Reviewerly/exHarmony huggingface dataset:
Digital devices in developing artistic and technical creativity of children with intellectual disabilities. Pedagogical innovation

The article analyses the role of digital devices in the artistic and technical creativity in children and adolescents with intellectual disabilities. Based on examples supported by practical work, the author presents the creative impact of the use of digital tools on art education and describes the concept of creativity, development of a child’s drawing, art and technical education, digital devices, which are a starting point for further considerations. The author describes her work with students with intellectual disabilities and the creation of the digital professionalisation studio, where she combines artistic creativity with work with digital devices. With the appropriate use of specific devices, she supports the development of children’s creativity and sense of being a creator. By introducing pedagogical innovation, the author experiences positive changes in the functioning of students, in the form of a sense of being important in the process of creating and experiencing satisfaction with the developed competencies.
Handicrafts in developing children's creative attitude. Application in educational practice

In the modern world, there is a need to foster creative attitudes from an early age. Guilford’s and Lowenfeld’s research has proven that developing a creative attitude through fields related to art (art and music) and technology is reflected in the development of creativity in later life, e.g., in learning process. Children have an innate need to create, transform and experiment. This is especially noticeable at pre-school and early school age when they are keenly interested in the objects and phenomena surrounding them. Handicrafts use natural materials in an unconventional way and provide children with real tools. They create surprising, new and curiosity-stimulating situations in which children can develop creative attitudes. The article describes the method of conducting classes according to a selected scenario (tic-tac-toe game) in a private kindergarten near Warsaw, where a pedagogical innovation was carried out involving the implementation of handicraft classes in the 2022/2023 school year. Pedagogical observation made it possible to draw the conclusion that handicraft workshops support the development of creative attitudes in preschool children and influence their agency, while the adult’s openness to implementing children’s ideas into practice strengthens children’s self-motivation and sense of coherence.
Psychological aspects of recognising the ability to be critical. Ontological and methodological dilemmas

The article is an analysis of theoretical dilemmas and methodological problems related to the study of the ability to be critical. Based on the literature, it presents an overview of positions related to the issues of ontological opacity and vagueness of this concept, including the following questions: 1) What is criticism (an attitude or a cognitive skill)?; 2) Is it innate or learned?; 3) What is the purpose of criticism – can we talk about one- or two-sidedness of this type of thinking/attitude?; 4) What are the competencies that determine critical abilities, and what hinders or inhibits the activation of critical thinking and critical attitude? Then, attention was focused on methodological issues related to epistemological dilemmas and limitations of methods of researching this construct. Research strategies and tools used in global research are described (primarily the CCTDI and CCTST tests, P. Facione’s The Delphi Report), as well as strategies related to attempts to measure the category of criticism. These analyses were considered as guidelines in the construction of the original Critical Competences Questionnaire and are connected with the preparation, initial phase of its creation related to the operationalisation of this construct and the theoretical and methodological precision of the research project, among others, an outline of the concept of relational treatment of criticism, explaining its personal and contextual selectivity. The summary presents the phases completed within the project, pilot research results and project standardisation and normalisation plans in order to use it as a diagnostic tool in psychology and pedagogy.
THE IMPORTANCE OF EXPERTʼS CONCLUSION FOR THE QUALIFICATION OF CRIMES RELATED TO THE HUMAN DEATH

This article emphasizes the importance of an expertʼs conclusion in determining the cause of death, the degree of injury, and the truth about the consequences. Also, some sources related to the emergence of the need for forensic medicine in cases related to human death and the opinions of world scientists and experts on the legal status of expert examinations appointed and conducted in criminal cases are highlighted. According to the results of the analysis: 1) to expose crimes by achieving adequate protection of individual rights in the conduct of criminal cases, to ensure justice and legality as a result of the truth in the case, and to improve the criminal procedure legislation established in the direction of development strategic program of the Uzbekistan, including amendments and additions to the code; 2) ensuring the reduction of regulatory burden as a result of optimization of legal and regulatory documents envisaged by the Strategy “Uzbekistan–2030” and further improvement of legal regulation of procedural actions and documents related to expert studies; 3) introduction of other procedures related to the issue of compensation for damages in criminal cases resulting in the death of a person; 4) relevant proposals on qualification of the crime of intentional murder of motherʼs own child are included.
SYMMETRICALLY DISTRIBUTED EIGENSTRAINS IN A THIN SHEET OF MATERIAL

