Reviewerly/exHarmony huggingface dataset:
Planning and content of educational activities and training in educational organizations of the Ministry of Internal Affairs of the Russian Federation

Introduction. The relevance of the research is determined by amendments to Federal Law No. 273-FZ dated December 29, 2012 «On Education in the Russian Federation», which fixed general requirements for the organization of educational activitiesfor students. In accordance with these changes, it became necessary to work on the creation and approval of a standard work program for education and a standard calendar plan for educational activities and trainingin educational organizations of the Ministry of Internal Affairs of the Russian Federation.&#x0D; Research methods. The author used a set of general scientific methods of cognition (analysis, synthesis, induction, deduction).&#x0D; The results of the research. The analysis of regulatory legal acts of the Russian Federation regulating educational activities in educational institutions of higher education is carried out. Based on this analysis and taking into account the orientation of educationalactivities in the system of the Ministry of Internal Affairs of the Russian Federation, an approximate structure and content of a standard work program of educational activities and a standard calendar schedule of educational activities are proposed, as well as legal, organizational, methodological and methodological foundations for their development are outlined. The proposed guidelines for planning educational activities with cadets and students of educational organizations of the Ministry of Internal Affairs of Russia are recommended to be used by members of the educational process. The article presents a detailed list of educational activities. The following modules are highlighted: patriotic education, professional education, legal education, moral and ethical education, aesthetic education, spiritual and moral education, scientific and educationalactivity, physical education, environmental education.The allocation of the abovementioned modules is justified by the logic of building educational activities and the need to create an educational environment in educational organizations of the Ministry of Internal Affairs of Russia, as well as the requirements of legislation in the field of education and moral and psychological support for the activities of internal affairs bodies of the Russian Federation.
Fundamentals of documenting thefts of mobile communication devices

Introduction. The article analyses the current state and transformation of the mechanism of theft of mobile communication devices. The main ways of committing thefts are studied, as well as the techniques and methods used in this process. The relevance of the research is determined both by the high number of committed thefts of mobile communication devices and by certain aspects of legal regulation of the documentation process in their investigation.&#x0D; Methods. In the research various methods were used, including the universal scientific method of historical materialism; general scientific methods of cognition (comparison, ascent from abstract to concrete, analysis, synthesis, induction, deduction, generalisation, classification, abstraction), private scientific methods (formal-legal, comparative-legal, method of interpretation of legal norms, dogmatic), interdisciplinary method of formalisation.&#x0D; Results. The author analysed the most typical investigative situations in the investigation of mobile communication devices thefts and concluded that the process of documenting such crimes is related to their investigation being aimed at collecting, recording and analysing evidence related to the crime in order to identify the responsible parties and ensure the identification of the perpetrators of mobile communication device thefts. Certain difficulties are associated with the fact that documenting the theft of mobile communication devices is a complex integrated multi-stage task that requires careful attention and co-operation between all participants in the criminal justice process involved in the investigation of a particular criminal case.
Two-Level Integrity-Monitoring Method for Multi-Source Information Fusion Navigation

To address the issue of integrity monitoring for a multi-source information fusion navigation system, a theoretical framework of two-level integrity monitoring is proposed. Firstly, at the system level, a system-integrity-monitoring method based on the Kalman filter weighted least-squares form is established to detect and isolate faulty navigation sources. Secondly, at the sensor level, considering the redundancy of the faulty navigation sources, this paper presents the design of a multi-mode comprehensive fault-detection method for non-redundant navigation sources. Additionally, an extended-dimension matrix optimized sensor-fault detection and verification method for redundant navigation sources is proposed. Finally, integrity risk allocation criteria are established based on the effectiveness of navigation sources to calculate the system protection level and trigger integrity alarms. The two-level integrity-monitoring method was tested on a multi-source information fusion navigation system integrated with an inertial navigation system (INS), global positioning system (GPS), BeiDou satellite navigation system (BDS), Doppler velocity log (DVL), barometric altimeter (BA), and terrain-aided navigation (TAN). Test results demonstrate that the proposed method can effectively isolate the faulty navigation source within 10 s. Furthermore, it can detect the faulty sensors within the faulty navigation sources, thereby enhancing the reliability and robustness of the multi-source information fusion navigation system.
Design and Evaluation of an Asynchronous VR Exploration System for Architectural Design Discussion Content

