Reviewerly/exHarmony huggingface dataset:
Solving and Learning Nonlinear PDEs with Gaussian Processes

We introduce a simple, rigorous, and unified framework for solving nonlinear partial differential equations (PDEs), and for solving inverse problems (IPs) involving the identification of parameters in PDEs, using the framework of Gaussian processes. The proposed approach: (1) provides a natural generalization of collocation kernel methods to nonlinear PDEs and IPs; (2) has guaranteed convergence for a very general class of PDEs, and comes equipped with a path to compute error bounds for specific PDE approximations; (3) inherits the state-of-the-art computational complexity of linear solvers for dense kernel matrices. The main idea of our method is to approximate the solution of a given PDE as the maximum a posteriori (MAP) estimator of a Gaussian process conditioned on solving the PDE at a finite number of collocation points. Although this optimization problem is infinite-dimensional, it can be reduced to a finite-dimensional one by introducing additional variables corresponding to the values of the derivatives of the solution at collocation points; this generalizes the representer theorem arising in Gaussian process regression. The reduced optimization problem has the form of a quadratic objective function subject to nonlinear constraints; it is solved with a variant of the Gauss--Newton method. The resulting algorithm (a) can be interpreted as solving successive linearizations of the nonlinear PDE, and (b) in practice is found to converge in a small number of iterations (2 to 10), for a wide range of PDEs. Most traditional approaches to IPs interleave parameter updates with numerical solution of the PDE; our algorithm solves for both parameter and PDE solution simultaneously. Experiments on nonlinear elliptic PDEs, Burgers' equation, a regularized Eikonal equation, and an IP for permeability identification in Darcy flow illustrate the efficacy and scope of our framework.
Advances and marine applications of optical neural network

With the development of artificial intelligence technology, such as artificial neural networks, the increasing demand for computing drives the upgrading of computing accelerators. It’s known that the semiconductor process is approaching physical limits and the Von Neumann architecture of storage-computing separation affects the computing efficiency, which both lead to the gradual failure of electronic devices to meet application requirements. Optical neural networks (ONNs) can take full advantage of high speed, high bandwidth, high parallelism, and low power consumption of optical transmission to overcome the deficiencies of electronic devices. In this paper, we summarize and analyze previous researches on optical neural networks according to different physical implementations. And we conclude that most studies apply the characteristics of special materials to realize the dense matrix multiplication and nonlinear activation function of ONNs. Less research focuses on the nonlinear characteristics inherent in the optical signal transmission to realize important components of traditional neural networks. ONNs show great potentials in analog computing and information processing, such as marine in-situ imaging and optical receiver of underwater optical communication. And ONN is possible to be a new generation of neural network accelerator. But the large-scale application of ONNs requires more studies in optical implementation of nonlinear activation function and loss function, and accuracy improvement of optical computing.
Is Deflationism Compatible With Compositional and Tarskian Truth Theories?

What requirements must deflationary formal theories of truth satisfy? This chapter argues against the widely accepted view that compositional and Tarskian theories of truth are substantial or otherwise unacceptable to deflationists. First, two purposes that a formal truth theory can serve are distinguished: one descriptive, the other logical (i.e., to characterise the correctness of inferences involving ‘true’). The chapter argues that the most compelling arguments for the incompatibility of compositional and Tarskian theories concern descriptive theories only. Second, two requirements that any deflationist truth theory intended to serve a logical purpose must satisfy are put forward. These requirements, it is argued, suggest that (i) many well-known compositional and Tarskian theories are acceptable from a deflationist standpoint (including CT); (ii) certain other popular theories of truth (including KF and FS) are not similarly acceptable; (iii) there are no conclusive reasons to impose a conservativeness requirement on deflationary theories of truth.
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition

Adversarially trained models exhibit a large generalization gap: they can interpolate the training set even for large perturbation radii, but at the cost of large test error on clean samples. To investigate this gap, we decompose the test risk into its bias and variance components and study their behavior as a function of adversarial training perturbation radii ($\varepsilon$). We find that the bias increases monotonically with $\varepsilon$ and is the dominant term in the risk. Meanwhile, the variance is unimodal as a function of $\varepsilon$, peaking near the interpolation threshold for the training set. This characteristic behavior occurs robustly across different datasets and also for other robust training procedures such as randomized smoothing. It thus provides a test for proposed explanations of the generalization gap. We find that some existing explanations fail this test--for instance, by predicting a monotonically increasing variance curve. This underscores the power of bias-variance decompositions in modern settings-by providing two measurements instead of one, they can rule out more explanations than test accuracy alone. We also show that bias and variance can provide useful guidance for scalably reducing the generalization gap, highlighting pre-training and unlabeled data as promising routes.
Pandemic Parallels: What Can Cybersecurity Learn From COVID-19?

