Reviewerly/exHarmony huggingface dataset:
Automated Dating of Galaktion Tabidze’s Handwritten Texts

The study of Georgian history greatly involves the fundamental investigation of the handwritten texts. The scientists use different methods for this investigation, but they face different problems. It is related to the fact that the time of the text creation of manuscript in the most cases differs from the date of the manuscript creation and the identification of each of them needs special approach. The automatic dating approaches of the manuscripts is now very common in practice, and it involves the work of computer scientists. The paper studies the approaches of the dating the manuscripts and offers the novel methodology for dating Georgian ones using neural networks. The authors offer the neural network and train it with the existing data. The system is trained using the classified writings of Galaktion Tabidze, which is the greatest Georgian poet of XX century. The system is needed for dating his manuscripts, which are not dated yet. It is very important for the study of Georgian history. The paper also offers the trained model, which can classify Georgian handwritings of Galaktion Tabidze. The received system has the accuracy score, which is considered good in comparison with the related research in this field. The offered approach can be used to date the handwritings of different authors.
Approaches to Improving the Accuracy of Machine Learning Models in Requirements Elicitation Techniques Selection

Selecting techniques is a crucial element of the business analysis approach planning in IT projects. Particular attention is paid to the choice of techniques for requirements elicitation. One of the promising methods for selecting techniques is using machine learning algorithms trained on the practitioners' experience considering different projects' contexts. The effectiveness of ML models is significantly affected by the balance of the training dataset, which is violated in the case of popular techniques. The paper aims to analyze the efficiency of the Synthetic Minority Over-sampling Technique usage in Machine Learning models for elicitation technique selection in case of the imbalanced training dataset and possible ways for positive feature importance selection. The computational experiment results confirmed the effectiveness of using the proposed approaches to improve the accuracy of machine learning models for selecting requirements elicitation techniques. Proposed approaches can be used to build Machine Learning models for business analysis activities planning in IT projects.
Design and Development of a Hybrid Eye and Mobile Controlled Wheelchair Prototype using Haar cascade Classifier: A Proof of Concept

According to the wheelchair foundation, about 1.86% of the world’s population requires a functional wheelchair. Most of these wheelchairs have manual control systems which puts millions of people with total paralyzes (total loss of muscle control including the head) at a disadvantage. However, the majority of those who suffer from muscular and neurological disorders still retain the ability to move their eyes. Hence the concept of eye-controlled wheelchair. This paper focused on the design and development of a hybrid control system (eye and mobile interface) for a wheelchair prototype as a proof of concept. The system was implemented using the pre-trained Haar cascade ML classifier in open CV. Focus was shifted from high accuracy common to lab-based studies to deployment and power consumption which are critical to usability. The system consists of a motor chassis that takes the place of a wheelchair, a raspberry pi4 module which acts as a mini-computer for image and information processing, and a laser sensor to achieve obstacle avoidance. The Bluetooth module enables serial communication between the motor chassis and the raspberry pi, while the power supply feeds the raspberry pi and the camera. The system performance evaluation was carried out using obstacle avoidance and navigation tests. An accuracy of 100% and 89% were achieved for obstacle avoidance and navigation, respectively, which shows that the system would be helpful for wheelchair users facing autonomous mobility issues.
A Modified Method and an Architecture of a Software for a Multivariate Polynomial Regression Building Based on the Results of a Conditional Active Experiment

The application scope of the results presented in this paper is the creation of mathematical support for intelligent expert and diagnostic systems based on nonlinear regression models. The authors own an original method of estimating the coefficients at nonlinear terms of a multivariate polynomial regression given by its redundant representation based on the results of an active experiment. We reduce this problem solution to the sequential building of univariate polynomial regressions. The coefficients at nonlinear terms of the regressions are estimated by the original algorithmic procedure. For each univariate polynomial regression, we successively formulate nondegenerate systems of linear equations in which variables are the coefficients at nonlinear terms of the multivariate polynomial regression given by the redundant representation. We proposed to estimate the coefficients at linear terms by known methods. The main disadvantages of the proposed method were the need for the existence of a common range of possible values of all deterministic input variables and independence of the range of possible values of a part of the input variables at fixed values of all other input variables. These are significant drawbacks when designing, in particular, medical diagnostic systems. For example, if we fix the patient’s age as a constant parameter, then this age will determine the range of changes to other input characteristics according to which the disease is diagnosed. This work presents a modification of the method that allows extending its scope to the cases formulated above, that is, when the common range of possible values of the input variables is an empty set or when the range of possible values of the input variables depends on the values of the fixed input variables in all tests of an active experiment. Also, with the help of an original fact of the general theory of the least squares method that we mention here, we have achieved an increase in the efficiency of estimating the coefficients at linear terms of a multivariate polynomial regression.
Web Application State Management Performance Optimization Methods

