Reviewerly/exHarmony huggingface dataset:
ON THE BASIS OF A BLOCK MODEL FOR THE DEVELOPMENT OF PEDAGOGICAL COMPETENCE OF FUTURE MASTER'S DEGREE PROGRAM PSYCHOLOGY TEACHERS

The article reveals the essence and content of the author's model of developing pedagogical competence of a future psychology teacher. The block model of formation of pedagogical competence of a psychology teacher, which consists of conceptual-value, value-motivational, content-processual and result-criterial blocks, is presented.
A Comparative Study on the Performance of Bio-inspired Algorithms on Benchmarking and Real-World Optimization Problems

Biologically inspired computing, shortly known as bio-inspired computing (BiC), follows the models of biology to solve the problems by computing. The main objective of the study presented in this paper is to present the working principle of three BiC algorithms with different biological bases. The algorithms considered were genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA). These algorithms were implemented, to solve a set of benchmarking problems and a real-world image segmentation problem, and their performance were compared. The performance metrics used for the comparison were the solution obtained (So), number of generations (NoG), and the execution time (ExeTime). It was observed from the results, of benchmarking problems, that PSO has given better solutions followed by SA and GA. For the real-world problem, it was concluded with the results that GA has segmented the image better than SA and PSO.
Solving Operating Room Scheduling Problems with Surgical Teams via Answer Set Programming

The optimization of daily operating room surgery schedule can be problematic because of many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of surgical teams for complete surgical procedures. Recently, Answer Set Programming (ASP) has been successfully employed for addressing and solving real-life scheduling and planning problems in the healthcare domain. In this paper we present an enhanced solution using ASP for scheduling operating rooms taking explicitly into consideration availability of surgical teams, that include a surgeon and an anesthetist in different specialties for the entire duration of the surgery. We tested our solution on different benchmarks with realistic parameters for schedule’s length up to the target 5-days planning. The results of our experiments show that ASP is a suitable methodology for solving also such enhanced problem.
The influence of clutter on search-based learning, long-term memory, and memory-guided attention in real-world scenes: an eye-movement research protocol

Previous studies have shown that visual clutter degrades visual search performance. This performance decrement is also reflected in several eye movement metrics, such as mean fixation duration, scan path length, and first saccade latency. However, whether and if so, how visual clutter might impact other cognitive processes that are important for adaptive functioning, like learning, long-term memory, and attention, remains poorly understood. Here, we present the rationale for the use of a three-stage experimental paradigm combined with eye-tracking to better understand the effects of visual clutter observed in real-world scenes on cognition and eye movement behavior. We also present preliminary behavioral findings on this topic from our lab and discuss areas with significant potential for future research.
A Multi-view Active Learning Approach for the Hierarchical Multi-label Classification of Research Papers

In this paper, we focus on the hierarchical multi-label classification task of scientific papers, which consists in assigning to a paper the set of relevant classes, which are organized in a hierarchy. The difficulty of manually constructing sufficient labeled datasets renders challenging the automatic classification task of research papers according to hierarchical labels. Multi-view active learning is a widely adopted method to address this issue, by iteratively selecting the most useful unlabeled samples for the multi-view classifiers exploiting disjoint data’ views, and querying an oracle on their real labels. However, none of the state of the art studies in this field is proposed for the hierarchical multi-label classification task. In this paper, we propose an effective multi-view active learning framework for the hierarchical multi-label classification task, applied on scientific papers. Our approach adopts a novel selection strategy that relies on both uncertainty and representativeness criteria when selecting the most informative unlabeled samples in each iteration. Experimental results on a real world dataset of ACM research papers show the efficiency of our approach over several baseline methods.
CreepOnto: An Avant-Garde Scheme for Framing and Evaluating Ontologies Using Domain Knowledge of Creep Mechanism

A good analysis about a specific field of study is a basic and essential requirement for clarifying various terminologies. Ultimately, this has led to the need for development of ontologies which has proven to be a great asset for individuals and groups that seek to ameliorate the usage of useful unorganised particulars and regenerate it to ensure optimum reusability of the same. This has been further kindled by the contemporary desire for artificial intelligence based systems on content-related theories and mechanisms related to a particular domain. This has created the need for developed ontologies to build on cohesive reasoning purposes. An important domain under material sciences termed creep mechanism has been conceptualised due to lack of well-defined ontologies based on it. In subsequent sections, various concepts related to the study of interest have been identified, defined, and established using the relations between them. Web Protégé, a widely used tool for developing ontologies, aides to provide a well-structured framework for CreepOnto. A flowchart designed based on various classes and acts as a primary source of information for readers by acting as a ready reckoner. An OWL representation of the established ontology presents itself as a cornerstone in making the foundation of ontology using axioms. Finally, its effectiveness is measured using various qualitative and quantitative performance metrics which resulted in a reuse ratio of 0.84.
Sources and Corpora

