Reviewerly/exHarmony huggingface dataset:
State regulation of public libraries in Russia in the first half of the XIX century

The article reveals the content of state regulation of the activities of public libraries in Russia in the first half of the 19th century. It is shown that the government showed interest in involving the public in the development of the cultural life of urban centers, and therefore created relatively comfortable legal conditions and an organizational environment for the work of public libraries. In the 1830, the authorities tried, using bureaucratic methods, to introduce a public initiative to create public libraries in provincial cities. In response to the proposal of the head of the Free Economic Society P. S. Mordvinova, the Minister of Internal Affairs attracted the heads of regions — governors — to create a kind of library partnership. Local authorities, for their part, had to guarantee legal status to provincial libraries, provide them with organizational assistance, and representatives of the public, for their part, had to accept funding and carry out ongoing library work. The government established a licensing procedure for opening public libraries, which received legal status and the ability to independently stock book depositories. In an effort to prevent public libraries from becoming centers of anti-government propaganda, the authorities exercised administrative and police supervision over them. The results of the implementation of the ministerial project turned out to be modest: instead of the planned fifty-two, only twenty-nine public libraries were opened, most of which ceased to exist within 10–15 years. However, the general principles of state regulation of the activities of provincial libraries were extended to all other public libraries in Russia. In the middle of the 19th century, a library boom began in the country, which was caused by numerous public initiatives from below.
Enhancing Efficiency in Healthcare Supply Chains: Leveraging Machine Learning for Optimized Operations

In this complex strings of healthcare supply chain, optimal efficiency is the most supreme to ensure cost and time effectiveness of medical resources. This paper scrabbles through Machine Learning (ML) classifiers such as, Na¨ıve Bayes, K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM) and Linear Regression to intensify the performance of healthcare supply chain management. This study focuses on five classes: ’Inspection Results’, ’Defect Rate’, ’Transportation Modes’, ’Routes’ and ’Cost’. According to my findings, Random Forest classifier indicated 87% accuracy in the ‘Inspection Results’ and ‘Transportation Modes’ classifications whereas KNN classifier signified an impressive accuracy of 86% in the ‘Routes’ classification. These findings underline the need for ML approaches within different classes. The diversified performances of classifiers within classes highlight the importance of selecting the most suitable algorithm based on supply chain aspect. This research not only shows the effectiveness of ML classifiers in healthcare supply chain optimization but also highlights the possibility of automation for different surface of supply chain management. It provides as a fundamental step for optimizing healthcare supply chain
Features of Information and Communication Technologies in the Educational Process: Neuroscience in Education

Society is intensively developing in the context of informatization and the use of innovative technologies, which causes the need for changes in various spheres of life and relevant social institutions. Such a trend determined the relevance of the research problem, in particular, a hypothesis was formed about the effectiveness of neuropedagogical and information and communication technologies as a factor in reforming modern education. The purpose of the research is the role of neuropedagogy in modern, the effectiveness of informatization as a reforming factor. The tasks of the research are aimed at determining the aspects of the effectiveness of the application of innovative and neuropedagogical technologies in the educational process, in particular, the neuroscientific aspect and the computer as a set of innovative technologies. The article characterizes the categorical and conceptual context of the educational trends of the modern information society, which is in the process of formation and reformation. In order to determine the specifics of the research problem, the theoretical and methodological foundations of scientific publications in the relevant field were analyzed. The concepts of neuropedagogical technologies, reforming modern education, computerization of education are summarized, methodological technologies of the educational process are analyzed in the context of neuroscientific technologies. In the course of the study, the method of abstraction, descriptive and research method, method of analysis and synthesis was applied.
A hybrid computational approach to anticipate individuals in sequential problem solving

