Reviewerly/exHarmony huggingface dataset:
Integrative nearest neighbor classifier for block-missing multi-modality data

In modern biomedical classification applications, data are often collected from multiple modalities, ranging from various omics technologies to brain scans. As different modalities provide complementary information, classifiers using multi-modality data usually have good classification performance. However, in many studies, due to the high cost of measures, in a lot of samples, some modalities are missing and therefore all data from those modalities are missing completely. In this case, the training data set is a block-missing multi-modality data set. In this paper, considering such classification problems, we develop a new weighted nearest neighbors classifier, called the integrative nearest neighbor (INN) classifier. INN harnesses all available information in the training data set and the feature vector of the test data point effectively to predict the class label of the test data point without deleting or imputing any missing data. Given a test data point, INN determines the weights on the training samples adaptively by minimizing the worst-case upper bound on the estimation error of the regression function over a convex class of functions. Our simulation study shows that INN outperforms common weighted nearest neighbors classifiers that only use complete training samples or modalities that are available in each sample. It performs better than methods that impute the missing data as well, even for the case where some modalities are missing not at random. The effectiveness of INN has been also demonstrated by our theoretical studies and a real application from the Alzheimer’s disease neuroimaging initiative.
PENGEMBANGAN BIG BOOK BERBASIS KEARIFAN LOKAL UNTUK PEMBELAJARAN MEMBACA PERMULAAN DI SD

This study aims to describe the effectiveness of developing big book media based on local wisdom on early reading learning in first grade elementary school students. This study uses a literature review method that focuses on the use of literature in similar research journals using a Research and Development (R&amp;D) approach. Based on the literature review, the results obtained in the form of the conclusion that any research using the big book media shows the effectiveness of the media when used in early reading learning for students in lower grades. Bigbook media makes learning closer to students, fun and meaningful. This media is in accordance with the characteristics and needs of first grade elementary school students and improves reading skills in students as evidenced by the positive response in student learning outcomes in understanding information
Parameter identification of a physical model of brass instruments by constrained continuation

Numerical continuation using the Asymptotic Numerical Method (ANM), together with the Harmonic Balance Method (HBM), makes it possible to follow the periodic solutions of non-linear dynamical systems such as physical models of wind instruments. This has been recently applied to practical problems such as the categorization of musical instruments from the calculated bifurcation diagrams [V. Fréour et al. Journal of the Acoustical Society of America 148 (2020) https://doi.org/10.1121/10.0001603 ]. Nevertheless, one problem often encountered concerns the uncertainty on some parameters of the model (reed parameters in particular), the values of which are set almost arbitrarily because they are too difficult to measure experimentally. In this work we propose a novel approach where constraints, defined from experimental measurements, are added to the system. This operation allows uncertain parameters of the model to be relaxed and the continuation of the periodic solution with constraints to be performed. It is thus possible to quantify the variations of the relaxed parameters along the solution branch. The application of this technique to a physical model of a trumpet is presented in this paper, with constraints derived from experimental measurements on a trumpet player.
Nonadiabatic Geometric Gates with a Shortened Loop in a Superconducting Circuit

Scalable quantum computation demands high‐fidelity gate operation, which is required to be fast and robust. The geometric gate is considered to be robust to certain types of noises, while its duration time is normally longer than a dynamical routine. Herein, a universal nonadiabatic geometric gate with a shortened loop (NAGGSL) in a superconducting circuit is demonstrated. The methods of quantum state tomography (QST) and randomized benchmarking (RB) are used to characterize the gate fidelity, proving that the decoherence effect can be reduced by this protocol. Furthermore, the robustness of this approach against certain errors is demonstrated, indicating that this scheme is promising for future scalable quantum circuits.
Study of positioning estimation with user position affected by outlier: a case study of moving-horizon estimation filter

Many applications which require accurate location point positioning systems utilize global position system for pseudosciences. One of the main challenges faced by the system occurs due to the inherent errors that are a resultant of outliers. This considerably reduces the accuracy of the observations of global position system device. In this paper, we briefly introduce the problem of position estimation when the pseudo range measurements have an outlier. Moving horizon estimation algorithm has been adapted for the simulation result compared with the extended Kalman filter model, which is still imperfect for the case of outlier. The point at which a pseudo range becomes an outlier is considered at a fixed time instance. A simulation example is presented using an existing model with a moving horizon estimator and an extended Kalman filter. The moving horizon filter turns to be more robust than Kalman filtering with presence of outlier under certain choice of tunning parameter
Models and analysis for user-driven reconfiguration of rule-based IoT applications