Design discussion is crucial in the architectural design process. To enhance the spatial understanding of 3D space and discussion effectiveness, recently, some systems have been proposed to support design discussion interactively in an immersive virtual environment. The entire design discussion can be archived and potentially become course materials for future learners. In this paper, we propose an asynchronous VR exploration system that aims to help learners explore content effectively and efficiently anywhere and at any time. To improve effectiveness and efficiency, we also propose a summarization-to-detail approach with the application space by which students can observe the visualization of spatial summarization of actions and participants’ dwell time or the temporal distribution of dialogues and then locate the important or interesting region or dialogue for further exploration. To further explore the discussion content, students can call the preview to see the time-lapse animation of the object operation to understand the change in models or playback to view the discussion details. We conducted an exploratory user study with 10 participants to evaluate user experience, user impression, and effectiveness of learning the design discussion course content using our asynchronous VR design discussion content exploration system. The results indicate that the interactive VR exploration system presented can help learners study the design discussion content effectively. Participants also provided some positive feedback and confirmed the usefulness and value of the system presented. Our applications and lessons learned have implications for future asynchronous VR exploration systems, not only for architectural design discussion content, but also for other applications, such as industrial visual inspections and educational visualizations of design discussions.
STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection

Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy.
Cross-Regional Seismic Event Discrimination via Convolutional Neural Networks: Exploring Fine-Tuning and Ensemble Averaging

ABSTRACT The gradual densification of seismic networks has facilitated the acquisition of large amounts of data. However, alongside natural tectonic earthquakes, seismic networks also record anthropogenic events such as quarry blasts or other induced events. Identifying and distinguishing these events from natural earthquakes requires experienced interpreters to ensure that seismological studies of natural phenomena are not compromised by anthropogenic events. Advanced artificial intelligence methods have already been deployed to tackle this problem. One of the applications includes Convolutional Neural Networks (CNN) to discriminate different kinds of events, such as natural earthquakes and quarry blasts. In this study, we investigate the effects of ensemble averaging and fine-tuning on seismic event discrimination accuracy to estimate the potential of these methods. We compare discrimination accuracy of two different CNN model architectures across three datasets. This was done with the best models from an ensemble of each model architecture, as well as with ensemble averaging and fine-tuning methods. Soft voting was used for the CNN ensemble predictions. For the transfer learning approach, the models were pretrained with data from two of the datasets (nontarget regions) and fine-tuned with data from the third one (target region). The results show that ensemble averaging and fine-tuning of CNN models leads to better generalization of the model predictions. For the region with the lowest numbers of one event type, the combination of ensemble averaging and fine-tuning led to an increase in discrimination accuracy of up to 4% at station level and up to 10% at event level. We also tested the impact of the amount of training data on the fine-tuning method, showing, that to create a global model, the selection of comprehensive training data is needed.
Key Technologies of Intelligent Question-Answering System for Power System Rules and Regulations Based on Improved BERTserini Algorithm

With the continuous breakthrough of natural language processing, the application of intelligent question-answering technology in electric power systems has attracted wide attention. However, at present, the traditional question-answering system has poor performance and is difficult to apply in engineering practice. This paper proposes an improved BERTserini algorithm for the intelligent answering of electric power regulations based on a BERT model. The proposed algorithm is implemented in two stages. The first stage is the text-segmentation stage, where a multi-document long text preprocessing technique is utilized that accommodates the rules and regulations text, and then Anserini is used to extract paragraphs with high relevance to the given question. The second stage is the answer-generation and source-retrieval stage, where a two-step fine-tuning based on the Chinese BERT model is applied to generate precise answers based on given questions, while the information regarding documents, chapters, and page numbers of these answers are also output simultaneously. The algorithm proposed in this paper eliminates the necessity for the manual organization of professional question–answer pairs, thereby effectively reducing the manual labor cost compared to traditional question-answering systems. Additionally, this algorithm exhibits a higher degree of exact match rate and a faster response time for providing answers.
Introducing a New Metric for Improving Trustworthiness in Real Time Object Detection

