Reviewerly/exHarmony huggingface dataset:
FEATURES OF THE DEVELOPMENT OF MATHEMATICAL SKILLS OF FIRST-GRADERS IN DIFFERENT EDUCATIONAL PROGRAMS

Background. Relevance of this study is determined by the increased interest in mathematical education in recent years. Teachers and researchers are concerned about the conditions and means of developing mathematical education. Along with this, there is no consensus on what constitutes mathematical abilities, what is their structure and development. Objective. Th e aim of this study is to compare the development of mathematical skills of fi rst-graders in diff erent educational programs and to identify specifi c to each program features. Sample. Th e study sample consisted of 434 fi rst-graders of Moscow schools (54.8% of boys), among which: 37.1% of fi rst-graders learn programs of increased complexity, 44% — traditional programs, 18.9% — developmental education program. Methods. An author’s set of tasks was developed to diagnose the mathematical skills of fi rst-graders in this study. It included “classical” tasks children must perform regardless of the training program and “specifi c” tasks that test the awareness of meaning of the mathematical actions performed. Results. Th e analysis showed that most of the classical mathematical skills are not related to the character of the program and it developed by all fi rst-graders in the course of training. However, complex arithmetic skills (working with twodigit numbers and searching for an unknown sum component) developed more eff ectively in programs of increased complexity. Specifi c skills related to reasonable action with a number (tasks for a number line or measurement) showed an ambiguous development dynamic. Conclusion. Th is study allows us to form a hypothesis that the selecting students for programs aff ects only the rate of development of a number of classical mathematical skills. Whereas specifi c skills are more dependent on the content of the program. It requires further study.
Penggunaan Media Pembelajaran Kartu Doli Untuk Meningkatkan Hasil Belajar Siswa SMP Pada Materi Listrik

The purpose of the study was to determine the increase in student learning outcomes about electricity in class IX.G through the use of Doli Cards. This research was carried out in a junior high school (SMP Negeri 19 Bogor) in the 2019/2020 school year with the research subjects being class IX.G students. The method used is a classroom action research method which consists of two cycles. In the first cycle the learning was carried out by using Doli Cards in groups without a list of questions, while in the second cycle the use of Doli Cards in groups was accompanied by a list of questions. Each cycle consists of four stages of research, namely planning, implementing actions, observing, and reflecting. The research data were analyzed using comparative descriptive followed by reflection. Comparative descriptive is done by comparing the initial condition data, cycle 1 and cycle 2, both for learning outcomes. The results of this study indicate that: First, the use of Doli Cards can increase students' activities and learning outcomes about electricity in Natural Science subjects in class IX.G. It is proven that the percentage of students in the good learning activity category increased from the initial condition in the first cycle of 62.50% to 84.15 in the second cycle or in the final condition increased by 21.75% from the initial condition. Second, the use of the Doli Card can improve student learning outcomes about electricity in science subjects in class IX.G. It is proven that the percentage of student learning mastery increases from the initial condition of 18.75% to 8556.25 in the first cycle and becomes 93.75% in the second cycle or in the final condition, it increases by 75% from the initial condition.
First Steps toward Voice User Interfaces for Web-Based Navigation of Geographic Information: A Spanish Terms Study

This work presents the first steps toward developing specific technology for voice user interfaces for geographic information systems. Despite having many general elements, such as voice recognition libraries, the current technology still lacks the ability to fully understand and process the semantics that real users apply to command geographic information systems. This paper presents the results of three connected experiments, following a mixed-methods approach. The first experiment focused on identifying the most common words used when working with maps in a web browser. The second experiment developed an understanding of the chain of commands used for map management for a specific objective. Finally, the third experiment involved the development of a prototype to validate this understanding. Using data and fieldwork, we created a minimum corpus of terms in Spanish. In addition, we identified the particularities of use and user profiles to consider in a voice user interface for geographic information systems, involving the user’s proprioception concerning the world and technology. These user profiles can be considered in future designs of human–technology interaction products. All the data collected and the source code of the prototype are provided as additional material, free to use and modify.
Two Stage Multi Modal Deep Learning Kannada Character Recognition Model Adaptive to Discriminative Patterns of Kannada Characters

