Reviewerly/exHarmony huggingface dataset:
Fast anti-turbidity underwater topography measurement based on rotating structured light

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/m<sup>3</sup>, which satisfy the requirements of underwater 3D high-precision measurement.
Blind multi-Poissonian image deconvolution with sparse log-step gradient prior

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

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

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 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

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

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

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

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

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

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

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

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.&#x0D; 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.&#x0D; Results: Among the patients with corona virus, service innovation had a positive and significant effect on the patient satisfaction and 0.11 (p&lt;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&lt;0.05); the effect of the patient satisfaction on loyalty was positive and significant at the rate of 0.41 (P&lt;0.05).&#x0D; 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

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&amp;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.&#x0D;
Educational Transformation in The Digital Era

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

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.