This preliminary study, investigates the possible benefits of using a novel metric in real-time evaluation of outputs generated by Real-Time Object Detection Algorithms. Our primary goal is to improve the reliability of the detection process through the spatial analysis of the sequence of video frames used as input data for a Convolutional Neural Network (CNN). The method focuses on the analysis of the variations between consecutive output values from the CNN. By leveraging established similarity metrics, we try to identify patterns that signal potential instances of false positive predictions and develop a methodology-agnostic assessment of the CNN’s output quality. The paper concludes with some preliminary computational results that support the efficacy and potential applications of the proposed method.
Improving Handwritten Cyrillic OCR by Font-Based Synthetic Text Generator

In this paper, we propose a straight-forward and effective Font-based Synthetic Text Generator (FbSTG) to alleviate the need for annotated data required for not just Cyrillic handwritten text recognition. Unlike standard GAN-based methods, the FbSTG does not have to be trained to learn new characters and styles; all it needs is the fonts, the text, and sampled page backgrounds. In order to show the benefits of the newly proposed method, we train and test two different OCR systems (Tesseract, and TrOCR) on the Handwritten Kazakh and Russian dataset (HKR) both with and without synthetic data. Besides, we evaluate both systems’ performance on a private NKVD dataset containing historical documents from Ukraine with a high amount of out-of-vocabulary (OoV) words representing an extremely challenging task for current state-of-the-art methods. We decreased the CER and WER significantly by adding the synthetic data with the TrOCR-Base-384 model on both datasets. More precisely, we reduced the relative error in terms of CER/WER on (i) HKR-Test1 with OoV samples by around $$20\%$$ / $$10\%$$ , and (ii) NKVD dataset by $$24\%$$ CER and $$8\%$$ WER. The FbSTG code is available at: https://github.com/mhlzcu/doc_gen .
DB-BERT: making database tuning tools “read” the manual

Abstract DB-BERT is a database tuning tool that exploits information gained via natural language analysis of manuals and other relevant text documents. It uses text to identify database system parameters to tune as well as recommended parameter values. DB-BERT applies large, pre-trained language models (specifically, the BERT model) for text analysis. During an initial training phase, it fine-tunes model weights in order to translate natural language hints into recommended settings. At run time, DB-BERT learns to aggregate, adapt, and prioritize hints to achieve optimal performance for a specific database system and benchmark. Both phases are iterative and use reinforcement learning to guide the selection of tuning settings to evaluate (penalizing settings that the database system rejects while rewarding settings that improve performance). In our experiments, we leverage hundreds of text documents about database tuning as input for DB-BERT. We compare DB-BERT against various baselines, considering different benchmarks (TPC-C and TPC-H), metrics (throughput and run time), as well as database systems (PostgreSQL and MySQL). The experiments demonstrate clearly that DB-BERT benefits from combining general information about database tuning, mined from text documents, with scenario-specific insights, gained via trial runs. The full source code of DB-BERT is available online at https://itrummer.github.io/dbbert/ .
THE HOCKEY COLLECTIVE IDENTITY: COGNITIVE AND PSYCHOLOGICAL APPROACHES TO THE DISCOURSE ANALYSIS (Verbal and Multimodal Aspects)