The Internet of Things consists of devices and software interacting altogether in order to build powerful and added-value services. One of the main challenges in this context is to support end users with simple, user-friendly, and automated techniques to design such applications. IFTTT-style rules are a popular way to build IoT applications as it addresses this challenge. Given the dynamicity of IoT applications, these techniques should also consider that these applications are in most cases not built once and for all. They can evolve over time and objects may be added or removed for several reasons (replacement, loss of connectivity, upgrade, failure, etc.). There is a need for techniques and tools supporting the reconfiguration of rule-based IoT applications to ensure certain correctness properties during this update tasks. In this paper, we propose new techniques for supporting the reconfiguration of running IoT applications, represented as a set of coordinated rules acting on devices. These techniques compare two versions of an application (before and after reconfiguration) to check if several functional and quantitative properties are satisfied. This information can be used by the user to decide whether the actual deployment of the new application should be triggered or not. The analysis techniques have been implemented using encodings into formal specification languages and verification is carried out using corresponding analysis frameworks. All these techniques for designing new applications, analysing the aforementioned reconfiguration properties, and deploying the new applications have been integrated into the WebThings platform and applied on real-world examples for validation of the approach.
SELF-EVALUATION INSTRUMENT FOR MEASURING EDUCATORS’ PEDAGOGICAL DIGITAL COMPETENCE

With the rapid development of digital technologies they are also more widely used in education. It is possible that in future most learning will involve digital solutions. Nevertheless, it is clear that the use of technology in education poses various risks. For the educators to be able to work with technologies, support students' knowledge development in a technology enhanced learning process as well as organize learning remotely and mitigate the risks posed by digital solutions, they need to acquire not only digital skills but also develop pedagogical digital competence. This is a new competence area, so educators need support to ensure technology enhanced learning. To understand the learning needs of educators for the acquisition of pedagogical digital competence, a tool for identifying their learning needs in this area has been developed and tested. It is based on the DigCompEdu framework with its six dimensions divided into 38 indicators that describe the application of pedagogical digital competence in practice. The educators have to evaluate these indicators through their own experience in terms of:(1) how important each indicator is in the educational process and (2) how often they implement this indicator in their teaching practice. The indicators are evaluated through both previously mentioned perspectives using a Likert scale. A sequential research design was used in this study and four consecutive research cycles were implemented. In the first cycle researchers developed the tool based on analysis of scientific papers published on pedagogical digital competence and analysis of already existing tools used for measuring pedagogical digital competence. In the second cycle, the tool was approbated with 30 compulsory education educators of computer science and programming to determine the validity of the tool and improve it for wider use. In the third cycle, the tool was approbated with 7 experts of educator professional development in the field of pedagogical digital competence. A focus group discussion was organized to gather and implement their suggestions on necessary improvements of the tool developed. In the last cycle the research sample consisted of educators from preschool to high school level who entered courses of professional development to improve their pedagogical digital competence and become mentors of educational technologies. 414 educators used the tool to determine their learning needs concerning pedagogical digital competence and set their learning goals for the professional development course. This article provides information on the rationale for the development of the tool, and its approbation. As well as data analysis confirming the validity of the developed tool for measuring educators’ pedagogical digital competence improvement needs.
Distribution probability‐based self‐adaption metric learning for person re‐identification

Person re-identification addresses the problem of pedestrian image matching in a non-overlapped surveillance network. Traditional metric learning methods try to learn a fixed pedestrian images matching metric model. However, existing metric learning-based methods have the problem of overfitting the training data. In order to solve this problem, a sample-specific metric learning-based method is proposed. In the perspective of probability distribution, the over-fitting problem is attributed to the problem that the generalisation ability of projection features is weak. Firstly, the proposed method learns a metric subspace, which projects the raw feature to a discriminative subspace. Then, a projection feature selection method is established based on the probability distribution of positive image pairs. According to the proposed method, the probability that the test samples following the training data distribution is closely related to the adaptability. The proposed projection feature selection method selects different projection features for each individual's similarity distance measure. Finally, extensive experiments on four published datasets verify the effectiveness of the proposed metric learning method. It performs favourably against the comparison methods, especially on the rank-1 rate.
Douglas Walton’s Contributions in Education

