Reviewerly/exHarmony huggingface dataset:
FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing

Federated learning (FL) is an emerging decentralized machine learning framework enabling private global model training by collaboratively leveraging local client data without transferring it centrally. Unlike traditional distributed optimization, FL trains the model at the local client and then aggregates it at the server. While this approach reduces communication costs, the local datasets of different clients are non-Independent and Identically Distributed (non-IID), which may make the local model inconsistent. The present study suggests a FL algorithm that leverages regularization and data sharing (FedRDS). The local loss function is adapted by introducing a regularization term in each round of training so that the local model will gradually move closer to the global model. However, when the client data distribution gap becomes large, adding regularization items will increase the degree of client drift. Based on this, we used a data-sharing method in which a portion of server data is taken out as a shared dataset during the initialization. We then evenly distributed these data to each client to mitigate the problem of client drift by reducing the difference in client data distribution. Analysis of experimental outcomes indicates that FedRDS surpasses some known FL methods in various image classification tasks, enhancing both communication efficacy and accuracy.
Cube-SSIM: A Metric for Evaluating 360-degree Images as Cube Maps

360-degree image data is a crucial aspect in graphics applications where they are typically used for lighting purposes. Fields like mixed reality generally rely on lighting estimation techniques to estimate the 360-degree environment. To evaluate such approaches, accurate image assessment in this domain is important. However, traditional image evaluation metrics like SSIM, PSNR, and IMED are problematic when analyzing 360-degree image data as they would require two-dimensional representations like equirectangular panoramas or unfolded cubes that introduce image distortions. In this paper, we address this problem by presenting Cube-SSIM, a variant of SSIM designed specifically for cube maps. For this, we modify SSIM to take the solid angles of cube map pixels into account which gives more consistent results than using SSIM for the individual cube faces. The computations can run efficiently on graphics hardware due to their native support for cube maps and no further image conversions are required. We show that our approach allows for more accurate results than other comparison metrics that largely depend on 2D images. While SSIM is especially important due to its wide usage, the modification can also be applied to other image metrics for which we include IMED as an example.
Review on audit data visualization method based on R language

To be competitive in today's market, companies must constantly assess and improve their operations, and financial data analysis has emerged as a crucial tool for this purpose. Executives can utilize data analysis to gain a deeper understanding of the underlying facts in their data and make better informed decisions about their company and the market. Multiple data models in the language make it possible to have a fully functional language environment with tools for statistical analysis and visual visualization of data. With its ability to enhance the quality of work in statistical computations and graphical analysis, the R language is ideally suited to the industrial data analysis environment. In this paper, we use a literature review methodology to examine the domestic and international literature on R language big data visualization auditing, analyze the visualization application areas of R language big data auditing, discuss the benefits and challenges of big data auditing, demonstrate how R language can be adapted to real-world auditing scenarios, and offer reasonable recommendations and optimistic outlooks for the field's future.
IMPLEMENTATION OF V.A. SUKHOMLINSKY IN THE PRACTICE OF TRAINING FUTURE TEACHERS OF PHILOLOGY

Reforming the education system and changing the educational paradigm, due to a change in the system of value orientations of society in the conditions of industrialization, informatization, have identified the main directions for improving the educational process, among which the comprehensive and harmonious development of the younger generation is one of the priorities, since it is the basis for the formation of moral and ethical norms accepted in society. The didactic ideas of Vasily Alexandrovich Sukhomlinsky, an outstanding Russian teacher, whose implementation in the middle of the last century made it possible to educate a generation of conscious citizens, have not lost their relevance in modern society. This should especially apply to the training of future teachers, including teachers of philology, who should actively use the ideas of the national teacher V. A. Sukhomlinsky in their pedagogical activities. The actualization of the ideas of an outstanding teacher allows future teachers of philology to form a stable system of professionally significant values, the foundation of which is such personal qualities as love and respect for their wards, a sense of duty and justice, tolerance, patience, responsiveness, diligence, creative thinking, etc. The article describes the experience of introducing the ideas of an outstanding teacher into the practice of training future teachers of philology at Donetsk State University. Particular attention is paid to the formation of the following types of pedagogical activity among philologists: the implementation of the verbal method in teaching schoolchildren, which allows using the right words and intonation to find an approach to each student; attracting students to creative work, a characteristic feature of which is diversity in the search for solutions to achieve the goals; the application of an individual approach to each individual member of the student team, ensuring the development of his personal qualities, abilities and inclinations.
Aligning Large Language Models through Synthetic Feedback