A solution for the eigenstress problem in a half-space can be derived based on the following method: stresses in the finite body with traction-free boundary conditions can be derived from those corresponding to the infinitely extended body, by imposing the boundary relief: the stress field induced by the false normal stresses remaining on the boundary from the full-space solution, is subtracted from the latter solution. The method can be further extended to the case of a thin sheet of material, by applying the boundary relief twice, for each of the two limiting parallel planes bounding the sheet. Compared to the half-space case, an additional difficulty arises, as the Boussinesq fundamental solutions needed for the surface relief cannot be directly applied, because the thin sheet is not infinitely extended in one direction as the half-space. A suitable Green’s function for the thin elastic sheet compressed between two equal and collinear concentrated forces is employed in this paper. Four elastic states, three employing the infinite medium solution and one the calculation of stresses in a thin sheet, are superimposed to replicate the needed eigenstresses. Convolution and correlation products are calculated with state-of-the-art numerical methods based on the fast Fourier transform. Spherical and cuboidal inclusions, symmetrically distributed about the sheet mid plane, are simulated with the newly advanced Matlab code, and the results are compared with the ones obtained under the half-space assumption. In the first case, deviations of up to 15% are found, whereas the cuboidal inclusion exhibits more significant differences, due to the eigenstrains concentrated near the sheet limiting planes.
Automated Hematology: Blood Cells Detection and Classification Using Deep Learning Algorithms

A comprehensive assessment of blood cell components serves as a pivotal step in recognizing specific health issues in individuals. The analysis of life-threatening conditions like myeloma and thrombocytopenia underscores the quantification of platelets, erythrocytes (RBCs), and leukocytes (WBCs). Conventional methods, comprising automated analyzers, and manual counting were frequently employed, but they are laborious, lengthy, costly, and require the skills of numerous medical professionals. To mitigate the aforementioned slow and expensive techniques, this study utilizes a deep learning-driven system for object recognition and categorization, employing the You Only Look Once (YOLO) algorithm to count blood cells. The Tiny YOLO model was trained on the BCCD dataset, utilizing a customized architecture designed specifically for detecting the different components of blood cells. Following this, a Convolutional Neural Network (CNN) was utilized to classify the identified WBCs.
License compatibility for Python packages: the FASTEN PyPI plugin

This work presents an early and functioning version of the PyPI plugin. This FASTEN component runs on the developer’s side, aiming at providing licensing and vulnerability analysis on the source code provided. The paper focuses on the licensing components involved in FASTEN, which are helpful to the plugin execution. The workflow at the FASTEN architectural and plugin level is described in detail. We evaluated the PyPI plugin, using a reduced version focusing on the license detection and compliance verification, by analyzing selected GitHub repositories finding several license issues that we commented on in our result evaluation.
Perancangan Kantor Dinas Pendidikan Pemuda Dan Olahraga Kabupaten Sikka Dengan Pendekatan Arsitektur Post-Moderen

The building of youth education office and sport of Sikka District is currently located in the Nasiona l Housing complex on Jalan Mawar. This Kantor building has a simple building and only one floor. The area of the location is very small and there are minimal supporting facilities. In addition, this office is also locatedin a residential area with a fairly high density. Based on the author's initial observations and studies, it needs to be proposed to be relocated to a new place, namely on Eltari Street. The reason for choosing the location with consideration is that the location is the office area of the Sikka Regency Government. Thus, it can facilitate and optimize access to services and relationships between government offices. The planning and design of this office is expected to reflect the fusion and mixing of local architectural values with today's architecture. Therefore, the selection and approach used as a design concept is Post-Modern Architecture.This method in designing is carried out by analyzing the data by means of; 1). quantitative analysis, 2). qualitative analysis, and 3). comparative analysis. Data collection is carried out in the following ways; 1). literature study, 2). interviews, 3). observation. Post-Modern Architecture applied to the concept of designis; a). mcontaining elements of communication that are local or popular, b). urban context, c). metaphorical form, d). is eclectic, e). reflects general aspirations, and f). reapply the technique of ornamentation.
The Effectiveness of Case Method to Students’ Paraphrasing Write Skills

