Reviewerly/exHarmony huggingface dataset:
Structural Design of Intelligent Transport Robot based on Non-Structured Environments

The intelligent transport robot is widely used in large-scale intelligent logistics and warehousing centers. In daily life, it is commonly applied in structured environments, such as flat and sparsely populated campuses, industrial parks, or planned robot-specific pathways. However, the application of intelligent transport robot in non-structured environments is relatively limited. Therefore, this paper proposes a structural design for intelligent transport robot tailored to non-structured environments, which focus on overcoming step obstacles. The motion mode adopts a double-deformable wheel self-balancing approach. By selecting the appropriate design and parameters, the transmission system is designed, and the chassis structure of the robot is established, ultimately completing the three-dimensional modeling of the intelligent transport robot. Through animation simulation, the designed intelligent transport robot exhibits small size, high obstacle-crossing capability, and strong adaptability. Combined with visual recognition algorithms and unmanned driving systems, the robot offers a wide range of application scenarios.
Alleviating Training Bias with Less Cost via Multi-expert De-biasing Method in Scene Graph Generation

Scene graph generation (SGG) methods have suffered from a severe training bias towards frequent (head) predicate classes. Recent works owe it to the long-tailed distribution of predicates and alleviate the long-tailed problem to conduct de-biasing. However, the "unbiased'' models are in turn biased to tail predicate classes, resulting in a significant performance loss on head predicate classes. The main cause of such a trade-off between head and tail predicates is the fact that multiple predicates from the head or tail ones can be labeled as the ground-truth. To this end, we propose a multi-expert de-biasing method (MED) for SGG that can produce unbiased scene graphs with minor influence on recognizing head predicates. We avoid the dilemma of balancing between head and tail predicates by adaptively classifying the predicates with multiple complementary models. Experiments on the Visual Genome dataset show that MED provides significant gains on mRecall@K without harming the performance on Recall@K, and achieves a state-of-the-art on the mean of Recall@K and mRecall@K.
Gang Theft Crime Behavior and Prevention Control System Based on Computer Data Analysis

The Gang Theft Crime Prevention System is an information-based prevention and control system that utilizes computer data analysis technology to predict, warn, and crack down on gang theft crime behavior. with the continuous development of modern science and technology, criminal behavior has gradually shown a trend of professionalization, organization, dynamic, high-tech and high intelligence. In particular, gang theft has brought new challenges to crime control, law enforcement efficiency, investigation strategy and prevention strategy to a certain extent. This system is designed to collect, store, analyze, and process big data to improve public safety management and maintain social stability. The system architecture design includes the overall structure and functional module design, which lays the foundation for data collection, storage, processing, analysis, and output. In summary, the Gang Theft Crime Prevention System is an advanced technology-based system designed to improve public safety and prevent gang theft crime by leveraging big data analysis.
ICT Services Market’s Impact on ICT-Enabled Services Market: Comparative Cross-Country Analysis

This article examines the impact of the ICT services market on the ICT-enabled services market in advanced and developing economies using multiple linear regression. The research methodology involved comprehensive data collection for 38 advanced economies and varying numbers of developing economies: from 83 to 118 depending on the availability of data. The analysis included control variables such as GDP growth, GDP per capita, trade openness, political stability, and consumer price index. The research concluded that the ICT services market has a positive impact on most of components of the ICT-enabled services market in advanced countries, while its impact on developing countries is limited and mixed.
Presence System Based On Face Recognition And Body Temperature Detection

Employee attendance is an important operational process for the company because it's used for employee evaluation. The presence system must maintain the integrity and validity of the presence data. The common presence system used is fingerprint. The fingerprint mechanism required contact between the device and finger, it potentially leaves dirt on the device. The covid-19 pandemic is required social distancing to prevent the spread of the coronavirus. One of the symptoms of being infected with coronavirus is fever, so checking body temperature is the protocols that are widely used. The physical contact mechanism on the fingerprint is a risk of spreading the coronavirus, so an alternative system is needed that hasn't a physical contact mechanism and at the same time also capable of measured body temperature. The purpose of this paper is to determine the measuring body temperature. This system design method uses waterfall as a software development life cycle, C# as a programming language, the sensor with Arduino Uno as a microcontroller, and SQL Server as the database. Based on the results of research, the user interface complies with the eight golden rules and all features work according to the planned tasks.
Javanese Letters Recognition Using Canny Edge Detection, Principal Component Analysis, and Support Vector Machine (SVM)