Human-awareness is an ever more important requirement for AI systems that are designed to assist humans with daily physical interactions and problem solving. This is especially true for patients that need support to stay as independent as possible. To be human-aware, an AI should be able to anticipate the intentions of the individual humans it interacts with, in order to understand the difficulties and limitations they are facing and to adapt accordingly. While data-driven AI approaches have recently gained a lot of attention, more research is needed on assistive AI systems that can develop models of their partners' goals to offer proactive support without needing a lot of training trials for new problems. We propose an integrated AI system that can anticipate actions of individual humans to contribute to the foundations of trustworthy human-robot interaction. We test this in Tangram, which is an exemplary sequential problem solving task that requires dynamic decision making. In this task the sequences of steps to the goal might be variable and not known by the system. These are aspects that are also recognized as real world challenges for robotic systems. A hybrid approach based on the cognitive architecture ACT-R is presented that is not purely data-driven but includes cognitive principles, meaning heuristics that guide human decisions. Core of this Cognitive Tangram Solver (CTS) framework is an ACT-R cognitive model that simulates human problem solving behavior in action, recognizes possible dead ends and identifies ways forward. Based on this model, the CTS anticipates and adapts its predictions about the next action to take in any given situation. We executed an empirical study and collected data from 40 participants. The predictions made by CTS were evaluated with the participants' behavior, including comparative statistics as well as prediction accuracy. The model's anticipations compared to the human test data provide support for justifying further steps built upon our conceptual approach.
Advanced robust control design and experimental veriﬁcation for trajectory tracking of model-based uncertain collaborative robots

Abstract At the core of this research is the pursuit of enhancing the trajectory tracking performance of six-degree-of-freedom collaborative robots, with a particular focus on addressing the challenges posed by uncertainties in real-world applications. One of the primary issues encountered with existing methods is the susceptibility of trajectory tracking to uncertainties, which can significantly hinder the performance of robotic systems. To address these challenges, we propose an advanced control method, known as the model-based proportional-derivative controller, or MPDP controller for short, which represents an innovative fusion of model-based PD control principles with a robust control algorithm. This amalgamation is driven by the need to mitigate the impact of uncertainties and external disturbances on trajectory tracking. A comprehensive assessment grounded in Lyapunov theory has been undertaken to validate the effectiveness of our approach. The analysis has firmly established that our method ensures not only the ultimate boundedness but also the uniform boundedness of the robotic system, which is critical for its operational stability. Both experimental and simulation studies have been meticulously conducted to benchmark the performance of the MPDP controller against the conventional proportional-integral-derivative controller, which serves as a widely adopted baseline in the field. The results unequivocally demonstrate the superiority of the MPDP controller across multiple dimensions. It exhibits exceptional robustness, resulting in a smaller steady-state tracking error, a critical advantage when addressing inherent uncertainties and external disturbances that can perturb the robot system. This translates to a more stable trajectory tracking performance. Furthermore, the MPDP controller empowers the robot with the capability to precisely follow predefined trajectories, thus ensuring high-precision and reliable execution of tasks. This feature significantly contributes to an overall enhancement of system performance and productivity.
Conceptual Legal Challenges in Tax Automation via Blockchain Technology

The blockchain technology has been projected to substantially disrupt numerous aspects of tax processes. This prediction has by and large not come to fruition yet. While every disruptive technology must overcome some barriers before realizing its full potential, the authors inquire whether there is something inherently hindering in the way current laws are drafted and constitutional guarantees formulated that prevents the blockchain from flourishing. The authors argue that, to reap the full benefits of automation via blockchain, the existing social institutions (legal framework) and the creative achievement (new technologies) must meet half way. On the one hand, new technologies such as blockchain must be established within the framework of the existing social structure and be fully subjected to the fundamental rights guaranteed by the legal order. On the other hand, the legal order itself must be transformed in a way that permits binary automated outcomes while preserving equity. This paper looks at the ways of achieving that.
Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss $\mathcal{L}_{EC}$ is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug-and-play nature of the EAE module in our method.
Graph based Hiding of Sensitive Knowledge

The Frequent Itemset Hiding Problem (FIHP) is an important research topic in the field of Privacy-Preserving Data Mining. Knowledge Graphs (KGs) have also recently emerged as a popular data representation model for various applications. However, the integration of KGs and FIHP is a relatively unexplored area of research. In this paper, we propose a novel approach to address the FIHP by incorporating Community Detection graph-based algorithms to hide sensitive knowledge in our data. Our approach preserves the structure and utility of the KG and efficiently produces the set of Itemsets that must be hidden. We evaluate our approach on popular FIHP datasets and demonstrate its effectiveness in terms of time efficiency.
Abnormal Behavior Detection Method based on Spatio-temporal Dual-flow Network for Surveillance Videos