The use of various libraries often leads to a decrease in the speed of the web application and the complexity of the execution of the program code. The object of this research is the process of storing and managing the data of the client part of the web application, the subject of the research is the software methods of managing the local state of the data of the web application. The research objective is the data processing time reduction of web applications relative to existing software methods. The main idea of the proposed method is to use an atomic approach to the state of the web application data. Having an arbitrary entity, in the general state of the web application, a state fragment is created that is responsible only for this entity. Such a fragment is independent of other state fragments and can only work with the encapsulated entity. Using encapsulation, the configuration of an entity is passed to the React Context API as an object containing data and functions that modify it. The developed software method was compared with popular state management libraries Redux, MobXState-Tree and Recoil. Comparing each of the test scenarios in a percentage ratio, an average decrease in program execution time by 17% was obtained. The analysis of methods was performed using the SonarQube utility. To evaluate the results of the software methods, the Google Chrome browser utility DevTools was used. Based on decreased program execution time it can be stated that proposed optimized software method allows reduce the data processing time and optimize the state management of web applications. The research value is that such a software method can replace existing popular libraries with better proposed solution.
Accounting for diverse feature-types improves patient stratification on tabular clinical datasets

Tabular Clinical and Biomedical Routine Data (CBRD) contains diverse feature types. Recent research shows that the conventional application of Uniform Manifold Projection and Approximation (UMAP) to extract clusters from the low dimensional embedding can prove ineffective due to the diverse feature types in such datasets. Feature-type Distributed Clustering (FDC) workflow accounts for these diverse feature types resulting in a more informative low-dimensional embedding. However, a rigorous assessment of the FDC algorithm is missing so far. In this work, we conducted comprehensive benchmarking experiments to compare the quality of the cluster distributions and low dimensional embeddings generated by the FDC against that of the ones generated by UMAP using standard objective measures: Silhouette score, Dunn index, and ANOVA. Our results confirm that FDC can indeed be the better choice to embed tabular data with diverse feature types in low dimensions and thereby extract clusters from such an embedding. In addition, we provide a rationale behind the choice of metrics proposed in the FDC workflow. Moreover, we also point out some problems with the original Canberra metric used to reduce ordinal features in the FDC workflow and provide a solution in the form of a modified version of the Canberra metric. Using seven datasets from the medical domain for benchmarking, we demonstrate that FDC leads to improved patient stratification.
Overhead object projector: OverProjNet

Despite the availability of preventive and protective systems, accidents involving falling overhead objects, particularly load-bearing cranes, still occur and can lead to severe injuries or even fatalities. Therefore, it has become crucial to locate the projection of heavy overhead objects to alert those beneath and prevent such incidents. However, developing a generalized projection detector capable of handling various overhead objects with different sizes and shapes is a significant challenge. To tackle this challenge, we propose a novel approach called OverProjNet, which uses camera frames to visualize the overhead objects and the ground-level surface for projection detection. OverProjNet is designed to work with various overhead objects and cameras without any location or rotation constraints. To facilitate the design, development, and testing of OverProjNet, we provide two datasets: CraneIntenseye and OverheadSimIntenseye. CraneIntenseye comprises actual facility images, positional data of the overhead objects, and their corresponding predictions, while OverheadSimIntenseye contains simulation data with similar content but generated using our simulation tool. Overall, OverProjNet achieves high detection performance on both datasets. The proposed solution's source code and our novel simulation tool are available at https://github.com/intenseye/overhead_object_projector. For the dataset and model zoo, please send an email to the authors requesting access at https://drive.google.com/drive/folders/1to-5ND7xZaYojZs1aoahvu6BkLlYxRHP?usp=sharing.
ICDAR 2023 Competition on Reading the Seal Title

