Reviewerly/exHarmony huggingface dataset:
Linear Constant Discrete System Based Evaluation of Equipment System-of-Systems

To meet the demand that analyzing the impact of architecture and equipment performance on capability and cost of equipment system-of-systems, an evaluation method is investigated based on linear constant discrete system. Model of equipment system-of-systems is established using state space equation of linear time invariant discrete system, which describes equipment composition, connection relation, operation process and equipment performance of the equipment system-of-systems. Operation characteristic of the equipment system-of-systems is analyzed using state motion equation of linear time invariant discrete system. Then, evaluation models for adaptation capability, survivability capability, task capability and response capability are established, as well as a cost evaluation model for equipment system-of-systems are given. The method proposed in this paper is demonstrated with a hypothetical air defense equipment system-of-systems. Evaluation results of the above capabilities and cost of the air defense equipment system-of-systems are derived. These results can support optimization of the equipment system-of-systems architecture and equipment performance.
A short-wave reconnaissance equipment scheduling technology based on Hopfield network

Short-wave band signal density, complex electromagnetic environment and relatively limited detection equipment often lead to low detection efficiency. Aiming at this situation, a scheduling method of short-wave detection equipment based on Hopfield neural network is proposed to carry out cooperative detection of short-wave signals. In this paper, the definition of effective detection probability is given, the constraints of effective detection are sorted out, and the mathematical model of detection equipment scheduling is designed, which is realized by Hopfield neural network. This method uses the global optimization technology to schedule multiple detection sensors, so that different detection sensors can cooperate reasonably and maximize the overall benefit of detection system. Simulation results show the feasibility and effectiveness of the proposed method.
Improvement of the protocol for millionaires’ problem

As the basic problem that introduces the idea of secure multi-party computation (SMC), millionaires’ problem (MP) is one of the research highlights in the field of SMC. However, most of current protocols for MP are only secure in semi-honest model, which using public key cryptography schemes, and there are few secure schemes in malicious model exist security defects. Therefore, this paper analyzes the Li et al.’s protocol for MP that secure in malicious model. Firstly, we point out several behaviours that can be successfully implemented in Li et al.’s protocol. Secondly, by using the zero-knowledge proof (ZKP) for the equality of discrete logarithm (DL), cut-and-choose method etc. We propose the improved protocol and show how to detect malicious behaviours. Finally, we analyze the correctness and security against malicious behaviours of the improved protocol.
IMPLEMENTATION OF THE CONCEPT OF STEM EDUCATION IN THE SYSTEM OF QUALIFICATION DEVELOPMENT OF PEDAGOGICAL EMPLOYEES

The concept of STEM-education today is the subject of research and interest among educators-practitioners, as a way to implement reforms in the field of education and improve teaching methods of natural sciences and mathematics. The article describes the method of organizing professional development of teachers within the STEM field and their continuing education at the Ternopil Regional Municipal Institute of Postgraduate Pedagogical Education. Foreign and domestic approaches and concepts of STEM implementation in educational institutions are analyzed. In order to study and adapt the model of STEM-education as an approach to integrated learning in educational activities, an analytical study was conducted. The SWOT-analysis allowed to determine the hierarchy of positioning opportunities and threats of STEM education in the relevant matrices and to identify strategies for using strengths to realize opportunities and prevent threats, as well as to identify recommendations for the implementation of these strategies. The educational environment is analyzed and the system of measures for the development of STEM-competencies of teachers in the period of professional development is proposed. The proposed model provides a combination of formal (training sessions with STEM elements provided by the curriculum), non-formal (events in educational and research institutions) and informal education (self-education, scientific contacts, unscheduled casual conversations about STEM-education). The emphasis is placed on the fact that the implementation and development of STEM-education requires the preparation of an appropriate learning environment and improvement of the pedagogical system. An effective way to test the effectiveness of innovation in the education system is an experiment or research work. There was described for the formative stage of the innovative educational experiment of the regional level on the topic of “ECO-school” for the formation of environmentally safe STEM-educational environment in educational institutions of Ternopil region.
Research and Application of Artificial Intelligence Planning Recognition

As a core technology and an important research direction in the field of artificial intelligence research, artificial intelligence planning recognition has been widely used in many aspects of computer systems, and its application prospects and application value should not be underestimated. This paper systematically expounds the artificial intelligence planning recognition from the concept, classification, method, domestic and foreign research status and application fields of intelligent planning recognition. This paper provides a certain theoretical basis for the further research of artificial intelligence planning recognition planner.
Depth-Map-Based Augmented Reality Holographic Stereogram: Method and Verification

