Reviewerly/exHarmony huggingface dataset:
A 2D image 3D reconstruction function adaptive denoising algorithm

To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into regions based on the entropy value of each region. The background region is subject to threshold denoising, while the target region undergoes processing using an adversarial generative network. This network effectively handles 2D target images with noise and generates a 3D model of the target. The proposed algorithm aims to enhance the noise immunity of 2D images during the 3D reconstruction process and ensure that the constructed 3D target model better preserves the original image’s detailed information. Through experimental testing on 2D images and real pedestrian videos contaminated with noise, our algorithm demonstrates stable preservation of image details. The reconstruction effect is evaluated in terms of noise reduction and the fidelity of the 3D model to the original target. The results show an average noise reduction exceeding 95% while effectively retaining most of the target’s feature information in the original image. In summary, our proposed adaptive denoising algorithm improves the 3D reconstruction process by preserving image details that are often compromised by conventional denoising techniques. This has significant implications for enhancing image quality and maintaining target information fidelity in 3D models, providing a promising approach for addressing the challenges associated with noise reduction in 2D images during 3D reconstruction.
A Transparent, Distributed, and Secure Crowdfunding Platform Based on Blockchain

Crowdfunding refers to a way of raising funds from individuals and/or firms for business purposes. Conventionally, it is a manual process, in which, the collected fund is available with a single entity (firm or an individual). In such cases, it has been observed that the funds could be diverted elsewhere or misused. Online crowdfunding platform powered through blockchain technology brings in transparency and accountability to such transactions, which could possibly improve the scale of business since more and more people would now like to fund the ventures as each individual is aware of the utilization of the funds. The blockchain technology builds upon the Industrial Internet (Industry 4.0), a transparent and secure environment for the people and the transactions. In this chapter, one such application based on blockchain technology is proposed that has the potential to improve the security and reliability of the transactions. Since the control in blockchain-based technology is decentralized, it effectively addresses the monopoly over the funds and improves the accountability of the one who is making the investments.
Analysis of Physics-Based Optical Flow

This chapter describes the mathematical aspects of the variational optical flow solution to preserve discontinuities in a velocity field extracted from flow visualization images. The uniqueness of the solution and the convergence of a successive approximation sequence are proven. First, the bounded variation (BV) space is briefly reviewed, and an integral representation of the measure ∇·(gu) is given to analyze the physics-based optical flow equation. Then, the general variational formulation with the L1-norm is applied to the equation, which regularizes a velocity filed while preserving its discontinuities in a velocity field. In this case, the minimization problem admits a unique solution in the BV space. Since it is difficult to directly solve the nonlinear Euler-Lagrange equation associated with the functional with the L1-norm, a successive approximate method is proposed, and a convergence analysis of the successive approximation is given. Next, the numerical algorithm is developed and simulations are conducted to evaluate the accuracy of the algorithm.
Comparison of classical machine learning approaches with hybrid quantum approaches in applied problems

&lt;p&gt;The work is aimed at analyzing the potential advantages of using quantum approaches in applied problems of artificial intelligence. In this paper, the task of classifying medical images extracted from histopathological images of sections of lymph nodes is set. The theoretical basis used for the construction of quantum and hybrid-quantum computing elements used in the article will be given. Quantum analogues of classical machine learning algorithms and neural networks will be considered. The paper will give a step-by-step description of the data transformation, the construction of models and their training, followed by an analysis of the results obtained and the performance of the simulation of quantum computing.&lt;/p&gt;
The impact of ChatGPT on human data collection: A case study involving typicality norming data

Abstract Tools like ChatGPT, which allow people to unlock the potential of large language models (LLMs), have taken the world by storm. ChatGPT’s ability to produce written output of remarkable quality has inspired, or forced, academics to consider its consequences for both research and education. In particular, the question of what constitutes authorship, and how to evaluate (scientific) contributions has received a lot of attention. However, its impact on (online) human data collection has mostly flown under the radar. The current paper examines how ChatGPT can be (mis)used in the context of generating norming data. We found that ChatGPT is able to produce sensible output, resembling that of human participants, for a typicality rating task. Moreover, the test–retest reliability of ChatGPT’s ratings was similar to that of human participants tested 1 day apart. We discuss the relevance of these findings in the context of (online) human data collection, focusing both on opportunities (e.g., (risk-)free pilot data) and challenges (e.g., data fabrication).
PROGRAMMATIC ADVERTISING: Evolution, Efficacy, and Ethical Implications In The Digital Age