Objectives: Designing optical character recognition systems for Kannada character is challenging due to higher self-similarity in characters and higher number of character classes.This work addresses the two major problems of reduced accuracy and higher false positives due to higher self-similarity in characters.Methods: This work proposes a two stage multi modal deep learning technique to handle the complexity in Kannada character recognition.The characters are first grouped based on morphological and structural similarity.A novel morphological/structural difference maximization convolution kernel based deep learning modal is trained for each of character group to recognize characters in that group.This divide and conquer strategy reduce the complexity of deep learning model in learning discriminative features for Kannada character recognition.Findings: The proposed two stage multi modal deep learning provides 89% recognition accuracy which is at least 6% higher compared to existing works.The false positives in proposed solution are at least10% lower compared to existing works.Novelty: A novel sector intensity distribution feature specific to curve structure of Kannada characters for deciding the number of groups of characters.Classifier is designed for each group of characters.Classification is done using convolutional neural network with a novel morphological/structural difference maximization convolution kernel to solve the structural similarity problem in Kannada character recognition.
National Digital Infrastructure and India’s Healthcare Sector: Physician’s Perspectives

Patient-centric digital infrastructure can potentially enhance the efficiency of healthcare systems. However, even in developed nations, evidence suggests low adoption rates for such infrastructure and lack of support from clinicians is considered as one of the most critical hindering factors. In this study, we examine physicians' perceptions of the proposed large-scale information technology initiative in India that aims to transform the health sector and provide universal health coverage to all residents of India. We employed the information ecology lens to understand the broader changes in the healthcare system that could result from the initiative. We use focus group discussion and in-depth interviews to comprehend the perceptions of doctors about the initiative. Drawing upon Foucault’s conceptualization of power, we find that physicians, the key stakeholders in this initiative, are skeptical about the changes in the locus of power in the new ecosystem. Specifically, they perceive that knowledge power has shifted from a historical “expert knowledge power” to power related to “data management.” The physicians believe that changes are expected to manifest through monitoring, controlling, and managing the data rather than providing knowledge-based services. We present recommendations to engage physicians' perspectives in implementing large-scale patient-centric digital infrastructure.
Multi-modal pedestrian detection with misalignment based on modal-wise regression and multi-modal IoU

Multi-modal pedestrian detection, which integrates visible and thermal sensors, has been developed to overcome many limitations of visible-modal pedestrian detection, such as poor illumination, cluttered background, and occlusion. By adopting the combination of multiple modalities, we can efficiently detect pedestrians even with poor visibility. Nevertheless, the critical assumption of multi-modal pedestrian detection is that multi-modal images are perfectly aligned. In general, however, this assumption often becomes invalid in real-world situations. Viewpoints of the different modal sensors are usually different. Then, the positions of pedestrians on the different modal images have disparities. We proposed a multi-modal faster-RCNN specifically designed to handle misalignment between two modalities. The faster-RCNN consists of a region proposal network (RPN) and a detector. We introduce position regressors for both modalities in the RPN and the detector. Intersection over union (IoU) is one of the useful metrics for object detection but is defined only for a single-modal image. We extend it into multi-modal IoU to evaluate the preciseness of both modalities. Our experimental results with the proposed evaluation metrics demonstrate that the proposed method has comparable performance with state-of-the-art methods and outperforms them for data with significant misalignment.
Retracted: Blockchain‐based covert software information transmission for bitcoin

Abstract Retraction: [Gaurav Dhiman, Marcello Carvalho dos Reis, Paulo C. S. Barbosa, Victor Hugo C. de Albuquerque, Sandeep Kautish, Blockchain‐based covert software information transmission for bitcoin, IET Software 2023 ( https://doi.org/10.1049/sfw2.12120 )]. The above article from IET Software , published online on 8 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Active Lifelong Anomaly Detection with Experience Replay

Anomaly detection tools present the potential to enhance defense policies and protection against different types of threats, supporting public safety and national security. Lifelong anomaly detection showcases new and challenging scenarios in which models are challenged to automatically adapt to changing conditions without forgetting past knowledge. However, the presence of anomalies in incoming data may significantly impact the robustness of models in such scenarios. Although active learning strategies could be an asset to increase model longevity and robustness, they have never been explored in this context. In this paper, we propose an active lifelong anomaly detection framework for class-incremental scenarios that supports any memory-based experience replay method, any query strategy, and any anomaly detection model. While experience replay allows models to consolidate past knowledge and simultaneously adapt to new knowledge, an active learning module reduces the number of anomalies memorized in the replay buffer. We propose two strategies that automatically identify and remove additional data points that are likely to be anomalies based on the oracle’s feedback. Our experiments on popular host-based and network-based intrusion detection datasets show that our framework can improve the anomaly detection performance of models under low labeling budget constraints.
FedBiKD: Federated Bidirectional Knowledge Distillation for Distracted Driving Detection

