Research on Imbalanced Data Regression Based on Confrontation

Abstract: The regression model has higher requirements for the quality and balance of data to ensure the accuracy of predictions. However, there is a common problem of imbalanced distribution in real datasets, which directly affects the prediction accuracy of regression models. In order to solve the problem of data imbalance regression, considering the continuity of the target value and the correlation of the data and using the idea of optimization and confrontation, we propose an IRGAN (imbalanced regression generative adversarial network) algorithm. Considering the context information of the target data and the disappearance of the deep network gradient, we constructed a generation module and designed a composite loss function. In the early stages of training, the gap between the generated samples and the real samples is large, which easily causes the problem of non-convergence. A correction module is designed to train the internal relationship between the state and action as well as the subsequent state and reward of the real samples, guide the generation module to generate samples, and alleviate the non-convergence of the training process. The corrected samples and real samples are input into the discriminant module. On this basis, the confrontation idea is used to generate high-quality samples to balance the original samples. The proposed method is tested in the fields of aerospace, biology, physics, and chemistry. The similarity between the generated samples and the real samples is comprehensively measured from multiple perspectives to evaluate the quality of the generated samples, which proves the superiority of the generated module. Regression prediction is performed on the balanced samples processed by the IRGAN algorithm, and it is proven that the proposed algorithm can improve the prediction accuracy in terms of the imbalanced data regression problem.


Interval Type-2 Fuzzy-Model-Based Sampled-Data Control of an AUV Depth System with Input Saturation

Abstract: This paper proposes a sampled-data fuzzy controller design technique for an autonomous underwater vehicle (AUV) depth system represented by an interval type-2 (IT-2) fuzzy model, considering input saturation. In the Takagi–Sugeno (T–S) fuzzy model of an AUV depth system, surge velocity is chosen as a premise variable. To address the associated uncertainty with this variable, we employ the IT-2 fuzzy modeling technique. Also, the controller proposed in this paper utilizes time-varying gains, ensuring superior exponential stability compared with traditional fixed gain approaches. Furthermore, a membership function-dependent (MFD) H∞ criterion is employed to enhance robustness for each subsystem individually. Taking into account the mentioned aspects, the controller design condition is derived in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method is validated through simulation examples.


Demonstration Training and Test Datasets for a 60/40 split that passes univariate KS 2 sample test and WALD test.

Abstract:
Random random seeds are used, but captured. The KS 2 sample univariate tests are then performed. All columns must pass.

If the tests are passed, the WALD test is performed and the WALD test statistic scores captured. The dataset with the best WALD p-value is then selected, with the accompanying random seed for reproducibility.


Developing Public Speaking Ability For the Teachers of SMP Jamiah Al-Aziziyah Batee Iliek Samalanga

Abstract: Teachers are the backbone of an educational institution. The progress or setback of an educational institution is determined by the role of teachers. Through observations and interviews conducted with the Headmaster of SMP Jamiah Al-Aziziyah Batee Iliek, it was found that the majority of teachers lack proficient public speaking skills. Based on this issue, it is necessary to organize activities to enhance public speaking skills, which are crucial for the teachers of SMP Jamiah Al-Aziziyah Batee Iliek. The method of implementing these activities is through lecture-based methods. The stages of the activities include pre-implementation, implementation, and evaluation. Technically, a theoretical explanation related to public speaking skills is provided, and participants then engage in practical exercises. After the lectures, participants are assigned tasks and provided guidance to create speech scripts on themes relevant to the school's needs. The results of the community service activities aimed at enhancing public speaking skills have shown improvement among the participants. According to the participants' questionnaire responses, there was an increase in understanding the criteria for effective speakers, with 20% previously not understanding and 80% having an average level, which improved to 40% understanding and 60% having a high level of understanding. During the practical exercises, all participants successfully overcame confidence issues while speaking. The results of the task to compose speech scripts were quite satisfactory, with an average score of 77.5 out of 100.


Leveraging Chain-of-Thought to Enhance Stance Detection with Prompt-Tuning

Abstract: Investigating public attitudes towards social media is crucial for opinion mining systems to gain valuable insights. Stance detection, which aims to discern the attitude expressed in an opinionated text towards a specific target, is a fundamental task in opinion mining. Conventional approaches mainly focus on sentence-level classification techniques. Recent research has shown that the integration of background knowledge can significantly improve stance detection performance. Despite the significant improvement achieved by knowledge-enhanced methods, applying these techniques in real-world scenarios remains challenging for several reasons. Firstly, existing methods often require the use of complex attention mechanisms to filter out noise and extract relevant background knowledge, which involves significant annotation efforts. Secondly, knowledge fusion mechanisms typically rely on fine-tuning, which can introduce a gap between the pre-training phase of pre-trained language models (PLMs) and the downstream stance detection tasks, leading to the poor prediction accuracy of the PLMs. To address these limitations, we propose a novel prompt-based stance detection method that leverages the knowledge acquired using the chain-of-thought method, which we refer to as PSDCOT. The proposed approach consists of two stages. The first stage is knowledge extraction, where instruction questions are constructed to elicit background knowledge from a VLPLM. The second stage is the multi-prompt learning network (M-PLN) for knowledge fusion, which learns model performance based on the background knowledge and the prompt learning framework. We evaluated the performance of PSDCOT on publicly available benchmark datasets to assess its effectiveness in improving stance detection performance. The results demonstrate that the proposed method achieves state-of-the-art results in in-domain, cross-target, and zero-shot learning settings.


First-Order Logic with Adverbs

Abstract: This paper introduces two languages and associated logics designed to afford perspicuous representations of a range of natural language arguments involving adverbs and the like: first-order logic with basic adverbs (FOL-BA) and first-order logic with scoped adverbs (FOL-SA). The guiding logical idea is that an adverb can come between a term and the rest of the statement it is a part of, resulting in a logically stronger statement. I explain various interesting challenges that arise in the attempt to implement the guiding idea, and provide solutions for some but not all of them. I conclude by outlining some directions for further research.


Spatial Relation Awareness Module for Phase Unwrapping

Abstract: Phase unwrapping is a technique used to recover the original phase from the wrapped phase in the range (−π,π]. Various methods have been proposed for phase unwrapping. In particular, methods using convolutional neural networks (CNNs) have been extensively researched because of their high robustness against noise and fast inference speed. However, conventional CNN-based methods discard the local position information and relationships between pixels in the convolution process, resulting in poor phase-unwrapping performance. To obtain better phase unwrapping results, we propose a module that combines a global convolution network, which applies convolutional layers with a kernel size equivalent to that of the feature maps, and CoordConv, which acquires the positional relationships between pixels. We validated the performance of the proposed method by comparing it with a quality-guided path algorithm and deep learning-based phase unwrapping methods and found that the proposed method is highly robust against noise.


Exploring JASP as a data analysis tool in L2 research: a snapshot

Abstract: This paper explores the potential of JASP (Jeffreys’s Amazing Statistics Program, https://jasp-stats.org/) as a robust statistical tool in advancing Second Language Acquisition (SLA) research, with a specific emphasis on its application within the domain of L2 writing. Second language writing proficiency is a complex and multifaceted skill, demanding rigorous empirical investigation to uncover nuanced patterns and insights. JASP, known for its user-friendly interface and advanced statistical capabilities, emerges as a promising instrument for researchers seeking to unravel the intricacies of L2 writing development. The paper begins by providing an overview of the features embedded in JASP and continues to discuss some of the extant research within the field of SLA that implements JASP as a data analysis tool. Follows is a detailed description of the use of JASP in two L2 writing papers, in which the data analysis decisions are discussed. Furthermore, the discussion delves into the practical implications of utilizing JASP in L2 writing research, including its ability to accommodate small sample sizes, handle complex interactions, and facilitate transparent and reproducible analyses. The paper concludes by advocating for the widespread adoption of JASP in SLA research, positing that its integration holds the promise of advancing our understanding of the intricacies of L2 writing development and contributing to the refinement of pedagogical approaches in second language education.


A Hierarchical Orthographic Similarity Measure for Interconnected Texts Represented by Graphs

Abstract: Similarity measures play a pivotal role in automatic techniques designed to analyse large volumes of textual data. Conventional approaches, treating texts as paradigmatic examples of unstructured data, tend to overlook their structural nuances, leading to a loss of valuable information. In this paper, we propose a novel orthographic similarity measure tailored for the semi-structured analysis of texts. We explore a graph-based representation for texts, where the graph’s structure is shaped by a hierarchical decomposition of textual discourse units. Employing the concept of edit distances, our orthographic similarity measure is computed hierarchically across all components in this textual graph, integrating precomputed similarity values among lower-level nodes. The relevance and applicability of the presented approach are illustrated by a real-world example, featuring texts that exhibit intricate interconnections among their components. The resulting similarity scores, between all different structural levels of the graph, allow for a deeper understanding of the (structural) interconnections among texts and enhances the explainability of similarity measures as well as the tools using them.