While the threats may appear to be vastly different, further investigation reveals that the cybersecurity community can learn much from the COVID-19 messaging response.
TEACHING CONSTRUCTION AND PROGRAMMING OF ROBOTS IN A DISTANCE FORM

V. Stoffová1, M. Zboran2 1Trnava University, Faculty of Education (SLOVAKIA) 2Comenius University in Bratislava, Faculty of Mathematics, Physics and Informatics (SLOVAKIA)
Optimization of Persons Localization Using a Thermal Imaging Scanner Attached to UAV

With the increasing incorporation of new technologies into everyday life, unmanned aerial vehicles (UAV) have found, in addition to entertainment, also use in practice. The article is focused on optimizing and increasing the efficiency of UAV use in the “Search and Rescue” process. Different search situations, such as rugged terrain, outdoor temperature, area of search, require different procedures when using UAVs. In the research, the authors compared different infrared camera mount settings for specific situations along with different flight path arrangements during a UAV rescue mission. The outcomes are recommendations and a proposed methodology for more effective use of UAVs with thermal imaging in practice, which will help to more quickly locate the person or object sought.
Augmented reality to promote and visualize scientific knowledge in the RNPLS&amp;T’s Open Archive

The author examines library services based on augmented reality technology. The concept of augmented reality comprises various instruments and approaches to their application. The author demonstrates the significance of and prospects for using modern augmented reality technologies to popularize and visualize scientific knowledge accumulated over many years in the library's open archive collections. The scope of practical applications of augmented reality interfaces in the areas of library services and knowledge management systems or Big Data is hard to overestimate. However, the depth of theoretical research in the area indicates the dynamic development and the scale of further research and development in the field of library services and knowledge management systems or Big Data. The author examines the augmented reality instruments as part of ALS IRBIS64+.
Blockchain: An Overview

Blockchain is an emerging technology that started in the cryptocurrency sphere with bitcoin but expanded to include numerous applications. This chapter provides an overview of the book. It begins by identifying the three main components of a blockchain. Next, it discusses the book's purpose, distinguishing features, and its intended audience. The chapter then outlines the book's structure, consisting of 22 chapters divided into four main parts. It offers a brief synopsis of each section and chapter. Finally, it ends with a summary and conclusions.
Towards Assessing the Readability of Programming Error Messages

Programming error messages have proven to be notoriously problematic for novices who are learning to program. Although recent efforts have focused on improving message wording, these have been criticized for attempting to improve usability without first understanding and addressing readability. To date, there has been no research dedicated to the readability of programming error messages and how this could be assessed. In this paper we examine human-based assessments of programming error message readability and make two important contributions. First, we conduct an experiment using the top twenty most-frequent error messages in three popular programming languages (Python, Java, and C), revealing that human notions of readability are highly subjective and dependent on both programming experience and language familiarity. Both novices and experts agreed more about which messages are more readable, but disagreed more about which messages are not readable. Second, we leverage the data from this experiment to uncover several key factors that seem to affect message readability: message length, message tone, and use of jargon. We discuss how these factors can help guide future efforts to design a readability metric for programming error messages.
Critical Traffic Analysis on the Tor Network

Tor is a widely-used anonymity network with more than two million daily users. A prominent feature of Tor is the hidden service architecture. Hidden services are a popular method for communicating anonymously or sharing web contents anonymously. For security reasons, in Tor all data packets to be send over the network are structured completely identical. They are encrypted using the TLS protocol and its size is fixed to exactly 512 bytes. In this work we describe a method to deanonymize any hidden service on Tor based on traffic analysis. This method allows an attacker with modest resources to deanonymize any hidden services in less than 12.5 days. This poses a threat to anonymity online.
Knowledge and behavioral attitudes of the academic community of the high school and university levels towards the prevention of covid-19: a cross-sectional study in Guasave, Sinaloa