The Javanese letters are one of the cultural heritage of the Javanese tribe with unique letter shapes and writing procedures. This uniqueness makes the Javanese letters an interesting material in pattern recognition research. Canny edge detection, Principal component analysis (PCA), and Support vector machine (SVM) are methods commonly used and considered to be superior in pattern recognition. This research was conducted to determine the ability of Canny edge detection, PCA, and SVM in recognizing Javanese letters. Canny edge detection and PCA are used to perform feature extraction. While SVM will be used as a method of recognizing Javanese letters in this study. Through this research, the performance of Canny edge detection, PCA, and SVM methods was obtained in recognizing 20 Javanese letters. The best average accuracy of recognizing Javanese letters is 91.582% using Canny, PCA, and SVM. PCA and SVM have 91.500% accuracy. The lowest average accuracy is 89.917% using Canny and SVM.
Algorithm for Radar Signal Pre-sorting Based on Correlation Distance Differential

Radar signal pre-sorting plays a crucial role in the entire radar signal sorting process. Currently, the primary approach to radar pre-sorting is unsupervised clustering. However, common clustering algorithms face challenges: they may struggle to handle "non-cluster" shaped data caused by parameter variations; exhibit high algorithmic complexity, leading to time-consuming processes that fail to meet real-time requirements; necessitate pre-configured parameters, wherein improper settings can result in clustering errors; or encounter difficulties in dealing with the constant influx of new radar styles, exhibiting limitations in various aspects. To address these issues, this paper proposes a radar pre-sorting algorithm based on Correlation Distance Differences(CDD). This algorithm computes the correlation between pulses, representing it as correlation distance, performs a first-order differentiation on the sorted distances, and subsequently identifies local extreme points. These points indicate boundaries between different classes of radars. Notably, the algorithm doesn't require preset parameters, boasts a straightforward principle, and maintains a relatively low computational complexity. Particularly, it excels in clustering performance for special radar styles characterized by changing carrier frequencies and complex pulse repetition patterns. Simulation experiments demonstrate the algorithm's robust clustering performance and high accuracy, achieving up to over 90% accuracy in ideal noise-free simulation environments. As a result, the algorithm holds promise for practical engineering applications.
Based on the Heuristic Bias Method of Efficient Algorithm of RRT - Connect

Aiming at the problems of Rapidly Exploring Random Tree (RRT) algorithm with a large number of redundant points and slow convergence in the sampling stage, An efficient RRT-Connect algorithm based on Heuristic Bias RRT-Connect (HB-RRT-Connect) is proposed. The algorithm divides the sampling process into global random sampling and biased target sampling, and determines the next sampling method by judging the environment of the previous sampling point, so as to realize the heuristic biased sampling. At the same time, the concept of double optimized dynamic step is also introduced to improve the expansion efficiency. The simulation results show that the proposed algorithm has less convergence time and less number of nodes than other algorithms, and can effectively improve the convergence speed.
A Blockchain-based Electronic Mental Health Records Model

In the context of mental health, safeguarding patient data poses numerous challenges due to the sensitive nature of the field. The psychiatric sector heavily relies on data collection, processing, and sharing, which necessitates a robust and privacy-preserving system. By leveraging the unique features of blockchain, such as decentralization and cryptographic techniques, we establish a secure environment for managing psychiatric data. Our model addresses concerns related to data privacy and unauthorized access by guaranteeing data integrity and confidentiality. The blockchain-based shared case registry model also facilitates seamless collaboration and data sharing among healthcare providers, effectively overcoming interoperability challenges. By securely exchanging patient records, healthcare professionals can ensure continuity of care and gain a comprehensive understanding of a patient’s mental health history. This study investigates the potential of blockchain technology as an enabling solution for effective psychological health data management. Our research proposes a blockchain-based shared case registry model that ensures secure and efficient access to electronic psychiatric records.
A New Method Identification of Interval Discrete Models of Objects with a Randomized Selection of Structural Elements

The article deals with the problem of identification of interval discrete models of complex objects. A new method of structural identification of these models based on behavioral models of bee colonies (BMBC) is proposed and substantiated, which includes computational procedures of adaptive setting of the random selection of structural elements for the formation of new structures. These procedures are based on the calculation of probability of the selection after the completion of the corresponding phase of BMBC taking into account the history of success of the formation of applicant models for each element. The peculiarities of the application of the given method have been studied. A comparative analysis of computational complexity of application of existing and proposed methods was made on the example of the construction of interval models of the dynamics of concentration of harmful emissions of carbon monoxide CO in homogenious environment due to the uniform movement of traffic flow with constant power of emissions.
A Lightweight Traffic Sign Detection Algorithm based on Improved YOLOv7