Aligning large language models (LLMs) to human values has become increasingly important as it enables sophisticated steering of LLMs. However, it requires significant human demonstrations and feedback or distillation from proprietary LLMs such as ChatGPT. In this work, we propose a novel alignment learning framework with synthetic feedback not dependent on extensive human annotations and proprietary LLMs. First, we perform reward modeling (RM) with synthetic feedback by contrasting responses from vanilla LLMs with various sizes and prompts. Then, we use the RM to simulate high-quality demonstrations to train a supervised policy and further optimize the model with reinforcement learning. Our resulting model, Aligned Language Model with Synthetic Training dataset (ALMoST), outperforms recent open-sourced models, which are trained on the outputs of InstructGPT or human-annotated demonstrations, in alignment benchmarks. In human evaluation, our model is preferred to Alpaca and Dolly-v2, 55.0% and 58.5% of the time, respectively. Further analyses demonstrate the efficacy and importance of synthetic feedback in our framework.
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, Hannaneh Hajishirzi. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification

Authorship verification (AV) is a fundamental task in natural language processing (NLP) and computational linguistics, with applications in forensic analysis, plagiarism detection, and identification of deceptive content. Existing AV techniques, including traditional stylometric and deep learning approaches, face limitations in terms of data requirements and lack of explainability. To address these limitations, this paper proposes PromptAV, a novel technique that leverages Large-Language Models (LLMs) for AV by providing step-by-step stylometric explanation prompts. PromptAV outperforms state-of-the-art baselines, operates effectively with limited training data, and enhances interpretability through intuitive explanations, showcasing its potential as an effective and interpretable solution for the AV task.
“Fifty Shades of Bias”: Normative Ratings of Gender Bias in GPT Generated English Text

Language serves as a powerful tool for the manifestation of societal belief systems. In doing so, it also perpetuates the prevalent biases in our society. Gender bias is one of the most pervasive biases in our society and is seen in online and offline discourses. With LLMs increasingly gaining human-like fluency in text generation, gaining a nuanced understanding of the biases these systems can generate is imperative. Prior work often treats gender bias as a binary classification task. However, acknowledging that bias must be perceived at a relative scale; we investigate the generation and consequent receptivity of manual annotators to bias of varying degrees. Specifically, we create the first dataset of GPT-generated English text with normative ratings of gender bias. Ratings were obtained using Best–Worst Scaling – an efficient comparative annotation framework. Next, we systematically analyze the variation of themes of gender biases in the observed ranking and show that identity-attack is most closely related to gender bias. Finally, we show the performance of existing automated models trained on related concepts on our dataset.
MM-HAR: Multi-Modal Human Activity Recognition Using Consumer Smartwatch and Earbuds

Human Activity Recognition (HAR) is one of the important applications of digital health that helps to track fitness or to avoid sedentary behavior by monitoring daily activities. Due to the growing popularity of consumer wearable devices, smartwatches, and earbuds are being widely adopted for HAR applications. However, using just one of the devices may not be sufficient to track all activities properly. This paper proposes a multi-modal approach to HAR by using both buds and watch. Using a large dataset of 44 subjects collected from both in-lab and in-home environments, we demonstrate the limitations of using a single modality as well as the importance of a multi-modal approach. Moreover, we also train and evaluate the performance of five different machine learning classifiers for various combinations of devices such as buds only, watch only, and both. We believe the detailed analyses presented in this paper may serve as a benchmark for the research community to explore and build upon in the future.
Archiving individual science blog posts

Sometimes we want to preserve a blog post to read or reuse later. There are generic tools for that purpose, including read-it-later apps such as Instapaper or Pocket. For scholarly blog posts, the right place can also be a reference manager, which stores the content and the metadata, especially if a DOI was registered for each blog post. Reference managers can store full-text documents and typically use PDF as the file format.
Demand for and Efficiency of Innovative Educational Technologies: Results of Qualitative Study

