Reviewerly/exHarmony huggingface dataset:
Organization of an Individual Approach to Teaching Mathematics to Non-Mathematical Pupils Under the Covid-19 Conditions

The problem of teaching mathematics in modern Ukrainian schools is general, but it has become more difficult for students with a non-mathematical background. Despite numerous studies of this problem, no specific recommendations have been made. Therefore, to develop and implement an experimental method of teaching mathematics aimed at activating the cognitive activity of non-mathematical specialties pupils. The goal was solved by conducting a questionnaire among students and teachers, which allowed us to reveal and deepen the aspects of the specified problem. Two groups were created: experimental and control. The experimental group studied according to the new model of education, and the control group - according to traditional methods of teaching mathematics. The study revealed a complex of interrelated problems, both for teachers and students. Among the problems, the lack of motivational mechanisms and a complex pedagogical approach to explaining mathematics and the limited amount of teaching mathematics to students with a non-mathematical background are of primary importance. The results of the study indicate the need to introduce the specifics of conducting classes, which would focus on understanding the subject through imaginative thinking. The need to develop textbooks and manuals, which would focus on a more in-depth and understandable teaching of the subject with exercises and tasks for humanitarian areas, has been proven. At the same time, such measures became urgent due to the introduction of quarantine measures of the Covid-2019 pandemic.
Machine Learning-Based Audio Interface Model for Sign Language Recognition

Due to the fact that most offices and educational institutions now operate from home, the work-from-home and study-from-home cultures have made it difficult to interact with persons who are deaf or hard of hearing. These people communicate within their society using sign language, which is not widely understood by others. Most of the time, as a result of this, they miss out on the opportunity to express their point in front of every one since they are ignored/passed over without receiving the necessary attention. In real-time, having an independent translator that can process photos and interpret signs quickly at the speed of streaming images is critical. We'll utilize TensorFlow Object Detection and Python to bridge the gap by creating an end-to-end bespoke object detection model that not only translates sign language in real time but also speaks it to others.
The Pursuit of Algorithmic Fairness: On "Correcting" Algorithmic Unfairness in a Child Welfare Reunification Success Classifier

The algorithmic fairness of predictive analytic tools in the public sector has increasingly become a topic of rigorous exploration. While instruments pertaining to criminal recidivism and academic admissions, for example, have garnered much attention, the predictive instruments of Child Welfare jurisdictions have received considerably less attention. This is in part because comparatively few such instruments exist and because even fewer have been scrutinized through the lens of algorithmic fairness. In this work, we seek to address both of these gaps. To this end, a novel classification algorithm for predicting reunification success within Oregon Child Welfare is presented, including all of the relevant details associated with building such an instrument. The purpose of this tool is to maximize the number of stable reunifications and identify potentially unstable reunifications which may require additional resources and scrutiny. Additionally, because the algorithmic fairness of the resulting tool, if left unaltered, is unquestionably lacking, the utilized procedure for mitigating such unfairness is presented, along with the rationale behind each difficult and unavoidable choice. This procedure, though similar to other post-processing group-specific thresholding methods, is novel in its use of a penalized optimizer and contextually requisite subsampling. These novel methodological components yield a rich and informative empirical understanding of the trade-off continuum between fairness and accuracy. As the developed procedure is generalizable across a variety of group-level definitions of algorithmic fairness, as well as across an arbitrary number of protected attribute levels and risk thresholds, the approach is broadly applicable both within and beyond Child Welfare.
Structural-component analysis of the phenomenon «manager’s competitiveness» in psychological and pedagogical discourse

The urgency of the problem of formation of competitiveness of future managers in the process of their professional training is substantiated in the article. The problem of competition and competitiveness, which was introduced in the field of economics, management and production, over time, due to the strengthening of its humanistic context, has received wide and comprehensive coverage in the field of education. The relevance of the study is due to the dynamic development of the education sector, which requires highly qualified competitive professionals capable of effective action at the level of world standards. The author made a theoretical analysis of the main approaches to understanding the nature and components of «competitiveness». It is noted that "competitiveness of the manager of education» is a multilevel integrated personal education, characterized by a set of values, personally and professionally significant qualities, basic knowledge, skills and abilities and is designed for all spheres of life and provides future leaders with competitive advantages, educational activities for creative self-realization. As a result of the analysis, the author summarizes the existing approaches and considers the studied phenomenon as a four-component phenomenon, the components of which are: motivational-value, cognitive, activity and personality-reflexive components. Motivational and value component determines the value orientation and motivation of the individual to self-development, covers the motivation for professional growth and reflects the value orientations of the individual. The cognitive component is characterized by the possession of a system of knowledge necessary for the effective performance of professional tasks; understanding the essence, content, goals and objectives of professional activity; awareness of methods and ways of performing management tasks. The activity component is characterized by the ability of future managers in the field of education to use the acquired knowledge in solving professional problems; ability to adapt to new situations, justify and make decisions for independent professional tasks. The personality-reflexive component determines the suitability for the chosen profession and is characterized by the presence and level of formation of priority professional and personal qualities that affect the ability of future professionals in the field of education to compete.
FEATURES OF THE FORMATION OF ARTISTIC CREATIVITY OF CHILDREN 4–5 YEARS OLD USING DRAWING IN THE CONDITIONS OF SCHOOL