In this paper, a perspective image fusion algorithm of real-world and artificial scenes based on depth maps is proposed and applied to holographic printing to realize augmented reality display of holographic stereograms. The basic principle of the fusion algorithm is introduced, and the perspective image fusion with correct occlusion relation is implemented theoretically. Under the condition of sampling only once, an image generation algorithm with adjustable depth of artificial scenes is proposed and implemented. We use effective perspective image segmentation and mosaicking method for optical experiments, and verify the effectiveness of the proposed method.
Fall Detection Based on Action Structured Method and Cascaded Dilated Graph Convolution Network

The research of fall detection is a hot topic in computer vision. Most existing methods only detect the fall in simple scenes of a single person. Moreover, these methods only extract fall action features from RGB images, and neglect to extract features from human joint coordinates, resulting in a decrease in recognition accuracy. In order to extract discriminative action features, a fall detection method based on action structured method and cascade dilated graph convolution neural network is proposed. The action structured method (ASM) is proposed to model the skeleton of human action through the pose estimation algorithm, which removes the interference of complex background. Besides, the object detection algorithm is utilized to locate multiple people to transfers the fall detection issue of multi-person to single person fall detection. The proposed cascaded dilated graph convolution network (CD-GCN) enlarges the receptive field by the dilated operation, effectively extracts action features from joint node coordinates, and fuses multichannel features with different dilation rates, then finally obtains the classification results. The proposed method achieves the best accuracy on three public datasets and one self-collected dataset, which is out-performing other state-of-art fall detection methods.
Artificial Intelligence for Image-Based HPV Status Prediction in Head and Neck Cancer: A Systematic Review and Meta-Analysis

Background: Accurate early detection of the human papillomavirus (HPV) status in head and neck cancer (HNC) is crucial to identify at-risk populations, stratify patients, personalize treatment options, and predict prognosis. Artificial intelligence (AI) is an emerging tool to dissect imaging features. This systematic review and meta-analysis aims to evaluate the clinical performance of AI on medical image-based HPV status prediction in HNC.Methods: A systematic literature search was conducted in databases including Ovid-MEDLINE, Embase, and Web of Science Core Collection for studies continuously published from inception up to 30th October 2022. Pertinent studies with synonymous keywords 'AI', 'HPV', 'performance', and 'HNC' were included. A random-effects model was used for meta-analysis and we mainly reported pooled sensitivity (SE), specificity (SP), and area under the curve (AUC) for all studies and studies only reporting the highest performance. Four subgroup analyses were further explored according to (1) imaging modalities; (2) internal validation types; (3) sample size; (4) geographical distribution. The adapted QUADAS-AI tool was used to assess the risk of bias. Findings: Totally, 22 original studies were included in the systematic review, 15 of which were eligible to generate 33 contingency tables for meta-analysis. The pooled SE and SP for all studies were 79% (95% CI: 75-82%) and 74% (95% CI: 69-78%) respectively, with an AUC of 0·83 (95% CI: 0·79-0·86). When only selecting one contingency table with the highest accuracy from each study, our analysis revealed a pooled SE of 79% (95% CI: 75-83%), SP of 75% (95% CI: 69-79%), and an AUC of 0·84 (95% CI: 0·81-0·87). The respective heterogeneities were moderate (I2 for SE and SP were 51·70% and 51·01%) and only low (35·99% and 21·44%).Interpretation: This evidence-based study showed an acceptable and promising performance of AI algorithms to predict HPV status in HNC but was not comparable to the routine p16 immunohistochemistry. The exploitation and optimization of AI algorithms warrant further research.Funding Information: This study was supported by the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (Grant #: CAMS 2021-I2M-1-004).Declaration of Interests: All authors declare no competing interests.
Learning to Make Digital Maps Using Project-Based Learning Based on Lesson Study in Cartography Course

Lack of support for learning facilities owned by students in learning cartography, especially in making digital maps, causes less achievement of learning objectives. Learning to make digital maps using the PJBL model and carried out in the form of Lesson Study activities The purpose of this research is to describe Learning to make digital maps using the PJBL model and implemented in Lesson Study activities The method used is descriptive which describes in full the information obtained by researchers from the implementation of lesson studies. The results obtained in the Do stage for positive things are, First, there is a high enthusiasm from some students to learn, second, students gain new knowledge in making digital maps. While for the negative things are First, one computer is used by several students, second, the number of students is too large for the type of skills courses, and third, students are less focused on learning. The See stage is first, the need for lecturers to pay attention to the number of students in one class, second, the need for lecturers to make preparations before learning begins.
Automating App Review Classification based on Extended Semantic