The associative and interpretative analysis of speech units is a traditional practice in discourse analysis that is involved in discourse studies to demonstrate how participants of communication mutually agree on the meaning they structure during the discourse. The controlled experiments are held under conditions that are relatively unusual for the natural discourse practice as in the real-time discourse the process of meaning structuring and reconstruction is due to deep involvement in the situational context. That is why the purpose of our study was to complete the experiment’s results with the mental models or situation models reflecting the categories of thinking during structuring hockey discourse in order to develop the idea of collective hockey identity that arises from the discourse because of some typical discourse structures and typical language behaviour in the process of discourse. The purpose of the research enabled several tasks to be solved. The first one was to define whether there are any peculiarities of the hockey discourse. Secondly, to reconstruct a collective hockey identity according to the discourse they construct during communication. These tasks helped us to characterize the participants of hockey discourse as the ones with the status of “in-group” members and gives them a special level of trust from other members of the social group. The methods of the discourse analysis together with the cognitive modeling compose the basis of the research. In experiments 1 and 2, we found that the “in-member” and “off-member” statuses are drawn and ascribed by the participants according to the ability to correctly structure the meaning in hockey discourse. The cognitive analysis enabled us to build a cognitive model of collective hockey identity that is based on the representations of the hockey world drawn from the discourse in social media (Instagram pages of the NHL teams). Results of the experiments and cognitive modeling of the dominant concepts of the hockey collective identity prove several hypotheses. We found social effects of status demonstration via the construction of meaning in the process of discourse and the importance of being “in-member” for effective communication and satisfaction of the participants. Differences in hockey-oriented conversation between in-group members and off-group members are proved as we assumed in the hypothesis. We provide some evidence that in-group members` and off-group members` reconstruction of the meaning in communication can differ in conversations that are focused on a specific collaborative goal, though their verbal behavior is moderated by the employment of clear general vocabulary.Though the methods of the study introduce literal and statistical noise, putting people into more naturalistic contexts and examining discourse between interlocutors who have various levels of the English language competence can reveal differences that are hidden or discouraged in the laboratory.
ChatGPT as a Tool for User Story Quality Evaluation: Trustworthy Out of the Box?

Abstract In Agile software development, user stories play a vital role in capturing and conveying end-user needs, prioritizing features, and facilitating communication and collaboration within development teams. However, automated methods for evaluating user stories require training in NLP tools and can be time-consuming to develop and integrate. This study explores using ChatGPT for user story quality evaluation and compares its performance with an existing benchmark. Our study shows that ChatGPT’s evaluation aligns well with human evaluation, and we propose a “best of three” strategy to improve its output stability. We also discuss the concept of trustworthiness in AI and its implications for non-experts using ChatGPT’s unprocessed outputs. Our research contributes to understanding the reliability and applicability of Generative AI in user story evaluation and offers recommendations for future research.
AMEMD-FSL: fuse attention mechanism and earth mover’s distance metric network to deep learning for few-shot image recognition

In computer vision, image recognition is one of the classic tasks. Currently, with the foundation of big data and advanced hardware, deep learning has achieved high accuracy. However, deep learning often fails to perform well when faced with a small number of samples. Therefore, few-shot learning has become a key technology to solve this problem. The learning paradigm of few-shot learning is different from that of deep learning. It aims to learn a universal representation from multiple training categories, used for recognition in new categories. Each few-shot learning training instance consists of a group of images and an unlabeled sample. The goal is to enable the model to perform well in recognizing new categories. To achieve this, the model needs to extract representative and highly generalizable features that enable the correct recognition of new category samples. To address the problem of small sample space being unable to describe enough dataset’s semantic features, we propose the attention mechanism and earth mover’s distance for few-shot learning (AMEMD-FSL) method. First, we fuse the attention mechanism (AM) to deep learning to help the model extract more semantically rich features. Then we use the earth mover’s distance (EMD) metric method to calculate the distance between samples, enabling better classification. Finally, we combine the deep-learning residual network and AMEMD to perform few-shot learning. We validate our algorithm on the Caltech-UCSD Birds-200-2011 dataset and the few-shot public dataset mini-ImageNet, which comes from the DeepMind team. The experimental results demonstrate that we have proposed an end-to-end and effective method in the field of few-shot image classification.
Reviewing Crypto-Agility and Quantum Resistance in the Light of Agile Practices

Abstract The term crypto-agility means the ability to quickly and securely change cryptographic algorithms and related data, in the case of their compromise. In this context, the advent of quantum computing constitutes a new paradigm, which poses existential threats to current cryptographic algorithms. Even if these attacks are not an imminent danger, we must be prepared to change the cryptographic algorithms at risk with new, quantum resistant ones. This is by no means an easy task, because cryptographic algorithms are used everywhere and are often also implemented on the hardware. In this paper, we analyze the similarities and the differences between traditional agility and crypto-agility, and investigate the prospects of using agile and lean practices in the context of crypto-agility to introduce quantum resistant algorithms. In particular, for the main agile and lean practices we discuss if and how they can be useful for obtaining crypto-agility. We also investigate how the features key to crypto-agility can be helped by the agile and lean approach.