To address the issues that current surveillance videos abnormal behavior detection methods are affected by complex environments such as video blurring and long distances, resulting in low detection accuracy and slow speed, we propose an abnormal behavior detection method based on spatio-temporal dual-flow network for surveillance videos. Firstly, the input surveillance videos are sampled with different frame rates and a spatio-temporal dual-flow partial convolution network is constructed to extract spatial and motion information features from the spatial and temporal flow video sequences, respectively. Then, a cross-modal dual-attention fusion mechanism is introduced after each feature extraction of the dual-flow partial convolution network to enhance the exchange of feature information between spatial and temporal flows. Finally, the extracted motion and spatial features are fused to output detection results. Experiments show that our method reduces the number of floating-point operations and model parameters on the Kinetics-600 dataset compared to the MViT-L algorithm with guaranteed accuracy. Compared to lightweight networks such as X3D-XL, it improves the accuracy in Top-1 and Top-5 by 4.3% and 3.6%, respectively. The experiments prove that the method can reduce the model’s computational complexity while obtaining more accurate abnormal behavior detection results when the surveillance videos is blurred or the abnormal behavior occurs far away.
ACER: An AST-based Call Graph Generator Framework

We introduce ACER, an AST-based call graph generator framework. ACER leverages tree-sitter to interface with any language. We opted to focus on generators that operate on abstract syntax trees (ASTs) due to their speed and simplicitly in certain scenarios; however, a fully quantified intermediate representation usually provides far better information at the cost of requiring compilation. To evaluate our framework, we created two context-insensitive Java generators and compared them to existing open-source Java generators.Code: https://github.com/WM-SEMERU/ACER
Towards Autocomplete Strategies for Visualization Construction

Constructive visualization uses physical data units - tokens - to enable non-experts to create personalized visualizations engagingly. However, its physical nature limits efficiency and scalability. One potential solution to address this issue is autocomplete. By providing automated suggestions while still allowing for manual intervention, autocomplete can expedite visualization construction while maintaining expressivity. We conduct a speculative design study to examine how people would like to interact with a visualization authoring system that supports autocomplete. Our study identifies three types of autocomplete strategies and gains insights for designing future visualization authoring tools with autocomplete functionality.A free copy of this paper and all supplemental materials are available on our online repository: https://osf.io/nu4z3/?view_only= 594baee54d114a99ab381886fb32a126.
Mixed Reality for health and safety monitoring

MR can create immersive environments for workers to practice safety protocols, identify potential hazards, and visualize safety information in real-time, improving safety awareness and training effectiveness. In this study, we take advantage of MR’s capability to connect seamlessly to IoT sensors, wearables and mobile apps, and visualize real-time information to design and develop an MR application that allows enhanced visualization, real-time data monitoring, documentation and reporting, as well as safety performance analytics. The MR application is integrated to the IoT infrastructure of a large construction site contributing to a safer working environment, increased worker awareness, improved training efficiency, and better compliance with safety standards. The study elaborates on the evaluation activities performed at a construction site in Poland, wherein workers were provided with health monitoring devices and location tags and a Health and Safety (H&S) officer operated the MR application.
Comprehensive benefit evaluation method for photovoltaic power generation projects based on cloud model

Engineering projects exert a profound influence on diverse facets of society, encompassing individuals’ livelihoods, the broader economy, and the environment. Assessing the benefits of projects is crucial in governmental decision-making processes and in determining investments in social capital. This paper introduces a cloud-based method for evaluating the comprehensive benefits of engineering projects, specifically focusing on photovoltaic power generation projects. First, the paper constructs a comprehensive benefit evaluation system tailored to photovoltaic power generation projects. Next, it calculates the weights of the indicators within the evaluation system. By merging the subjective weights derived from the fuzzy analytic hierarchy process (FAHP) with the objective weights gathered from grey relational analysis (GRA), the method yields a more scientifically sound combined weight. Finally, the combined weight is assimilated into the cloud model, leading to the creation of a comprehensive evaluation benefit cloud model tailored to the projects. The effectiveness of the proposed methodology is substantiated through a real-world example of a photovoltaic power generation project.
A Numeral and Affective Knowledge Enhanced Network for Aspect-based Financial Sentiment Analysis