Universities traditionally play a major role in creating innovations and implementing them elsewhere and during the educational process. Whether innovations are efficient and boost quality of university edu­cation is still debatable. The qualitative study conducted by means of focus group interviews presents students’ opinions on capacity and efficiency of various innovative technologies in education. Some advantages and limitations of both current and prospective technolo­gies (augmented reality, hologram) are revealed. The limitations result from painstaking and costly process of introducing some innovative technologies, besides, both faculty and students could be psychologically unprepared and lack certain competencies to make use of them. The factors influencing successful implementation and relevance of innovations are analyzed. It has been concluded and further substantiated that traditional and innovative technologies should be flexibly combined to fully meet capabilities and needs of students. Technological innovations in the educational process should be accompanied by pedagogical innovations and incentivize students as well. Only in this case, students alongside with teachers will be involved in the generation of knowledge.
Application of Rice Pest Repellent Using ESP32 Microcontroller and Internet of Things Based Sound Wave

This research aims to create an automatic pest-repellent system using the Internet of Things. This system uses a microcontroller model ESP32 Devkit and PIR sensor to detect the attack of rice field leafhopper pests. In addition, the application is also equipped with a buzzer and a servomotor that can repel leafhoppers. The circuit can be used to expel leafhoppers because it can emit ultrasonic waves in the range of 40 kHz. The resulting system is IoT-based, so it can be controlled via smartphone. The application can eradicate leafhoppers at a radius of 3 meters and can kill leafhoppers within 2 hours. However, if the distance is 6 meters, this tool can kill leafhoppers and locusts in a 4-hour time interval.
Transformation and Reform of Broadcasting and Hosting Education from the Perspective of "Internet Plus"

The growth of the Internet has created new avenues for the information media industry to expand and change. It is essential to modify and reform the broadcasting and hosting education major in order to foster skills in these areas. However, there are still some problems in the transformation and reform of traditional broadcasting and hosting education majors. In response to the problems of students' poor oral expression and voice shaping abilities and low satisfaction with teaching models in the current transformation and reform of broadcasting and hosting education majors, this article aimed to use internet technology to promote the transformation and reform of their majors, in order to better cultivate broadcasting and hosting professionals. The article first analyzed the development of broadcasting and hosting education in the internet environment, and then elaborated on new media technologies under the internet. Then, dynamic group strategy teaching optimization algorithms were used to optimize its transformation and reform methods, and then specific transformation and reform strategies were elaborated. To test the effectiveness of the Internet in the transformation and reform of broadcasting and hosting education majors, this article compared it with traditional methods. The research results showed that the average scores of oral expression and voice shaping abilities of Class 1 students using internet methods during the 8th assessment were 89.5 and 88.5, respectively; the average scores for oral expression and voice shaping abilities of students using traditional methods were 78.4 and 77.8, respectively. The results indicate that students majoring in broadcasting and hosting education under the Internet method have better oral expression and voice shaping abilities, and their overall professional level is also better than traditional methods. This study has highlighted the important role of "Internet plus" in improving the professional ability of students majoring in broadcasting and hosting education, and provided more methods to promote the transformation and reform of broadcasting and hosting education.
Dynamic Multi-Target Tracking Using Heterogeneous Coverage Control

A coverage-based collaborative control strategy is developed in this paper for a multi-robot system with heterogeneous effective sensing ranges and safe operation zones to simultaneously estimate the states of and follow multiple targets governed by stochastic dynamics. Multiplicatively weighted Voronoi diagrams are exploited to define each robot's dominant region considering its limited sensing radius. The asymptotically stable system dynamics enabling the heterogeneous multi-robot system to (locally) optimally cover the time-varying probability density distributions that characterize the uncertainties of the targets' positions is derived, and minimally perturbed by control barrier functions designed to ensure that each robot moves within its safe operation zone in a collision-free manner. Specific target dynamics and measurement models are chosen in the experiment, whose results demonstrate the effectiveness of the proposed dynamic multi-target tracking approach.
Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments. (Video <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Video demonstration: https://youtu.be/efnPERm0Rco.)
Charging Quantum Batteries via Indefinite Causal Order: Theory and Experiment

