Reviewerly/exHarmony huggingface dataset:
Multi-Modal OCR System for the ICT Global Supply Chain

Optical Character Recognition (OCR) tools have been widely used to extract text content from images in many applications including Information and Communications Technology (ICT) supply chains. Due to the characteristics of datasheets in global ICT supply chains, models trained with popular public datasets often suffer from domain adaptation problems. First, popular open source text recognition datasets do not contain all the characters and symbols that appear in the ICT documents, meaning that models trained with these datasets cannot recognize these special characters and symbols. Second, these datasets also do not contain the samples with multiple words and multiple lines assuming that there is an Text Detection model that can extract text areas perfectly, which is not practical for ICT documents. Besides, as far as we know, there is no open-source dataset specifically designed that can be used to evaluate the OCR tools in the ICT domain. Therefore, in this study, we first build a benchmark dataset for the text recognition problem in the ICT domain, which includes the special characters and symbols in the ICT domain, and samples with multiple lines and multiple words. Then we propose a novel multi-modal sequence-to-sequence model, which not only take images as input but also their corresponding their texts generated by a pre-trained model. We conducted extensive experiments to evaluate the proposed multi-modal method on the proposed dataset, and the empirical results show that the proposed method can recognize special characters and symbols, samples with multiple lines and multiple words, outperform benchmark models consistently.
Differentially-Private Federated Intrusion Detection via Knowledge Distillation in Third-party IoT Systems of Smart Airports

With the increasing deployment of IoT and Industry 4.0, the federated learning system was presented to preserve the privacy between the third-party IoT systems and the security operation center in smart airports. Nonetheless, the extremely skewed distribution of cyber threats increases the complexity of intrusion detection system (IDS) in smart airports, while privacy preservation limits the utility of IDS in the process of server model update. In this article, we have devised a knowledge distillation (KD)-based Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) model to improve the accuracy of multiple intrusion detection. In addition, the tradeoff between privacy and accuracy is achieved by denoising the adaptive parameter update mechanism to upgrade the optimizer of Differentially-Private (DP) Federated IDS. The results indicate high effectiveness and robustness of DP Federated KD-based IDS for third-party IoT systems of a smart airport.
ADM-OSC: an industry initiative for communicating object-based audio data

Spatial and immersive audio has become increasingly mainstream, presented in concert halls and more recently (e.g. Apple: June 2021) through music streaming services. There is a diverse ecosystem of hardware and software controllers and renderers in both live and studio settings that would benefit from a standardized communication protocol. Since 2021 a growing group of industry stakeholders has been working to develop ADM-OSC to fill this need. ADM-OSC proposes a standard for transmitting position data for object-based audio by implementing a namespace in parallel with the Audio Definition Model (ADM), a metadata standard developed in the broadcast industry. Open Sound Control (OSC) is a well-established data transport protocol developed for flexible and accurate communication of real-time performance data. By leveraging these open standards, we have created a lightweight specification that can be easily implemented in audio software, plugins, game engines, consoles, and controllers.This paper will discuss the design principals of ADM-OSC, developed to meet the needs of specific stakeholders and use cases. The core address space for position data is described, including a solution for representing both physics-based and cinematic distance. Proposed extensions to the next version of the specification will add comprehensive support for additional broadcast use cases, including different types of audio content (e.g., different languages or assistive descriptions), user interactivity, and cues for reducing channel count. We conclude with an overview of future ADM-OSC development, including next steps in bringing together ideas and discussion from multiple industry partners.
Performance Improvement of Pure Persuit Alogorithm using PID Control

Self-driving cars are making very important technological advances in modern society and contribute greatly to increasing driver convenience and road safety. a sense of ride is one of the important factors in increasing the comfortable driving and reliability of drivers and passengers, and this paper proposes a way to improve the performance of the pure pursuit algorithm by combining pid control and integration accumulation prevention. The pure pursuit algorithm is a technique that is widely used for path tracking and is an algorithm that uses only the vehicle's equation of motion and path tracking. Therefore, in order to solve the error caused by integration accumulation occurring at high speed, the cumulative prevention method is applied to improve the performance of the algorithm.
OPTIMIZATION OF THE ANALOG MODULE ACCORDING TO NOISE CHARACTERISTICS IN THE ON-BOARD DETECTOR OF THE RECORDER-ANALYZER FOR THE IDENTIFICATION OF CHARGED PARTICLES