The digital landscape has witnessed significant transformations over the past few decades, with programmatic advertising emerging as a pivotal force reshaping the world of brand communication. This article delves deep into the evolution of online advertising, highlighting the inefficiencies of traditional digital ad-buying methods that paved the way for the algorithmic and data-driven paradigm of programmatic advertising. While offering advantages like precision targeting, automation, scalability, and real-time analytics, programmatic advertising has challenges, especially concerning data privacy, regional nuances, and ethical considerations. Through a lens of global and regional case studies, the article underscores the intricate dynamics and cultural sensitivities crucial for successful campaigns. As we stand at the cusp of technological advancements, integrating Artificial Intelligence, Augmented Reality, and 5G into the programmatic ecosystem promises further evolution. The article concludes with robust recommendations for researchers, emphasizing the importance of interdisciplinary studies, ethical considerations, and the need for continuous adaptation in this ever-evolving digital world.
Object Recognition Scheme for Digital Transformation in Marine Science and Engineering

With the advancement of deep learning (DL), researchers and engineers in the marine industry are exploring the application of DL technologies to their specific applications. In general, the accuracy of inference using DL technologies is significantly dependent on the number of training datasets. Unfortunately, people in marine science and engineering environments are often reluctant to share their documents (i.e., P&amp;ID) with third-party manufacturers or public clouds to protect their proprietary information. Despite this, the demand for object detection using DL technologies in image-formatted files (i.e., jpg, png, or pdf format) is steadily growing. In this paper, we propose a new mechanism, called a no-training object picker (NoOP), which efficiently recognizes all objects (e.g., lines, tags, and symbols) in image-formatted P&amp;ID documents. Notably, it can recognize objects without any training dataset, thus reducing the time and effort required for training and collection of unpublished datasets. To clearly present the effectiveness of NoOP, we evaluated NoOP using a real P&amp;ID document. As a result, we confirmed that all objects in the image-formatted P&amp;ID file are successfully detected over a short time (only 7.11 s on average).
MINIMALISM IN DESIGN: A TREND OF SIMPLICITY IN COMPLEXITY

Minimal design is an approach to communicate a concept in a simple manner. The goal of the minimalist design is to remove unnecessary components from an image. With the intention of grabbing consumers attention, minimizing clutter and seeming elegant, this type of visual design emphasizes on having just the most significant components of the design effortlessly prominent. Minimalism seeks to understand the core of things, emphasizing on a simple way of dealing with complexity. This paper offers a conceptual foundation for the minimalism design trend and elaborates on the creative philosophy that underlies it, as well as its influences, applications, implications, and multiple perspectives. The influence of minimalism on graphic design, user interface and web design are also discussed in the paper.
The Effect Of Using Question-Answer Relationship (Qar) Strategy On Grade Eleven Students’ Ability In Reading Comprehension Of Narrative Text At Sma Negeri 5 Pematang Siantar

This research aims to see the effect of using the Question-Answer Relationship (QAR) strategy on the ability of eleventh grade students in reading comprehension of narrative texts at SMA Negeri 5 Pematang Siantar. In this study, researchers used a method called quasi-experimental design, which allows researchers to collect numerical data. The population is all class XI students of SMA Negeri 5 Pematang Siantar for the 2023/2024 academic year, totaling 10 classes. The number of students in each class varies, so the total is 360 students. This research sampling involved two classes, namely XI PMIA-1 and XI PMIA-3. Data analysis shows that the experimental class obtained an average post-test score of 86.52, compared to the control class's average score of 80.13. The post-test standard deviation for the experimental class was 6.95, while the control class had a standard deviation of 3.77. Additionally, the standard error of the mean difference was calculated as 3.22. With a significance level of 5%, the T-count value exceeds the T-table value (1.8 &gt; 1.672), so it can be concluded that the T-count is greater than the T-table. This also shows that (Ho) is rejected and (Ha) is accepted.
Real-infraredSR: real-world infrared image super-resolution via thermal imager

Infrared image super-resolution technology aims to overcome the pixel size limitation of the infrared focal plane array for higher resolution images. Due to the real-world images with different resolutions having more complex degradation processes than mathematical calculation, most existing super-resolution methods using the synthetic data obtained by bicubic interpolation achieve unsatisfactory reconstruction performance in real-world scenes. To solve this, this paper innovatively proposes an infrared real-world dataset with different resolutions based on a refrigerated thermal detector and the infrared zoom lens, enabling the network to acquire more realistic details. We obtain images under different fields of view by adjusting the infrared zoom lens and then achieve the scale and luminance alignment of high and low-resolution (HR-LR) images. This dataset can be used for infrared image super-resolution, with an up-sampling scale of two. In order to learn complex features of infrared images efficiently, an asymmetric residual block structure is proposed to effectively reduce the number of parameters and improve the performance of the network. Finally, to solve the slight misalignment problem in the pre-processing stage, contextual loss and perceptual loss are introduced to improve the visual performance. Experiments show that our method achieves superior results both in reconstruction effect and practical value for single infrared image super-resolution in real scenarios.
A Contrastive Analysis Of Adjective Clause In English And Indonesia On Explanation Text