Automatic classification of app reviews can help developers quickly read reviews to identify and fix software bugs or add new software functions to meet user requirements. Text mining technologies have been widely used in reviews classification in recent years. However, the accuracy of app reviews classification is limited because of the generally short length of reviews and limited information, and classification models are prone to overfitting due to the diversity and unstructured characteristics of app reviews. In this paper, we propose an automatic classification approach for app reviews based on extended semantic. Specifically, we first reduce noisy data in app reviews by preprocessing, and annotate app reviews using frame semantics and splice the annotation results with reviews to extend the semantic information and text length of reviews. Then, to reduce the probability of overfitting, we integrate the pre-trained models to learn the semantic information of extended app reviews and classify reviews. We evaluate the effectiveness of proposed approach in multiple popular apps, and the experimental results show that it outperforms the state-of-art baselines.
Design and implementation of a new power consumption monitoring and analysis system based on intelligent neural network

This paper focuses on the overall design and implementation of the monitoring and analysis system. The evaluation of interference load, the design of Switching virtual circuit compensation capacity and the design of filter is discussed in detail. This system gives full play to the superiority of extra segment over qualitative analysis. The reasoning path is clear. The advantages are ease of interpretation and user engagement. The data processing and self-learning ability of the original power quality monitoring system are improved. The steady state power quality and transient power quality in power system are monitored accurately. Provides a variety of statistical reports. Provide comprehensive, detailed and real-time power quality information. It provides the basis for power quality management.
Vibration Failure Analysis of Civil Aircraft Engine Blades Based on Virtual Reality

With the development of new aircraft, the flight speed of aircraft is faster and faster, and its requirements for engine performance are higher and higher. If the vibration accuracy of the blade is reduced, it will lead to its vibration failure. Therefore, this study designs a vibration failure analysis method of civil aircraft engine blades based on virtual reality. Firstly, the vibration state of single blade of civil aircraft engine is analyzed by finite element method, and then combined with the analysis results of virtual reality technology, the vibration failure analysis equation of blade is designed. The experimental results show that the analysis effect of this method is good, and can be used as a reference for subsequent aircraft engine research.
Full length image stitching algorithm for spinal deformity surgery

In the orthopedic surgery of the spine, obtaining a full-length spine image play an important role in evaluating the quality of the procedure. The most common imaging devices used to obtain spine images are C-arm x-ray machines, which requires multiple imaging of the patient's spine at different locations, then stitching together multiple images to obtain an entire spine. This paper proposes an approach based on an improved SIFT feature descriptor for the spine image stitching problem. Considering that the contrast of medical spine X-ray images is generally low and the contrast difference of individual images is significant, an improved SIFT algorithm is proposed based on image contrast information to adjust the contrast threshold. The improved SIFT was introduced into the stitching of spine X-ray images to achieve orthopedics spine images stitched quickly, accurately, and intra-operatively.
Light–Matter Interaction

The interaction between light and matter is essential for the generation, manipulation, and detection of quantum states used in quantum information processing. In this chapter, we describe the quantum treatment of light–matter interaction. The Jaynes–Cummings Hamiltonian is derived and used to explain spontaneous emission and Rabi oscillations. Cavity quantum electrodynamics and Rydberg atoms are introduced as a means of enhancing light–matter interaction. Collapse and revival of Rabi oscillations is examined for coherent light in a cavity. The dressed atom–cavity states are derived and used to explain vacuum Rabi splitting.
RAWtoBit: A Fully End-to-end Camera ISP Network

Image compression is an essential and last processing unit in the camera image signal processing (ISP) pipeline. While many studies have been made to replace the conventional ISP pipeline with a single end-to-end optimized deep learning model, image compression is barely considered as a part of the model. In this paper, we investigate the designing of a fully end-to-end optimized camera ISP incorporating image compression. To this end, we propose RAWtoBit network (RBN) that can effectively perform both tasks simultaneously. RBN is further improved with a novel knowledge distillation scheme by introducing two teacher networks specialized in each task. Extensive experiments demonstrate that our proposed method significantly outperforms alternative approaches in terms of rate-distortion trade-off.
A Robust and Flexible Geometry calibration Method for Phase Measuring Deflectometry

