a precise indoor navigation concept is needed that even works without a continuous internet connection. This paper describes the location and navigation concept with optical barcodes that are stuck at the top of each floor. The information of the barcodes is coded in the way, that they contain orientation features and are spanning a hierarchical structure.
OntoDRE: An ontology for the requirements engineering decision process

The decision-making process in the initial phase of software development is highlighted by the amount and importance of decisions and how these decisions can impact the entire development cycle and, consequently, the final results. Requirements Engineering (RE) is the sub-area of Software Engineering dedicated to establishing the rules, procedures, and metrics for an adequate understanding of the expectations involving software. In this context, this paper presents an application ontology for formalizing the decision-making process during the RE phase, aiming to provide mechanisms to assist the automation of this process and, consequently, to base the decisions to be taken. As a proof of concept, a case study was carried out to demonstrate the practical use of ontology for creating an application on the Semantic Web. The results attest to the applicability of the ontology, as well as its contribution to the RE decision-making process.
Some topics in English newsmagazines in 2020, with special reference to mineral resources, quality of construction aggregates, and closing a geoscience department

Information collected from geological newsmagazines in 2020 is reviewed. First topic is mineral resources such as mineral commodity summary in USA, porphyry deposits, international cooperation for critical minerals, an environmental analysis of the smelters with a use of tree rings, and mineralogy of lithium. Second topic is the pyrrhotite distribution map in USA. It is prepared for construction aggregates to prevent failing concrete. Third, the event that led to closing a geoscience departure is reviewed.
Unsupervised sign language validation process based on hand-motion parameter clustering

Automatic sign language translation process relies mainly on dictionaries of signs to interpret the right meaning of gestures. Due to the lack of large multi sign language dictionaries covering all the aspect of sign languages, the collaborative approach to create signs becomes essential. In fact, the collaborative sign creation process based on Kinect motion capture tool requires the collaboration of non expert users to make sign language dictionaries. However, due to the availability constraint of sign language experts to validate the created signs and the huge amount of signs to be validated manually, the automatic sign language validation process becomes the most suitable solution. In this paper, we present a new automatic and unsupervised sign validation process based on machine learning techniques applied on sign replicas. Given a set of replicas (records) of the same sign created by different non expert sign language user, our main goal is to select the adequate sign records to be used to generate the closest sign signature compared to the one created by sign language expert. For this aim, we present an automatic sign selection and validation solution based on unsupervised clustering of sign motion parameters related to the different sign replicas. We conducted an experimental study to validate 300 ASL signs based on four unsupervised clustering methods, namely, Kernel PCA Kmeans, GMM, Spectral clustering and kernel Kmeans. We concluded that the use our sign validation process using Spectral clustering method allows us to select the right sign replicas to be used to generate the user sign signature. The use of our unsupervised sign validation process onto 3000 ASL sign replicas (300 sign * 10 replicas) lead us to enhance the R2 score average from 0.4830 without sign validation to 0.9123 with sign validation compared to expert sign signature.
Randomized Smoothing Variance Reduction Method for Large-Scale Non-smooth Convex Optimization

We consider a new method for minimizing the average of a large number of non-smooth and convex functions. Such a problem often arises in typical machine learning problems, but is computationally challenging. We apply an implementable randomized smoothing method and propose a multistage scheme to progressively reduce the variance of the gradient estimator of the smoothed functions. Our algorithm achieves a linear convergence rate. Both its time complexity and gradient complexity are superior to the current standard algorithms for non-smooth minimization as well as subgradient-based algorithms. Besides, our algorithm works well without the error-bound condition on the minimizing sequence as well as the commonly imposed (but strong) smoothness and strongly convexity condition. We show that our algorithm has wide applications in optimization and machine learning problems. As an illustrative example, we demonstrate experimentally that our algorithm performs well on large-scale ranking problems and risk-aware portfolio optimization problems.
Growth and evolution of deep neural networks from gene regulatory networks

A simple population based Evolutionary Algorithm (EA) was used to evolve convolutional neural networks for solving an image classification problem (CIFAR10). Each member of the population was defined by a genome. This work proposes the construction of a genome based closely on the naturel world. The genes within such a genome regulate each other's expression and hence build a gene regulatory network (GRN). In the proposed approach, the genome contains no information from the problem space and could be applied to any application in principle. The genome behaves as an evolved program that grows multi-cellular organisms through a developmental process from an initial single cell. The cellular structure is an intermediate phenotype which is then mapped to its final form, a convolutional neural network in this case. The proposed GRN approach was able to evolve successful networks whose level of performance is comparable to a LeNet5 implementation.
Individual and Socioeconomic Variables as Predictors of Middle School Students’ Intrinsic Reading Motivations