In analog amplifiers of weak signals, the question arises of finding the optimal signal/noise ratio based on noise characteristics for further discretization of the signal by digital methods. Using computer simulations is not a solution to this problem. Because the developers of electronics simulators do not reveal the progress of calculations as usual, or provide the opportunity to independently enter formulas for calculation with the unpredictable probability of forecasting results.In current work, a method for calculating and optimizing the noise level has been developed. It is proposed to be used for debugging circuits based on operational amplifiers, which will save time and money during further testing and optimization of the circuit before its manufacture. And the developers in the datasheet specify the noise parameters without taking into account all the cascade (with open feedback loop, etc.). That is why, a method is needed that takes into account the noise parameters of the switching circuit. The key parameters of the cascades, which have an impact on the reliability of sampling, are among other types: inherent noises of operational amplifiers and thermal noises of the elements of the switching circuit, which are optimized by reduction to a single basis and checked for compliance with the research objectives. In addition, the sequence of actions that establishes the accuracy of discretization of the system with the influence of noise voltage is described.As a result, the solution of the noise level minimization problem by simplified computer calculation is accelerated. Signal-to-noise ratio optimization achieved by non-linear programming method.The proposed method can be used both for analysis discrete and integrated circuits. The results of the work have been used for optimization of the existing analog scheme of the small-caliber spectrometer MiRA_ep that is planning to be launched into low near-Earth orbit to study the nature of the high-energy electron and proton microbursts.
COMBINED APPROACHES TO THE STATIC CODE ANALYSIS USING NEURAL NETWORKS

This article presents an overview of possible approaches to the application of neural networks in the process of static code analysis. It explores the current state of affairs in existing approaches to improving program analysis using machine learning methods, including postprocessing of static analysis alerts, preprocessing of source code, or direct use of machine learning for analyzing source code. Additionally, the article examines the main directions for applying approaches from each category. Both classical approaches and machine learning methods in program analysis possess distinct strengths and weaknesses that should be considered when implementing them in practice. One of the main theses of this research is that understanding the capabilities of combining these approaches, leveraging the flexibility offered by neural networks while maintaining a sufficient level of reliability provided by classical algorithms, is crucial for building a high-quality system. This article covers the following three basic directions of the application of neural networks for the static source code analysis. The first direction is a specification tuning: a refinement of specifications produced by a ‘classic’ static code analyzer (a removal, clustering, ranking of warnings or just assistance in manual warning analysis, etc.). The second direction is a specification inference, to find specifications hidden in code (feature extraction, selection, or code transformation retaining its behaviour, e.g. to make it more suitable for the ‘classic’ static analysis tools). The third way is a black box analysis to discover and fix code defects (syntactic, semantic ones or vulnerabilities), to assist in manual code checking, to format the code automatically or to find code smells (in this direction only a machine learning model is used, its training is performed on the source code directly). The article outlines directions for the future research which will focus on the development and combining of the approaches covered here.
Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.
Dual Attention Graph Convolutional Neural Network to Support Mocap Data Animation

The analysis of movements is one of the notable applications within the field of computer animation.Sophisticated motion capture techniques allow to acquire motion and store it in a digital form for further analysis.The combination of these two aspects of computer vision enables the presentation of data in an accessible way for the user.The primary objective of this study is to introduce an artificial intelligence-based system for animating tennis motion capture data.The Dual Attention Graph Convolutional Network was applied.Its unique approach consists of two attention modules, one for body analysis and the other for tennis racket alignment.The input to the classifier is a sequence of three dimensional data generated from the Mocap system and containing an object of a player holding a tennis racket and presenting fundamental tennis hits, which are classified with great success, reaching a maximum accuracy over 95%.The recognised movements are further processed using dedicated software.Movement sequences are assigned to the tennis player's 3D digital model.In this way, realistic character animations are obtained, reflecting the recognised moves that can be further applied in movies, video games and other visual projects.
SOME LEGAL AND GENERAL THEORETICAL ASPECTS