Distracted driving behavior is known as a leading factor in road traffic injuries and deaths. Fortunately, rapidly developing deep learning technology has shown its potential in distracted driving detection. Nevertheless, deep learning-based solutions need to collect large amounts of driving data captured by camera sensors in the vehicle, which will cause serious privacy concerns. As a privacy-preserving distributed learning paradigm, federated learning (FL) has achieved competitive performance in many applications recently. Inspired by this, we introduce FL into distracted driving detection tasks. However, we observe that the heterogeneous data distribution across drivers leads to significant performance degradation of the model learned in FL. To address this challenge, we propose a simple and effective federated bidirectional knowledge distillation framework, FedBiKD. Specifically, FedBiKD utilizes the knowledge from the global model in guiding local training to mitigate the issue of local deviation. Meanwhile, the consensus from the ensemble of local models is also employed to fine-tune the aggregated global model for less volatility in training. Our extensive experiments demonstrate the effectiveness of FedBiKD in distracted driving detection. The results show that FedBiKD significantly outperforms other FL algorithms in terms of accuracy, communication efficiency, convergence rate, and stability.
A novel particle swarm optimization based on hybrid-learning model

&lt;abstract&gt;&lt;p&gt;The convergence speed and the diversity of the population plays a critical role in the performance of particle swarm optimization (PSO). In order to balance the trade-off between exploration and exploitation, a novel particle swarm optimization based on the hybrid learning model (PSO-HLM) is proposed. In the early iteration stage, PSO-HLM updates the velocity of the particle based on the hybrid learning model, which can improve the convergence speed. At the end of the iteration, PSO-HLM employs a multi-pools fusion strategy to mutate the newly generated particles, which can expand the population diversity, thus avoid PSO-HLM falling into a local optima. In order to understand the strengths and weaknesses of PSO-HLM, several experiments are carried out on 30 benchmark functions. Experimental results show that the performance of PSO-HLM is better than other the-state-of-the-art algorithms.&lt;/p&gt;&lt;/abstract&gt;
Aspects of Developing Students’ Individual Educational Trajectory

The paper examines the organisational and methodological aspects of individualization behind professional higher education through the implementation of an individual learning trajectory of higher education candidates, taking into account the forced activation of distance learning.It analyses a survey of students on the individual learning trajectory as well as the organisational and methodological approaches to its implementation in one of the typical higher educational institutions of Ukraine.The authors discuss ways of using elements of different types of training in the process of realisation of the student's individual learning trajectory.The conducted analysis allowed them to pinpoint the main organisational and methodological directions of updating the individual educational trajectory.The theoretical basis of the study became the main principles and regulations of higher education organisation.The study used historical-logical and systemic approaches, general scientific and special methods, in particular: statistical (when analysing and interpreting data from higher education students on the individual trajectory of education); monographic and abstract-logical methods (when analysing the influence of individualisation of students' set of competences on the format of higher education); methods of observation and generalization (when discussing the use of elements of various forms of education in the implementation of the students' individual learning trajectory); methods of systemic analysis and synthesis.As a result, the directions for the development of the means of implementing the individual learning trajectory of higher education students in specialty 073 "Management" were formed.
Design of a Genetic based Optimized Fuzzy Logic Controller for Enhanced Trajectory Tracking Accuracy of a 3P Robot

Robot positioning precision is critical in industrial automation. This research attempts to analyze three distinct approaches for controlling a 3P robot. The designed controllers are position PID (P-PID), position and velocity PID (PV-PID) and genetic algorithm optimized fuzzy logic controller (GA-FLC). In this regard, the mentioned system is initially described, then a position PID controller is applied to the system. In the next step, an attempt has been made to reduce the system error by adding a PID on the velocity to the previous controller as a feed forward term. Then the fuzzy logic controller (FLC) is expressed in which the input variables are the position and velocity error signals and the output is the required force desired path. Ultimately by means of genetic algorithms optimized fuzzy membership functions are achieved. The proposed method outperforms its conventional counterpart and enhances robot precision for applications that require high positioning accuracy, such as riveting, drilling, and precise assembly. The robot modeling and control design were simulated using MATLAB and Simulink software, with the obtained numerical results serving as the criterion for evaluation.
Sentiment Analysis on Twitter Social Media towards Climate Change on Indonesia Using IndoBERT Model