Design of a Generic Dynamically Reconfigurable Convolutional Neural Network Accelerator with Optimal Balance

Abstract: In many scenarios, edge devices perform computations for applications such as target detection and tracking, multimodal sensor fusion, low-light image enhancement, and image segmentation. There is an increasing trend of deploying and running multiple different network models on one hardware platform, but there is a lack of generic acceleration architectures that support standard convolution (CONV), depthwise separable CONV, and deconvolution (DeCONV) layers in such complex scenarios. In response, this paper proposes a more versatile dynamically reconfigurable CNN accelerator with a highly unified computing scheme. The proposed design, which is compatible with standard CNNs, lightweight CNNs, and CNNs with DeCONV layers, further improves the resource utilization and reduces the gap of efficiency when deploying different models. Thus, the hardware balance during the alternating execution of multiple models is enhanced. Compared to a state-of-the-art CNN accelerator, Xilinx DPU B4096, our optimized architecture achieves resource utilization improvements of 1.08× for VGG16 and 1.77× for MobileNetV1 in inference tasks on the Xilinx ZCU102 platform. The resource utilization and efficiency degradation between these two models are reduced to 59.6% and 63.7%, respectively. Furthermore, the proposed architecture can properly run DeCONV layers and demonstrates good performance.


Sunda Script Detection Using You Only Look Once Algorithm

Abstract: The Sundanese script is a writing system used in the Sundanese language, one of the regional languages of West Java, Indonesia. This study investigates the use of the YOLO v8 algorithm for the real-time video detection of Sundanese script. Various versions of YOLO v8, including YOLO v8n, v8s, v8m, v8l, and v8x, were tested to determine the most effective model. After a comprehensive evaluation involving the analysis of mean Average Precision (mAP), F1-Confidence, and precision, the study selected the YOLO v8s model as the primary detection method. YOLO v8s demonstrated superior performance with the highest mAP of 98.835%, an F1-Confidence of 98%, and a precision of 76,2%. This choice was based on a balance between high accuracy and computational efficiency. The results indicate significant potential for object recognition technology in the learning and preservation of Sundanese script.


Web-Based Tuition Payments Information System At Al-Wafi Kindergarten

Abstract: The tuition payment system at AL-WAFI Kindergarten is still done manually, that is, recording is done using a tuition payment notebook. Which can cause errors in recording SPP payment data or mistakes in calculating SPP payments, the process of recording and making SPP payment reports takes a long time. Several other problems were also found that were often complained about, namely the difficulty of collecting separate SPP payment data in several financial books. This slows down the payment process, recording and reporting payments. This causes the process related to SPP payments to not run optimally. To overcome all the problems above, the author proposes to design and build supporting applications such as database systems so that processing of tuition payments is more effective and efficient, and can also simplify operational activities in schools. This system was designed and built using the PHP (Hypertext Preprocessor) programming language and MYSQL as the database.


Pattern Recognition in Medical Images Through Innovative Edge Detection with Robert's Method

Abstract: This research introduces an innovative approach for pattern recognition in medical images through the application of Robert's edge detection method. Pattern recognition in medical images has great significance in disease diagnosis and patient care management. Edge detection is an important stage in image processing which aims to determine the boundaries of objects in the image. Robert's edge detection method is one of the classic methods that has been used in image processing. However, improving edge detection performance is needed to improve accuracy in pattern recognition in medical images. In this study, we propose a modified variation of Robert's method to increase the accuracy in finding edges in medical images. The proposed innovative approach is tested using a large and diverse medical image dataset. Evaluation is carried out by comparing the edge detection results using the conventional Robert method with the results using the proposed modified method. Quantitative analysis is carried out to measure the performance improvements achieved. Experimental results show that the modified Robert edge detection method produces significant improvements in precision and accuracy in finding edges in medical images. These results indicate that the proposed innovative approach has the potential to improve pattern recognition in medical images and can make valuable contributions in the diagnosis and management of diseases.


Implementation of the Laplacian of Gaussian Algorithm in Edge Detection Image Processing of Zebra Cross Damage on Highways in the Langkat Regency Area

Abstract: Walking is part of the traveler's movement and is the simplest means of transportation, but it is in a weak position and prone to conflict or accidents when they mix with other modes of transportation. To protect pedestrians, special facilities are needed, one of which is a crossing place (zebra crossing) that is able to serve according to pedestrian needs. Based on Law No. 22 of 2009 concerning Traffic Polytechnic Land Transportation Bali 46 Cross and Road Transportation, article 131 paragraph (2), it is stated that "Pedestrians are entitled to priority when crossing the road at the crosswalk". One of the important meanings for human life is the Way. Roads are used as a means of transportation that has a very useful role in efforts to develop human life. In 2018, based on statistical data, the number of motorized vehicle users in Indonesia is increasing every year to reach 146,858,759 units. The impact that occurs is that there are many Zebra Cross roads damaged with conditions that are very troubling and worrying for road users. Among the causes of zebra crossing being damaged will be traffic accidents where the vehicle does not lag obeying the path of the vehicle following the predetermined lane. So this study detects image processing with the Laplacian of Gaussian algorithm with edge detection making it easier for the government to improve traffic signs of zebra crossing images on highways that are worthy of improvement so that accidents do not occur. The results of this study illustrate the image of being able to see damaged zebra crossings with calculations of the Laplacian of Gaussian algorithm.


An Informed-Bi-Quick RRT* Algorithm Based on Offline Sampling: Motion Planning Considering Multiple Constraints for a Dual-Arm Cooperative System

Abstract: Aiming to address problems such as low sampling success rate and long computation time in the motion planning of a dual-arm cooperative system with multiple constraints, this paper proposes an Informed-Bi-Quick RRT* algorithm based on offline sampling. First, in the process of pre-sampling, the new algorithm relaxes the approximation of constrained manifolds by introducing the idea of incremental construction, and it incorporates the stochastic gradient descent method to replace global random sampling with local random sampling, which enriches the data set and shortens the offline sampling time of the data set. Second, the new algorithm improves the original Quick-RRT* algorithm by combining the two-tree idea and the multi-target bias expansion strategy, and it improves the adaptability of the algorithm to different obstacle environments. In addition, the loosely constrained motion and tightly constrained motion in the two-arm cooperative system are analyzed, and the adaptive planning of the two-arm trajectory in different motions is described in detail. In this paper, the two-arm cooperative model constructed with UR5 and UR10 robot arms is studied, and the ability of the proposed algorithm to deal with multiple constraints is verified by simulating assembly and handling tasks. The experimental results show that compared with other methods, the proposed algorithm has obvious advantages in path quality and planning efficiency.


ANALYSIS OF THE SPATIAL-SEMANTIC COHERENCE IN THE GETTY AAT VOCABULARY DATA STRUCTURE - AN EDUCATIONAL EXPERIENCE

Abstract: Abstract. The paper explores the integration of Geomatics and architectural surveying into the educational context of Geomatic for Built Heritage Conservation course within the Master on Architectural Design, in which a multidisciplinary teaching approach is applied, emphasizing the importance of holistic knowledge encompassing historical, geometric, material, and structural aspects for the conservation and restoration of architectural heritage.A central theme is the role of Geomatics and 3D modelling in historical built heritage documentation, assuming the creation of three-dimensional models as a foundation for integrating various thematic data, enabling interdisciplinary studies, and facilitating knowledge representation and communication.The paper delves into the challenges and strategies of semantic 3D modelling. It discusses the necessity for a flexible and layered informational model, integrating spatial, geometric, and semantic data. The complexity of semantic annotation is explored, considering the subjectivity and cultural context inherent in identifying and describing architectural elements. The educational approach employed in fostering critical thinking among students, particularly in interpreting cultural heritage issues related to documentation and recording, is presented.A case study involving Pitti Palace in Florence is considered. An attempt to implement the Getty Vocabulary on Art &amp; Architecture Thesaurus for semantic annotation is described, emphasizing the need for a coherent alignment between the 3D model and controlled vocabulary hierarchy. The study concludes with insights from the student's experience, highlighting the difficulties in aligning spatial and semantic elements within existing controlled vocabularies and suggesting avenues for future developments in refining these vocabularies for enhanced spatial-semantic coherence in 3D modelling applications.


NEURAL RADIANCE FIELDS (NERF) FOR MULTI-SCALE 3D MODELING OF CULTURAL HERITAGE ARTIFACTS

Abstract: Abstract. This research aims to assess the adaptability of Neural Radiance Fields (NeRF) for the digital documentation of cultural heritage objects of varying size and complexity. We discuss the influence of object size, desired scale of representation, and level of detail on the choice to use NeRF for cultural heritage documentation, providing insights for practitioners in the field. Case studies range from historic pavements to architectural elements or buildings, representing diverse and multi-scale scenarios encountered in heritage documentation procedures. The findings suggest that NeRFs perform well in scenarios with homogeneous textures, variable lighting conditions, reflective surfaces, and fine details. However, they exhibit higher noise and lower texture quality compared to other consolidated image-based techniques as photogrammetry, especially in case of small-scale artifacts.


