Reviewerly/exHarmony huggingface dataset:
A Big Data Infrastructure in Support of Healthy and Independent Living: A Real Case Application

This chapter illustrates how the SMART BEAR project aims to integrate heterogeneous smart devices, including wearables and environmental sensors, to enable the continuous data collection from the everyday life of the elderly, which will be processed by an affordable, accountably secure, and privacy-preserving eHealth platform applying Machine Learning algorithms to deliver interventions such as personalized notifications and alerts to each patient, thus promoting their healthy and independent living.
Beyond Homophily with Graph Echo State Networks

Graph Echo State Networks (GESN) have already demonstrated their efficacy and efficiency in graph classification tasks. However, semi-supervised node classification brought out the problem of over-smoothing in end-to-end trained deep models, which causes a bias towards high homophily graphs. We evaluate for the first time GESN on node classification tasks with different degrees of homophily, analyzing also the impact of the reservoir radius. Our experiments show that reservoir models are able to achieve better or comparable accuracy with respect to fully trained deep models that implement ad hoc variations in the architectural bias, with a gain in terms of efficiency.
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees

Current abstractive summarization models either suffer from a lack of clear interpretability or provide incomplete rationales by only highlighting parts of the source document. To this end, we propose the Summarization Program (SP), an interpretable modular framework consisting of an (ordered) list of binary trees, each encoding the step-by-step generative process of an abstractive summary sentence from the source document. A Summarization Program contains one root node per summary sentence, and a distinct tree connects each summary sentence (root node) to the document sentences (leaf nodes) from which it is derived, with the connecting nodes containing intermediate generated sentences. Edges represent different modular operations involved in summarization such as sentence fusion, compression, and paraphrasing. We first propose an efficient best-first search method over neural modules, SP-Search that identifies SPs for human summaries by directly optimizing for ROUGE scores. Next, using these programs as automatic supervision, we propose seq2seq models that generate Summarization Programs, which are then executed to obtain final summaries. We demonstrate that SP-Search effectively represents the generative process behind human summaries using modules that are typically faithful to their intended behavior. We also conduct a simulation study to show that Summarization Programs improve the interpretability of summarization models by allowing humans to better simulate model reasoning. Summarization Programs constitute a promising step toward interpretable and modular abstractive summarization, a complex task previously addressed primarily through blackbox end-to-end neural systems. Supporting code available at https://github.com/swarnaHub/SummarizationPrograms
Research on pedestrian detection based on multi-level fine-grained YOLOX algorithm

Purpose The purpose of the study is to address the problems of low accuracy and missed detection of occluded pedestrians and small target pedestrians when using the YOLOX general object detection algorithm for pedestrian detection. This study proposes a multi-level fine-grained YOLOX pedestrian detection algorithm. Design/methodology/approach First, to address the problem of the original YOLOX algorithm in obtaining a single perceptual field for the feature map before feature fusion, this study improves the PAFPN structure by adding the ResCoT module to increase the diversity of the perceptual field of the feature map and divides the pedestrian multi-scale features into finer granularity. Second, for the CSPLayer of the PAFPN, a weight gain-based normalization-based attention module (NAM) is proposed to make the model pay more attention to the context information when extracting pedestrian features and highlight the salient features of pedestrians. Finally, the authors experimentally determined the optimal values for the confidence loss function. Findings The experimental results show that, compared with the original YOLOX algorithm, the AP of the improved algorithm increased by 2.90%, the Recall increased by 3.57%, and F1 increased by 2% on the pedestrian dataset. Research limitations/implications The multi-level fine-grained YOLOX pedestrian detection algorithm can effectively improve the detection of occluded pedestrians and small target pedestrians. Originality/value The authors introduce a multi-level fine-grained ResCoT module and a weight gain-based NAM attention module.
Bag-of-Words Similarity in eXplainable AI

eXplainable AI (XAI) does not only lie in the interpretation of the rules generated by AI systems, but also in the evaluation and selection, among many rules automatically generated by large datasets, of those that are more relevant and meaningful for domain experts. With this work, we propose a method for evaluation of similarity between rules, which identifies similar rules, or very different ones, by exploiting techniques developed for Natural Language Processing (NLP). We evaluate the similarity of if-then rules by interpreting them as sentences and generating a similarity matrix acting as an enabler for domain experts to analyse the generated rules and thus discover new knowledge. Rule similarity may be applied to rule analysis and manipulation in different scenarios: the first one deals with rule analysis and interpretation, while the second scenario refers to pruning unnecessary rules within a single ruleset. Rule similarity allows also the automatic comparison and evaluation of rulesets. Two different examples are provided to evaluate the effectiveness of the proposed method for rules analysis for knowledge extraction and rule pruning.
Programmable Silicon Photonic Circuits