&lt;p style="text-align: justify;"&gt;Individuals’ engagement with reading and maintaining it depends on personal motivations. Having a multidimensional structure, reading motivation is affected by intrinsic factors. Studies on reading motivation can make a significant contribution to identifying students’ intrinsic motivation profiles closely, encouraging them to read, and increasing their reading engagement. The present study aimed to determine the individual and socioeconomic variables predicting middle school students’ intrinsic reading motivations. Employing the predictive correlational design, the study was carried out with 459 students attending six different public schools in the city of Sanliurfa. The participants were determined by stratified purposeful sampling method. The study data were collected by the Intrinsic Reading Motivation Scale and the Personal Information Form. According to the hierarchical regression analysis results, all the models constructed in the study were significant. All of the variables explained the intrinsic reading motivation with a 22% variance explanation percentage. As a result, it can be said that the change observed in intrinsic reading motivation variance mostly stemmed from reading engagement (ΔR&lt;sup&gt;2&lt;/sup&gt;=.12), family (ΔR&lt;sup&gt;2&lt;/sup&gt;=.07), demographic characteristics (ΔR&lt;sup&gt;2&lt;/sup&gt;=.03), and school (ΔR&lt;sup&gt;2&lt;/sup&gt;=.01), respectively. It can be stated that the predictors of the intrinsic reading motivation determined in this study should be taken into consideration by educators.&lt;/p&gt;
Challenges of combining structured and unstructured data in corpus development

Recent advances in the availability of ever larger and more varied electronic datasets, both historical and modern, provide unprecedented opportunities for corpus linguistics and the digital humanities. However, combining unstructured text with images, video, audio as well as structured metadata poses a variety of challenges to corpus compilers. This paper presents an overview of the topic to contextualise this special issue of Research in Corpus Linguistics. The aim of the special issue is to highlight some of the challenges faced and solutions developed in several recent and ongoing corpus projects. Rather than providing overall descriptions of corpora, each contributor discusses specific challenges they faced in the corpus development process, summarised in this paper. We hope that the special issue will benefit future corpus projects by providing solutions to common problems and by paving the way for new best practices for the compilation and development of rich-data corpora. We also hope that this collection of articles will help keep the conversation going on the theoretical and methodological challenges of corpus compilation.
A Robust and Efficient Micropayment Infrastructure Using Blockchain for e-Commerce

The rapid evolving of Internet and e-commerce technology in recent years has led to rapid growth of online shopping which has become a central part of our daily life. Micropayment systems are emerging rapidly in e-commerce sites although their lack of security and robustness. In this paper, we provide a micropayment infrastructure using the blockchain technology for securing and verifying the payment transactions achieved between buyers and sellers. Moreover, our proposed infrastructure provides ways for managing the trust level of the buyer based on his historical purchases, controlling the size of the block of tokens that will be uploaded into the blockchain network for later validation, and estimating the risk of loss to be handled. Furthermore, we propose three trust models that compute the buyer’s trust value. The behavior of the buyer has an impact on the block size and the risk of loss. Our micropayment infrastructure ensures payment transaction authentication, prevention from double-spending and double-selling, prevention from tokens forging, tracing of payment transaction, protection and prevention against cyber attack, and reduction of the delay of transaction verification and the waiting time of the buyer. Finally, we conduct a simulation to evaluate the performance of our three trust models and show how the behavior of the buyer has an impact on the decision made by the auditing entity.
Machine Learning-Based Analysis of Free-Text Keystroke Dynamics

The development of active and passive biometric authentication and identification technology plays an increasingly important role in cybersecurity. Keystroke dynamics can be used to analyze the way that a user types based on various keyboard input. Previous work has shown that user authentication and classification can be achieved based on keystroke dynamics. In this research, we consider the problem of user classification based on keystroke dynamics features collected from free-text. We implement and analyze a novel a deep learning model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU). We optimize the resulting model and consider several relevant related problems. Our model is competitive with the best results obtained in previous comparable research.
Jumple: Interactive Contents for the Virtual Physical Education Classroom in the Pandemic Era

The COVID-19 pandemic has disrupted all aspects of life, including children’s education. As classrooms move to online space, physical education teachers face unique challenges in facilitating physical activity. As a solution, we proposed Jumple, a virtual physical education classroom that allows students to jump and play in a remote learning environment. Artificial intelligence (AI) pose estimation technology is used to detect the position of the body parts and provide real-time feedback. We developed five augmented reality games that provided new possibilities to connect teachers and students.
WordErrorSim: An Adversarial Examples Generation Method in Chinese by Erroneous Knowledge