Efficient genetic algorithm for spam mail detection and classification

Abstract: E-mail is the quickest tool to convey information from one to others. Individual users and organizations have become more reliant on e-mails as technology advances. At the moment, all email inboxes are swamped with spam and these cause significant and diverse difficulties, such as the loss of crucial information and the theft of the recipient’s identity. Organizations may suffer significant losses. As a result, users cannot avoid spam emails, which come in various formats such as advertisements and messages. Spam filtering removes spam messages and prevents them from being accessed. This paper focuses on categorization of e-mails by using genetic algorithm. The proposed approach uses entropy to evaluate information gain, which is subsequently employed by the genetic algorithm classifier to choose important features from the spam database. The model performance is then assessed by using the testing dataset with all 57 features, a crossover probability of 0.3, and a mutation probability of 1.0 and obtained good accuracy.


Improved video motion magnification method assisted by digital image correlation

Abstract: Video Motion Magnification (VMM) has gained considerable attention in the field of engineering measurement due to its impressive ability to amplify subtle motions. However, traditional algorithms often suffer from image artifacts and noise due to improper parameter settings, especially when dealing with weak motion. This paper introduces an improved VMM method that addresses this limitation by incorporating the Digital Image Correlation (DIC) technique. The proposed method utilizes DIC-measured image displacement results to analyze the dominant motion frequencies. Based on this analysis, the parameters of the VMM time domain filter are set accordingly. This approach enables motion magnification in videos while preserving image details and reducing noticeable artifacts. Simulation experiments conducted on an indoor precision displacement platform demonstrate the effectiveness of the proposed method. It eliminates the need for repetitive manual parameter adjustment through trial, producing clear and amplified motion videos. Additionally, it enables accurate measurement of small-scale motions. Overall, the proposed method enhances the performance of VMM by leveraging DIC and provides a more reliable and efficient approach for motion magnification in engineering measurement applications.


Statement-Grained Hierarchy Enhanced Code Summarization

Abstract: Code summarization plays a vital role in aiding developers with program comprehension by generating corresponding textual descriptions for code snippets. While recent approaches have concentrated on encoding the textual and structural characteristics of source code, they often neglect the global hierarchical features, causing limited code representation. Addressing this gap, our paper introduces the statement-grained hierarchy enhanced Transformer model (SHT), a novel framework that integrates global hierarchy, syntax, and token sequences to automatically generate summaries for code snippets. SHT is distinctively designed with two encoders to learn both hierarchical and sequential features of code. One relational attention encoder processes the statement-grained hierarchical graph, producing hierarchical embeddings. Subsequently, another sequence encoder integrates these hierarchical structures with token sequences. The resulting enriched representation is then fed into a vanilla Transformer decoder, which effectively generates concise and informative summarizations. Our extensive experiments demonstrate that SHT significantly outperforms state-of-the-art approaches on two widely used Java benchmarks. This underscores the effectiveness of incorporating global hierarchical information in enhancing the quality of code summarizations.


Apriorics: Information and Graphs in the Description of the Fundamental Particles—A Mathematical Proof

Abstract: In our earlier work, we suggested an axiomatic framework for deducing the fundamental entities which constitute the building block of the elementary particles in physics. The basic concept of this theory, named apriorics, is the ontological structure (OS)—an undirected simple graph satisfying specified conditions. The vertices of this graph represent the fundamental entities (FEs), its edges are binary compounds of the FEs (which are the fundamental bosons and fermions), and the structures constituting more than two connected vertices are composite particles. The objective of this paper is to focus the attention on several mathematical theorems and ideas associated with such graphs of order n, including their enumeration, showing what is the information content of apriorics.


K-Means Algorithm for District/City Classification in West Java Based on Stunting Toddler Data

Abstract: Health issues, especially related to stunting, remain a major concern in Indonesia. Stunting is a condition where toddlers have a height that is too short for their age, which is a sign of chronic malnutrition. The purpose of this study is to group the number of stunting by district/city in West Java. The dataset was obtained from data on the number of stunting toddlers by district/city in West Java Province from 2014 to 2022. This dataset related to the topic of Health is produced by the health Office which is issued every 1 year. The results showed that there are two main groups in the classification of stunting numbers in West Java. The first group (cluster 0) includes Ciamis Regency, Kuningan Regency, Majalengka Regency, Sumedang Regency, Subang Regency, Purwakarta Regency, Bekasi Regency, West Bandung Regency, Pangandaran Regency, Bogor City, Sukabumi City, Bandung City, Cirebon City, Depok City, Cimahi City, Tasikmalaya City, Banjar City. The second group (cluster 1) consists of Bogor Regency, Sukabumi Regency, Cianjur Regency, Bandung Regency, Garut Regency, Tasikmalaya Regency, Cirebon Regency, Indramayu Regency, Karawang Regency, Bekasi City


INTERCULTURAL DIALOGUE AS THE BASIS OF INTERCULTURAL EDUCATION

Abstract: Intercultural education is a very important part of general andhigher education today. It is mainly based on thecommunicative aspects, which are closely connected with adialogue.The article deals with the intercultural dialogue. It is the basisof the theoretical and methodological support of the spiritualdevelopment of a man.Definition of the earlier nature of the dialogue, from aphilosophical point of view, as an element of the theoretical,methodological, spiritual and ethical principles, in particular asa fundamental principle that is subjectively a fundamentalrequirement and a prerequisite for the thinking and behaviorof the individual (maxim), and objectively leading the spiritualand practical norm of human co-existence, makes it possibleto conclude that such a methodological guideline is notleading in existing cultural and educational practices thatremain beyond the scope of dialogue, without the use ofdialogue in the cultural and educational process.


Microscale strain distribution measurement before and after crack and delamination occurrence in CFRP laminates by multiplication sampling moiré method

Abstract: The multiplication sampling moiré (MSM) method achieves a strong noise-immunity deformation measurement by performing phase analysis of the second harmonic of grating patterns, which surpasses the limitation of the conventional sampling moiré method that produces phase errors when the first harmonic is submerged by the background noise. In this study, the multiplication sampling moiré method was utilized to investigate the fracture behavior of a [±15°]2s carbon fiber reinforced plastic (CFRP) laminate specimen under different tensile loads. The full-field microscopic strain distribution maps, including the normal, shear, and principal strains, were successfully measured on the cross-section of the CFRP laminates with fiber discontinuities. The results show strain distribution characteristics before and after transverse crack occurrence in the matrix resin region of the CFPR laminates, and the changes in shear strain at the interlayer interfaces before and after the emergence of delamination. The MSM method holds promise for evaluating mechanical properties, fracture behavior, characterizing strain distributions, and residual stresses in deformation measurements of various structural and composite materials.


Stitching in fixed wide-field-of-view scenarios

Abstract: In certain scenarios, due to the limitation of a single camera in capturing the entire scene, we need to employ multiple cameras to gather information and seamlessly fuse multiple local images into a panoramic image, showcasing significant potential across a wide range of application domains. This paper focuses on the theory and practice of image stitching in fixed wide-field-of-view scenarios. Firstly, we delve into the fundamental principles and methods of image stitching, including feature extraction, feature matching, and image blending. Secondly, we provide an overview and analysis of current state-of-the-art image stitching algorithms, exploring their advantages and limitations, particularly in the context of fixed field of view. To address the common challenges in fixed wide-field-of-view image stitching, this paper proposes a novel stitching algorithm based on grid distortion. By utilizing multiple sets of scene images to guide the new image stitching process, we achieve higher precision in image matching and blending while retaining fine details. Experimental results demonstrate the effectiveness of our approach in a self-constructed scene.


The Role of International Efforts in Combating Cybercrimes

Abstract: Cybercrimes have become a modern type of crime pattern. They are characterised by the fact that they transcend the borders of states. This led to the direction of the international community to cooperate to confront cybercrimes, as they have a great impact on the national security of states. Therefore, states and international organisations have sought to take joint measures to protect individuals, societies and companies from cybercrimes by concluding international and regional agreements. The most important of these agreements is the Budapest Convention on Combating Cybercrime, the latest of which is the Paris Call for Confidence and Security in Cyberspace. The increase in the use of the internet results in an increase in exposure to the risk of cybercrime. Moreover, the increasing involvement of individuals and organised criminal groups with the internet can harm states, individuals and companies. This invites regional and international organisations to play an important role in concluding international agreements that consolidate international efforts in cybersecurity. However, although there are important international steps in combating cybercrimes, this effort faces several challenges. In addition, the implementation of international agreements approved by states in combating cybercrime is still problematic. Cybercrime is a global challenge, and any effective response requires coordination between the public and private sectors, including law enforcement agencies across many international jurisdictions. This paper discusses the role of international efforts in combating cybercrime in terms of international legal instruments and the role of international organisations in this context. The paper also presents the most important challenges facing the enforcement of these efforts to achieve international co-operation in confronting this type of emerging crime. The paper uses descriptive and interpretive approaches with analytical methods. It concludes with several recommendations for ways to improve international efforts in combating cybercrimes, including the establishment of an international system for responsibility sharing among states to combat cybercrimes.