This research aims to determine the ability of students of the 6th semester English study program at HKBP Nommensen Pematangsiantar University in using adjective clauses in Indonesian and English. This research aims to show the dominant function and types of adjective clauses in explanatory text. Apart from that, this journal also shows differences in the use of adjective clauses between English and Indonesian. Researchers looked at adjective-clause material based on English and Indonesian. The method used in this research is called library research, or in other words library research. From this research it can be concluded that the number of words most often found in the use of adjective clauses for explanatory text is 7, with a total of 7 words from 10 sentences. In Indonesia, 7 words out of 10 sentences. This means that, in both English and Indonesian, adjective clauses in explanatory texts have differences and similarities between English and Indonesian.
Developing a reconstruction algorithm for 3D birefringence from tomographic polarimetry

Stress-induced birefringence is created by the manufacturing process of injection-molded lenses. Characterizing the degradation in optical performance is important information to guide manufacturing improvements. The 3D birefringence distribution is proportional to the inhomogenous stress field. Birefringence is an anistropic property therefore retardance measurements change if the optical component is rotated. Reconstruction of a 3D birefringence distribution from a series of retardance measurements poses a challenge due to the extension of tomographic algorithms to tensor-valued quantities. Our approach is a closed-form forward model to linearly relate the polarimetric measurements to a line projection through an discrete arrangement of index ellipsoids. The rank of this linear system is investigated for varying arrangements of index ellipsoids. For example, given an homogenous birefringence distribution, only three non-parallel line projections are required for a full-rank operator that reconstructs the index ellipsoid of a planar slice. These three line projections are full-rank for some arrangements of three unique index ellipsoids and rank deficient for others. This mathematical framework is developed to design a tomographic polarimeter for inspecting the stress-induced 3D birefringence distribution of injection-molded optics.
A NARRATIVE TURN: HUMAN AGENCY IN ROCK CARVINGS AT NÄMFORSEN, NORTHERN SWEDEN

Summary The idea to create pictorial narratives seems to have occurred long after humans learned to produce iconic images, that is, depictions based on visual similarity to external objects. In Scandinavia, e.g. in Gärde, Sweden or Stykket and Bøla, Norway, early Mesolithic images (e.g. rock carvings from before c.5000 BCE) often feature animals that are solitary or without suggestion of causal or narrative relations to other figures, although they sometimes are grouped by proximity or are superimposed on each other. Notable is also the almost complete absence of human figures. However, in subsequent Neolithic configurations (after c.5000 BCE), there are renderings of human agents involved in various forms of interaction with each other or with animals, often within distinguishable ‘scenes’ or compositions. In this paper we outline the emergence of pictorial narratives in Scandinavian rock carvings. In particular, we focus on some of the first depictions of human agency as displayed in Nämforsen, Sweden (c.5000–1800 BCE). Further, we present an analysis of occurring human‐animal relationships, interpretable as early attempts to create narrativizing images.
Machine Learning-Based Classification for Scholarship Selection

University of Muhammadiyah Sukabumi (UMMI) is a university that accepts KIP scholarship every year. However, KIP student applicants always exceed the quota, so it requires a re-selection process to determine KIP Shcolarship Awardee. UMMI does not have a clear method to support decisions in the selection process for KIP Shcholarship Awardee. To solve this problem, a classification modeling process will be carried out from previous data using machine learning algorithms, namely with Decision Tree (DT) and Support Vector Machine (SVM) algorithms. The general method for its development uses the SEMMA method (Sample, Explore, Modify, Model, Assess). Starting with collecting a dataset of KIP recipients studying at UMMI from 2021-2022 which amounted to 519 data with 16 attributes. From the results of exploration, the main attributes that became features for modeling were DTKS Status, P3KE Status, Combined income of father and mother and achievement. These attributes are converted into numeric data for easy data modeling. The results of K-Fold Cross-Validation for the DT model in the case of classification of KIP Kuliah recipients resulted in an accuracy of 78.44% of the entire test dataset, a precision of 0.73107 indicating that 73.11% of the model's predictions were correct, recall (sensitivity level) of 78.45% and an F1 score of 73.20%. The results of modeling and validation with SVM are 80.17% accuracy, 84.44% precision and 80.17% recall. The SVM model shows slightly better in terms of accuracy and precision, both models show competitive performance in classifying KIP scholarship recipients studying at UMMI.
Application Areas, Benefits, and Research Challenges of Converging Blockchain and Machine Learning Techniques