Programmable photonics configure the flow of light on a photonic chip using software and electrical actuators. Such flexible chips are useful for prototyping new applications. We discuss the underlying technologies for hardware and software.
Development and Evaluation of Two Approaches of Visual Sensitivity Analysis to Support Epidemiological Modeling

Computational modeling is a commonly used technology in many scientific disciplines and has played a noticeable role in combating the COVID-19 pandemic. Modeling scientists conduct sensitivity analysis frequently to observe and monitor the behavior of a model during its development and deployment. The traditional algorithmic ranking of sensitivity of different parameters usually does not provide modeling scientists with sufficient information to understand the interactions between different parameters and model outputs, while modeling scientists need to observe a large number of model runs in order to gain actionable information for parameter optimization. To address the above challenge, we developed and compared two visual analytics approaches, namely: algorithm-centric and visualization-assisted, and visualization-centric and algorithm-assisted. We evaluated the two approaches based on a structured analysis of different tasks in visual sensitivity analysis as well as the feedback of domain experts. While the work was carried out in the context of epidemiological modeling, the two approaches developed in this work are directly applicable to a variety of modeling processes featuring time series outputs, and can be extended to work with models with other types of outputs.
Javanese Character Recognition Based on K-Nearest Neighbor and Linear Binary Pattern Features

Javanese script (Hanacaraka) is one of the cultures owned by Indonesia. Javanese script is found in temples, inscriptions, cultural and prehistoric sites, ancient Javanese manuscripts, Gulden series banknotes, street signage, and palace documents. Javanese script has a form with an article, and the use of reading above the script is a factor that affects the character detection process. Punctuation marks, clothing, Swara script, vowels, and consonants are parts of the script that are often found in Javanetest scripts. Preserving Javanese script in the digital era, of course, must use technology that can support the digitization of Javanese script through the script detection process. The concept of script image is the image of Javanese script in ancient manuscripts. The process of character detection using certain techniques can be carried out to extract characters so that they can be read. Detection of Javanese characters can be found by finding a testing image. Here, we had been used 10 words images consisting of 3 to 5 syllables with the vowel aiu. Dataset process by Linear Binary Pattern (LBP) feature extraction, which is used to characterize images and describe image textures locally. LBP has been used in r=4 and preprocessing is also done by thresholding with d=0.3. This process can be done using the K-Nearest Neighbor algorithm. In 10 datasets of Javanese script words, an average accuracy value of 90.5% was obtained. The accuracy value of 100% is the highest and 50% is the lowest.
PROFESSIONAL DEVELOPMENT OF ACADEMIC STAFF BY MEANS OF INFORMATION AND COMMUNICATION TECHNOLOGIES: THE UKRAINIAN EXPERINCE

The article explores the issue of professional development and academic staff readiness for implementing innovations. Japanese higher education is well-known for its academic excellence, internationalization, and progressivity. Among several its unbiased advantages there is innovative encouragement, entrepreneurial culture of higher education institutions, collegiate professional support. All these issues are the matter of interest for the Ukrainian education system. The article examines focal points: recent innovation trends in higher education globally, academic staff readiness for implementing innovations in two education systems. It was found that the readiness of scientific and pedagogical staff to introduce key innovations in higher education is built on the basis of specific components of professional competence. In particular, such components focus on the organized acquisition of additional knowledge and skills of professional implementation, aimed at the professional development of specialists in the information society.This concept is related to the desire and the ability of the teaching staff to acquire additional professional knowledge and skills to meet the demand of modern education. This is especially urgent under uncertain socio-economic circumstances, particularly, in Ukraine where teaching is not considered an attractive career to choose. The paper seeks to examine the level of university staff readiness for implementing innovations in Ukraine by means of information and communication technologies. The analysis of university staff readiness to implement innovations was carried out on the base of a remote survey. All the participants attended at least two training programs (advanced training courses, seminars, workshops) in years prior to the survey. The analysis showed the evident need of research and teaching staff to improve their own information and communication competence, and also the need to refer to the modern information and communication technologies. It was found that the use of the information and communication technologies in the educational process of higher education is a favorable factor in the development of readiness of research and teaching staff to implement educational innovations in their own professional activities. In future research, it is recommended improving Ukrainian experience for professional development of academic staff in the dimension of information and communication technologies adapted for different levels of educational management.
An Optimized Monte Carlo Approach for Multidimensional Integrals Related to Intelligent Systems