The article examines the results of an empirical study of the development of artistic creativity in drawing in four- to five-year-old children. The development of drawing as one of the types of children’s artistic activity was evaluated according to certain criteria: artistic perception and artistic-aesthetic emotions and feelings; independent and creative artistic actions; artistic-aesthetic evaluation, and cognitive, emotional-valuable, and behavioral were the structural components of the formation of four-five-year-old preschoolers’ creativity in drawing. The authors of the article focus on the general indicators of artistic creativity of children aged 4–5 years, which were focused on: enthusiasm for artistic activities, originality in creating artistic images in drawing. The study of the peculiarities of drawing as one of the types of artistic creativity of 4–5-year-old children took place on the basis of the methodology developed by the authors, which consisted of the following methods: conversations with children; experimental tasks of a creative nature in drawing, observation of the process of creativity in drawing; survey of educators and parents. It was found that the vast majority (30 %) of 4–5-year-old preschoolers are characterized by a low level of development of artistic creativity, which is manifested in their artistic activity, 53 % of four-five-year-old children have an average level of artistic creativity. The least number of four- to five-year-old preschoolers (17 %) were found, who have a high level of artistic creativity. In total, 83 % of children of the middle group of SEN are characterized by an insufficient degree of formation of artistic creativity in drawing. Studying educational influences on the development and improvement of artistic creativity, it was found that teachers of special education do not sufficiently assess the potential of drawing in the development of children’s creativity and use a limited arsenal of tools in the educational process. The vast majority of educators only sporadically help four- to five-year-old children master the most significant means of drawing, forming the artistic skills of preschoolers, and very few educators cooperate in this regard with children’s parents. Only a small number of teachers interact with parents of middle school students on this issue. Also, not all educators correctly understand the very phenomenon of children’s creativity, although they emphasize its focus on learning about the world, but they are not sufficiently aware of how it should be supported The educational influence of parents on the artistic creativity of 4–5-year-old children cannot be called systemic. The vast majority of adults are not interested in the development of children’s creativity in drawing, only about half of parents are interested in expanding the means of drawing. At the same time, a small part of the children’s parents is determined to cooperate with teachers of special education in this context of the educational process.
OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation

Learning performant robot manipulation policies can be challenging due to high-dimensional continuous actions and complex physics-based dynamics. This can be alleviated through intelligent choice of action space. Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. Nonetheless, its strength depends on the underlying modeling fidelity, and is prone to failure when there are modeling errors. In this work, we propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors by inferring relevant dynamics parameters from online trajectories. OSCAR decomposes dynamics learning into task-agnostic and task-specific phases, decoupling the dynamics dependencies of the robot and the extrinsics due to its environment. This structure enables robust zero-shot performance under out-of-distribution and rapid adaptation to significant domain shifts through additional finetuning. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines. For more results and information, please visit https://cremebrule.github.io/oscar-web/.
Optical observation of single spins in silicon

The global quantum internet will require long-lived, telecommunications band photon-matter interfaces manufactured at scale. Preliminary quantum networks based upon photon-matter interfaces which meet a subset of these demands are encouraging efforts to identify new high-performance alternatives. Silicon is an ideal host for commercial-scale solid-state quantum technologies. It is already an advanced platform within the global integrated photonics and microelectronics industries, as well as host to record-setting long-lived spin qubits. Despite the overwhelming potential of the silicon quantum platform, the optical detection of individually addressable photon-spin interfaces in silicon has remained elusive. In this work we produce tens of thousands of individually addressable `$T$ centre' photon-spin qubits in integrated silicon photonic structures, and characterize their spin-dependent telecommunications-band optical transitions. These results unlock immediate opportunities to construct silicon-integrated, telecommunications-band quantum information networks.
Establishing Interoperability in Blockchain Enabled Interconnected Smart Microgrids Using Ignite CLI

