Reviewerly/exHarmony huggingface dataset:
Sign identification model of the Colombian Sign Language (CSL) alphabet based on Computational Intelligence

Different methods of sign language recognition have been proposed, some based on devices that must be worn by the person to identify signs, these have the disadvantage of having a noisy output, or constant use deteriorates the elements of the instrument. This article proposes a model that allows recognizing the static signs within the LSC alphabet. The signs are captured using a device with an infrared camera called Leap Motion, capable of graphically obtaining the hand that is being displayed in front of it, building a database made up of 38 people. The recognition system is developed using three (3) computational intelligence techniques: Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forests (RF), and a Staking model with the results of individual techniques. Obtaining a recognition percentage of 97.41%, surpassing the SVM by 1.49%, MLP by 3.91% and RF by 4.08%.
The House Edge and the Basic Strategy of Blackjack

The fact about the blackjack is that the dealer gains advantages all the time in the normal casino since the rules can be changed by the casino. The way they change rules affects the probabilities of winning for both players and dealers. Therefore, it is important for the players to learn how could the house edge occur and the strategy to maximize their advantages individually. The theoretical analysis and simulations of the experiment by computer programs, for the sake of data, are the tools for solving the problems mentioned in this paper. The house edge is resulted by difference of the bet and the probability of winning the game, and the basic strategy created slightly advantages for the players in the case that house edge always exists.
Observing Pre-Trained Convolutional Neural Network (CNN) Layers as Feature Extractor for Detecting Bias in Image Classification Data

Detecting bias in data is crucial since it can pose serious problems when developing an AI algorithm. The research aims to propose a novel study design to detect bias in image classification data by using pretrained Convolutional Neural Network (CNN) layers as a feature extractor. There are three datasets used in the research with varying degrees of complexity, those are low, medium, and high complexity. There are Modified National Institute of Standards and Technology (MNIST) Digits, batik collections (Parang, Megamendung, and Kawung), and Canadian Institute for Advanced Research (CIFAR-10) datasets. Then, the researchers make a baseline workflow and substitute a step-in feature extraction with a convolution using the first pre-trained CNN layer and each of its kernels. Then, the researchers evaluate the effect of the experiments using accuracy. By observing the effect of the individual kernel, the research can better make sense of what happens inside a CNN layer. The research finds that color in the image is an essential factor when working with CNN. Furthermore, the proposed study design can detect bias in image classification data where it is related to the color of the image. Detecting this bias early is important in helping developers to improve AI algorithms.
Kelpie

The latest generations of Link Prediction (LP) models rely on embeddings to tackle incompleteness in Knowledge Graphs, achieving great performance at the cost of interpretability. Their opaqueness limits the trust that users can place in them, hindering their adoption in real-world applications. We have recently introduced Kelpie, an explainability framework tailored specifically for embedding-based LP models. Kelpie can be applied to any embedding-based LP model, and supports two explanation scenarios that we have called necessary and sufficient. In this demonstration we showcase Kelpie's capability to explain the predictions of models based on vastly different architectures on the 5 major datasets in literature.
Morphological Teaching Strategies to Enhance Students’ Vocabulary Knowledge and Reading Comprehension

A number of studies have shown that morphological awareness contributes to the improvement of the comprehension of student reading and vocabulary learning, interest in the importance of language acquisition and instruction of morphological awareness is increasing. This research emphasizes the use of morphological techniques used in reading comprehension to enhance the vocabulary awareness of prefixes and suffixes of students and to contribute to the development of vocabulary acquisition. This literature review was conducted to incorporate research results to assess whether such training led to increased understanding of reading and development of vocabulary. First, researchers need to collect relevant information related to morphological and teaching strategies in the teaching and learning process of English. Second, researchers can analyze data regarding the advantages and disadvantages of morphological strategies and instructions in the English teaching and learning process. Third, researchers identified the important role of morphological awareness that has been applied by students and the need to implement morphological strategies to help second language learners to acquire English language skills. Finally, learners can conclude some important things related to the application of morphology learning strategies to engage the teaching and learning process so that learners can achieve learning objectives regularly.
Achieving Light-Weighted Secure Scheme for Communication in a Smart Grid