RELEVANT ASPECTS OF FOREIGN LANGUAGE TEACHING DURING THE WAR IN UKRAINE

Abstract: The education system is one of those areas that have felt the most negative impact ofmilitary operations in Ukraine. The use of a wide range of ICT improved the actualquality of teaching disciplines in institutions of higher education, and ensured thetransition of Ukrainian education to a new level. However, the rapid spread of distanceeducation in the world not only opened wide opportunities for students and teachers,but also posed many challenges to them. The work emphasizes that distance learningmethods are not appropriate enough in the conditions of war in Ukraine, and mighteven lose their effectiveness, need to be revised, adapted and improved. The articlestates that the integration of ICT in education serves many purposes, the mostimportant of which are free access to educational materials, high mobility andconvenience. The authors highlighted the most effective methods for ensuring asufficient level of foreign language training in the conditions of distance education,among which they singled out the communicative method, the project method, and thesimulation method, the implementation of which is supported by the use of videoconferencing tools and distance learning environments. The main shortcomings andobstacles in the organization of the learning process were characterized and solutionswere proposed taking into account modern realities in Ukraine. Problems of atechnical nature, the instability of the security situation, being in different countries ofthe world lead to the fact that the existing teaching methods need to be revised,constantly adapted to the rapidly changing situation, and in some places, theycompletely lose their relevance. At present, the success of students' training dependsprimarily on the readiness and ability of the teacher to organize the most flexibletraining process.


Morphological and Chemical Analysis of Farzi (Fake) Indian Currency

Abstract: In Forensic Science, Morphological and chemical analysis of farzi currencies plays a very important role in identifying the counterfeit currency. The term “counterfeit” refers to fake or forgery which is an exact replica of the original. As there is a rapid advancement in technology there is a drastic increase in crime rate with respect to technology as well where culprits are using advanced and modern technologies for committing crime, here counterfeit currency falls one among them, where culprits are using wellversed technologies in manufacturing fake notes and coins which cannot be easily recognized by the public. In India though RBI is taking a lot of precautions to overcome counterfeiting by implementing high security features in Indian currency such that it cannot be duplicated easily, some or the other way these fake currencies are circulating in the society and people are lacking knowledge in identifying these fake notes and coins. Some of the security features for identifying these fake currencies is revealed to public by the RBI in its guidelines. Though criminals disguise the original by circulating fake currencies, it is impossible to implement the authenticated security features in it. The composition and its ratio of the original varies from the duplicate which is a loophole, and this can be easily analysed by forensic scientists by keen observations and conducting certain physical and chemical tests. We tried to differentiate between fake and original currencies of India by using RBI guidelines and by performing certain chemical tests through which counterfeit currencies can be analysed.


Online Deterministic 3D Trajectory Generation for Electric Vertical Take-Off and Landing Aircraft

Abstract: The use of non-piloted eVTOL aircraft in non-segregated airspace requires reliable and deterministic automatic flight guidance systems for the aircraft to remain predictable to all the users of the airspace and maintain a high level of safety. In this paper we present a 3D trajectory generation module based on clothoid transition segments in the horizontal plane and high order polynomial transition segments in the vertical plane. The expressions of the coefficients of the polynomial are derived offline are used to generate the trajectory online, making the system capable of running in real time without requiring enormous computational power. For the horizontal plane, we focus on the flyby transition, and therefore present a thorough analysis of the flyby geometry and the limitations linked to this geometry and the construct of three-segment trajectory generation around a fixed turn rate. We present feasible solutions for these limitations, and show simulation results for the combined horizontal and vertical plane concepts, allowing the system to generate complex 3D trajectories.


MOTIVATIONAL ASPECT OF MUSIC AND PERFORMANCE TRAINING OF THE FUTURE TEACHER-MUSICIAN

Abstract: The article highlights the specifics of the motivation of musical and performingactivities. The problem of increasing motivation in student self-development andachieving high learning results has been updated. Attention is focused on taking intoaccount the individual characteristics of each student in connection with the differentlevel of pre-university training and on the importance of revealing the professionalpotential of an individual for his further self-improvement. The components of musicalperformance activity are defined. The direct connection of executive activity withmotivation is substantiated. The process of formation and development of positivemotivation is analyzed. The structure of the process of formation of motivation formusical and performing activities is considered. The components of the complex ofpedagogical actions, which are able to ensure a positive attitude towards theperforming activities of the future teacher-musician, have been identified. Theimportance of finding an appropriate ratio of external and internal motives of musicalactivity in the process of performance preparation is indicated. Examples of thepositive effect of internal motivation on a person are given.The mechanisms of stimulation of the internal and external motivation of theindividual are considered. The "flow state of consciousness" in educational activity issingled out as the one that triggers the process of increasing motivation.The role of the teacher in stimulating the positive motivation of future music teachersto improve performance skills is emphasized. It has been proven that motivation is adriving force in the professional development of a teacher-musician.


DEVELOPMENT OF ARTISTIC AND AESTHETIC INTERESTS OF STUDENTS WITH SEVERE SPEECH DISORDERS IN MUSIC LESSONS

Abstract: The article defines and substantiates the problem of artistic and aestheticeducation of younger schoolchildren with severe speech disorders (SSD) inmusic lessons. It is noted that in the process of artistic and aestheticdevelopment of students with SSD there is a positive influence of art on theformation of general cultural literacy of children. Scientific approaches to theorganization and management of the process of artistic and aestheticeducation of students with speech disabilities have been analyzed. It wasestablished that the main task of the method of forming artistic and aestheticskills in students with speech disorders is the development of their emotionalintelligence, communicative and creative abilities. The following means ofactivating interests have been determined: selection of moderately difficulttasks that awaken independent thought in the student; reliance on children'sexperience and interests that have already arisen in them. A model of theformation and development of the aesthetic culture of students with SSD ispresented, which consists of three stages. The development of artistic andaesthetic interests is a necessary prerequisite for the successful assimilationof knowledge and, accordingly, correction of the speech development ofelementary school students. This process involves the creation of a positiveattitude to the subjects of the aesthetic cycle, an emotional atmosphere inthe organization of classes, as well as stimulating the success of students bypedagogically appropriate means. It was concluded that the use of apedagogical system (based on artistic and aesthetic complexes) in musiclessons and in extracurricular time will contribute to a deeper understandingof music by students; will cause certain visual and auditory impressions inthem, will promote speech development.


IMPLEMENTATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN MUSIC CLASSES IN PRE-SCHOOL EDUCATION INSTITUTIONS

Abstract: The author deals with the new problem of the specifics of theimplementation of information and communication technologies in musiclessons in preschool education institutions. A detailed analysis oftheoretical, applied, practical, scientific research devoted to the questionof the expediency of using information and communication technologiesin the modern educational space has been carried out. An interpretationof the meaning of the terms «the age of digital technologies»,«information and communication technologies», «preschool child» isprovided, the main developments of foreign and domestic scientists onthe investigated problem are presented. The main legislative documentsof Ukraine, which focus on the implementation of information andcommunication technologies in the educational practice of preschooleducation institutions, were reviewed and analyzed. It is emphasized thatthe increasing pace of development and application of information andcommunication technologies in Ukraine and the world actualizes the needto improve the quality of knowledge of modern Ukrainian music directorsand teachers of preschool education institutions in matters of musicaleducation of preschool children. The main ways of introducing informationand communication technologies in music lessons in preschool educationinstitutions are characterized. The main goal of the article is totheoretically substantiate the importance of using ICT in music classes forpreschool children in preschool education institutions.


A calibration method for LED point light sources in near-field photometric stereo

Abstract: In near-field photometric stereo vision, the accurate calibration of the light source position directly affects the precision of the reconstruction results. Traditional calibration techniques rely on a highly reflective sphere and exploit the specular reflection properties at highlight points for determining light source positions. However, in real-world scenarios, nonuniform lighting conditions often leads to errors in extracting the sphere's image edges, affecting the accuracy of light source position calibration. Therefore, we propose a method for calibrating light source position based on a novel target. This method involves detecting planar reference points that are less sensitive to the lighting conditions and leveraging the pose relationship provided by the planar reference points to effectively overcome the adverse effects of lighting conditions on light source position calibration. Experimental results demonstrate that this method significantly enhances the precision of light source position calibration under non-uniform lighting conditions, eliminating the constraint of light source position calibration on specific lighting environments.


Multiple-image authentication scheme based on the phase-only holograms

Abstract: A multiple-image cryptographic authentication scheme based on phase-only holograms is proposed in this paper. First, computer-generated holograms of original images to be authenticated are encoded to phase-only holograms using the Floyd-Steinberg error diffusion algorithm. Second, each phase-only hologram image is randomly sampled as the sparse representation with the help of its random binary mask. Finally, the phase-only ciphertext containing the information of original images is obtained by integrating all sparse representations using their binary masks. The existence of each original image can be verified by calculating the nonlinear correlation map between it and its corresponding decrypted result. High security level of this cryptosystem can be achieved by considering random binary masks as secret keys. This work provides an effective alternative for the related research based on computer-generated holograms.


CycleSR: unsupervised learning for 3D fingerprint super-resolution

Abstract: Fringe projection profilometry has been applied to measure 3D information of fingertip and collect contactless 3D fingerprints. When low-resolution (LR) camera is used in the system due to reasons such as cost, the captured fringe patterns may appear blurry, which results in less obvious contrast between valleys and ridges in the reconstructed contactless 3D fingerprints. To address this issue, we introduce an unsupervised super-resolution (SR) method that solely relies on low-resolution fringe patterns. Our approach combines a two-loop generative adversarial network. In the forward loop, a binarized interpolation loss function is designed to ensure that the upsampling generator preserves ridge and valley details. In the backward loop, the discriminator ensures that the fringe patterns produced by the downsampling generator are both repeatable and similar to the original fringe patterns. Finally, the fringe patterns are reconstructed to obtain 3D fingerprints. Experimental results demonstrate the advantages of our proposed method.