The COVID-19 pandemic impacts, worldwide, the daily activities of the population, in view of this, various preventive measures have been adopted to reduce the risks of contagion. In the educational field in Mexico, face-to-face classes have been suspended, adopting the use of electronic platforms; this, to avoid the risks of contagion. Measures like the aforementioned, the use of face masks, gels, healthy distance, face shields, among others, are already part of the daily routine. However, the level of perception, responsibility and behavior in the face of these measures by the various sectors of the population is unknown. In this study, the perception of high school and university students from Guasave, Sinaloa, Mexico is analyzed. To know the aforementioned aspects, a descriptive cross-sectional study was carried out using the convenience sampling technique, applying a survey made up of 18 items of Likert-type, designed on the Google Forms platform, to a sample of 940 students of both sexes, in 10 public and private schools. The results show that, more than 50% of the students of both educational levels, have a perception that deviates from reality, they do not measure the importance of the preventive measures that the health sector recommends to reduce the contagion and lethality rate. Despite recognizing that the viral disease is contagious, they do not perceive the importance of measures such as washing hands with soap, using gels, masks, face masks, maintaining a healthy distance, among other sanitary measures. They assume the measure of confinement as necessary, although it generates stress and a decrease in their academic performance, reflected in low grades. The results found in the studied educational sector indicate that the educational authorities should strengthen the scientific learning of the students in relation to the epidemiological phenomenon of covid-19, in order to contribute to their conduct and implementation of health protection measures and prevention of this scourge.
Low-Cost Assistive System for Deaf People Based on Artificial Vision

Hearing-impaired people have troubleNaranjo, Amanda E. communicating with the rest of society because they do not have an effective and inexpensive means of communication.Alarcon-O, Andrea People who learn sign language find it difficult to locate qualified interpreters who can get their message across.Amancha-P, Giovanny This article proposes the development of a software application,Ortiz-Espinosa, Jose using artificial vision and digital image processing to translate the Ecuadorian Sign Language (ESL) into the written language of the Latin alphabet.Naranjo, Jose E. The objective of this research is to create an economic support system for people with hearing disabilities to improve their autonomy and reduce the cost of medical care. For the creation of the system, digital image processing was performed through four stages: acquisition, preprocessing, the extraction of characteristics, and recognition-translation into the Latin alphabet language. Within the controlled tests conducted in this research, an average processing time of 905.88 ms was obtained, i.e., less than one second for each letter. Regarding the precision in the translation of the system, the efficiency of 97% was obtained.
Quality Diversity Genetic Programming for Learning Decision Tree Ensembles

Quality Diversity (QD) algorithms are a class of population-based evolutionary algorithms designed to generate sets of solutions that are both fit and diverse. In this paper, we describe a strategy for applying QD concepts to the generation of decision tree ensembles by optimizing collections of trees for both individually accurate and collectively diverse predictive behavior. We compare three variants of this QD strategy with two existing ensemble generation strategies over several classification data sets. We then briefly highlight the effect of the evolutionary algorithm at the core of the strategy. The examined algorithms generate ensembles with distinct predictive behaviors as measured by classification accuracy and intrinsic diversity. The plotted behaviors hint at highly data-dependent relationships between these metrics. QD-based strategies are suggested as a means to optimize classifier ensembles along this performance curve along with other suggestions for future work.
A non-overshooting controller for vehicle path following

Unmanned pavement construction is of great significance in China, and one of the most important issues is how to follow the designed path near the boundary of the pavement construction area to avoid curbs or railings. In this paper, we raise a simple yet effective controller, named the proportional-integral-radius and improved particle swarm optimization (PIR-IPSO) controller, for fast non-overshooting path-following control of an unmanned articulated vehicle (UAV). Firstly, UAV kinematics model is introduced and segmented UAV steering dynamics model is built through field experiments; then, the raw data collected by differential global positioning system (DGPS) is used to build the measurement error distribution model that simulates positioning errors. Next, line of sight (LOS) guidance law is introduced and the LOS initial parameter is assigned based on human driving behavior. Besides, the initial control parameters tuned by the Ziegler-Nichols (ZN) method are used as the initial iterative parameters of the PSO controller. An improved PSO fitness function is also designed to achieve fast non-overshoot control performance. Experiments show that compared with the PSO, ZN and ZN-PSO controller, the PIR-PSO-based controller has significantly less settling time and almost no overshoot in various UAV initial states. Furthermore, compared with other controllers, the proposed PIR-IPSO-based controller achieves precise non-overshoot control, relatively less settling time and centimeter-level positioning error in various initial deviations.
Object Description Based on Local Features Repeatability