There are many security risks in the communication between the various nodes of the smart grid.Among these security risks, data leakage is a common one, which occurs in communicating between any two smart meters.In the existing studies, the data leakage is tackled by proposing various security strategies.In this paper, we propose a novel security communication scheme to ensure the security of transmission data between two smart meters against external malicious attacks.Specifically, an authentication scheme based on chaos theory is proposed for verifying the identity trustworthiness of smart meters.In the authentication phase, we use a chaos algorithm and a hash function to compute a shared session key that is used for authentication of both communicating parties, as well as for encryption of private information.Our proposed scheme implements a one-time session approach, i.e., each round of communication keys are unrelated to each other.In relevant validation experiments, we verify that the chaotic algorithm is sufficiently time efficient and compare the computational efficiency with other literature in terms of communication expenses, and the results prove that the proposed scheme is of lightweight nature.Finally we use the Proverif formal verification tool the BAN logic to verify the security as well as the feasibility of the proposed scheme.
Strain distribution on vibrating synthetic and natural saxophone reeds measured with digital image correlation

The mechanical behavior of the reed is an essential part of the sound production in single reed instruments such as the saxophone and the clarinet. To investigate effect of mouthpiece design, reed materials and reed shapes, it is important to understand reed dynamics. In this paper, stroboscopic digital image correlation (DIC) is used to measure displacement and strain distribution of the reed surface as function of time while the reed is vibrating. Measurements are performed on synthetic and natural cane reeds in dry and wet condition. Data are recorded on 30 phase steps within the vibration period. Results show that DIC allows to obtain reliable quantitative results of strain values in both the direction along the reed fiber length as well as perpendicular to the fibers. On the synthetic and the dry cane reed maximal x-strain values, averaged over the visible part of the reed tip, are respectively 0.06% and 0.08%. Maximal y-strain values are respectively 0.07% and 0.09%. On the wet cane reed, x-strain amplitude is smaller (0.06%), but during the measurement time both x-strain and y-strain change strongly because of dehydration artifacts. Over a period of a few minutes, displacements and strains of the wet reed change strongly.
Psychological support of cognitive and psychosocial development of primary schoolchildren in the context of e-Learning

The article presents an analysis of modern research on international educational practices of distance learning from the point of view of psychological support of cognitive and psychosocial development of schoolchildren. There are three stages of the development of these studies: catching up, operational and advanced. In catch-up studies, the authors focus on the analysis of psychological risks arising in the context of global digitalization of education; the negative impact of educational IT products on the cognitive, psychosocial and psychophysical development of a child’s personality is noted. Operational studies are works that are of an ascertaining nature and proceed from the need to adapt digital tools and electronic means to traditional learning in order to increase its effectiveness. The main targets of advanced research are not risks and attempts of psychological adaptation to widespread and newly created digital educational technologies, the main attention of their supporters is paid to understanding the very fact of digital transformation of education. Hybrid forms of educational organization, allowing to comfortably connect virtual and physical spaces, are considered by them as the inevitable perspective of education. The authors of the article see the lack of systematic research, as well as theories and concepts of the mental development of a modern child in the digital world as an obstacle to the progress of research and practical tools for psychological support of schoolchildren in distance education.
Efficient neural network accelerators with optical computing and communication

Conventional electronic Artificial Neural Networks (ANNs) accelerators focus on architecture design and numerical computation optimization to improve the training efficiency. However, these approaches have recently encountered bottlenecks in terms of energy efficiency and computing performance, which leads to an increase interest in photonic accelerator. Photonic architectures with low energy consumption, high transmission speed and high bandwidth have been considered as an important role for generation of computing architectures. In this paper, to provide a better understanding of optical technology used in ANN acceleration, we present a comprehensive review for the efficient photonic computing and communication in ANN accelerators. The related photonic devices are investigated in terms of the application in ANNs acceleration, and a classification of existing solutions is proposed that are categorized into optical computing acceleration and optical communication acceleration according to photonic effects and photonic architectures. Moreover, we discuss the challenges for these photonic neural network acceleration approaches to highlight the most promising future research opportunities in this field.
NL2RT: A Tool to Translate Natural Language Text into Requirements Templates (RTs)

This paper aims to develop a tool for automated translation of NL text into RTs. As a result, we developed a prototype to translate NL text within EARS and RUPP’s RTs. The prototype also computes six quality metrics values before and after processing the NL text into RTs. Preliminary results show improvement in the quality of NL requirements and translation approach. We have evaluated the working process and capabilities of the prototype by applying 16 problem specifications. Demonstration video: https://youtu.be/4Ac3jZpLacc Source code & Artifacts: https://tinyurl.com/nl2rt-git
Should there be a national bibliographic database