The phenomenon of climate change is a change in temperature and weather patterns in the long term. This incident became a frightening specter for everyone because consciously or unconsciously the bad effects of climate change are already in sight. This has become an urgency for all levels of society so that this topic has become quite hot on Social Media, especially on Twitter. The topic of climate change in Indonesia on Twitter Social Media can be analyzed so that it can be seen how people's sentiments towards this phenomenon. This research utilizes the Transformer architecture, namely IndoBERT, IndoBERT itself is the development of the BERT architecture by the IndoNLU team which has 74 million words from various Bahasa Indonesia sources. Therefore, this method was chosen in the hope of helping sentiment analysis on the topic of climate change so that public sentiment can be mapped. The test results obtained an F1-Score values of 95.6% with a tuning parameter of 0.00002 learning rate and 16 of batch size. Hopefully the results of this research can be used in future research.
Assessment of digital transformation in the education system of Kazakhstan

Today, the issue of e-learning is very relevant. The concept of "informatics" has entered all spheres of our life, gradually transforming many processes of activity from mechanized to automated, with the help of information systems. In the modern world, we can no longer imagine many life cycles without informatization or digitalization. Digital transformation is the introduction of modern technologies into the business processes of an enterprise. This approach implies not only the installation of modern equipment or software, but also fundamental changes in approaches to management, corporate culture, and external communications. Digitalization of processes is relevant not only at the level of individual enterprises, but entire industries choose this path of development for themselves as the only opportunity to meet the rapidly changing conditions of the surrounding world. Thanks to this, the digital transformation of industry, trade, public sector, education, and other spheres is already changing the life of every person and every company. The article examines the current state of digital transformation in the education system based on the implementation of e-learning in the Republic of Kazakhstan, identifies the main problems in the implementation of state programs for digitalization of the national education system as a whole. The purpose of the article is to outline measures for the development of the education system in the direction of digitalization of processes based on the assessment of the implementation of e-learning.
Data Center Based on Cloud Computing Technology

With the rapid development of Internet applications, the impact on the development of data centers is huge. Domestic data centers attach great importance to the acceptance of cloud computing technology and the construction of application systems. Nowadays, data centers can be effectively transformed into cloud computing development. The operating support environment has become the main consideration and focus of today's data center development. Under the concept of cloud computing, this article analyzes and builds a new data center that is more in line with the needs of resource management and information construction. Taking the development of data centers based on cloud computing technology as the research object, building data centers through cloud computing technology realizes the acquisition and organization of data and makes full use of resources. A new information resource management system with functions such as classification and query of data, overall processing and analysis of data, backup of data, information management and services has been realized. Before using the cloud computing model, the network deployed a total of 40 virtual servers, and the average CPU utilization rate was less than 40%. Since the establishment of the data center model in this article, the utilization rate of the processor has stabilized at around 95%. Therefore, the data center proposed in this paper greatly improves the utilization of data and speeds up the overall construction of the data center.
Two-level quantum walkers on directed graphs. I. Universal quantum computing

In the present paper, the first in a series of two, we propose a model of universal quantum computation using a fermionic or bosonic multiparticle continuous-time quantum walk with two internal states (e.g., the spin-up and -down states of an electron). A dual-rail encoding is adopted to convert information: A single qubit is represented by the presence of a single quantum walker in either of the two parallel paths. We develop a roundabout gate that moves a walker from one path to the next, either clockwise or counterclockwise, depending on its internal state. It can be realized by a single-particle scattering on a directed weighted graph with the edge weights 1 and $\ifmmode\pm\else\textpm\fi{}i$. The roundabout gate also allows the spatial information of the quantum walker to be temporarily encoded in its internal states. The universal gates are constructed by appropriately combining several roundabout gates, some unitary gates that act on the internal states, and two-particle scatterings on straight paths. No ancilla qubits are required in our model. The computation is done by just passing quantum walkers through properly designed paths. Namely, there is no need for any time-dependent control. A physical implementation of quantum random access memory compatible with the present model will be considered in the second paper [R. Asaka et al., following paper, Phys. Rev. A 107, 022416 (2023)].