Abstract In this chapter, we focus on the sources of financial opinions; we group these sources by the opinion holders: insiders (Sect. 3.1), professionals (Sect. 3.2), social media users (Sect. 3.3), and journalists (Sect. 3.4). Each opinion holder may have his/her own goals when expressing opinions, resulting in different opinions from unique viewpoints.
A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things

This paper presents a scoping review of federated learning for the Internet of Medical Things (IoMT) and demonstrates the limited amount of research work in an area which has potential to improve patient care. Federated Learning and IoMT – as standalone technologies – have already proved to be highly disruptive but there is a need for further research to apply federated learning to the IoMT.
Fast and Robust Segmentation of Images Using FRFCM Clustering with Active Contour Model

The segmentation of images is the basis on which it is immediately implemented by the computer, and within the comprehension of images, it is still in the correct position. For image segmentation, there are distinct approaches. The hottest tool used for image segmentation is grouping. Since the fuzzy c-means clustering (FCM) algorithm is noise-sensitive, the normal feedback of local spatial information is to a function which is objective. The implementation of local spatial knowledge, however, leads to high complexity in computing. If you suggest an FCM algorithm that is better than morphological reconstruction and my brothers’ filter (FRFCM) that is dramatically faster and more stable than FCM, to solve this problem. Second, with the implementation of the morphological reconstruction operation, local spatial imaging information is introduced into FRFCM to take immunity from noise and retain image detail. Second, based on the distances between the walls in the room premises and the grouping centers, the alteration of the membrane information whether it is replaced by a local membership filter which depends only on the membership division's space neighbors. Using the active contour model (ACM) for detailed segmentation and robustness to optimize the FRFCM clustering outcomes. With active contour model efficiency, which is superior to the current FCM algorithm, the FRFCM is proposed
The Cybersecurity Aspects of New Entities Need a Cybernetic, Holistic Perspective

In our connected world security and proof (evidence constituted in Verifiable Credentials (VC, W3C)) is distributed over what an individual can attest, what my objects tell about me (that is why AI = inferences from that data, is so important), and my behavior: “apply shaving foam” is a number in coelition.org. It is clear that we can no longer isolate the notion of security as in securing devices or securing infrastructure. In this brief article which is the background to a number of workshops that the authors and the Journal will host together, we sketch what we believe to be the end of a paradigm of a government model that has outsourced capabilities to the market. It is in the process of privatizing its last public capability: identity management. This is causing tremendous stress in systems, services, organizational procedures, and individuals. We propose a holistic perspective, distributing security at two points: at the device level and a moral movement at a societal level. As a time out to create room to discuss this broadly, we propose a particular model of SSI and disposable identities.
Humanities quantorium as a technological digital environment

The purpose of the article was to formulate a general problem and clarify the hypothesis of research on the design of a humanitarian quantorium as a technological digital environment for the formation of teacher competencies in the subject areas “History” and “Social Sciences”, to determine the set and content of teacher competencies in the field of applying humanitarian education technologies. The leading research methods described in this article were the method of foresight design, the method of expert panels and methods for the diagnostic study of occupational deficiencies. The materials presented in the article contain a description of the results of a pilot study on identifying professional deficiencies of a history and social science teacher, a generalized, refined set and content of the future teacher’s competencies in the field of applying humanitarian education technologies in a digital environment, a conceptual rationale for equipping a humanitarian quantorium as a technological environment for the formation of teacher competencies. In the substantiation, an integrated approach was applied, which allows to combine the content of humanitarian education, teaching technologies and digital means into a single innovative didactic space. The conclusions are drawn to the peculiarities of the application of the humanitarian quantorium model in the wide practice of professional training of history and social studies teachers for the implementation of the developmental capabilities of the subjects of the humanitarian cycle and improving the quality of humanitarian education.
Automatic Evaluation of Difficult-to-Recognize Characters for Efficient Transcription of Japanese Cursive Characters