Electronic Scientific Library eLibrary.ru And the Russian Science Index of Citation (RSIC) could well become the basis for creating a National Bibliographic Database. But to do this, the RSIC should have regular monitoring of the completeness and reliability of statistical and bibliometric indicators of journals, the quality of procedures for selecting reference groups of journals for building thematic ratings. We welcome the creation of an independent, open, permanent analytical structure that would monitor the quality of scientific products uploaded to the RSIC at a meaningful level, and the adequacy of the constructed RSCI ratings.
SCIENTOMETRIC ANALYSIS OF THE DEVELOPMENT OF DOMESTIC THESIS RESEARCH CONTAINING THE CATEGORY "RURAL SCHOOL" IN THE TITLES

The relevance of the research is due to the increased interest and need of specialists of various categories in studying the history of the formation and development of rural schools in Russia. The research problem is to conduct a scientometric analysis of thesis research containing the category "rural school" in the titles. The purpose of the research is based on the analysis of the research fund, to show the main directions of research activities to disclose the content of the main category - "rural school". The research objectives are to show the role of scientometric analysis as a quantitative assessment of the characteristics of dissertation research, the dynamics of the presentation of rural school problems in the titles of dissertations, to draw conclusions for further study of this problem and to determine the possibilities for the direction of this work. The research methodology is made up of a set of methods of scientific knowledge: scientometric analysis, synthesis, generalization. The research results. Based on the scientometric method, the article examines the study of dissertation topics containing the category "rural school" in their titles, presented in the section "Abstracts of dissertations" of the Unified Electronic Resource of the Russian State Library, which made it possible to fix the given semantic units of the studied scientific and pedagogical content and conduct their scientometric analysis, design further relevant research. Key findings. The quantitative analysis and content of the thematic clusters presented in the article allows us to conclude that the process of turning researchers to this issue is not intensive, orderly and methodologically secure and requires further and comprehensive study in the theoretical and methodological in a practice-oriented field.
On Expressiveness of Visualization Systems' Interfaces

The paper discusses elements of logical models of graphical user interfaces used in both universal and specialized scientific visualization systems.Criteria of expressiveness of programming language that are discussed in "Structure and Interpretation of Computer Programs" book are applied to graphical interfaces.It is shown that graphical interfaces allow users to operate on the same digital substance and with the same logical approaches as in textual programming languages.Both give basic elements, allow their combination, and support the procedures of abstraction.Authors suggest considering these aspects when developing graphical interfaces.This perspective is applied to the following presentation of the paper.The idea of modifiers (also known as behaviors or effects) and the idea of extensions (also known as plugins, modules, and applications) are discussed.Some methods of programming of scene dynamics are presented.Also languages and ontologies of scientific visualization are discussed, e.g.models for editing visualization pipeline: adding data to projects, filtering of that data, and methods of description of data representation on screen.Finally, we discuss additional ideas on systems analysis of visualization systems.
STUDYING THE DEVELOPMENT OF CHEMICAL EDUCATION OF SCHOOLCHILDREN IN SOVIET RUSSIA: METHODOLOGICAL ASPECT

The work considers the features of the methodology of historical and pedagogical research. In the article, from modern scientific positions, a set of methodological approaches and means of organizing an optimal methodological basis for the historical and pedagogical study of the development of chemical education of schoolchildren in Soviet Russia is determined. The approaches used at various levels of methodological knowledge are analysed.
Manajemen Strategi Badan Penanggulangan Bencana Daerah Dalam Penanggulangan Banjir Di Kabupaten Bekasi

Floods are a social problem that often occurs almost every year. Bekasi Regency in recent years has become a regular flood with the number of submerged areas increasing every year and causing losses in various aspects of life. This is quite a concern and raises questions from the public about how the government's role in flood prevention is, so it is necessary to know the strategic management carried out by the BPBD which is the hand of the Bekasi Regency Government in dealing with floods and their prevention and mitigation. This study uses a descriptive method with a qualitative approach, data collection techniques are carried out through interviews, observations, and documentation using strategic management theory according to Wheelen and Hunger (2008) which have four stages: environmental analysis, strategy formulation, strategy implementation, evaluation and control. The results of this study indicate that the strategic management carried out by BPBD in flood prevention in Bekasi Regency has not been optimal. This is because there are still shortcomings and weaknesses both from BPBD itself and from the community, lack of resources and the absence of strong legality regarding disaster risk studies and education that lacks bad habits of people who still throw garbage in rivers so that it can affect the implementation of the program that is used as a strategy by BPBD.
Correction to: Signature2Vec - An Algorithm for Reference Frame Agnostic Vectorization of Handwritten Signatures