This chapter describes a methodology for selecting characteristic local features, to facilitate the detection of specified objects on power supply lines in diversified scenes. The objective of this works was to automate a process intended to search for characteristic objects in digital images, utilizing a limited training set. Description of the insulator in the test was based on a set of distinctive local features, which in the individual images of the test set, demonstrated the highest degree of repeatability. Such set allowed the location of insulators in other samples obtained from a diagnostic flight. To verify the method, the verification sets were proposed. To search and describe local features the SURF algorithm was used. The elaborated training set and adopted methodology of the test provided promising results within the analyzed scenario (flight along the power supply line). Analyses that were carried out, aimed at the development of methods supporting the power network maintenance, basing on the advanced techniques of digital image processing.
How to Plan Lessons in the New Normal Education: A Reintroduction to Selected Instructional Design Processes

Amid the continuing risks posed by the Coronavirus Disease 2019 (COVID-19) crisis, teachers are in a state of a predicament as schools have reopened. A problem that has been often raised is how to plan the lessons in the context of ‘new normal’ education. The answer lies in some extant instructional design processes which may have not only been given purposeful attention since the past up to the recent scholarly discussions. Thus, this article reintroduces these selected instructional design processes that may assist the teachers in planning their lessons in the new normal education. The researcher discusses unpacking the curriculum standards, designing the events of instruction, selecting instructional materials, and using constructive alignment. These selected instructional design processes are expected to bring about better responses, thus improve the current work of the teachers in the field amid the tough period.
Pattern Matching Compression Algorithm for DNA Sequences

Scientifically, the DNA contains hereditary information about the biological species. The DNA databases are considered to be the repository of a huge collection of DNA sequences. Ceaseless advancement in DNA researches has led to various key difficulties in the process of transferring, maintaining, as well as storing data. Such a huge size of DNA sequences often results in the requirement of abundant storage space which needs to be handled and minimized with the help of an effective methodology. One such technique being the data compression using which the DNA sequences can be minimized in size thus saving upon space and bandwidth requirement. There is a recommendation of Improved_Compress algorithm which aids in determining DNA compression along with biological sequence’s pattern matching. The matching information can be stored in an offline dictionary. Hence, the proposed algorithm Improved_Compress leads to an average better compression ratio of 89% when compared to the existing algorithm compression ratio through the dictionary-based method and encoded ASCII value with the National Center for Biotechnology Information (NCBI) datasets.
Interactive radio, social network sites and development in Africa: a literature review study

Purpose The technology determinism theory facilitated in assessing the impact of interactive radio and social network sites (SNSs) on development factors such as education, agriculture, health, and governance, by conducting an integrative and comprehensive literature review focusing on African countries. This paper aims to conduct this literature review to provide comprehensive empirical evidence on the impact of interactive radio and SNSs on development in Africa. Design/methodology/approach This study examined articles that were retrieved from online databases including EBSCOhost, Elsevier, Science Direct, SAGE Journals, Springer and Wiley Online Library. The keywords used included interactive radio, radio, development in Africa, SNS, agriculture, education, health, peace and governance. Search phrases were formulated using boolean operators “AND” and “OR.” Findings Study results revealed that interactive radio and SNSs improve knowledge among farmers and allow the dissemination of information on innovative agricultural techniques, which supports the adoption of sustainable practices. Interactive radio promotes political accountability because the strategies provide the voiceless and powerless communities with a platform to express themselves. This paper discovers that the incorporation of SNS with existing multimedia communication facilitates the dissemination of health-related information on illnesses such as Ebola, HIV, hypertension, diabetes and Polio, and interactive radio and SNS promote education among marginalized communities and under-served rural schools. Research limitations/implications The findings on the impact of interactive radio and SNSs do not represent all 54 countries in Africa. Although the studies included in this literature review were conducted in several countries such as South Africa, Nigeria, Somalia, Kenya, Malawi, Ghana, Tanzania, Uganda and Zambia, this limited the generalizability of the findings and recommendations. Also, the other potential limitation is that using the inclusion-exclusion criteria could have resulted in bias when selecting the studies to include in the review. Practical implications The paper might serve as a valuable source of information for students, academics and entrepreneurs where the impact of interactive radio and SNSs on agriculture, education, health and governance, which are core determinants of development in Africa, has been assessed for further case studies in this area. Social implications The use of interactive radio has helped in decreasing health issues caused by a deficiency in vitamin A among children in sub-Saharan Africa. Originality/value The development of sustainable and effective interactive radio programs is dependent on the collaboration of the core stakeholders such as governmental ministries, donor organizations and the mass communication sector. Numerous open sources on technology radio stations are available to employ social media managers to help in the application of knowledge.