Deep learning is widely used in many kinds of network applications, but errors in texts challenge the model's performance. In order to study the impact of erroneous knowledge on the Chinese text classification model, in this paper we propose a word-level black box adversarial example generation method called WordErrorSim. The algorithm uses SIGHAN Back-off 2013 and ZDIC to construct erroneous knowledge space. The adversarial examples generate by replacing character with pronounce similar character, shape similar character or other commonly used erroneous character. The adversarial example can achieve the attack without changing the semantic or grammar of the original sentence. Besides, we design and implement Chinese Pronunciation-Shape Similarity (CPSS) algorithm to measure the change between the adversarial example and the original text. We verify the effectiveness of the proposed method with real dataset on Bi-LSTM and TextCNN models. The experiment results show that our method can significantly reduce the classification accuracy of the model.
DistC: Distributed Quantum-safe Consensus for Secure IoT Data Processing

As networks evolve into 5G and beyond, data processing in such networks will be performed in increasingly virtualized deployments. Multiple IoT data streams will need to be securely processed concurrently in such networks with automated consensus processing before being recorded in blockchain ledgers across stakeholders. This paper describes the design, implementation, and performance analysis of a quantum-safe consensus processing system for such data streams. The proposed consensus system, DistC, could be instantiated with a varying number of consensus nodes that can be selected dynamically in emerging virtualized realization of blockchain and IoT data processing systems. DistC utilizes hierarchical consensus with an intra-node consensus for validating endorsements and policies associated with transactions followed by a distributed inter-node consensus across nodes in the blockchain system. A small number of nodes are selected for inter-node consensus to enhance trust and provide fault tolerance in the system while minimizing the energy required for consensus processing in virtualized infrastructure. Performance results with respect to delay and throughput are studied for both intra-node validation and inter-node consensus for 3 different elliptic curves of increasing complexity.
A topological extension of movement primitives for curvature modulation and sampling of robot motion

Abstract This paper proposes to enrich robot motion data with trajectory curvature information. To do so, we use an approximate implementation of a topological feature named writhe , which measures the curling of a closed curve around itself, and its analog feature for two closed curves, namely the linking number . Despite these features have been established for closed curves, their definition allows for a discrete calculation that is well-defined for non-closed curves and can thus provide information about how much a robot trajectory is curling around a line in space. Such lines can be predefined by a user, observed by vision or, in our case, inferred as virtual lines in space around which the robot motion is curling. We use these topological features to augment the data of a trajectory encapsulated as a Movement Primitive (MP). We propose a method to determine how many virtual segments best characterize a trajectory and then find such segments. This results in a generative model that permits modulating curvature to generate new samples, while still staying within the dataset distribution and being able to adapt to contextual variables.
On the use of feature-maps for improved quality-diversity meta-evolution

Quality-Diversity (QD) algorithms evolve a behaviourally diverse archive of high-performing solutions. In QD meta-evolution, one evolves a population of QD algorithms by modifying algorithmic components (e.g., the behaviour space) to optimise an archive-level objective, the meta-fitness. This paper investigates which feature-map is best for defining the behaviour space for an 8-joint robot arm. Meta-evolution with non-linear feature-maps yields a 15-fold meta-fitness improvement over linear feature-maps. On a damage recovery test, archives evolved with non-linear feature-maps outperform traditional MAP-Elites variants.
Introduction: A Hoot in the Light

Our senses are minute windows into an external world, from which we would otherwise be disconnected. They afford us several incomplete pictures that we merge in our minds to provide a rough and ready map to the exterior, the not us. This chapter introduces the inspiration and scope of my research into the overlapping worlds of biology, culture, and persuasion. Inspired by Marshall McLuhan’s often misunderstood statement that the medium is the message, and Jakob von Uexküll’s emphasis on the importance of Umwelten in the worldviews of creatures and how that shapes their interactions with all other living things, the book uncovers the sensory modes of human and nonhuman animal communication and persuasion.
A Decision Based Asymmetrically Trimmed Modified Geometric Mean Algorithm for the Removal of High Density Salt and Pepper Noise in Images and Videos