In the standard quantum theory, the causal order of occurrence between events is prescribed, and must be definite. This has been maintained in all conventional scenarios of operation for quantum batteries. In this study we take a step further to allow the charging of quantum batteries in an indefinite causal order (ICO). We propose a nonunitary dynamics-based charging protocol and experimentally investigate this using a photonic quantum switch. Our results demonstrate that both the amount of energy charged and the thermal efficiency can be boosted simultaneously. Moreover, we reveal a counterintuitive effect that a relatively less powerful charger guarantees a charged battery with more energy at a higher efficiency. Through investigation of different charger configurations, we find that ICO protocol can outperform the conventional protocols and gives rise to the anomalous inverse interaction effect. Our findings highlight a fundamental difference between the novelties arising from ICO and other coherently controlled processes, providing new insights into ICO and its potential applications.
Solving the Hidden Number Problem for CSIDH and CSURF via Automated Coppersmith

We define and analyze the Commutative Isogeny Hidden Number Problem which is the natural analogue of the Hidden Number Problem in the CSIDH and CSURF setting. In short, the task is as follows: Given two supersingular elliptic curves $$E_A$$ , $$E_B$$ and access to an oracle that outputs some of the most significant bits of the $$\textsf{CDH}$$ of two curves, an adversary must compute the shared curve $$E_{AB}=\textsf{CDH} (E_A,E_B)$$ . We show that we can recover $$E_{AB}$$ in polynomial time by using Coppersmith’s method as long as the oracle outputs $$\frac{13}{24}+ \varepsilon \approx 54\%$$ (CSIDH) and $$\frac{31}{41}+ \varepsilon \approx 76\%$$ (CSURF) of the most significant bits of the $$\textsf{CDH}$$ , where $$\varepsilon > 0$$ is an arbitrarily small constant. To this end, we give a purely combinatorial restatement of Coppersmith’s method, effectively concealing the intricate aspects of lattice theory and allowing for near-complete automation. By leveraging this approach, we attain recovery attacks with $$\varepsilon $$ close to zero within a few minutes of computation.
PRIORITY DIRECTIONS OF RESEARCH ON THE METHODS OF TEACHING MATHEMATICS

The article analyzes the main areas of research on the methodology of teaching mathematics. Currently, the issues of determining the directions of research by branches of science are showing their relevance. Based on the analysis of normative and strategic documents, four directions were identified in the methodology of teaching mathematics. The problem of moral education in teaching mathematics is an important area of research. The relevance of this direction is confirmed by the Decree of the President of the Kyrgyz Republic on moral education. Another important research topic on the methodology of teaching mathematics corresponds to the Concept of Digital Kyrgyzstan. Another important area of research is the development of scientific foundations for the formation of functional literacy of students. The implementer of these areas is the teacher, therefore, the study of the scientific foundations for the development of the activities of a mathematics teacher is an important direction in the methodology of teaching mathematics.
Federated Learning in IoT: A Survey on Distributed Decision Making

The proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented data generation, necessitating innovative approaches to extract valuable insights while respecting privacy and resource constraints. Federated Learning (FL) has emerged as a promising paradigm, enabling decentralized model training across a network of edge devices. This paper presents a comprehensive survey on the application of Federated Learning in IoT, with a specific focus on the challenges and solutions related to distributed decision making. The survey begins by elucidating the foundational concepts of Federated Learning and IoT, highlighting their convergence and the potential benefits of leveraging FL in decentralized IoT ecosystems. The paper explores the diverse applications of FL in various IoT domains, including smart cities, healthcare, industrial IoT, and smart grid systems. It investigates how FL can address the challenges posed by the distributed nature of IoT data, such as data heterogeneity, privacy concerns, and communication constraints. A significant portion of the survey is dedicated to examining the methodologies and algorithms employed in federated learning for distributed decision making in IoT. This encompasses a discussion on federated optimization techniques, communication-efficient algorithms, and privacy-preserving mechanisms. The survey also delves into the role of edge computing in facilitating efficient FL in IoT, considering the resource constraints inherent in edge devices. Furthermore, the paper reviews the current state-of-the-art federated learning frameworks and platforms tailored for IoT environments. It evaluates their capabilities in handling real-world challenges and providing scalable solutions for distributed decision making. The survey concludes by identifying open research challenges and potential avenues for future developments in federated learning for IoT, emphasizing the need for novel algorithms, robust security measures, and standardized frameworks. In summary, this paper offers a comprehensive overview of Federated Learning in the context of IoT, shedding light on its potential, challenges, and solutions with a specific emphasis on the critical aspect of distributed decision making. The insights provided aim to guide researchers, practitioners, and policymakers in navigating the complex landscape of FL in IoT and fostering advancements in this rapidly evolving field.