We study an optimized Monte Carlo algorithm for solving multidimensional integrals related to intelligent systems.Recently Shaowei Lin consider the difficult task of evaluating multidimensional integrals with very high dimensions which are important to machine learning for intelligent systems.Lin multidimensional integrals with 3 to 30 dimensions, related to applications in machine learning, will be evaluated with the presented optimized Monte Carlo algorithm and some advantages of the method will be analyzed.
Digital Transformation of Russian Universities during the Pandemic

The purpose of this study was to identify the main trends in the digitalization of Russian universities, which, recently, have been largely associated with the need to use mixed forms of education against the background of the development of the pandemic. The object of research is the system of higher professional education. The subject is the current trends of digitalization caused by external and internal factors, and the attitude towards them on the part of participants in the educational process. The pandemic has made significant changes not only in the educational process, but also in the management system of the entire knowledge acquisition system. Significant problems of digital inequality were identified not only among students, but also educational institutions. In a situation of forced transition to a mixed and even fully online learning format, the Russian higher education system has proven its viability and effectiveness. Most educational institutions were forced to take advantage of the opportunities of distance learning technologies. In the article, the author uses a classical approach to the study of the problem, based on desk and field research, analysis of secondary and obtaining primary data on the studied problem. The uniqueness of the study lies in the fact that a comparative analysis of the attitude to the digitalization processes of Russian universities before and after the pandemic is presented. To obtain primary information, the author's tools of longitudinal research were implemented and a survey was conducted to study the features of the use of communication technologies in the educational process under the influence of social, economic and political factors, as well as global processes taking place in society, including under the influence of the COVID-19 Pandemic. The results of the study can be practically significant for scientists, specialists, researchers and various government structures directly involved in solving issues related to the education system and higher education.
Coastline matching via a graph-based approach

Abstract This paper studies the problem of unsupervised detection of geometrically similar fragments (segments) in curves, in the context of boundary matching. The goal is to determine all pairs of sub-curves that are geometrically similar, under local scale invariance. In particular, we aim to locate the existence of a similar section (independent of length and/or orientation) in the second curve, to a section of the first curve, as indicated by the user. The proposed approach is based on a suitable distance matrix of the two given curves. Additionally, a suitable objective function is proposed to capture the trade-off between the similarity of the common sub-sequences and their lengths. The goal of the algorithm is to minimize this objective function via an efficient graph-based approach that capitalizes on Dynamic Time Warping to compare the two subcurves. We apply the proposed technique in the context of geometric matching of coastline pairs. This application is crucial for investigating the forcing factors related to the coastline evolution. The proposed method was successfully applied to global coastline data, yielding a bipartite graph with analytical point-to-point correspondences.
Recovery Scheme with Resource Abstraction in Multi-Domain Quantum-Key-Distribution Networks

A recovery scheme with resource abstraction in multi-domain quantum-key-distribution networks is proposed to re-supply keys after failures. Simulation results show it can achieve good performance in terms of key distribution success ratio.
Systematization Based on the Physical Characteristics of Whistling in a Broad Sense

Musical whistling competitions have been regularly held in Japan since 2005. The author serves as a judge at some of these competitions. However, defining what constitutes whistling may be problematic and may pose a challenge at the time of judging. Pucker whistling, which is achieved by puckering the lips, is the most common type of whistling. However, other styles may be used in competitions. Recently, K. Harvest introduced 45 different types of whistling methods in a video, which are classified in this study according to the sound source, the fundamental frequency determined by the vocal tract resonance, and the lip-opening characteristics. In addition, we subdivided these 45 types of whistling into 99 categories based on the distinction between blowing sound and sound absorption and the presence or absence of overtone in the generation process. Similar to the speech generation process, the whistle generation process can be regarded as a combination of three actions, that is, sound source generation, vocal tract tuning, and radiation from the lips.
Multiview deep learning-based attack to break text-CAPTCHAs

