Reviewerly/exHarmony huggingface dataset:
The influence of gender personal and professional qualities of heads of general educational institutions on the effectiveness of management

Introduction. The relevance of the study is due to the fact that it is necessary to study the characteristics of male and female managers in order to develop ways to form general professional personality traits, as well as to compile programs for improving the skills of managers. The process of developing personal and professional qualities is especially important for a leader in the education system, as it affects the effectiveness of management in education. At the same time, the personal and professionally significant features of male and female managers from educational institutions have not been studied enough. Materials and methods. The experimental study was conducted on the basis of the Republican Center for Extracurricular Work (Russia, Kazan). Principals and head teachers of secondary schools, as well as methodologists and inspectors of the district departments of education of the Republic of Tatarstan of the Russian Federation took part in the experiment. A sample of 120 male and female principals were studied (30 persons in each group). The subjects ranged in age from 35 to 65 years old. The work experience was not less than 3 years. The components of self-attitude and professionally important personal qualities of male and female managers were evaluated. Gender differences were determined. Methods of mathematical statistics: Student's t-test and Pearson's χ2 agreement test. The results of the study. A comparative analysis of the data showed that female heads of educational institutions have high values of self-confidence (Хave=7.4) and self-leadership (Хave=8.6). Male managers have high values of self-confidence (Xave=7.2), self-leadership (Xave=8.3) and reflection of self-attitude (Xave=8.3). Female leaders are less diplomatic, collected, with a high super-ego, they have high intelligence, strength of I. Male leaders are less practical and conformal than female leaders. Conformity and practicality distinguish female managers from managers of the opposite sex (at p&lt;0.05). Conclusion. The materials of the article are of practical value for the heads of general educational institutions. The results obtained will enrich the theoretical material on the problem of professionally important qualities of female and male leaders in general. At the same time, the results of this study can help improve the quality of retraining of managers in the system of professional development. They will help the selection, recruitment, and placement of personnel. Keywords: education, manager, management
Symbolic Solution of Emerson-Lei Games for Reactive Synthesis

Emerson-Lei conditions have recently attracted attention due to their succinctness and compositionality properties. In the current work, we show how infinite-duration games with Emerson-Lei objectives can be analyzed in two different ways. First, we show that the Zielonka tree of the Emerson-Lei condition gives rise naturally to a new reduction to parity games. This reduction, however, does not result in optimal analysis. Second, we show based on the first reduction (and the Zielonka tree) how to provide a direct fixpoint-based characterization of the winning region. The fixpoint-based characterization allows for symbolic analysis. It generalizes the solutions of games with known winning conditions such as B\"uchi, GR[1], parity, Streett, Rabin and Muller objectives, and in the case of these conditions reproduces previously known symbolic algorithms and complexity results. We also show how the capabilities of the proposed algorithm can be exploited in reactive synthesis, suggesting a new expressive fragment of LTL that can be handled symbolically. Our fragment combines a safety specification and a liveness part. The safety part is unrestricted and the liveness part allows to define Emerson-Lei conditions on occurrences of letters. The symbolic treatment is enabled due to the simplicity of determinization in the case of safety languages and by using our new algorithm for game solving. This approach maximizes the number of steps solved symbolically in order to maximize the potential for efficient symbolic implementations.
BranchNorm: Robustly Scaling Extremely Deep Transformers

Recently, DeepNorm scales Transformers into extremely deep (i.e., 1000 layers) and reveals the promising potential of deep scaling. To stabilize the training of deep models, DeepNorm (Wang et al., 2022) attempts to constrain the model update to a constant value. Although applying such a constraint can benefit the early stage of model training, it may lead to undertrained models during the whole training procedure. In this paper, we propose BranchNorm, which dynamically rescales the non-residual branch of Transformer in accordance with the training period. BranchNorm not only theoretically stabilizes the training with smooth gradient norms at the early stage, but also encourages better convergence in the subsequent training stage. Experiment results on multiple translation tasks demonstrate that BranchNorm achieves a better trade-off between training stability and converge performance.
Development of object detection from point clouds of a 3D dataset by Point-Pillars neural network