In this study, we approached Japanese cursive character (kuzushiji) recognition using machine learning to improve the efficiency of their transcription process. In particular, we discussed methods that automatically distinguish difficult-to-recognize characters or characters that were not included in the training, and put them on hold as unknown characters. We analyzed two methods that deferred recognition of characters: In the first method, characters with low confidence values obtained from the recognition model were put on hold as unknown characters. In the second method, we trained a “miscellaneous class” that combined multiple character classes and deferred the recognition of characters if they were recognized as belonging to the “miscellaneous class.” Experiments showed that by narrowing down the characters to be recognized, a recognition rate of more than 90% was achieved for over 1,500 character classes, confirming the usefulness of temporarily deferring recognition during the transcription process.
A generic framework for decomposing block cipher structure with secret components

In this paper, we present a generic framework for decomposing block cipher structure with secret components. The generic framework is based on the zero-correlation linear cryptanalysis and exploits clusters of zero-correlation linear hulls to establish a system of equations to recover the secret components. In order to solve such a system, we convert the operations into the field of real numbers. By utilizing the coordinate partition technique, both the time and data complexity will be mitigated to make the framework more competitive. We apply the framework to the Feistel structure with SP-type round functions and both the attack rounds and complexity perform better than previous results. Finally, we apply the proposed framework to the Type-1 Feistel structure to demonstrate its generality and improve the previous results against such structure. Our work provides further insights into the security of block cipher structure with secret components.
Measurement Error in Earnings Data: Replication of Meijer, Rohwedder, and Wansbeek's Mixture Model Approach to Combining Survey and Register Data

Meijer, Rohwedder, and Wansbeek (MRW, Journal of Business & Economic Statistics, 2012) develop methods for prediction of a single earnings figure per worker from mixture factor models fitted using earnings data from multiple linked data sources. MRW apply their method using parameter estimates of Kapteyn and Ypma's mixture factor model (KY, Journal of Labour Economics 2007) fitted to earnings data for Swedish workers aged 50+. First, we replicate MRW's empirical analysis using the Swedish model estimates. Second, we check the generality of their empirical finding with a new application. Using estimates of a KY model fit to a linked dataset on earnings for UK employees of all ages, we confirm that MRW's principal findings about the performance of their various predictors of true earnings also hold in this different setting.
The effectiveness of an interactive electronic booklet using the Quick Response Code (QR) and its effect on learning outcomes in the sport of fencing

The research aims to identify the design of an interactive electronic booklet using a quick response code and its effect on both: -1-        The level of cognitive achievement of the fencing course for the second year girls ’students of the Faculty of Physical Education, Sadat City University.2- The level of skillful performance of the skills of  Une-deux, Doublement, Une-deux-trois, Coup-droit-degagement,  for the Female students of the second year, Faculty of Physical Education, Sadat City University.
Social Acceptance of Robots and Artificial Intelligence in Preschooler Childcare

This paper reports social acceptance of a childcare support system with AI/robot through web-based survey and a field trial in Japan. The participants of both the web-based survey and the field trial showed positive attitudes toward the childcare support system with AI/robot. They also preferred a collaboration style between robots and people compared to fully autonomous/teleoperated style, although they preferred both a collaboration style and a fully autonomous style between AI and people.
Study of the relationship of electrical conductivity of porous moisture-saturated soils polluted with inorganic ecotoxicants

The relevance of the study of soils polluted with solutions of acids and alkalis is given in the article. The main theoretical dependences of the electrical conductive properties on the parameters of the soil body are considered. The database of the values of the parameters of the pore space of soils of the Kemerovo region - Kuzbass is given. The electrical properties of water with different salt content, water solutions with different content of acids and alkalis have been determined. A database for interpreting the results of experimental sounding when monitoring changes in the ratio of the pollutant content to the volume of the pore space, depending on the electrical resistivity, has been formed. The relationship between the electrical conductive properties of solutions of acids and alkalis and their content has been established. It was found that with an increase in the content of acids and alkalis in the solution, a monotonic increase in the electric resistivity occurs. It was found that with an increase in the content of acids and alkalis in the solution, a monotonic decrease in the resistivity occurs; this regularity is the physical basis of the method for diagnosing contaminated zones and monitoring the processes of their decontamination.
Grammar as a Network