Paraphrasing writing skills are useful for students in writing scientific papers, especially in providing an explanation of a concept using different language from the reference we quote without changing the meaning so as to avoid plagiarism. Writing paraphrases requires critical thinking skills and creativity in processing sentences without changing the meaning. In fact, there are still many students who are not skilled in writing paraphrases. One way to improve students' paraphrasing writing skills is to apply the case method. For this reason, this study aims to determine how much influence the case method has on students' paraphrasing abilities. This research used experimental method with pre-experimental designs. The research design used the One Groups Pretest-Posttest Design. Data collection using paraphrase writing test technique. Analysis of research data by: assessing the results of each student's paraphrasing writing, calculating data normality tests, and conducting hypothesis testing with the Paired Sample T Test using SPSS 23. The results showed that there was a significant effect of the case method on the paraphrasing ability of students of the Management Study Program, Faculty of Economics, Universitas PGRI Palembang. The influence is indicated by the Sig value. (2-tailed) of 0.01 is smaller than 0.05. Students are able to paraphrase using their own language in several techniques, namely changing active sentences to passive ones, shortening sentences, changing sentences to become longer, and replacing words with words that have the same meaning. It's just that there are still some students who paraphrase by only changing some of the words in the quoted sentence by writing the equivalent of the word.
Event Graphs: Syntax, Semantics, and Implementation

This tutorial aims to introduce Event Graphs (EGs), invented 40 years ago by Lee Schruben to allow eventbased modeling of discrete dynamic systems. Their simplicity and naturalness in causality modelling and simulation modelling made EGs popular in research and practice. In a simulation, an event causes state changes in a system as well as other events to happen in the future. EGs provide a parsimonious diagram representation for the Event Scheduling paradigm of Discrete Event Simulation. We first introduce their visual syntax and informal semantics, and then present a recent extension by adding objects to EGs. Our tutorial also includes an introduction to the formal semantics of EGs and a Python implementation for executing EGs.
Virtual Wearable Sensor Data Generation with Generative Adversarial Networks

This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offeringaninnovativealternativetotraditionalmetamodel-basedsimulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs’ capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems.
CLAVS/ODVS: Combining Class/Object Diagrams and DEVS

The Discrete EVent system Specification (DEVS) formalism is a modular discrete-event modeling formalism. It has a formal specification in terms of systems theory and is supported by several efficient and usable simulator implementations. In these implementations, the DEVS formalism is often "grafted" onto an existing Object-Oriented programming language. Examples are C++ in the case of ADEVS and Python in the case of PythonPDEVS. To match this grafting, we present CLAVS, the CLAss diagram and deVS formalism and its instance counterpart ODVS, the Object Diagram and deVS formalism, and their visual notations. These languages use an automaton-like visual notation for Atomic DEVS models and a Class Diagram notation augmented with port information and event structure specification. An implementation of a visual CLAVS/ODVS modeling environment built on draw.io is presented. The use and usefulness of the formalism is demonstrated by means of a simple traffic model whose detailed specification is presented.
Adaptive Ranking and Selection Based Genetic Algorithms for Data-Driven Problems

We present ARGA–Adaptive Robust Genetic Algorithm–to optimize zero-one simulation problems by incorporating input uncertainty. In ARGA, a surviving population of solutions evolves as more information about the high-dimensional problem affected by stochasticity becomes available. A ranking and selection operation in each iteration is enhanced with a debiasing mechanism of fitness values using fast iterated bootstraps and control variates. Debiasing reduces the model risk from input uncertainty bias, obtaining a more accurate ranking of the current surviving solutions. Given the double loop of function evaluations, we adaptively increase budget only if the current population’s proximity to optimality signals the need for a smaller standard error. In that case, we allocate additional replications to the input model of a current surviving solution that is most responsible for risk. The empirical results with a fixed optimization budget demonstrate that ARGA obtains significantly better solutions in a feature selection problem on various datasets.
System Simulation And Machine Learning-Based Maintenance Optimization For An Inland Waterway Transportation System

To continue operations of the inland waterway transportation system (IWTS), the interconnected infrastructure, such as locks and dam systems, must remain in good operating condition. However, as the IWTS ages, unexpected disruptions increase, causing significant transportation delays and economic losses. To evaluate the impacts of IWTS disruptions, a Python-enhanced NetLogo simulation tool is developed, where extreme natural events are also considered and characterized by a spatiotemporal model. Utilizing this tool, optimal maintenance strategies that maximize cargo throughput on the IWTS are determined via deep reinforcement learning. A case study of the lower Mississippi River system and the McClellan-Kerr Arkansas River Navigation System is conducted to illustrate the capability of the developed simulation and machine learning-based method for IWTS maintenance optimization.
Simulation of a Novel, Low Swap, Sparse Hyper-Dimensional Neural Network Architecture for Anomaly Detection AI AT the Edge