Aspect-based financial sentiment analysis (ABFSA) is a fine-grained task that can enrich and sharpen financial analysis by identifying sentiments towards specific entities (e.g., company, stock). As the application of ABSA in professional language, ABFSA is a challenging task requiring extensive domain knowledge while remaining understudied. Numeral understanding is crucial for financial text analysis, but existing NLP models for ABFSA lack such ability by mainly treating numerals as plain text. In addition, most studies on knowledge incorporation disregard necessary domain-specific connotations or suffer from the low coverage issue. In this paper, we propose a novel numeral-oriented network with a multi-source affective knowledge refinement strategy (NumAKEN) for ABFSA. NumAKEN utilizes a numeral encoding method based on DigitCNN to capture critical numeric concepts such as magnitude and category. A multi-source affective knowledge fusion strategy is designed for hybrid lexicon construction and incorporation, which can guide the model to capture significant sentiment clues as well as alleviate conflicting and coverage issues. Extensive experiments on two datasets illustrate that our NumAKEN model outperforms all state-of-the-art methods and verify the effectiveness of our model.
Smaller and more Accurate Swin-transformer Model Prediction for Tracking

Recently, Transformers have been excelling in the field of object tracking. However, most existing trackers tend to indiscriminately increase the number of model parameters in order to enhance tracking accuracy. In contrast, we introduce the Swin-Transformer-Tiny-based Predictive Model for Tracking (STMP). The STMP leverages Transformers for feature extraction and fusion, combines positional encoding and neighboring candidate information, and redesigns the model predictor to accurately locate and estimate target bounding boxes. This renders STMP as a compact yet powerful tracker, with only 32.56M model parameters. Evaluation experiments show that our STMP performs outstandingly on five challenging datasets. Particularly in the GOT-10k evaluation, our Average Overlap (AO) scored an excellent 75.2%, not only far surpassing trackers with similar levels of model parameters but also exceeding most larger model trackers. Simultaneously, we achieved commendable scores of 82.3% and 68.8% on the TrackingNet and LaSOT datasets respectively.
ICT significance for Community Engagement during COVID in Rural India: An application suite

The effectiveness of Community Engagement programs such as Women’s Empowerment Projects (WEP) relies heavily on efficient communication, allocation of resources, and reporting. Despite having a functional operational model, the COVID-19 situation exacerbated the challenges faced by these projects in terms of communication and monitoring. To address this, we propose an ICT-based operational model that incorporates a specially developed field operations tracker application, WE-FOT. This application digitizes information gathering, monitoring, and reporting processes, resulting in a centralized repository and data portal for improved centralized reporting. This paper presents the architectural design, functional features, and effectiveness of the toolset in the specific context of WEP implementation across the rural communities of India.
3D scanning technologies by optical RealSense cameras for SIREN-based 3D hand representation

Modern 3D scanning technologies have revolutionized various industries, including healthcare and biomedical engineering. This research explores the application of 3D scanning in the field of medicine, focusing on the representation of 3D hand data using the SIREN (Sinusoidal Representation Network) approach. 3D scanning plays a vital role in hand prosthetics, enabling the development of personalized models that accurately replicate the shape and size of real hands. This allows for the production of prostheses tailored to the specific needs of patients, facilitating their reintegration into active life. RealSense 3D cameras, developed by Intel, are among the leading technologies for 3D scanning. However, the effective utilization of implicit representation of 3D data, such as SIREN, presents challenges in ensuring compatibility with the features and limitations of existing 3D scanning technologies. This study analyses the potential of SIREN for 3D hand data representation, addressing the existing constraints and limitations. By leveraging the capabilities of RealSense cameras and the flexibility of SIREN, we aim to enhance the analysis and processing of 3D data, opening new avenues for prosthesis design.
Scientists link earthquakes to fast radio bursts

Researchers in Japan have found striking similarities between the statistical behaviour of repeating fast radio bursts (FRBs) and earthquakes.
International education and export of educational services in the age of intelligent machines: from training of digital personnel to machine learning

The goal of this paper is to find out the specifics and to substantiate various strategic approaches to increasing the export of educational services and international education for developed and developing countries in the age of intelligent machines based on the training of digital personnel and machine learning, as well as to develop recommendations on the practical implementation of these approaches during the COVID-19 pandemic and crisis. The originality of this research consists in consideration of the digital context - the age of intelligent machines - in which development of international education and export of educational services takes place. The vivid negative influence of the COVID-19 pandemic and crisis has been proved; due to it, the level of machine learning in developed countries will decrease by 23.50%, and the level of training of digital personnel in developing countries will decrease by 7.18%.