In order to achieve a lightweight network for traffic sign detection, we made improvements to the YOLOv7 object detection network, which include the following: 1. Replacing the original backbone with a lightweight PP-LCNet; 2. Using the CARAFE upsampling operator instead of the conventional upsampling methods; 3. Incorporating the SimAM attention module into the network; 4. Decoupling the detection head of YOLOv7. The training and testing were conducted on the CCTSDB dataset. The experimental results demonstrate that the improved YOLOv7 in this study has fewer parameters and lower computational complexity while achieving higher accuracy on the CCTSDB dataset. These findings indicate that the proposed modifications effectively enhance the performance of YOLOv7 for traffic sign detection while maintaining a lightweight network architecture.
Rethinking Person Re-Identification via Semantic-based Pretraining

Pretraining is a dominant paradigm in computer vision. Generally, supervised ImageNet pretraining is commonly used to initialize the backbones of person re-identification (Re-ID) models. However, recent works show a surprising result that CNN-based pretraining on ImageNet has limited impacts on Re-ID system due to the large domain gap between ImageNet and person Re-ID data. To seek an alternative to traditional pretraining, here we investigate semantic-based pretraining as another method to utilize additional textual data against ImageNet pretraining. Specifically, we manually construct a diversified FineGPR-C caption dataset for the first time on person Re-ID events. Based on it, a pure semantic-based pretraining approach named VTBR is proposed to adopt dense captions to learn visual representations with fewer images. We train convolutional neural networks from scratch on the captions of FineGPR-C dataset, and then transfer them to downstream Re-ID tasks. Comprehensive experiments conducted on benchmark datasets show that our VTBR can achieve competitive performance compared with ImageNet pretraining—despite using up to 1.4× fewer images, revealing its potential in Re-ID pretraining. Our source code is also publicly available at https://github.com/JeremyXSC/VTBR .
Reachability Analysis of Sigmoidal Neural Networks

This paper extends the star set reachability approach to verify the robustness of feed-forward neural networks (FNNs) with sigmoidal activation functions such as Sigmoid and TanH. The main drawbacks of the star set approach in Sigmoid/TanH FNN verification are scalability, feasibility, and optimality issues in some cases due to the linear programming solver usage. We overcome this challenge by proposing a relaxed star (RStar) with symbolic intervals, which allows the usage of the back-substitution technique in DeepPoly to find bounds when overapproximating activation functions while maintaining the valuable features of a star set. RStar can overapproximate a sigmoidal activation function using four linear constraints (RStar4) or two linear constraints (RStar2), or only the output bounds (RStar0). We implement our RStar reachability algorithms in NNV and compare them to DeepPoly via robustness verification of image classification DNNs benchmarks. The experimental results show that the original star approach (i.e., no relaxation) is the least conservative of all methods yet the slowest. RStar4 is computationally much faster than the original star method and is the second least conservative approach. It certifies up to 40% more images against adversarial attacks than DeepPoly and on average 51 times faster than the star set. Last but not least, RStar0 is the most conservative method, which could only verify two cases for the CIFAR10 small Sigmoid network, δ = 0.014. However, it is the fastest method that can verify neural networks up to 3528 times faster than the star set and up to 46 times faster than DeepPoly in our evaluation.
Sparsity-guided Discriminative Feature Encoding for Robust Keypoint Detection