METHODS AND TECHNIQUES OF OPTIMIZING THE CONDUCTING AND CHORAL TRAINING OF A MUSIC TEACHER IN THE CONDITIONS OF FORMAL AND INFORMAL EDUCATION

Abstract: In the article, the object of research is the process of optimization ofconducting and choral training of a music teacher in the conditions ofintegration of formal and informal education.The subject of the study is the uniqueness of the professional trainingof a music teacher in the context of the integration of formal andinformal education.The article analyzes the theoretical-methodical and acmeologicalbases of optimization of conducting and choral training of a musicteacher in the conditions of integration of formal and informaleducation; the methodological substantiation of the concept ofoptimization of conducting and choral training of a music teacher in theconditions of integration of formal and informal education is considered.The purpose of the work is to investigate and substantiate the methodsof optimization of conducting and choral training of a music teacher inthe conditions of integration of formal and informal education.The article uses the following research methods: theoretical methods –analysis and synthesis, induction and deduction, abstraction andidealization, generalization, analogy and modeling, the method ofascent from the abstract to the concrete; empirical methods:observation, study of documentation and products of educationalactivity, generalization of independent characteristics, study andgeneralization of advanced pedagogical experience.The practical significance lies in the scientific and methodologicalprovision of the process of professional development and selfrealizationof future music teachers in secondary education institutions.


RECOMMENDATIONS FOR PEDAGOGICAL WORKERS AND PARENTS REGARDING PEDAGOGICAL SUPPORT OF YOUNG CHILDREN IN EMERGENCY SITUATIONS

Abstract: Тhe article is devoted to the urgent problem of providing the youngestmembers of society – young children (from 1 to 3 years) with reliable andeffective pedagogical support during emergency events. An interpretationof the meaning of the terms: «early age», «pedagogical support»,«emergency situation» is provided, the main developments of foreign anddomestic scientists on the investigated problem are presented. An urgentproblem today is the need to highlight the importance of understanding byteachers and parents/guardians the peculiarities of children's developmentat an early age during stressful situations. The main goal of the article is toprovide specific pedagogical recommendations to both teachers andparents/guardians, which will effectively contribute to the adaptation,learning, upbringing and development of young children affected byemergency situations; to emphasize the importance of joint work andmutual understanding between pedagogical and family factors to improvethe quality of pedagogical support for young children in crisis situations.Specific pedagogical recommendations are described, both for pedagogicalworkers and for parents/guardians, who play a key role in shaping theresilience and well-being of young children in emergency situations.


Measurement depth modeling and extension for geometric constraint phase unwrapping method

Abstract: As an important phase unwrapping method in Fringe Projecting Profilometry, the geometric constraint enables pixel-by-pixel computation while avoiding the projection of additional patterns. However, its measurement depth is limited to one fringe period and influenced by multiple factors from the camera-projector system. There is still a lack of a generalized model that can describe the measurement depth. In this paper, to completely understand the variables related to the measurement depth, we analyze the unwrapping process of geometric constraint and derive a generalized model. To validate the key variables in the model, we establish a virtual camera-projector system and conduct simulations. Based on the simulation results, we propose four ways to extend the measurement depth, including (a) selecting a reference plane farther away from the camera, (b) designing the fringe patterns with fewer periods, (c) using the shorter focal lens and (d) reducing the camera-projector distance.


Adaptive-resolution-based high-resolution indoor 3D perceiving in fringe projection profilometry

Abstract: Fringe projection profilometry (FPP) is one of the most widely used optical three-dimensional (3D) perceiving techniques. However, in the realm of indoor 3D perceiving, achieving high-resolution data proves challenging due to the inherent trade-off between sampling resolution and measurement scale. This paper introduces an adaptive-resolution-based method to address this challenge. Specifically, the approach leverages the super-resolution reconstruction technique to enhance the resolution of captured fringe patterns, where an end-to-end fringe pattern super-resolution network (FPSRNet) is constructed to adaptively achieve different super-resolution values. The desired high-resolution 3D results can be reconstructed from these new patterns. Experimental results verify the effectiveness of the proposed method in indoor 3D perceiving.


Body measurement based on a multiview structured light 3D imaging system

Abstract: In this paper, a multi-view structured light three dimensional (3D) imaging system is descried, which can perform rapid 3D reconstruction of the human body and automatically extract anthropometric data from it. This system contains 12 sets of 3D imaging sensors distributed on four pillars. Each 3D imaging sensor consists of a binocular stereo system, an Infrared laser projector and a synchronous control system based on the Field Programmable Gate Array (FPGA). The projector provides phase-shifting fringe patterns and gray code for the binocular stereo system to make 3D reconstruction. The FPGA control system enables the sensor to achieve high speed scanning. A two-step calibration method is used to calibrate the internal and external parameters of each 3D imaging sensor and external parameters between these sensors. After the 3D human body data acquisition, major body joints will be extracted as key-points. In this process, the initial location of these key-points are extracted based on a deep learning method, and then they are further corrected with local point cloud analysis. With the assistance of these key-points, the anthropometric data, such as distances (lengths, breadths, heights) and circumferences of the human body, can be calculated from its 3D data. Based on the techniques described above, the multi-view 3D imaging system can complete the whole body scanning in 2 seconds and automatically measure more than sixty dimensional data after analyzing the reconstructed 3D human data.


3D measurement of large and complex parts based on phase matching and global marker points registration

Abstract: Current requirements resulting from high quality standards and mass production call for an efficient and reliable manufacturing technology. With the aim of meeting the needs of accurate and efficient inspection of massive industrial parts, we propose a novel large-field measurement technique to measure weakly textured, complex reflective surface on large-size parts. We introduce a small-field-of-view, high-spatial-resolution binocular fringe projection local measurement system, which realizes high-precision point cloud data acquisition of industrial parts' surface based on the principle of heterodyne multi-frequency phase shift and high dynamic measurement. A new point cloud registration method based on phase matching and global marker points is proposed. A global measurement system with large field of view and low spatial resolution system is applied to track the local measurement system and realize the local area point cloud registration of large industrial components. The global marker points on the surface of the turntable jointly calibrated by the laser tracker and the vision system is presented to realize the registration of all areas of the component. A global optimization with trimmed ICP is applied for adaptive fine registration of multi-view point clouds with different overlap rates to eliminate accumulated errors. The proposed system can achieve a global measurement range of 4m×4m×2.5m, and the experimental results demonstrate the effectiveness of the proposed method for high-precision measurement of industrial complex parts with large field of view, which effectively avoid the transmission error and benefits the inspection and manufacturing of industrial parts.


Quantum transport on multilayer generalized scale-free networks

Abstract: Abstract We study single-particle quantum transport on multilayer generalized scale-free networks using the continuous-time quantum walk model. Our focus is directed at the average return probability and its long-time average value as measures for the transport efficiency. In the continuous-time model these quantities are completely determined by all the eigenvalues and eigenvectors of the connectivity matrix. For all multilayer networks a nontrivial interplay between good spreading and localization effects is observed. The spreading is enhanced by increasing the number of layers L or the power-law exponent γ of the degree distribution. For our choice of the parameters, namely L (1 ≤ L ≤ 50) or γ (1 ≤ γ ≤ 4), the quantum efficiency is increased by at least one order of magnitude. The topological transition between networks without loops, which corresponds to a single scale-free network layer ( L = 1), and networks with loops ( L = 2) is the most impactful. Another important change occurs when L gets higher than the average diameter d of the layers, namely a new scaling behavior for random walks and lower fluctuations around the long-time average value for quantum walks. The quantum transport is more sensitive to changes of the minimum allowed degree, K min , than to the maximum allowed degree, K max . The same quantum efficiency is found by varying at least one of the parameters: L , γ , K min , or K max , although the network’s topology is different. The quantum efficiency of all multilayer scale-free networks shows a universal behavior for any size of the layers, more precise, is inversely proportional to the number of layers.


Uncertainty analysis and optimization design of long-distance laser triangulation displacement sensor applied to dynamic object

Abstract: The principle of laser triangulation has been widely used in various high-precision measurement due to its advantages of high accuracy, fast response speed and simple structure. However, the laser triangulation method still has some limitations for the measurement of dynamic object, and the accuracy of laser location will be significantly reduced especially when the working distance becomes longer. We have carried out the development and performance optimization of a long-distance laser triangulation displacement sensor for the application of dynamic object. The measurement uncertainty is analyzed when the laser dithering, laser drifting and object motion are all taken into account. Then, reflectors are added to the optical path structure of traditional laser triangulation system with the aim to improving its sensitivity and expanding its working range. The experiment results show that the repeatability of our designed optical system is 2.9μm and the imaging uniformity is 86.23% when the working distance is of 1300mm.