The article is devoted to the analysis of the essence and certain aspects of the admissibility of leading questions in criminal proceedings. Those provisions of the modern doctrine, which categorically refuse to use this type of question in proof only on the basis that their formulation contains an answer, a part of an answer, or a hint of it, are called into question. Each verbal-meaningful question, in general, really contains some initial information. The admissibility or inadmissibility of a leading question must be categorized not by the presence of introductory (basic) information in it and its suggestion, but by its origin, the moment of its appearance in the communicative act, compliance with the requirements, namely: 1) legality; 2) ethics, in particular: a) a manipulative leading question is inadmissible, which invariably prompts the addressee to provide the information desired by the addressee or to behave in accordance with his interests (make a decision, etc.); b) it is necessary to evaluate and take into account which social values the addressee will give priority to as a result of asking him a leading question (that is, the addressee must compare the purpose and objectives of criminal proceedings with familiar social values: family, family relations, etc.); c) that the information obtained as a result of the leading question (confession of guilt, positive reaction to involvement in a criminal offense or awareness of it, etc.) should not be considered (evaluated) as the only evidence of guilt, that is, that this information should be confirmed by other evidence as well; d) the question does not contain wording in an aggressive manner; e) a situation will not be created in which the addressee, by asking a question, directly or veiledly encourages the addressee to consider existing facts that have not yet been established in reality or are considered to exist based on information that has already been refuted; e) a situation will not be created in which the addressee, in the basis of the question, directly or veiledly operates with evidence that was previously recognized as inadmissible by the court; f) a situation will not be created in which the addressee, with a leading question, introduces a topic (idea) for discussion that was not previously the subject of direct questioning; g) a situation will not be created in which the addressee prompts the addressee to make an evaluative judgment with a leading question, etc.
TYPES OF RELIEF ELEMENTS ON BANKNOTES AND WAYS TO IMITATE THEM

The article is devoted to the consideration of methods for imitation of relief elements and perforation in the manufacture of counterfeit banknotes. In the manufacture of banknotes in modern conditions, the latest scientific and technical achievements are used. The reliability of protection of banknotes from forgery is ensured by the simultaneous use of a complex of security features of different levels. With the development of technological progress, new protective elements appear, and the reproduction of those already introduced into production gradually becomes available for reproduction by counterfeiters. In an effort to bring the appearance of counterfeit banknotes closer to those issued by issuing institutions, criminals use various techniques to imitate security elements and materials. Most often, they try to imitate the so-called public elements, known to most citizens and perceived by sight or by touch. A variety of relief elements are an indispensable attribute of the design of any banknote. Relief elements are among the important public security elements of banknotes issued for cash circulation. They are informative and easily perceived visually and tactilely. In addition to the protective function, they provide convenience for people with visual impairments to recognize the denomination of a banknote. At the same time, even a primitive forgery of the relief element of a banknote can mislead an ordinary citizen, because it imitates the presence of a corresponding sign or image. The article describes the technology of applying embossed signs, the concepts of «painted» and «unpainted» embossed signs, and elements with KIPP-effect or PEAK-effect, as well as these concepts are illustrated with examples using banknotes that are issued by issuing institutions. The methods of imitation of relief signs on counterfeit banknotes are highlighted and illustrated by cases from expert practice, namely: by making special relief forms and applying colourless embossing in the areas of the elements corresponding to the elements applied by intaglio printing on banknotes; imitation of relief elements on counterfeit banknotes, implemented by applying to the areas where relief elements should be located on a real banknote, convex strokes of arbitrary shape, or strokes formed by individual dots, which may not match the coloured image in content; creating relief by applying a layer of a transparent substance to the location of the corresponding inscription or image, made by inkjet or another non-relief printing method; by «powdering» a non-gravure print of a wet ink image with polymer powder on the face of the note to create a relief effect. The article gives a brief definition of micro-perforation, describes the technology of applying micro-perforation on banknotes, and provides illustrations of banknotes with elements applied by micro-perforation. Using examples from expert practice, methods for simulating perforation on counterfeit banknotes are considered, illustrating the features of the corresponding imitation method.
FEATURES OF SIGNATURES FORMATION IN THE CONDITIONS OF MODERN DEVELOPMENT OF INNOVATIVE TECHNOLOGIES OF DOCUMENT CIRCULATION