Existing handcrafted keypoint detectors typically focus on designing specific local structures manually while ignoring whether they have enough flexibility to explore diverse visual patterns in an image. Despite the advancement of learning-based approaches in the past few years, most of them still rely on the availability of the outputs of handcrafted detectors as a part of training. In fact, such dependence limits their ability to discover various visual information. Recently, semi-handcrafted methods based on sparse coding have emerged as a promising paradigm to alleviate the above issue. However, the visual relationships between feature points have not been considered in the encoding stage, which may weaken the discriminative capability of feature representations for keypoint recognition. To tackle this problem, we propose a novel sparsity-guided discriminative feature representation (SDFR) method that attempts to explore the intrinsic correlations of keypoint candidates, thus ensuring the validity of characterizing distinctive and diverse structural information. Specifically, we first incorporate an affinity constraint into the feature representation objective, which jointly encodes all the patches in an image while highlighting the similarities and differences between them. Meanwhile, a smoother sparsity regularization with the Frobenius norm is leveraged to further preserve the similarity relationships of patch representations. Due to the differentiable property of this sparsity, SDFR is computationally feasible and effective for representing dense patches. Finally, we treat the SDFR model as multiple optimization sub-problems and introduce an iterative solver. During comprehensive evaluations on five challenging benchmarks, the proposed method achieves favorable performances compared with the state of the art in the literature.
Transformation of technologies and forms of teaching mathematics to foreign students

Currently, the problem of teaching foreign citizens in higher educational institutions of the Russian Federation is relevant. There is an urgent need to choose the most rational and adequate forms, technologies, pedagogical tools and methods of teaching students from other countries. In this regard, it is important to comprehensively consider, develop and implement teaching methods that ensure the active and effective involvement of foreign students in the educational process of a Russian university. The problem of providing a teacher of a particular course with accelerated adaptation of foreign students to a new educational environment and the elimination of information, motivational and sociocultural barriers in the development of all necessary competencies comes to the fore. The paper focuses on the problem of designing an effective model for teaching higher mathematics to foreign students based on the interaction of traditional and innovative educational techniques and technologies. Specific possible ways of solving the problems posed are considered on the example of teaching mathematical courses to students majoring in 10.05.02 Information security of telecommunication systems at Volga State University of Telecommunications and Informatics, 10.05.03 Information security of automated systems at Samara National Research University and 44.03.05 Pedagogical education (Mathematics and Physics) at Samara State University of Social Sciences and Education.
Visualization of information in a digital educational environment

The development of a digital society as well as the digital transformation of education has a significant impact on the modern educational process. An increase in the volume of educational material involuntarily leads the student to the need to continuously process information flows both educational and extracurricular. Incoming data must be analyzed, comprehended, divided into useful and redundant, which in turn leads to the need to use innovative tools, digital content visualization technologies that would contribute to its perception, analysis, memorization, interpretation and reproduction. Infographics can become one of these tools, as well as improve the information and computer literacy of students as well as teachers in a digital educational environment. The skills of working with infographics in a digital educational environment can be useful for students in their future professional life and for social adaptation. The paper reveals the relationship between the development of digital literacy of students and the use of infographics and its elements for this purpose, which would allow students to master subject knowledge, skills and abilities as well as meta-subject competencies of the digital society. Infographics is a way of graphical representation of complex information, the purpose of which is to concisely, accurately and reliably represent large amounts of data without losing information. As a result of the analysis of the possibilities of using infographics as a way to visualize digital educational content and its elements in the classroom, for the development of digital literacy of students, it has been found that infographics can be used, for example, to explain the operation of various devices, when teaching programming, to visually illustrate facts or events, to create problematic situations, as reference materials in solving problems.
Rector-creator: To the 90th anniversary of the Honorary Rector of Samara State Pedagogical University A.A. Semashkin (1933–2023)

The article is devoted to the life and work of the Honorary Rector of Samara State Pedagogical University Anatoly Alekseevich Semashkin. The way of his professional formation is shown: from an assistant at the Department of History of the CPSU to the dean of the Faculty of Public Professions of the Kuybyshev State Polytechnical Institute, head of the Department of History of the CPSU and Political Economy, secretary of the Party Committee of the CPSU in Kuybyshev State Institute of Railway Engineers and rector of the Kuybyshev State Pedagogical Institute. The research work of A.A. Semashkin is considered. The article analyzes his policy as rector for the development of Kuybyshev State Pedagogical Institute in the leading pedagogical university of the country (19842000). The business and personal qualities of Anatoly Alekseevich Semashkin, his pedagogical principles, personnel, social and youth policy, the system of patriotic education and the formation of the principles of selfgovernment in the labor collective are highlighted. The work of Semashkin by the Kuybyshev/Samara regional branch of the Soviet/Russian Childrens Fund is shown. Semashkins contribution to the development of the pedagogical cluster of the Samara Region, to the preservation and modernization of the system of higher educational institutions of the region is reflected. The clash of the traditional model of domestic education with pro Western scholasticism is reflected. The work has been prepared on the basis of generalization and analysis of a wide range of sources.