In the originally published version of the chapter 12, the name of one of the co-authors was erroneously missed out. The author's name "Bhargav Kurma" has been added to the list of authors.
Hilbert-Huang Mel Frequency Cepstral Coefficient for Speech Stress Recognition System

Today, acoustic and spectral characteristics are commonly utilized to determine stress levels, and they have a high degree of accuracy. Mel frequency cepstral coefficients are the most successful spectral characteristic (MFCCs). On MFCC framework, each window was determined using a Fourier transformation based on its resolution. The window's size, on the other hand, causes frequency resolution issues, particularly for under-stressed speech, since the frequency of each speech can change due to emotional conditions. Hence, it is necessary to analyze the frequency spectrum over time and has an adaptive window. To address this issue, we apply Hilbert-Huang transform into MFCC (called HFCC) feature extraction technique for more robust stress speech recognition system. The extracted features are then used as trained data using neural network (NN) to identify the emotional stress of speaker. We used actual speech stress data from SUSAS Database in all experiments. The experimental result shows that HFCC outperforms MFCC and the existing feature extraction techniques.
ENHANCING THE 5E LEARNING MODEL (ENGAGEEXPLORE-EXPLAIN-ELABORATE-EVALUATE) AMONG UNIVERSITY STUDENTS IN KOSOVO

The aim of this study was to investigate the application of the 5E Learning Model (Engage-Explore-Explain-Elaborate-Evaluate) among university teachers, to understand how teachers deliver each ‘E’ phase at the Faculty of Education, and to evaluate whether they pay similar attention to all phases. The sample of the study consisted of 139 university students who responded to a standardized online questionnaire regarding the 5E Learning Model. The results revealed that all phases of the 5E Model were covered while teaching the tertiary level students; however, the initial phase, the engagement phase, was paid less attention compared to the other ‘E’ phases. The students also considered that most activities were performed in the exploration and explanation phase, reasoning that they are hands-on phases. Although all phases of the 5E Model were demonstrated while teaching, the conclusion is that not all are similarly distributed. The study recommends that the same attention should be paid to all phases of the 5E Learning Model. The practice and implementation of each phase while teaching and learning helps develop life-long learning and teaching skills. Thus, it should be thoroughly introduced, explained and revised for the purpose of its further appropriate and easy application by the students at the Faculty of Education in their future teaching.
Multi-Heuristic A*

We present a novel heuristic search framework, called Multi-Heuristic A* (MHA*), that simultaneously uses multiple, arbitrarily inadmissible heuristic functions and one consistent heuristic to search for complete and bounded suboptimal solutions. This simplifies the de- sign of heuristics and enables the search to effectively combine the guiding powers of different heuristic func- tions. We support these claims with experimental results on full-body manipulation for PR2 robots.
ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation

Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical application. In this work, we study a novel task on text-guided image manipulation on the entity level in the real world. The task imposes three basic requirements, (1) to edit the entity consistent with the text descriptions, (2) to preserve the text-irrelevant regions, and (3) to merge the manipulated entity into the image naturally. To this end, we propose a new transformer-based framework based on the two-stage image synthesis method, namely ManiTrans, which can not only edit the appearance of entities but also generate new entities corresponding to the text guidance. Our framework incorporates a semantic alignment module to locate the image regions to be manipulated, and a semantic loss to help align the relationship between the vision and language. We conduct extensive experiments on the real datasets, CUB, Oxford, and COCO datasets to verify that our method can distinguish the relevant and irrelevant regions and achieve more precise and flexible manipulation compared with baseline methods.
Real Case Studies Toward IoT-Based Cognitive Environments

Communication has been always a vital need for society, because it is a carrier of exchange of experiences that has allowed a more and more rapid evolution in every field of knowledge. As a prerogative of living beings, the “communication paradigm” in recent years has been evolved and adapted to new application domains like those tied to the cognitive environments by exploiting the advancements coming from the fields of artificial intelligence, edge computing, and Internet of Things. In this chapter, we will focus on several approaches used in some research projects with the aim of ensuring effective communications in the context of cognitive IoT environments (CoIoTEs). A CoIoTE is an environment augmented with IoT devices and technologies that autonomously learns how to act on itself and how to adapt based both on the surrounding context and on the needs of people living in it. The aforementioned approaches have been applied in some real operational scenarios.