Geometry calibration is a critical step for phase measuring deflectometry (PMD) system. Generally, a circle grid, a chessboard, or a group of fringes is used to determine the camera pose during the calibration. There are two main problems with using a circle grid or a checkerboard for geometry calibration: incomplete pattern capturing and uneven gray value distribution. For fringe patterns, the camera needs to take many images of the reflected patterns. This paper proposes a new geometry calibration method for PMD system, which combines square-based fiducial markers with a marker-less flat mirror. The proposed method can accomplish the calibration in the case the captured calibration patterns are incomplete and have uneven gray value distribution, and only require to take a minimum of three images. Experiment shows the proposed method can get accuracy calibration result simple and flexible.
Radio signal modulation pattern recognition method

With the continuous development of radio communication technology, the modulation methods of signals also show a diversified development trend, and the modulation identification of applied signals is a very critical step in radio spectrum resource regulation and non-cooperative communication systems. To this end, this paper firstly introduces the basic principles of modulation recognition methods; secondly, it describes the research status of two types of modulation recognition methods based on feature extraction and machine learning; then, it compares and analyzes the performance and advantages and disadvantages of various modulation recognition techniques; finally, it summarizes and outlooks on future research directions.
Can You Guess the Title? Generating Emoji Sequences for Movies

"In the culture of the present emojis play an important role in written/typed communication, having a primary role of supplementing the words with emotional cues. While in different cultures emojis can be interpreted and thus used differently, a small set of emojis have clear meaning and strong sentiment polarity. In this work we study how to map natural language texts to emoji sequences, more precisely, we automatically assign emojis to movie subtitles/scripts. The pipeline of the proposed method is as follows: first the most relevant words are extracted from the movie subtitle, and then these are mapped to emojis. In order to perform the mapping, three methods are proposed: a lexical matching-based, a word embedding-based and a combined approach. To demonstrate the viability of the approach, we list some of the generated emojis for a randomly selected movie subset, showing also the deficiencies of the method in generating guessable sequences. Evaluation is performed via quizzes completed by human participants. Keywords and phrases: natural language processing, emoji, keyword extraction, movie scripts, lexical matching, word embedding. "
SIP-YOLOv5: A Road Negative Obstacle Detection Network Based on Improved YOLOv5 and Prediction Box Correction Algorithm

Obstacle detection affects navigation and obstacle avoidance of autonomous vehicles, where negative obstacles are easy to be missed and falsely detected due to their variable shapes, varying depths, and complex environments. In this paper, we propose a SIP-YOLOv5 network to detect negative obstacles, specifically by adding a small target detection layer and improved coordinate attention to YOLOv5, and incorporating a prediction box correction algorithm to locate and identify negative obstacles. Experimental results show that SIP-YOLOv5 improves AP0.5 by 5.4% and AP05:0.95 by 1.7% compared to YOLOv5m, and it also detects road negative obstacles better compared to methods proposed by others.
Predictors of professional development for special and inclusive education pedagogues in the conditions of digital transformation of education

Introduction. The digital transformation of the educational system imposes new requirements caused by the need to implement new forms of subject-centered relations in the remedial pedagogical process (online learning, distance and mixed learning formats, active use of digital resources, etc.). At the same time, the possibility of professional growth and self-development and, consequently, the activity of the subjects of integrated pedagogical process, as well as the efficiency of their activities, are reduced. Purpose of the study: to analyse and evaluate the existing predictors of professional development for special and inclusive education pedagogues in the conditions of digital transformation of education. Materials and methods. The analysis and assessment of impact of the digital transformation of education on the professional development of special and inclusive education pedagogues was carried out through the content analysis of questioning results based on Google Forms. A total of 409 special (remedial, correctional) education teachers from different regions of the Russian Federation and post-soviet countries were interviewed. The average age of the respondents was 38 (92.9% women and 7.1% men). The obtained data were processed using Microsoft Excel and IBM SPSS Statistics 23 software. The quantitative statistical processing method (Spearman’s rank correlation coefficient) was used for statistical data processing. Results. The interpretation of the research results showed that the predictors of professional development identified in the respondents showed a specific correlation in such evaluation parameters as age, education level and service record. The correlation analysis values showed the existence of statistically significant relationship between the following indicators: attitude to one’s profession and educational level (r=0.32); educational level and competence development (r=0.395); competence development and age of the respondents (r=0.365). The undertaken content analysis made it possible to outline a range of problems accounting for the difficulties in realising the professional activity by special and inclusive education pedagogues in the conditions of digital transformation. It was established that the prognostically positive predictors of professional growth include: the level of general cultural and professional competencies, the level of preparedness for various types of professional activity, professional activity and motivation, organising skills, reflexivity. Conclusion. The undertaken research highlighted a number of regularities pointing to the need to create due conditions for timely professional development of special and inclusive education pedagogues. The results can be used for the development of methodological framework for the professional growth of this category pedagogues through digital technologies.