Deep learning algorithms are able to automatically handle point clouds over a broad range of 3D imaging implementations. They have applications in advanced driver assistance systems, perception and robot navigation, scene classification, surveillance, stereo vision, and depth estimation. According to prior studies, the detection of objects from point clouds of a 3D dataset with acceptable accuracy is still a challenging task. The Point-Pillars technique is used in this work to detect a 3D object employing 2D convolutional neural network (CNN) layers. Point-Pillars architecture includes a learnable encoder to use Point-Nets for learning a demonstration of point clouds structured with vertical columns (pillars). The Point-Pillars architecture operates a 2D CNN to decode the predictions, create network estimations, and create 3D envelop boxes for various object labels like pedestrians, trucks, and cars. This study aims to detect objects from point clouds of a 3D dataset by Point-Pillars neural network architecture that makes it possible to detect a 3D object by means of 2D convolutional neural network (CNN) layers. The method includes producing a sparse pseudo-image from a point cloud using a feature encoder, using a 2D convolution backbone to process the pseudo-image into high-level, and using detection heads to regress and detect 3D bounding boxes. This work utilizes an augmentation for ground truth data as well as additional augmentations of global data methods to include further diversity in the data training and associating packs. The obtained results demonstrated that the average orientation similarity (AOS) and average precision (AP) were 0.60989, 0.61157 for trucks, and 0.74377, 0.75569 for cars.
Additive Class Distinction Maps using Branched-GANs

We present a new model, training procedure and architecture to create precise maps of distinction between two classes of images. The objective is to comprehend, in pixel-wise resolution, the unique characteristics of a class. These maps can facilitate self-supervised segmentation and objectdetection in addition to new capabilities in explainable AI (XAI). Our proposed architecture is based on image decomposition, where the output is the sum of multiple generative networks (branched-GANs). The distinction between classes is isolated in a dedicated branch. This approach allows clear, precise and interpretable visualization of the unique characteristics of each class. We show how our generic method can be used in several modalities for various tasks, such as MRI brain tumor extraction, isolating cars in aerial photography and obtaining feminine and masculine face features. This is a preliminary report of our initial findings and results.
Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flow

Low-resolution point clouds are challenging for object detection methods due to their sparsity. Densifying the present point cloud by concatenating it with its predecessors is a popular solution to this challenge. Such concatenation is possible thanks to the removal of ego vehicle motion using its odometry. This method is called Ego Motion Compensation (EMC). Thanks to the added points, EMC significantly improves the performance of single-frame detectors. However, it suffers from the shadow effect that manifests in dynamic objects' points scattering along their trajectories. This effect results in a misalignment between feature maps and objects' locations, thus limiting performance improvement to stationary and slow-moving objects only. Scene flow allows aligning point clouds in 3D space, thus naturally resolving the misalignment in feature spaces. By observing that scene flow computation shares several components with 3D object detection pipelines, we develop a plug-in module that enables single-frame detectors to compute scene flow to rectify their Bird-Eye View representation. Experiments on the NuScenes dataset show that our module leads to a significant increase (up to 16%) in the Average Precision of large vehicles, which interestingly demonstrates the most severe shadow effect. The code is published at https://github.com/quan-dao/pc-corrector.
What Else Can Voronoi Diagrams Do For Diameter In Planar Graphs?

The Voronoi diagrams technique was introduced by Cabello to compute the diameter of planar graphs in subquadratic time. We present novel applications of this technique in static, fault-tolerant, and partially-dynamic undirected unweighted planar graphs, as well as some new limitations. 1. In the static case, we give $n^{3+o(1)}/D^2$ and $\tilde{O}(n\cdot D^2)$ time algorithms for computing the diameter of a planar graph $G$ with diameter $D$. These are faster than the state of the art $\tilde{O}(n^{5/3})$ when $D<n^{1/3}$ or $D>n^{2/3}$. 2. In the fault-tolerant setting, we give an $n^{7/3+o(1)}$ time algorithm for computing the diameter of $G\setminus \{e\}$ for every edge $e$ in $G$ the replacement diameter problem. Compared to the naive $\tilde{O}(n^{8/3})$ time algorithm that runs the static algorithm for every edge. 3. In the incremental setting, where we wish to maintain the diameter while while adding edges, we present an algorithm with total running time $n^{7/3+o(1)}$. Compared to the naive $\tilde{O}(n^{8/3})$ time algorithm that runs the static algorithm after every update. 4. We give a lower bound (conditioned on the SETH) ruling out an amortized $O(n^{1-\varepsilon})$ update time for maintaining the diameter in *weighted* planar graph. The lower bound holds even for incremental or decremental updates. Our upper bounds are obtained by novel uses and manipulations of Voronoi diagrams. These include maintaining the Voronoi diagram when edges of the graph are deleted, allowing the sites of the Voronoi diagram to lie on a BFS tree level (rather than on boundaries of $r$-division), and a new reduction from incremental diameter to incremental distance oracles that could be of interest beyond planar graphs. Our lower bound is the first lower bound for a dynamic planar graph problem that is conditioned on the SETH.
Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering

Graph neural networks (GNNs) based methods have achieved impressive performance on node clustering task. However, they are designed on the homophilic assumption of graph and clustering on heterophilic graph is overlooked. Due to the lack of labels, it is impossible to first identify a graph as homophilic or heterophilic before a suitable GNN model can be found. Hence, clustering on real-world graph with various levels of homophily poses a new challenge to the graph research community. To fill this gap, we propose a novel graph clustering method, which contains three key components: graph reconstruction, a mixed filter, and dual graph clustering network. To be graph-agnostic, we empirically construct two graphs which are high homophily and heterophily from each data. The mixed filter based on the new graphs extracts both low-frequency and high-frequency information. To reduce the adverse coupling between node attribute and topological structure, we separately map them into two subspaces in dual graph clustering network. Extensive experiments on 11 benchmark graphs demonstrate our promising performance. In particular, our method dominates others on heterophilic graphs.
Image Captioners Sometimes Tell More Than Images They See

Image captioning, a.k.a. "image-to-text," which generates descriptive text from given images, has been rapidly developing throughout the era of deep learning. To what extent is the information in the original image preserved in the descriptive text generated by an image captioner? To answer that question, we have performed experiments involving the classification of images from descriptive text alone, without referring to the images at all, and compared results with those from standard image-based classifiers. We have evaluate several image captioning models with respect to a disaster image classification task, CrisisNLP, and show that descriptive text classifiers can sometimes achieve higher accuracy than standard image-based classifiers. Further, we show that fusing an image-based classifier with a descriptive text classifier can provide improvement in accuracy.
OSDaR23: Open Sensor Data for Rail 2023

To achieve a driverless train operation on mainline railways, actual and potential obstacles for the train's driveway must be detected automatically by appropriate sensor systems. Machine learning algorithms have proven to be powerful tools for this task during the last years. However, these algorithms require large amounts of high-quality annotated data containing railway-specific objects as training data. Unfortunately, all of the publicly available datasets that tackle this requirement are restricted in some way. Therefore, this paper presents OSDaR23, a multi-sensor dataset of 45 subsequences acquired in Hamburg, Germany, in September 2021, that was created to foster driverless train operation on mainline railways. The sensor setup consists of multiple calibrated and synchronized infrared (IR) and visual (RGB) cameras, lidars, a radar, and position and acceleration sensors mounted on the front of a rail vehicle. In addition to the raw data, the dataset contains 204091 polyline, polygonal, rectangle, and cuboid annotations in total for 20 different object classes. It is the first publicly available multi-sensor dataset annotated with a variety of object classes that are relevant for the railway context. OSDaR23, available at data.fid-move.de/dataset/osdar23, can also be used for tasks beyond collision prediction, which are listed in this paper.
SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data

This paper describes the results of SemEval 2023 task 7 -- Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT) -- consisting of 2 tasks, a Natural Language Inference (NLI) task, and an evidence selection task on clinical trial data. The proposed challenges require multi-hop biomedical and numerical reasoning, which are of significant importance to the development of systems capable of large-scale interpretation and retrieval of medical evidence, to provide personalized evidence-based care. Task 1, the entailment task, received 643 submissions from 40 participants, and Task 2, the evidence selection task, received 364 submissions from 23 participants. The tasks are challenging, with the majority of submitted systems failing to significantly outperform the majority class baseline on the entailment task, and we observe significantly better performance on the evidence selection task than on the entailment task. Increasing the number of model parameters leads to a direct increase in performance, far more significant than the effect of biomedical pre-training. Future works could explore the limitations of large models for generalization and numerical inference, and investigate methods to augment clinical datasets to allow for more rigorous testing and to facilitate fine-tuning. We envisage that the dataset, models, and results of this task will be useful to the biomedical NLI and evidence retrieval communities. The dataset, competition leaderboard, and website are publicly available.
Calibration of Syndrome Measurements in a Single Experiment