Verification and identification of the speech signal using algebraic analysis and DWT

Abstract: We presented a flowchart, of a new approach that relies on the mathematical operations of matrices and to verify and identifies the speaker, this is a new method difficult to detect by attackers, and this is due to the difficulty to determine its base matrix used in the speaker verification and identification process, Although there are many artificial intelligence programs that can imitate voices and generating false conversations, which poses security and hacking problems.


Phase shift error estimation based on deep learning

Abstract: Phase-shifting interferometry is a high-precision and commonly used phase retrieval method. In practical applications, phase shift errors are usually introduced due to factors such as environmental disturbances and phase shifter error. In this paper, we propose a deep learning method for estimating phase shift error from phase-shifting interferograms. This method manages to process the three interferograms with phase shift <i>π</i>/2, and uses neural network to extract phase shift errors from three interferograms. The analysis shows that the proposed method can effectively estimate the phase shift error under the noisy interferograms. This method can be used to correct phase-shift errors for phase retrieval (e.g., the least squares phase retrieval method) and calibrate phase shifters.


Active thermal marker using thermal images of heated areas with visible semiconductor laser

Abstract: In situations where the object being captured or the camera itself is in motion, object tracking using conventional attached markers or detailed textures is useful to keep capture the object with high accuracy; however, tracking accuracy has been degraded in situations where texture features are scarce or where it is difficult to attach markers. Therefore, in this study, the temperature rise corresponding to the laser irradiation time to the object is detected by a thermal camera, and the points where the temperature reaches or exceeds a threshold value after the laser irradiation ends are used as markers. After the temperature drops, laser irradiation is performed again to repeatedly generate markers. Furthermore, by controlling the irradiation point with a 2-axis galvanometer mirror, a marker that can code arbitrary shapes and information can be generated on a 2-dimensional plane. In our experiments, we irradiated a red semiconductor laser onto black paper, and found that an irradiation time of 10 ms was the most efficient in terms of heat dissipation time, enabling the simultaneous generation of up to 33 markers. As a result, it was found that it is possible to draw distinguishable characters and graphics such as lines and circles by continuous drawing. Future plans include application to non-paper based on additional physical property investigations and actual tracking applications.


Bridge deflection measurement by drone aerial photography using the sampling moiré method

Abstract: Accurate deflection measurement is vital in evaluating the structural integrity of transportation infrastructures, with bridges being fascinating. In this study, we propose a novel image stabilization technique integrated into the sampling moiré method, which leads to a dependable approach for measuring bridge deflection through drone aerial photography. Our experimental verification entailed conducting drone tests on an actual bridge, utilizing a passing test vehicle, and the results showcased deflection measurements comparable to those obtained through conventional methods. This newly developed technology eliminates the need for ground-fixed cameras mounted on tripods, thus enabling precise deflection measurement at the millimeter level for bridges in challenging environments, including marine and mountainous areas.


TRANSLATION GLOVE WITH SENSOR FOR SIGN LANGUAGE

Abstract: Abstract-The translating glove with sign language sensors is a cutting-edge wearable device designed to close the communication gap between sign language users and non- users. The glove has a number of sensors, such as accelerometers, gyroscopes, and flex sensors, which are strategically placed on the hand and fingers to precisely detect the motions and gestures used in sign language.The glove can detect and interpret a wide range of sign language gestures by using sophisticated algorithms to assess sensor data in real-time. When the sensors record the movements of a sign language user, the algorithms convert their gestures into either spoken or written language. Consequently, real- time communication is possible between people who use sign language and others who do not.Reverse translation is another feature of the glove. The device's sensors convert spoken or written input into motions that are displayed on the glove's surface as the user talks or writes into it. By making it possible for sign language users to comprehend and respond to spoken information, this promotes inclusive communication.


Whisper-Steg: Unveil the Hidden

Abstract: Whisper-Steg represents a ground- breaking project at the intersection of secure messaging and advanced information concealment techniques. This innovative chat application transcends traditional boundaries by seamlessly integrating the art of steganography, offering users a novel and secure platform for communication. The project not only facilitates text and image sharing but introduces an ingenious Image Steganography feature, allowing users to embed confidential information within images. Incorporating an acknowledgment mechanism akin to popular messaging applications like WhatsApp, Whisper- Steg prioritizes the reliability of communication. This feature ensures that users receive confirmation of message delivery, enhancing the overall user experience. The project dynamically generates cover images based on the size of hidden messages, employing AI algorithms to optimize concealment. This not only enhances the security of the stenographic process but also underscores the project's commitment to leveraging cutting-edge technologies for user benefit. In an era where privacy concerns are paramount, Whisper-Steg embraces steganography and AI-driven solutions and stands as a testament to technological innovation, reshaping the landscape of secure digital communication. Key Words: Whisper-Steg, Steganography, chat application


Phase retrieval by extracting Zernike coefficients from two random phase-shifting interferograms based on deep learning

Abstract: This paper presents a method of extracting Zernike coefficients and then retrieving the phase based on deep learning. In the normal interferometry, the phase extraction algorithm is utilized to calculate the wrapped phase according to the registering phase shifting interferograms, and then unwrap it to obtain the phase map. Further, Zernike polynomials are used to fit the phase. The method proposed in this paper uses neural networks to extract 35 terms of Zernike coefficients from two random phase-shifting interferograms, then retrieve the phase based on Zernike polynomials. The method only needs two random phase-shifting interferograms, does not require phase extraction and unwrapping processing and greatly simplifies the computation for phase reconstruction. The paper presents the training processing, and provides the experimental results. The results show that the proposed method can reach high precision and are more suitable for the quick testing for workshop environment.


Fast anti-turbidity underwater topography measurement based on rotating structured light

Abstract: The visual measurement techniques play an important role in underwater three-dimensional (3D) topography reconstruction. Line structured light measurement are widely used in visual 3D information acquisition. Aiming at the refractive nonlinear imaging effects and image degradation problems in underwater measurements, this paper proposes a physical enhancement-based underwater nonlinear refraction measurement model (peUNR). First, the underwater images are corrected by physical enhancement based on scattering imaging theory to improve the image recognition. Second, a refraction relationship for underwater visual measurements is developed by ray tracing. In this model, the light plane is unchanged before and after reaching the surface of the underwater target. As a result, the system parameters can be calibrated in air, allowing high-precision 3D reconstruction underwater without the need for underwater calibration. At last, in order to reduce the dependence of structured light stripe extraction on image quality, a multiline rotating structured light projection mode is designed by using binary coding, which fulfils the need for fast and portable measurements and improves the measurement efficiency and resolution simultaneously. The experimental results of underwater measurement indicate that the residual error of the fitted plane obtained by the peUNR model is 1.81 mm in turbid water with a sand content of 125 g/m3, which satisfy the requirements of underwater 3D high-precision measurement.


Blind multi-Poissonian image deconvolution with sparse log-step gradient prior

Abstract: Blind image deconvolution plays a very important role in the fields such as astronomical observation and fluorescence microscopy imaging, in which the noise follows Poisson distribution. However, due to the ill-posedness, it is a very challenging task to reach a satisfactory result from a single blurred image especially when the power of the Poisson noise is at a high level. Therefore, in this paper, we try to achieve high-quality restoration results with multi-blurred images which are contaminated by Poisson noise. Firstly, we design a novel sparse log-step gradient prior which adopts a mixture of logarithm and step functions to regularize the image gradients and combine it with the Poisson distribution to formulate the blind multi-image deconvolution problem. Secondly, we incorporate the methods of variable splitting and Lagrange multiplier to convert the original problem into sub-problems, then we alternately solve them to achieve the estimation of all the blur kernels of corresponding blurred images. Besides, we also design a non-blind multi-image deconvolution algorithm which is based on the log-step gradient prior to reach the final restored image. Experimental results on both synthetic and real-world blurred images show that the proposed prior is very capable of suppressing negative artifacts caused by ill-posedness. The algorithm can achieve restored image of very high quality which is competitive with some state-of-the-art methods.


Photonic time-delayed reservoir computing based on series-coupled microring resonators with high memory capacity

Abstract: On-chip microring resonators (MRRs) have been proposed to construct time-delayed reservoir computing (RC) systems, which offer promising configurations available for computation with high scalability, high-density computing, and easy fabrication. A single MRR, however, is inadequate to provide enough memory for the computation task with diverse memory requirements. Large memory requirements are satisfied by the RC system based on the MRR with optical feedback, but at the expense of its ultralong feedback waveguide. In this paper, a time-delayed RC is proposed by utilizing a silicon-based nonlinear MRR in conjunction with an array of linear MRRs. These linear MRRs possess a high quality factor, providing enough memory capacity for the RC system. We quantitatively analyze and assess the proposed RC structure's performance on three classical tasks with diverse memory requirements, i.e., the Narma 10, Mackey-Glass, and Santa Fe chaotic timeseries prediction tasks. The proposed system exhibits comparable performance to the system based on the MRR with optical feedback, when it comes to handling the Narma 10 task, which requires a significant memory capacity. Nevertheless, the dimension of the former is at least 350 times smaller than the latter. The proposed system lays a good foundation for the scalability and seamless integration of photonic RC.