Douglas Walton, perhaps the most prolific author in Argumentation theory, has been of a great influence in the fields of Informal logic, Artificial intelligence, and Law. His contributions in the field of educational research, in particular in the field of argumentation and education, are less known. This review paper aims at shedding light on those aspects of Walton’s theory that have received educational researchers’ attention thus far, as well identifying existing lacks of consideration and open paths for future research.
Saliency-enhanced two-stream convolutional network for no-reference image quality assessment

We propose a saliency-enhanced two-stream convolutional network (SETNet) for no-reference image quality assessment. The proposed SETNet contains two subnetworks of image stream and saliency stream. The image stream focuses on the whole image content, while the saliency stream explicitly guides the network to learn spatial salient features that are more attractive to humans. In addition, the spatial attention module and dilated convolution-based channel attention module are employed to refine multiple levels features in spatial and channel dimensions. Finally, the image stream and saliency stream features fusion strategy is proposed to integrate features at the corresponding layer, and the final quality scores are predicted by using multiple levels of integrated features and weighting strategy. The experimental results of the proposed method and several representative methods on four synthetic distortion datasets and two real distortion datasets show that our SETNet has higher prediction accuracy and generalization ability.
Impact of Online Courses on University Student Visual Attention During the COVID-19 Pandemic

Under the threat of COVID-19, many universities offer online courses to avoid student gatherings, which prevent teachers from collecting responses and optimizing courses. This work collected eye movement data to analyze attention allocation and proposed instruction for improving the courses.Subjects were recruited to watch three online courses. Meanwhile, their eye movement data were collected through Dikablis Glasses. Mayer's multimedia cognitive theory was adopted to discriminate the pivotal components of online course, and the Mann-Whitney relevance analysis demonstrated that different representations of courses affected the viewers' attention differently.Three subjects watched three different types of political courses. Course 1, which combined text and explanation, attracted the most attention. Course 2 was shown to be less attractive than course 1 and better than course 3, but the subjects were distracted by the animations in course 2. Course 3, which did not use any technique to present learning content, attracts the least attention from the subjects. A correlation analysis shows that course 1 and course 3 have similar results compared with course 2.Online courses have become a norm during the COVID-19 pandemic. Improving the quality of online courses can effectively reduce the impact of the epidemic on teaching. These experiment results suggest that text + commentary in the design of online courses can effectively attract the attention of the listeners and achieve better learning results. Attention gradually rises in the early stage and then falls after reaching a peak. At this time, the proper introduction of animation can effectively reverse the attention curve, while individual text or commentary results in quickly losing the listener's attention.
Directional Pairwise Class Confusion Bias and Its Mitigation

Recent advances in Natural Language Processing have led to powerful and sophisticated models like BERT (Bidirectional Encoder Representations from Transformers) that have bias. These models are mostly trained on text corpora that deviate in important ways from the text encountered by a chatbot in a problem-specific context. While a lot of research in the past has focused on measuring and mitigating bias with respect to protected attributes (stereotyping like gender, race, ethnicity, etc.), there is lack of research in model bias with respect to classification labels. We investigate whether a classification model hugely favors one class with respect to another. We introduce a bias evaluation method called directional pairwise class confusion bias that highlights the chatbot intent classification model's bias on pairs of classes. Finally, we also present two strategies to mitigate this bias using example biased pairs.
A New Method for WebShell Detection Based on Bidirectional GRU and Attention Mechanism

With the rapid development of hacker technology, network security issues have become increasingly serious. Uploading WebShell is one of the most common attack methods used by network intruders. WebShell escape technology is changing with each passing day, and the traditional method based on feature matching is difficult to accurately detect. In order to detect WebShell more accurately and mitigate the threat caused by WebShell attacks, a WebShell detection method combining bidirectional GRU (gated recurrent unit) and attention mechanism is proposed for the first time. First, the sample is preprocessed to remove useless information such as annotations. Then, the sample is divided into a series of words, the word2vec model is used to obtain the word vector, and finally, the word vector is input into the network for prediction. According to the experimental results, compared with peer methods, the method in this study performs better in performance indicators such as accuracy rate, recall rate, and F1 value. The model not only detects the PHP-type WebShell but also has a good performance on the WebShell written in JSP, ASPX, or ASP languages. The detection accuracy of PHP-type, JSP-type, and ASP-type WebShell reached 99.36%, 99.23%, and 99.87%, respectively, and the recall rate was 98.6%, 99.13%, and 99.56%, respectively.
Development of Punctuation theory in First Quarter of 21&lt;sup&gt;st&lt;/sup&gt; Century: Main Trends