&lt;p&gt;This is a preprint only and has not been peer-reviewed or published anywhere so far. Abstract : Blockchain technology (BCT) is a game changer for many industries due to its distinct advantages like conducting peer-to-peer transactions in trustless environment, enhanced data security, greater transparency, and the inclusion of smart contracts. Its application in the energy industry in Blockchain Enabled Interconnected Smart Microgrids (BSMGs) is on the rise as it can automate local energy markets; execute energy trading; and implement market operations and management. However, they are limited by their scalability and low transactions rate. Also, with the increase in adoption of BSMGs, different types of BCT platforms will emerge, creating heterogeneity in the system. This shortcoming may lead to a monopoly of certain platforms over the rest. These drawbacks can be overcome by establishing interoperability between heterogeneous blockchains. Interoperability can also enable inter-microgrid transactions which will be hindered if there is no inter-chain communication and transaction. Cosmos is a network of blockchains which allows blockchains, applications and services to be interconnected through Inter Blockchain Communication (IBC) protocol. Ignite CLI is an open-source, command line interface which easily creates modular and customizable blockchains which are inherently connected to Cosmos through IBC. In this paper, Ignite CLI is used to create blockchains for BSMGs which exchange data and calls for inter-microgrid transactions via IBC. The procedure to establish inter-chain communication is defined. Interoperability between BSMGs is explored and demonstrated through different examples, for the first time in the energy domain. &lt;/p&gt;
iPET: Privacy Enhancing Traffic Perturbations for Secure IoT Communications

&lt;p&gt;IoT devices constantly communicate with servers over the Internet, allowing an attacker to extract sensitive information by passively monitoring the network traffic. Recent research works have shown that a network attacker with a trained machine learning (ML) model can accurately fingerprint IoT devices learned from the (encrypted) traffic flows of the devices. Such fingerprinting attacks are capable of revealing the make and model of the devices, which can further be used to extract detailed user activities.&lt;/p&gt; &lt;p&gt;In this work, we develop and propose iPET, a novel adversarial perturbation-based traffic modification system that defends against fingerprinting attacks. iPET design employs GAN (Generative Adversarial Networks) in a tuneable way, allowing users to specify the maximum bandwidth overhead they are willing to tolerate for the defense. A fundamental idea of iPET is to deliberately introduce stochasticity between model instances. This approach limits a counter attack, as it inhibits an attacker from recreating an identical perturbation model and using it for fingerprinting. We evaluate the effectiveness of our defense against state-of-the-art fingerprinting models and with three different attacker capabilities. Our evaluations on synthetic and real-world datasets demonstrate that iPET decreases the accuracy of even the potent attackers. We also show that the traffic perturbations generated by iPET generalize well to different fingerprinting schemes that an attacker may deploy.&lt;/p&gt;
SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering

Panoptic segmentation aims to address semantic and instance segmentation simultaneously in a unified framework. However, an efficient solution of panoptic segmentation in applications like autonomous driving is still an open research problem. In this work, we propose a novel LiDAR-based panoptic system, called SMAC-Seg. We present a learnable sparse multi-directional attention clustering to segment multi-scale foreground instances. SMAC-Seg is a real-time clustering-based approach, which removes the complex proposal network to segment instances. Most existing clustering-based methods use the difference of the predicted and ground truth center offset as the only loss to supervise the instance centroid regression. However, this loss function only considers the centroid of the current object, but its relative position with respect to the neighbouring objects is not considered when learning to cluster. Thus, we propose to use a novel centroid-aware repel loss as an additional term to effectively supervise the network to differentiate each object cluster with its neighbours. Our experimental results show that SMAC-Seg achieves state-of-the-art performance among all real-time deployable networks on both large-scale public SemanticKITTI and nuScenes panoptic segmentation datasets.
Self-Refine Learning For Data-Centric Text Classification

&lt;p&gt;In industry NLP application, our manually labeled data has a certain number of noise data. We present a simple method to find the noise data and remove them. We select the noise data whose human label is not contained in the top-K model's predictions. The experiment result shows that our method works. For industry deep learning application, our method improve the text classification accuracy from 80.5% to 90.6% in dev dataset, and improve the human-evaluation accuracy from 83.2% to 90.1%.&lt;/p&gt;
A Topology-based Approach for Uplink Transmission Optimization in Wireless Federated Learning