This paper details the simulation and performance results of a Sparse Hyper-Distributed Robust Efficient Neural Network (SpHyRE-Net) architecture that performs anomaly detection for real-world time-series data. SpHyRE-Net is an innovative, novel, low size, weight and power (SWaP) machine learning solution for devices operating at the tactical edge. It utilizes bit operations and sparse hyper-dimensional representations for bio-inspired learning via a Hebbian-like rule that results in a combined power-latency reduction of 2-orders of magnitude over ordinary deep networks. The paper details the application of SpHyRE-Net to real-world cell-traffic datasets as well as the simulation requirements needed to minimize latency and memory use. Also discussed are the mechanisms necessary for implementing the architecture on an FPGA as a precursor to realization on a neuro-morphic ASIC with ultra-low power profile.
Voice Modulation through Imaginative Exercises

The present article proposes an analytical cartography of voice exercises from the perspective of the material force through which they act at the psychosomatic level. The analyzed exercises are identified in the most known and used training methods developed throughout the 20th century. The analysis of exercises follows the effect of imagination in the development of qualities such as: flow, lightness, efficiency. The maximization of vocal qualities requires awareness of the interdependencies between body-voice- imaginary stimulus through training. The types of training and their applicability will produce results, being correlated with the specific disciplines of the actor’s art that integrate processes such as body awareness and the influence of the imaginal stimulus in the creative act. The various methods of learning and assimilating vocal expression techniques are recommended to be practiced in an individual system. The aim is to concretely and systematically follow the progress made by the practitioner under the assistance of the vocal coach.
Advanced Privacy-Preserving Federated Relationship Recommendation

Federated relationship recommendation is a growing technology trend in future intelligent system. Over the recent years, federated recommendation has recorded an exponential growth, leading to millions of intelligent equipments, and still increasing. However, privacy-preserving is an intractable problem in modern digital systems. In this paper, a comprehensive review of privacy-preserving federated recommendation is presented. Particularly, three kinds of federated recommendation methods are reviewed based on matrix factorization, neural network and other algorithms. Moreover, five authoritative datasets and three general evaluation metrics are mainly described. Simultaneously, a serious of outstanding federated recommender systems with related research discussions are provided. Finally, some potential future works will be tried to improve the application performance.
New Optimization: Design of a Neural Network Cat and Dog Recognition Prediction Model

With the advent of the big data era, there has been a surge in research focused on the application of convolutional neural networks (CNNs) and image processing. Similar to how humans effortlessly identify cats and dogs in our everyday lives, modern machines are also capable of performing this task. One effective approach to achieving this is through leveraging Kaggle's automatic cat and dog identification technology. This study aims to tackle the challenge of recognizing cats and dogs in images with complex appearances by developing a Sequential object-based CNN model. To accomplish this, the cutting-edge deep learning framework PyTorch is utilized, along with high-performance GPUs for computational power. The network is trained and tested separately on images of dogs and cats. Experimental results demonstrate that the CNN model achieves remarkably high recognition accuracy and exhibits exceptional performance in distinguishing between different breeds of dogs and cats.
Lateral Maneuver A-Star Path Planning Algorithm of Two UAV Air Combat Confrontation

The lack of autonomous decision ability of UAV has always been the main factor restricting UAV to enter the field of air combat. Using heuristic algorithm to construct the attack path of UAV lateral maneuver can get rid of the dependence on training method, and has strong expansibility in the future, which is easy to optimize. Based on A-star algorithm, a path planning algorithm for UAV lateral maneuver decision-making in medium range air combat is proposed. The algorithm can plan position and speed of UAV. Through the simulation experiment, the feasibility of the algorithm in achieving tactical effect is verified.
Action Recognition in Still Images Based on Cross-Modality and Knowledge Distillation

Action recognition in still images is a challenging task compared to video action recognition due to the limited information provided by a single image. Typically, many approaches for action recognition in still images involve two parts: feature extraction and classification. This paper follows the basic paradigm and proposes a Text-Image Alignment(TIA) method that utilizes a text-image alignment classifier based on ground truth labels to assist recognition. Besides, TIA adopts various prompt templates to improve the generalization ability of the TIA classifier. Additionally, this paper proposes Knowledge Distilllation(KD) training strategy by introducing a pre-trained image classifier that serves as a teacher classifier to improve the performance of TIA classifier. And the way to optimize the performance is to minimize the distance between two classifiers. As a result, a novel method is proposed in this paper. To validate the generalization and effectiveness of TIA-KD, experiments are conducted on two different basic archi-tectures: Transformer and CNN on Stanford40, PascalVOC, and HICO, respectively. The experiment results show that the proposed method outperforms baselines by 1~4.5% on above three datasets, respectively.