Reading seal title text is a challenging task due to the variable shapes of seals, curved text, background noise, and overlapped text. However, this important element is commonly found in official and financial scenarios, and has not received the attention it deserves in the field of OCR technology. To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2). We constructed a dataset of 10,000 real seal data, covering the most common classes of seals, and labeled all seal title texts with text polygons and text contents. The competition opened on 30th December, 2022 and closed on 20th March, 2023. The competition attracted 53 participants and received 135 submissions from academia and industry, including 28 participants and 72 submissions for Task 1, and 25 participants and 63 submissions for Task 2, which demonstrated significant interest in this challenging task. In this report, we present an overview of the competition, including the organization, challenges, and results. We describe the dataset and tasks, and summarize the submissions and evaluation results. The results show that significant progress has been made in the field of seal title text reading, and we hope that this competition will inspire further research and development in this important area of OCR technology.
An Analysis Tool for Cryptographic Designs Based on Chaotic Systems

Chaos-based cryptography research is one of the application areas for chaotic systems. Numerous design studies have been put up that take use of the connection between chaos and cryptography. This study has demonstrated how to exploit this relationship to decrypt cryptography designs. It has been looked at if chaos analysis techniques may be used to analyze cryptography protocols. The effectiveness of random number generators has been evaluated using Lyapunov exponents, a chaos analysis technique. The findings of the investigation demonstrated that Lyapunov exponents can be utilized as a standard in assessing random number generators. The paper highlights the issues with the NIST test suite, a popular method of analysis for assessing the statistical characteristics of random number generators. These issues have been seen to not exist with the new test tool that has been suggested. These findings demonstrate that the suggested strategy can be successfully applied in a variety of future applications.
ICDAR 2023 Competition on RoadText Video Text Detection, Tracking and Recognition

In this report, we present the final results of the ICDAR 2023 Competition on RoadText Video Text Detection, Tracking and Recognition. The RoadText challenge is based on the RoadText-1K dataset and aims to assess and enhance current methods for scene text detection, recognition, and tracking in videos. The RoadText-1K dataset contains 1000 dash cam videos with annotations for text bounding boxes and transcriptions in every frame. The competition features an end-to-end task, requiring systems to accurately detect, track, and recognize text in dash cam videos. The paper presents a comprehensive review of the submitted methods along with a detailed analysis of the results obtained by the methods. The analysis provides valuable insights into the current capabilities and limitations of video text detection, tracking, and recognition systems for dashcam videos.
ICDAR 2023 CROHME: Competition on Recognition of Handwritten Mathematical Expressions

This paper overviews the 7th edition of the Competition on Recognition of Handwritten Mathematical Expressions. ICDAR 2023 CROHME proposes three tasks with three different modalities: on-line, off-line and bimodal. 3905 new handwritten equations have been collected to propose new training, validation and test sets for the two modalities. The complete training set includes previous CROHME training set extented with complementary off-line (from OffRaSHME competition) and on-line samples (generated). The evaluation is conducted using the same protocol as the previous CROHME, allowing a fair comparison with previous results. This competition allows for the first time the comparison of the on-line and off-line systems on the same test set. Six participating teams have been evaluated. Finally the same team won all 3 tasks with more than 80% of expression recognition rate.
Research on the Development Path of Innovation and Entrepreneurship Education in Colleges and Universities from the Perspective of Marx's Educational Equity Thought

Based on the foundation of the historical materialism and dialectical materialism, Marx's thought of educational equity is the theoretical source of guiding the development of educational equity in China. Marx's educational equity thought is an important part of Marx's educational thought. Innovation and entrepreneurship education is an important part of current higher education and the breakthrough of reform, the embodiment of social equity in the field of education, and the extension of fair rights in political, economic, cultural and other fields in the field of innovation and entrepreneurship education. Since the 18th National Congress of the Communist Party of China, the development of the Party's educational thought has made new achievements. This paper, based on Marx's thought of educational equity, combined with the new development of the thought and practice of educational equity since the 18th National Congress of the Communist Party of China, and taking the reform effect of innovation and entrepreneurship education in colleges and universities as the object, further puts forward the high-quality development path of innovation and entrepreneurship education in the new era.
Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers

Specifying and solving Constraint-based Optimization Problems (COP) has become a mainstream technology for advanced motion control of mobile robots. COP programming still requires expert knowledge to transform specific application context into the right configuration of the COP parameters (i.e., objective functions and constraints). The research contribution of this paper is a methodology to couple the context knowledge of application developers to the robot knowledge of control engineers, which, to our knowledge, has not yet been carried out. The former is offered a selected set of symbolic descriptions of the robots' capabilities (its so-called "behavior semantics") that are translated in control actions via "templates" in a "semantic map"; the latter contains the parameters that cover contextual dependencies in an application and robot vendor-independent way. The translation from semantics to control templates takes place in an "interaction layer" that contains 1) generic knowledge about robot motion capabilities (e.g., depending on the kinematic type of the robots), 2) spatial queries to extract relevant COP parameters from a semantic map (e.g., what is the impact of entering different types of "collision areas"), and 3) generic application knowledge (e.g., how the robots' behavior is impacted by priorities, emergency, safety, and prudence). This particular design of, and interplay between, the application, interaction, and control layers provides a structured, conceptually simple approach to advance the complexity of mobile robot applications. Eventually, industry-wide cooperation between representatives of the application and control communities should result in an interaction layer with different standardized versions of semantic complexity.
Pruning DETR: efficient end-to-end object detection with sparse structured pruning

Deep learning methods in the field of object detection have made significant progress in terms of performance, but end-to-end implementations still face challenges. Recently, the transformer-based DETR model successfully introduced the attention mechanism into object detection tasks, achieving end-to-end object detection. However, despite its competitive accuracy, DETR still falls short in terms of inference speed and computational costs. To address this issue, this paper proposes an optimization of the DETR model using structured pruning through sparsity-induced pruning, aiming to improve its inference speed and reduce computational costs. We adjust the importance of module outputs through parameter scaling factors and sparse regularization terms, and optimize the parameter scaling factors using an improved Accelerated Proximal Gradient (APG) method. Experimental results on the COCO dataset demonstrate that our approach achieves a computational cost reduction of over 32% while maintaining an AP value of 42.7%, resulting in an inference speed improvement of over 17% to reach 32.7 FPS. This study provides an effective solution for further enhancing the computational efficiency of transformer-based object detection models.
Multi-party private size comparison protocol under the semi-quantum condition

Abstract Based on <?CDATA $d$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> </mml:math> -dimensional multiparticle maximal entangled states, a novel multi-party private comparison protocol under semi-quantum condition is proposed. With the help of a semi-honest quantum third party, <?CDATA $N$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>N</mml:mi> </mml:math> classical participants can determine the size relationship of their privacies while keeping them private in the devised protocol. Compared with some similar protocols, the presented protocol performs better in terms of security and qubit efficiency. In addition, the effectiveness and the correctness of our protocol are verified with some examples and proofs. By the decoy particles and the pre-shared keys, the presented protocol can defend against the internal and the external attacks.
A fuzzy cognitive map-based algorithm for predicting water consumption in Spanish healthcare centres

Abstract The management of water consumption in healthcare centres can have positive impacts on both the environmental performance and profitability of health systems. Computational tools assist in the decision-making process of managing the operation and maintenance of healthcare centres. This research aimed to integrate the empirical knowledge of experts in Healthcare Engineering and the historical data from 66 healthcare centres in a Fuzzy Cognitive Map. The outputs of the predictive model included water consumption, water cost, and CO 2 emissions in healthcare facilities, along with eleven variables to discover the causes and consequences of water consumption in healthcare centres. A healthcare centre with about 12 350 users, located in a city that experiences an average of 1100 heating degree days, whose facilities be moderately energy-efficient contributing over 50% with renewable energies is expected to consume 8.4 dam 3 of water with 32.1 k€ of cost, and contribute realising 30.8 ton CO 2 eq emissions. The use of Fuzzy Cognitive Maps for prediction can provide a high level of effectiveness in identifying the factors that contribute to water consumption and in designing key performance indicators to manage the environmental performance of healthcare buildings. This tool is extremely effective in enhancing the performance of the management division of health systems.