A generalization of the main trends in the development of the Russian and English punctuation theory in the first quarter of the 21st century is presented, namely, relatively stable trends in the analysis of punctuation within the framework of the communicative-pragmatic approach and punctuation activity in its functional diversity. The relevance of the study lies in the following: there is still no idea of what has been done in the theory of punctuation in the first quarter of the 21st century and what are the main trends in its development. The novelty of the study is in the disclosure of how the theory of punctuation is covered through the prism of a communicative-pragmatic approach. An analysis of the development of punctuation theory in the first quarter of the 21st century is based on Russian and English publications from 2000 to 2021. It is shown how in the theory of punctuation of the first quarter of the 21st century, on the basis of a communicative-pragmatic approach, the principles and functions of punctuation, the relationship between the punctuation mark and the punctuation position, the grounds for the priority of punctuation marks, the verbalization of punctuation marks, punctuation variance, the semantic potential of punctuation, textual punctuation are considered. In addition, it is demonstrated how the functional diversity of punctuation activity, including the use of punctuation marks in computer-mediated communication is covered.
Global trends in the education system of high-ranking countries according to PISA-2018

Problem and aim. The modern education system is a complex of developed social institutions that form a system of connections and social norms that correspond to promising tasks focused on personal development and socialization. The PISA program evaluates the educational achievements of students and thereby identifies and interprets new global trends in the field of education. The aim of the article is to study the global trends in the education system of the high-ranking countries according to the PISA-2018. Materials and research methods. Empirical research has three stages. The first stage is the actualization of materials generated according to the Report PISA-2018 from three groups of ratings: TOP-10 OECD countries, TOP-10 OECD partner countries and an integral TOP-10 of all participating countries. The second stage is the study of the TOP-10 of all participating countries, where the focus is shifted to a specific group of indicators that identify global trends: globalization, practice-oriented learning, digitalization, individual educational track, sustainable development, social climate and emotional intelligence. At the third stage, a comparative analysis of global trend indicators was carried out. The empirical study included descriptive statistics (range of variation, standard deviation and coefficient of variation) to assess the homogeneity of the group of countries identified by the authors and the degree of difference between them. Research results. TOP-10 countries of the world with the best education systems according to PISA-2018 were selected, and those that maximally reveal the global trends in education systems in the high-ranking countries were identified. The highest indicators were found: practical orientation of education in Estonia – 100%; having digital devices at home in Finland – 82.6%; individual educational track (teacher's agreement with the student's opinion) in Chinese Taipei – 82.4%; emotional intelligence and a sense of belonging to school in Korea – 78.7%; social climate and parental participation in school activities in China (Beijing – Shanghai – Jiangsu – Guangdong) – 52.3%. Discussion and conclusion. The infiltration of global trends in the educational activities of a modern school makes it competitive in the context of global digitalization and strengthens the position of the entire country in the PISA ratings. All countries of the world recognizing the high role of education in the development of their country are introducing new digital technologies and improving existing ones. As a result, a picture emerges in which the leading countries of the world set global trends for digitalization and globalization of education, and other countries are implementing them.
OWGC-HMC: An Online Web Genre Classification Model Based on Hierarchical Multilabel Classification

Web genre plays an important role in focused crawling, web link analysis, and contextual advertising. In this paper, web genre is defined as the functional purpose and the information type contained in the website. The intelligent classification of web genre can predict the content and functional type of website. However, there are several critical challenges to solve the web genre classification problem: lack of web genre classification dataset and efficient web genre classification mechanism. To improve web genre classification performance, we crawled Chinese websites of different web genres and converted crawled data into a hierarchical multilabel classification dataset. A website knowledge graph is constructed based on the relationship of website and meta tag features. Using entity features extracted from the knowledge graph, we propose an online web genre classification model based on hierarchical multilabel classification (OWGC-HMC) to mine the functional purpose of the corresponding website. Experimental results show that our OWGC-HMC model can mine hierarchical multilabel structure of web genre and outperform other web genre classification methods.
Commercialization of Dispelix LBS Waveguides

Dispelix is a global leader in Augmented Reality Waveguide Displays. Dispelix is a fabless company specializing in the design, development and mass manufacturing of diffractive waveguides for use in Augmented and Mixed Reality glasses and headsets. Our solutions revolutionize industrial and consumer AR wearables for today and our vision answers to the future demands of our customers and partners. With our answer to customized waveguide displays and licensing, we turn your AR wearable visions into reality.