A Systematic Review on the use of Machine Learning and Deep Learning Techniques for Crime Prediction from Social Media

Abstract: The goal of crime prediction is to help law enforcement authorities to prevent crimes before they happen by identifying and reducing potential future crimes. In present days, crime cases are rapidly occur so it is a challenging task to accurately predict and classify the future crimes, as criminal patterns are adaptable and constantly evolve, making it a challenging task. Sentiment analysis is essential for deciphering the feelings conveyed in text since social media has grown into a popular venue for individuals to communicate their thoughts, feelings, views, and comments. This analysis is particularly valuable for making informed decisions in business, politics and government agencies especially to identify crimes. However, it faces the difficulties like lexical diversity and dataset imbalance. In order to make better decisions and maybe lower the risk of predicting the suspects in a crime, early crime prediction has lately made use of Artificial Intelligence (AI) models like Deep Learning (DL) and Machine Learning (ML). When applied to crime data, ML and DL models may assist pinpoint possible crime hotspots and foretell when crimes may occur. Crime prediction and categorization using social media posts is covered in depth in this paper, which offers a comprehensive overview of several ML and DL frameworks. Initially, different ML and DL based crime prediction models designed by many researchers are examined in brief. The next step is to do a comparison research to learn about the shortcomings of those frameworks and provide an alternative method for effectively predicting crimes based on social media postings.


Research on the Impact of Financial Structure on Economic Growth

Abstract: Abstract Financial support for economic growth is an important lever for developing countries to enhance their international competitiveness. This article selected provincial panel data from 31 provinces in China from 2006 to 2022. Research the impact mechanism of financial structure on economic growth from both linear and nonlinear perspectives. The linear research results indicate that market-oriented financial structures can effectively promote economic growth and have a robust impact. The non-linear research results indicate that the impact of market-oriented financial structures on economic growth is influenced by the non-linear effects of the capital market and labor market. Specifically, with the improvement of the allocation efficiency of the capital market and labor market, the promoting effect of market-oriented financial structure on economic growth shows a first strengthening and then weakening effect. Keywords: Financial structure, Economic growth, Linear influence, Nonlinear effects.


Applicability of education 4.0 in higher education: Engineering students’ survey

Abstract: The purpose of the study is to examine how Industry 4.0, and the digital environment, have together created a new situation for companies and universities in terms of soft skills for employability. While companies aim to introduce a new structure, universities try to align with these changes by developing new educational methods, curricula, and models. Many researches are focusing on - rightfully so - Industry 4.0 skills and competences to gain throughout higher education. However, we have shifted our research interest and asked students to what degree their university years added to their knowledge, self-management and skills. 147 engineering and technical manager students’ responses from two faculties at Óbuda University, Hungary were analyzed to see their evaluations of the role of the university in the framework of Education 4.0, and their chances in the job market. Quantitative and CHAID analyses were used. Students’ responses show significant differences between the two faculties regarding languages, teamwork, self-management and in the different order of the skills seen as most paramount to employability. Engineering students attach greater importance to field-relevant knowledge, while technical manager students find decision-making more useful. The research proved that Education 4.0 is here and Z generation studying at universities prefers problem-based learning, including creativity, analytical, and critical thinking while would like to have good communication skills. Universities need to adapt to these changes and integrate hard as well as soft skills development in tutoring. The importance of technical-IT knowledge is just as important as having interpersonal skills, excellence in problem-solving, so students need to be taught how to analyze problems, engage in scientific debate, or express themselves clearly as previous literature indicated.


The role of social networks in communication in the scientific research community

Abstract: Social networks have grown rapidly in recent years, enabling the application of social web technologies to the scientific process and creating platforms that enhance communication between researchers. The aim of this research is to go one step further and investigate whether the use of more general social networks, such as Twitter or Facebook, is also becoming more widespread for scientific research, thus contributing to the visibility of scientists and their collaborative networks. Social media analysis is carried out using the Brandwatch platform to assess the use of generalist social networks in research and compared with the use of scientific social networks through an online survey of university professors. The resulting conclusions show that scientific mentions in networks are rare and that, despite the importance of researchers having a profile in a social network, which allows them to give greater visibility to their results and receive feedback from their colleagues, many of them are still unaware of its usefulness.


Video synopsis method based on interaction determination using human pose estimation

Abstract: Video synopsis aims to condense long videos containing temporal redundancy into a more compact form by rearranging the active objects in the video. However, since each object in the video belongs to a different tube, moving the objects during the tube rearrangement process may disrupt the interaction relationship between the objects. To address this issue, we proposed a video synopsis optimization method based on interaction determination using human pose estimation. First, the inverse perspective mapping method is used to calculate the distance between objects from a bird’s-eye view. Then the body orientation is determined using the coordinates of human skeletal points obtained from the human pose estimation model. These two pieces of information are then combined to determine the interactions between objects. Second, the interaction energy cost term is introduced. By penalizing the cost term, the interactions between objects in the original video are preserved in the synopsis video. Finally, we propose a fusion optimization algorithm (FSOSA) that combines snake optimization and simulated annealing. FSOSA aims to minimize the energy cost function and achieve optimal tube rearrangement. Experimental results demonstrate that the proposed method effectively preserves the interactions between objects while improving the convergence speed.


Lightweight Saliency Target Intelligent Detection based on Multi-scale Feature Adaptive Fusion

Abstract: In order to solve the problems of small targets, variable shooting angles, and heights in drone images, the author proposes an adaptive drone target intelligent detection algorithm based on multi-scale feature fusion. The results show that after adding a deconvolution cascade structure to the network, mAP increased by about 2.5 percentage points and AP$^{50}$ increased by about 3 percentage points. Compared with Method 3, Method 4 uses GA-RPN instead of RPN, and when the IOU is 75, the AP increases by 3.5 percentage points, reflecting that the target prediction candidate boxes generated using semantic features adaptively match better than the manually designed target candidate boxes. This indicates that the proposed target detection framework has better classification ability and higher frame regression accuracy. Multi scale adaptive candidate regions are used to generate fused features of different scales generated by the network, weighted fused multi-scale features are used for target prediction, and semantic features are used to guide the network to adaptively generate target candidate frames, greatly enhancing the feature expression ability of various targets and improving the detection accuracy of aerial targets.


A Method for Specifying Yoga Poses Based on Deep Learning, Utilizing OpenCV and Media Pipe Technologies

Abstract: Yoga is a years discipline that calls for physical postures, mental focus, and deep breathing. Yoga practice can enhance stamina, power, serenity, flexibility, and well‐being. Yoga is currently a well-liked type of exercise worldwide. The foundation of yoga is good posture. Even though yoga offers many health advantages, poor posture can lead to issues including muscle sprains and pains. People have become more interested in working online than in person during the last few years. People who are accustomed to internet life and find it difficult to find the time to visit yoga studios benefit from our strategy. Using the web cameras in our system, the model categorizes the yoga poses, and the image is used as input. However, the media pipe library first skeletonizes that image. Utilizing a variety of deep learning models, the input obtained from the yoga postures is improved to improve the asana. The algorithms like VGG16 (Visual Geometric Group), VGG19, Convo2d, CNN.


A Study of Blockchain and Machine Learning-Enabled IoT Security in Time-Delayed Neural Network Vocal Pattern Recognition to Improve Web-Based Vocal Teaching

Abstract: With the development of information technology, online vocal teaching is becoming more and more popular, but the sound quality of teaching is also becoming more and more demanding. As online vocal instruction becomes more popular, the need for high-quality sound in these digital environments becomes more critical. This research tackles the problem of improving sound quality in real-time vocal teaching by integrating advanced technologies such as Blockchain and Machine Learning within the Internet of Things (IoT) security framework. We created a vocal recognition model using Time-Delay Neural Network (TDNN) and improved it with Generated Feature Vector (GFV). This integration yields a strong GTDNN vocal recognition system that is specifically designed to secure and optimize web-based vocal teaching. Our experiments show that GTDNN outperforms traditional TDNN and i-vector methods in feature vector extraction, adapting well to different speech environments. In various speech settings, GTDNN's Error Rates (EERs) are impressively low at 11.3%, 12.0%, 4.9%, 6.2%, and 6.1%, indicating superior performance over comparison models. GTDNN has an EER of 9.6% for short-duration speech and 2.3% for long-duration speech. Furthermore, the GTDNN system achieves an overall pass rate of 94% for target speech and an impressive rejection rate for non-target speech, ensuring high accuracy in a variety of speech environments.


Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

Abstract: In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques.