The article deals with the features of establishing the possibilities of researching signatures in the conditions of the modern development of general educational standards of writing and the changeability of writing skills in combination with the rapid development of electronic signatures. The regularities of handwriting and signature formation in the conditions of the modern development of innovative technologies are considered. It has been established that the use of an online signature (electronic digital signature) in everyday life creates a serious problem for handwriting experts. This is due to the fact that electronic digital signatures, as well as signatures made on electronic media, are not objects for handwriting studies. The traditional method of authentication and identification cannot be completely eliminated, due to the high risks of cyber-attacks.
An Introduction to Rov Based Intelligent Deep Sea Mining System

In view of difficulties existing in researches on deep-sea mining systems at home and abroad, including being stuck in seabed sediment for self-propelled deep-sea mining vehicles, uncontrollable movement of buffers, and the complications of pipeline transportation system, a remote-operated vehicle (ROV) is introduced in a new-type of intelligent deep-sea mining system, and structured the ROV-based intelligent deep-sea mining system. The components of the ROVbased intelligent deep-sea mining system, an overall concept design for it under a complex and dynamic ocean environment, are all presented in this paper.
Flood Susceptibility Index Analysis Using Overlay Method and GIS-based

High rainfall caused the overflow of the Wiroko river, causing flooding in Nguntoronadi. This study aims to analyze flood potential, provide evaluation and recommendations on areas that have high flood suscetibility. Type of research is quantitative descriptive by using the overlay method of scoring land characteristics that affect environmental conditions. Land characteristics are rainfall, altitude, slope, soil type and land use. The characteristics of the land are overlayed using the ArcGIS application to determine a map of flood susceptibility index, and the data were analyzed using descriptive methods. The result found three categories of flood susceptibility indexes: low, medium, and high. The study revealed a total area flood susceptibility of 6566.52 ha, with 33.79% having high flood susceptibility. The potential for high flood vulnerability is found in lowland, high rainfall and open land use. Areas which has a high flood susceptibility by improving river flow, drainage and terraces in rice fields. The high flood susceptibiity caused by rainfall, lowland and low vegetation as well as high human populations.
Opportunities and challenges brought by artificial intelligence to second language teaching: A case study of international Chinese language education

In the context of the widespread application of artificial intelligence technology, international Chinese language education is taking on new characteristics. The next generation of artificial intelligence is profoundly impacting the global education landscape and transforming the methods of knowledge production, driving the digitization of international Chinese language education and innovating all elements of education. It is systematically constructing a new ecosystem for the future of international Chinese language education, attracting extensive attention and lively discussions in the education field, including the domain of international Chinese language education. This paper, through a review of the application of computer technology in the field of Chinese language teaching and a discussion of the challenges and opportunities it currently presents, analyzes the strengths and weaknesses of artificial intelligence technology in the context of international Chinese language education. It offers strategies for Chinese language teachers to effectively utilize artificial intelligence technology, employ flexible teaching methods to address challenges, and enhance teaching effectiveness.
The emergence and need for explainable AI

Artificial Intelligence (AI) systems, particularly deep learning models, have revolutionized numerous sectors with their unprecedented performance capabilities. However, the intricate structures of these models often result in a "black-box" characterization, making their decisions difficult to understand and trust. Explainable AI (XAI) emerges as a solution, aiming to unveil the inner workings of complex AI systems. This paper embarks on a comprehensive exploration of prominent XAI techniques, evaluating their effectiveness, comprehensibility, and robustness across diverse datasets. Our findings highlight that while certain techniques excel in offering transparent explanations, others provide a cohesive understanding across varied models. The study accentuates the importance of crafting AI systems that seamlessly marry performance with interpretability, fostering trust and facilitating broader AI adoption in decision-critical domains.