Completely Automated Public Turing Test To Tell Computer and Humans Apart (CAPTCHA) is a computer program that prevents malicious computer users. Text-CAPTCHA schemes utilize less-computational costs. Hence, they are the most popularly used. This paper investigates the effectiveness of state-of-the-art (SOTA) text-CAPTCHA schemes, proposes a Multiview deep learning system to break them, and highlights their weaknesses. Rather than the usual single-view feature extraction, the proposed model explores correlational features from multiple views to increase the model's generalization and classification accuracy. The model combines convolutional neural networks and recurrent networks to preserve the input text-CAPTCHA's spatial and sequential order. The proposed system has successfully achieved average accuracies ranging from 93.6% to 100%, and the average time to break a text-CAPTCHA scheme ranges from 0.0032 to 0.21 seconds on eight different datasets. Furthermore, an ablation study on 71 human users was conducted to evaluate the effectiveness of the schemes. The results demonstrated that the proposed system effectively outperforms the human users whom the schemes were designed to serve. Lastly, when compared with existing systems, the proposed system outperforms existing SOTA systems with an accuracy gap of almost 40% higher.
Power Analysis for Causal Discovery

Abstract Causal discovery algorithms have the potential to impact many fields of science. However, substantial foundational work on the statistical properties of causal discovery algorithms is still needed. This paper presents what is to our knowledge the first method for conducting power analysis for causal discovery algorithms. The power sample characteristics of causal discovery algorithms typically cannot be described by a closed formula, but we resolve this problem by developing a new power sample analysis method based on standardized in silico simulation experiments. Our procedure generates data with carefully controlled statistical effect sizes in order to enable an accurate numerical power sample analysis. We present that method, apply it to generate an initial power analysis table, provide a web interface for searching this table, and show how the table or web interface can be used to solve several types of real world power analysis problems, such as sample size planning, interpretation of results, and sensitivity analysis.
UML-based Online Interactive Learning Platform Development and Data Specification for Foreign Language Courses

This course not only requires students to master the basic knowledge of IJL, but also requires students to master the application technology of UML through the experiments of this course, and have the basic skills to use UML modeling tools to support the software development process. This course is a course with strong engineering practice. In the traditional practice teaching process, according to the results of system requirements analysis, Rational Rose 2003 software is used to build a model of the system.
Implementation of a binary neural network on a passive array of magnetic tunnel junctions (Conference Presentation)

Magnetic tunnel junctions (MTJs) provide an attractive platform for implementing neural networks because of their simplicity, nonvolatility and scalability. In a hardware realization, however, device variations, write errors, and parasitic resistance will generally degrade performance. To quantify such effects, we perform experiments on a 2-layer perceptron constructed from a 15 × 15 passive array of MTJs, examining classification accuracy and write fidelity. Despite imperfections, we achieve accuracy of up to 95.3 % with proper tuning of network parameters. The success of this tuning process shows that new metrics are needed to characterize and optimize networks reproduced in mixed signal hardware.
Levels of formation of visual competence of future teachers

Introduction. Today, the main informational need of a person is to extract the meaning of the transmitted data in large amounts of information. A popular way to optimize the perception and transmission of information is its visualization. A modern teacher should be ready to convey to the students the necessary amount of knowledge in an accessible form, to increase and maintain interest in the subject being studied. Therefore, for the future teacher, it is relevant to have competencies in the field of perception and development of visual images that determine his visual competence. Understanding what level of formation visual competence corresponds to helps to evaluate the achieved result, to establish strategies for further learning. The purpose of the study is to determine and theoretically substantiate the levels of formation of the visual competence of a future teacher. Materials and Methods. A systematic approach was used as the methodological basis of the study. To achieve the goal of the study, various methods were used: analysis, synthesis, generalization, comparison, formalization, pedagogical design. Results. Four levels of development of visual competence of future teachers are indicated: initial, basic, practice-oriented, professionally-oriented. To determine the student's compliance with a specific level of visual competence, a number of criteria were defined: possession of graphic knowledge and skills, search and analysis of information, selection and analysis of the capabilities of software tools and services, use of the capabilities of graphic editors to build visual images, use of the capabilities of visual tools in professional activities. Compliance indicators are indicated for each criterion. Discussion and Conclusions. The formation of visual competence is an extended process and is carried out in the study of the subject-methodological module, the passage of educational and pedagogical practices, and the performance of research activities. Compliance with the designated criteria at a certain level gives an idea of the preparedness of the future teacher for professional activities.
A novel ultrasonic wave-based analog Fourier transform computing system

The Fourier transform is a powerful tool that has manifold applications in various disciplines. With the rise of new applications, such as real-time image processing, there is a need for more efficient Fourier transformation methods. This has led to a number of advancements in the field of wave-based analog computing, with the development of the ultrasonic Fourier transform most recently. This paper presents an alternative design to existing ultrasonic Fourier transform analog computing systems, with its relative compactness being the main advantage. In this study, its capabilities and limitations are extensively examined.