Chapter 2 provides a background on the use of network models in different scientific disciplines and introduces the general architecture of the grammar network. The proposed network model has two levels of analysis: a lower level, at which linguistic signs, notably constructions, are defined by three different types of associations, or relations: (1) symbolic relations connecting form and meaning, (2) sequential relations connecting linguistic elements in sequence and (3) taxonomic relations connecting linguistic patterns at different levels of abstraction. Together the three relations define the basic units of speech, i.e., lexemes and constructions. Every unit constitutes a (local) network shaped by language use, but these networks also serve as nodes of a higher-level network that involves three other types of relations: (4) lexical relations connecting lexemes with similar or contrastive forms and meanings, (5) constructional relations connecting constructions at the same level of abstraction and (6) and filler-slot relations connecting particular lexemes with constructional schemas.
Organizing Financial Opinions

Abstract In Chap. 10.1007/978-981-16-2881-8_2 , we discuss what we need to extract and understand when analyzing financial opinions. In Chap. 10.1007/978-981-16-2881-8_3 , we discuss where to find financial opinions. This chapter concerns how to extract and understand the financial opinions in these sources.
Asymmetric Attention Upsampling: Rethinking Upsampling For Biological Image Segmentation

Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China Rescaling a feature map could be a key issue of biological image segmentation. Nearly all the existing upsampling strategies concentrate only on local information and attempt to expand the reception field via deepening the network. In this paper, we present Asymmetric Attention Upsampling (AAU) for biological-image segmentation. AAU utilizes the information of low-level feature maps to rescale the high-level feature maps smartly through spatial pooling and attention mechanisms. It consists of two attention variants: Asymmetric Spatial Attention (ASA) and Asymmetric Channel Attention (ACA). The Asymmetric Attention Upsampling Network (AAU-Net) combines several AAU blocks to achieve better segmentation performance. Experiments on the Kvasir-SEG data set reveal the effectiveness of our work. AAU-Net outperforms other state-of-the-art methods for polyp segmentation while not consuming many resources.
Deviations in Online Communications in the Sphere of Youth Education

The transition of the Russian education system in the context of the Covid-19 pandemic to online platforms marked the emergence of new practices of interpersonal communication in the teacher-student relationship system. The article deals with deviant manifestations in the online space of communications. The analysis is based on the study of semantic deviations constructed by young people in the process of communication in a changing reality. The starting point of deviations is considered as a departure from the approved norms of communication in educational system, which leads to such phenomena as trolling, gaming, etc. The influence of virtualization of education on the rethinking of the norms and practices of communication by young people is considered, and the educational space itself, due to its greater openness, becomes a platform for the implementation of these deviant practices. Analysis of the data obtained during sociological research shows the nature of the differentiation of meanings in the field of online communication of young people, which is reflected in the education system.
Vate

Inspired by earlier work on Augur, Vate is a probabilistic programming language for the construction of JVM based probabilistic models with an Object-Oriented interface. As a compiled language it is able to examine the dependency graph of the model to produce optimised code that can be dynamically targeted to different platforms. Using Gibbs Sampling, Metropolis-Hastings and variable marginalisation it can handle a range of model types and is able to efficiently infer values, estimate probabilities, and execute models.
Specificity of Making, Existence and Development of Woodwind (Whistle) Instruments of the Russian Folklore Tradition in the Kuzbass Region

The article aims to substantiate forms and methods of introducing various types of flutes into folklore environment and educational groups of the Kuzbass region. Scientific novelty of the article lies in the fact that the authors analyse the process of making musical instruments in detail, giving credit to the materials used. The authors’ version of breathing and articulation techniques is described. A maker’s advice is also important for teachers and directors of musical ensembles. The attained results have shown that the use of the studied whistle musical instruments in concert practice of the Kuzbass region proves effectiveness of the approach to reconstruction and modernisation of instruments of the Russian folk tradition in the modern era.
Knowledge Management in Human-Robot Interaction Approaches and Processes

Cognitive robots, exhibiting cognitive characteristics and synthesizing knowledge to perform tasks and interacting with humans in both industrial and social settings, have become a big part of modern societies. In this chapter, the authors review the processes and approaches to knowledge management in cognitive robot agents for effective human robot interaction. They present the current state of the art in current robotics technology and human-robot interaction. They state current requirements of cognitive robot agents in human-robot interaction and examine the role of knowledge in human-robot interaction. They finally propose a knowledge management framework for cognitive robots that consist of three main stages: knowledge acquisition and grounding, knowledge representation and knowledge integration, and instantiation into robot architectures.