&lt;p&gt;Federated learning (FL) is a novel approach where user devices (UDs) train a shared model without exchanging their data, effectively preserving privacy in distributed machine learning. When FL is applied in wireless networks, one particular issue is the transmission bottleneck in the uplink, as most UDs are expected to transmit updated parameters to the server almost simultaneously. This paper proposes a topology-based approach to address this issue, with the objective of reducing the total uplink transmission time. The key idea of our approach is to apply the Tree All-reduce (TAR), a parameter aggregation scheme enabled by D2D communications, to identify the optimal transmission sequence and topology in wireless FL. We formulate the TAR structure formation as an NP-hard MINLP problem, and then solve the problem using two algorithms based on Minimum Spanning Tree and Upper Confidence bound applied to Trees, respectively. Theoretical analysis and numerical results show that our approach can significantly reduce the total uplink transmission time compared to existing benchmarks. Particularly, the reduction can be over $80\%$ in scenarios with high intensity of UDs.&lt;/p&gt;
Diagnosis of The Most Important Factors Affecting The Annual Performance Evaluation of The Teaching Cadres Using The Technique of Binary -Response Logistic Regression

The aim of the research is to identify the main factors affecting the evaluation of the annual performance of the teaching staff using the Binary Logistic Regression technique by applying to a random sample of size(217) teaching and teaching staff from four different disciplines (technology, administrative, agricultural, The results of the analysis showed that the completion and publication of scientific research was the most important factor affecting the evaluation of the performance of the teaching staff, followed by the teaching use of modern teaching methods and its incorporation of technology into education.
Using Maximum Likelihood Method to Estimate Parameters of the Linear Regression T Truncated Model

The estimation of the parameters is one of an important issues in the mathematical statistics. The development of estimation methods requires accurate estimation and finds the best estimator for parameters. The aim of this research is to build a regression model. A dependent variable has a truncated t-distribution for this model. It was depended to the method of maximum likelihood for finding the parameters of the model, the parameters ? and ? 2 were estimated when they were unknown, the approximation of the cumulative function of the truncated t-distribution from two sides is used to represent the function of the dependent variable. It was concluded that the value ? is equal ?ols plus a certain amount, this amount is equal zero when the values of the truncated points a, b are equal in value and different in sign. As a practical application, simulation was used in data generation by using the program (Matlab 2020a), the value of ? was estimated, The comparison was between maximum likelihood method and according to the formula that we reached with its estimated value according to the least squares method based on the mean square errors, where its value according to the maximum likelihood method was less than its value according to the method of least squares, which indicates the advantage of the first method over the second.
Theory of Machine Learning with Limited Data

Abstract Application of machine learning may be understood as deriving new knowledge for practical use through explaining accumulated observations, training set. Peirce used the term \textbf{abduction} for this kind of inference. Here I formalize the concept of abduction for real valued hypotheses, and show that 14 of the most popular textbook ML learners (every learner I tested), covering classification, regression and clustering, implement this concept of abduction inference. The approach is proposed as an alternative to Statistical learning theory, which requires an impractical assumption of indefinitely increasing training set for its justification.
A Conceptual Real-Time Deep Learning Approach for Object Detection, Tracking and Monitoring Social Distance using Yolov5

Objectives: To develop a computer vision-based model that can detect, track and recognize individuals for the purpose of measuring social distance in road traffic videos using surveillance cameras.Methods: The real-time traffic surveillance webcam dataset was applied to validate the model, with better performance metrics outperformed to those of comparable cuttingedge models such as RetinaNet, Faster R-CNN and SSD.Our proposed methodology utilized object detection methods to recognize individuals followed by multiple objects tracking approach to track identified individuals using detected bounding boxes.Our research shows that the conventional method is successful in detecting persons who violate social distances.Findings: Our finding shows that our proposed object detection model successfully recognizes human and those who violating the social distancing measurements.For the purpose of detecting social distance, develop a highly accurate detection technique.Our YOLOv5 with multiple objects tracking algorithm delivered great outcomes with appropriate Precision of 93%, Recall of 94%, F1-Score of 96% and mAP of 95% measures given by object categorization and localization to measure social distancing in real-time traffic surveillance videos.The YOLOv5 model's results are then compared to many other prominent state-of-the-art models.Novelty: The YOLOv5 and MOTSORT is appropriate for finding whether people are maintaining social distancing or not, intended to identify, monitor and track those who are not following or violating social distances with overall accuracy and efficiency.