Methods of quantum error correction are starting to be beneficial on current quantum computing hardware. Typically this requires to perform measurements which yield information about the occurred errors on the system. However, these syndrome measurements themselves introduce noise to the system. A full characterization of the measurements is very costly. Here we present a calibration method which allows to take the additional noise into account. Under reasonable assumptions we require only a single additional experiment. We give examples of how to apply this method to noise estimation and error correction. Finally we discuss the results of experiments carried out on an IBM quantum computer.
Development managing stages of hybrid intelligent learning environment for student’s mathematics research activities

Problem and aim. The search for relevant solutions to the restructuring of the architecture of modern education, elements of a new system through the organization of an intellectual learning environment adapted to the research activities of schoolchildren on the development of complex mathematical knowledge is a research problem. Materials and methods. 52 teachers and 234 students of schools in Moscow (Russian Federation) took part in the survey. Diagnostic methods were aimed at obtaining data on the use of various digital resources and tools in everyday learning activities and the level of their intellectualization. The methods of descriptive statistics were used (variation series, histograms, measures of central tendency and scatter), as well as Pearson's χ2-test. The results of the study. About 90% of the surveyed teachers and 80% of schoolchildren spoke in favor of the idea of introducing intelligent systems into the learning process, but more than 75% of respondents cannot give examples of such systems and environments. The opinion of trainees on the introduction of digital tools into the educational process differs significantly from the opinion of their mentors (χ2 emp=33,4&gt; χ2 cr (0,01)=2,65). The main stages of designing an intelligent learning system are presented: the development of methodological provisions for the intelligent management of the educational process; determination of personalized parameters of scientific potential that determine the structure of educational and research content; development of a database and knowledge of the subject of project activities; choice of architecture and functionality of the hybrid neural network. Conclusion. The author's methodological and technological solutions will provide scientists with a reference point for further research in the field of improving high-tech forms of e-learning, and teachers will have the opportunity to explore practical ways to implement personalized and adaptive distance learning through automated intelligent systems. Keywords: intelligent systems, research activities, teaching mathematics, complex knowledge
Impact of digital culture on shaping young people’s creative activity

Introduction. The object of research is the intensive impact of digital technologies on the development of culture, politics, economy and education, which gives rise to new socio-cultural practices and creative environments for human development. The goal of the article is to analyze digital culture and the degree of its influence on the formation of creative activity of adolescents and young people. Materials and methods. The research materials are based on the analysis of digital culture as a new concept of the globalisation epoch and the specifics of its influence on modern young people’s creative activity. Since the research has a complex character it synthesises the analytical methods of different humanities disciplines. Using such methods as cultural-historical, cultural-semiotic, social-analytical, contextual and competence-based allows for an opportunity to undertake a full-value complex analysis of the impact of digital culture on the formation of young people’s creative activity. Results. The digital transformation of educational environment, in the author’s opinion, manifests itself in the following trends: differentiated approach to designing the education system (at schools and universities) towards extending its models; transformation of education into a continuous process following the growth of new knowledge and the need for permanent updating of knowledge and competencies. The teacher’s functions are also changing – the pedagogue now acts more frequently as a tutor and instructor. Digital transformation of the educational environment also implies a new level of responsibility, new models of education; the above is rooted in the fact that a graduate of educational institution should not only be professionally competent and creative, but also be in demand in the labour market. Discussion on the results. Culture and its transformation in the conditions of globalisation and digitalisation is covered by research of scientists representing various fields of knowledge (Russian and foreign), which makes it possible to hold an interdisciplinary discussion on the impact of digital culture on the individual. The discussion materials make it possible to understand how digital culture influences the views on the meaning of existence, how it transforms the human life, helps to comprehend the new forms and methods of communication and education. Conclusion. The implemented research helps to outline the further ways of handling young people’s prospects, formation of their creative activity in the conditions of digital culture development with regard for current requirements for the educational system. Keywords: globalisation, digital culture, socio-cultural practices, internet culture, students, media education, creative activity