Impact of Service Innovation and its Quality on Patients' Satisfaction and Loyalty during the Covid-19 Pandemic

Abstract:
Introduction: In today's societies, the health sector is growing rapidly, and hospitals, as institutions that provide health and survival of the society and provide health services, have experienced many changes over time. People have understood that health is a valuable service for their lives, especially when the spread of new diseases threatens the life and health of society. Therefore, the main goal of this research was to investigate the relationship between innovation and service quality on customer satisfaction and loyalty during the Covid-19 pandemic.
 Methods: The present research has focused on the impact of service innovation and its quality on patients' satisfaction and loyalty during the Covid-19 pandemic in Yazd Shahid Sadoughi Hospital. The statistical population of the present study was the patients admitted to Shahid Sadoughi Hospital in Yazd, Iran. Because the size of the population was uncertain, Cochran's formula was used to calculate the number of samples, which resulted in 384 people. SPSS and Smart PLS3 software were also used for data analysis.
 Results: Among the patients with corona virus, service innovation had a positive and significant effect on the patient satisfaction and 0.11 (p<0.05) on loyalty, respectively. Likewise, service quality has affected the satisfaction and loyalty of the patients by 0.77 and 0.45 with (P<0.05); the effect of the patient satisfaction on loyalty was positive and significant at the rate of 0.41 (P<0.05).
 Conclusion: The results have shown that the relationship between service innovation and its quality had a positive and significant effect on patients' satisfaction and loyalty during the Covid-19 pandemic. The reason is that if the quality of the services provided is in accordance with the expectations of the patients, it will cause the satisfaction of the patients and will lead to their loyalty.


DEVELOPMENT OF SERIAL IMAGE MEDIA IN THE FORM OF POP-UP E-BOOKS TO IMPROVE THE READING SKILLS OF CLASS I STUDENTS IN THE INDEPENDENT CURRICULUM

Abstract: Media is a vehicle for transmitting learning information or transmitting messages. The word media comes from Latin, namely medius, which means middle, intermediary, or introduction. Media means an intermediary or messenger from the sender to the message recipient. The broad meaning is activities that can create conditions that enable students to acquire knowledge and attitudes. The aim of this research is to see whether the development of serial image media in the form of pop-up e-books can improve the reading skills of class I students in the independent curriculum. The type of research used in this research is research and development. Research and development (R&D) is a type of research that focuses on products. The results of the research show that serial image media in the form of pop-up e-books is said to be very valid, where the average for validator material is 83% while for media experts it is 85%. Based on the results of the research and discussions that have been carried out, it can be concluded as follows. Pop-up book media that is suitable for reading for grade I elementary school students is a pop-up book that pays attention to aspects of the material that supports the content of the learning material and is in accordance with the characteristics of elementary school students.


Educational Transformation in The Digital Era

Abstract: This research uses a literature study approach which aims to determine the quality of learning at SMA Negeri 1 Payaraman through the transformation of education in the Era of digital development. The method used in this study is a literature review of several previous studies using data collection techniques, reading and recording and processing research materials then a review is carried out. The review process carried out at SMA Negeri 1 Payaraman has improved the quality of learning through educational transformation including increased learning, increased knowledge, no distance, easier evaluation of learning outcomes, increased learning outcomes, the realization of a collaborative learning atmosphere, and made it easier for teachers and homeroom teachers to manage grades and write report cards. However, behind the advantages, there are disadvantages of the transformation of digital era education, including technology making students less productive and lazy, high cheating rates, less focus and decreased interest in learning, and decreased writing skills.


Abrasion Curative Using HerboGel

Abstract: Our project aims to develop a hydrogel-based wound healing formulation utilizing the medicinal properties of three potent herbs: Curcuma longa (turmeric), Ocimumtenuiflorum (holy basil), and Azadirachtaindica (neem). These herbs are renowned for their antimicrobial and anti-inflammatory properties, which are crucial for effective wound management. The hydrogel will serve as a vehicle for delivering the active compounds extracted from these herbs to the site of injury, promoting faster healing and reducing the risk of infection. Through a series of extraction, formulation, and characterization steps, we will optimize the herbal hydrogel for its physical, chemical, and biological properties. In vitro studies will assess its antimicrobial efficacy against common wound pathogens, while in vivo experiments will evaluate its ability to accelerate wound closure and reduce inflammation in animal models. Safety and stability assessments will ensure the viability of the formulation for clinical translation. Ultimately, this research holds promise for the development of a natural, effective, and accessible solution for wound care, addressing a critical need in healthcare.


A BIT of Protection for Non-Fungible Tokens: Digital Assets as a Catalyst for Economic Growth

Abstract: Abstract An inevitable consequence of technological advancement is that it triggers regulatory challenges for law and policymakers. The regulation of digital assets has generated much debate in this regard. The central objective of this article is to examine the regulation of one particular type of digital asset, NFTs, through an international investment law lens. The international investment regime offers investors high levels of protection against breaches of obligations by host states for covered investments. The aim of the article is to determine whether NFTs can be considered as covered investments for the purposes of the application of bilateral investment treaties (BITs) and treaties with investment provisions (TIPs), and further to examine whether that regime would provide an appropriate regulatory framework for investment in NFTs.


Assessment Of Witness Testimonials As Evidence In Civil Cases

Abstract: The purpose of this study is to investigate and explain the judge's judgment of witness testimony as evidence in the judge's decision. The study employed a normative legal research technique with primary legal sources. In accordance to the author, this judge's reasoning is incorrect due to the fact that there is an oral agreement between the plaintiff and the defendant, which is confirmed by testimony from witnesses and expert witnesses, it cannot be classified as a breach of contract because it does not meet the requirements set forth in Article 58 of the Indonesian Civil Code. The rules and regulations of Article 58 of the Indonesian Civil Code are coercive legal provisions (dwingend recht). Article 58 of the Indonesian Civil Code contains three conditions: 1. The vows of marriage must be registered in the civil registry; 2. Civil registration officials must declare the marriage plans on the notice board; and 3. Claims/lawsuits must be filed within 18 months of the marriage announcement. The judge's assessment that the defendant had broken his vow was incorrect. As a result, the participation of the witness in providing information in collaboration with other witnesses cannot be used to prove the marriage commitment.


How does AI create and recommend corresponding wallpapers based on the games played by users?

Abstract: The purpose of this paper is to give a comprehensive review of the related work that has everything to do with creating a wallpaper in artificial intelligence (AI) technology. Firstly, deep learning and neural network are summarized, especially generative adversarial networks that aim to effective generate images. Then, User interest modeling is summarized and analyzed, which is a key point to figure out the preference of the players. Further, main ideas are given about the trends and directions of wallpaper generation by AI.


Research on autonomous mobile robot maze navigation problem based on Dijkstras algorithm

Abstract: In recent years, the field of autonomous mobile robotics has garnered significant attention due to its potential applications in various domains such as logistics, surveillance, and search and rescue operations. A crucial challenge in this area is the efficient navigation of robots within complex and dynamic environments, particularly when navigating through maze-like structures. The maze navigation problem involves finding optimal paths for robots to traverse from their initial positions to designated destinations while avoiding obstacles and making intelligent decisions to ensure timely and safe navigation. This study aims to investigate and apply Dijkstras algorithm to solve the maze navigation problem for autonomous mobile robots. By analyzing the navigation challenges faced by autonomous mobile robots in maze environments, a solution based on Dijkstras algorithm is proposed. In conclusion, this study contributes to the field of autonomous mobile robotics by proposing and evaluating the application of Dijkstras algorithm for maze navigation. The experimental results validate its potential to address the challenges of navigating intricate maze environments. However, it is acknowledged that further refinement and innovation are possible to continue improving the performance of autonomous mobile robots in maze navigation scenarios.


Multimodal human-computer interaction for virtual reality

Abstract: Virtual reality (VR) is an interactive simulation environment based on computer technology that enables users to immerse themselves in a virtual world. With the continuous advancement of technology, VR has shown great potential in various fields. This article aims to review the application of VR in different fields and its impact on society. First, this paper introduces VR's basic principles and technical characteristics. VR systems are usually composed of head-mounted display devices, trackers, joysticks, etc., enabling users to experience immersive virtual environments by simulating real-world perception and interaction. Secondly, this paper discusses the application of VR in the field of education. VR technology can provide an immersive learning experience, enabling students to personally participate in the course content, greatly enhancing the effect of learning. At the same time, VR can also simulate experimental scenes, train operational skills, etc., to provide students with more abundant and practical learning resources. Then, this paper introduces the application of VR in the medical field. VR can be used for rehabilitation training of patients, psychological treatment, and other aspects, effectively improving the quality and effect of medical services. In addition, this paper also discusses the application of VR in entertainment, tourism, and other fields. VR technology can bring users immersive gaming experience, virtual tourism, etc., and inject new vitality into the entertainment industry. In tourism, VR can provide tourists with an immersive sightseeing experience, so they can feel the distant scenery at home.


The evolution of transformer models from unidirectional to bidirectional in Natural Language Processing

Abstract: Transformer models have revolutionized Natural Language Processing (NLP), transitioning from traditional sequential models to innovative architectures based on attention mechanisms. The shift from unidirectional to bidirectional models has been a remarkable development in NLP. This paper mainly focuses on the evolution of NLP caused by Transformer models, with the transition from unidirectional to bidirectional modeling. This paper explores how the transformer model has revolutionized NLP, and the evolution from traditional sequential models to innovative attention-driven architectures. In this paper, it mainly discusses the limitations of traditional NLP models like RNNs, LSTMs and CNN when handling lengthy text sequences and complex dependencies, highlighting how transformer models, employing self-attention mechanisms and bidirectional modeling (e.g., BERT and GPT), have significantly improved NLP tasks. It provides a thorough review of the shift from unidirectional to bidirectional transformer models, offering insights into their utilization and development. Finally, this paper concludes with a summary and outlook for the entire study.