A decision based asymmetrically trimmed modified geometric mean algorithm (DBATMGMA) for the removal of high density salt and pepper noise in images and videos is proposed. The algorithm uses fixed 3 × 3 windows for increasing noise densities. The algorithm initially checks for the presence of outliers (0 or 255) in the processed pixel. If it holds the outliers, the processed pixel is termed faulty. If the processed pixel is faulty, check for the four neighbors are noisy or not (0 or 255). If all the four neighbors are noisy, then check for all the pixels is noisy in the confined neighborhood. If the entire window is noisy, then the corrupted pixel is replaced by mean of all the elements else the corrupted pixel is replaced by mean of the four neighbors. If all the four neighbors are not noisy, then the faulty pixels are replaced with asymmetrically trimmed modified geometric mean. If the pixels does not hold the outlier, then the pixel is considered undamaged and left unaltered. The proposed algorithm is compared with standard algorithms on an image database. The noise suppression along with information preservation capability of the proposed algorithm was found to be very good both in terms of qualitative and quantitative measures.
On C <sup>2</sup> -finite Sequences

Holonomic sequences are widely studied as many objects interesting to mathematicians and computer scientists are in this class. In the univariate case, these are the sequences satisfying linear recurrences with polynomial coefficients and also referred to as D-finite sequences. A subclass are C-finite sequences satisfying a linear recurrence with constant coefficients.
An evolutionary approach to interpretable learning

Machine Learning (ML) interpretability is a growing field of computational research, of which the goal is to shine a light on black-box predictive models. We present an evolutionary framework to improve upon existing post-hoc interpretability metrics, by quantifying feature synergy, or the strength of feature interactions in high-dimensional prediction problems. In two problem instances from bioinformatics and climate science, we validate our results with existing domain research, to show that feature synergy is a valuable metric for post-hoc interpretability.
Effect of environment on quantum correlation in Heisenberg <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e690" altimg="si2.svg"><mml:mrow><mml:mi>X</mml:mi><mml:mi>Y</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:math> spin model

Quantum correlation of bipartite states (beyond entanglement) in presence of environment is studied using measurement-induced nonlocalities (MINs) for Heisenberg XYZ spin system. It is shown that if the system is allowed to exchange energy with environment, the initial state evolves and settles down to uncorrelated state in asymptotic limit. We have also demonstrated that fidelity based MIN is sensitive to such open system, and so useful for characterizing the correlation of the states.
간호대학생의 코로나19 관련 예방행위 영향요인: 지식, 감염위험지각, 우울, 불안을 중심으로

Purpose: The purpose of this study was to survey the Coronavirus Disease 2019 (COVID-19) related knowledge, risk perception, preventive behaviors, depression, and anxiety levels among nursing students, and to identify factors that influence preventive behaviors. Methods: An online survey was conducted among nursing students from October 7, 2020 to October 12, 2020. The data were analyzed using independent t-test, one-way ANOVA, Pearson's correlation coefficients, and multiple regression. Results: A total of 222 nursing students participated in this study. The correct answer rate for COVID-19 knowledge was 90.0%, risk perception was 5.51±1.26, and the rate for preventive behaviors was 92.5%. The depression score was 6.28±4.63; 59.9% of nursing students had depression, and the anxiety score was 5.56±3.98; 59.9% of nursing students had anxiety. COVID-19 preventive behaviors had a positive correlation with COVID-19 knowledge (r= .30, p< .001), COVID-19 risk perception (r= .18, p= .009), and anxiety (r= .21, p= .001). Factors influencing COVID-19 preventive behaviors by multiple regression were identified as COVID-19 knowledge, anxiety, physical health status before and after COVID-19, grade, practical experience after COVID-19, and COVID-19 risk perception (F = 12.46, p< .001, R2= 25.8%). Conclusion: The results of this research on factors influencing COVID-19 prevention behaviors can be used in the future to develop appropriate health policies for individual and community infectious disease prevention behaviors.
Hierarchical Bayesian Transfer Learning Between a Pair of Kalman Filters

Transfer learning strategies are typically designed in a deterministic manner, without processing uncertainty in the knowledge transfer mechanism. They also require the dependence between the participating learning procedures-Bayesian filters in this work-to be explicitly modelled. This letter develops an approach which relaxes both of these restrictive assumptions. We frame the proposed Bayesian transfer learning technique as fully probabilistic design of an unknown hierarchical probability distribution conditioned on knowledge in the form of an external probability distribution. This yields a randomized design around a base density for transfer learning which has been reported in previous work by the authors. In the Kalman filtering context, this hierarchical relaxation-which induces a knowledge-driven mixture state predictor-significantly improves tracking performance when compared to conventional transfer learning methods.