In recent years, machine learning (ML) has become a hot topic of research and application. ML model and huge amount of data growth difficulties still follow ML development. With the lack of new data and constant training, published ML models may soon become obsolete; unscrupulous data contributors may upload incorrectly labelled data, leading to poor training results; and data leakage and abuse are all possible outcomes. These issues can be effectively addressed by using blockchain, a new and rapidly evolving technology. With the advancement of various smart devices and the field of artificial intelligence and machine learning, interdisciplinary collaboration with blockchain technology may be incredibly valuable for future investigations. Collaborative ML and blockchain convergence can be studied here, with emphasis on how these two technologies can be combined and their application areas. On the other hand, look at the existing research’s shortcomings and future enhancements.
Blockchain-Based Infrastructure for Precision Agriculture

In agriculture, blockchain enables peer-to-peer transactions with extreme transparency and eliminates the need for a middleman or intermediary. Food attribution may be outlined using blockchain technology, in order to help the making of reliable food supply chains and the establishment of trust between producers and consumers. It encourages the adoption of data-driven technology in agriculture by offering a safe data storage system. In this chapter, we are aiming to discuss food supply chains, farming assurance, E-Agriculture, and farming resource transactions using blockchain technology in a theoretical and practical manner. We also confer the trials of monitoring farmer businesses and building a blockchain ecosystem for the food and agriculture industries. The Internet of Things (IoT), grid specimen, topographical information system (TIS), in-season decision-making, different current data collecting and analysis, and sensor technologies are all features of precision agriculture. The development of a comprehensive security system that supports the usage and administration of data is a critical aspect of constructing smart agriculture.
Blockchain-Based Organ Donation and Transplant Matching System

Organ donation is an important part of healthcare and can save countless lives. But the organ donation system has many loopholes and rampant organ trafficking exists. The organ donation rate in India is one of the lowest in the world – around 5 lakh people die each year due to lack of access to timely organ donation. Our study discusses a novel platform for organ transplant matching for end users – healthcare organizations, donors and receivers – in a secure and traceable manner, minimizing organ trafficking. The application can be extended to aid people to access plasma donations and medications in the current pandemic situation. With blockchain being popular, it has become one of the best choices for data security and scalability. Hence, the Ethereum blockchain approach to design a platform in achieving these goals is highly sought and could prove to be a breakthrough in solving a crucial real-world issue.
Cervical cell’s nucleus segmentation through an improved UNet architecture

Precise segmentation of the nucleus is vital for computer-aided diagnosis (CAD) in cervical cytology. Automated delineation of the cervical nucleus has notorious challenges due to clumped cells, color variation, noise, and fuzzy boundaries. Due to its standout performance in medical image analysis, deep learning has gained attention from other techniques. We have proposed a deep learning model, namely C-UNet (Cervical-UNet), to segment cervical nuclei from overlapped, fuzzy, and blurred cervical cell smear images. Cross-scale features integration based on a bi-directional feature pyramid network (BiFPN) and wide context unit are used in the encoder of classic UNet architecture to learn spatial and local features. The decoder of the improved network has two inter-connected decoders that mutually optimize and integrate these features to produce segmentation masks. Each component of the proposed C-UNet is extensively evaluated to judge its effectiveness on a complex cervical cell dataset. Different data augmentation techniques were employed to enhance the proposed model's training. Experimental results have shown that the proposed model outperformed extant models, i.e., CGAN (Conditional Generative Adversarial Network), DeepLabv3, Mask-RCNN (Region-Based Convolutional Neural Network), and FCN (Fully Connected Network), on the employed dataset used in this study and ISBI-2014 (International Symposium on Biomedical Imaging 2014), ISBI-2015 datasets. The C-UNet achieved an object-level accuracy of 93%, pixel-level accuracy of 92.56%, object-level recall of 95.32%, pixel-level recall of 92.27%, Dice coefficient of 93.12%, and F1-score of 94.96% on complex cervical images dataset.
The Effects of Leadership Style on Employee Sustainable Behaviour: A Theoretical Perspective

The main objective of this study is to examine the effects of leadership style on employee sustainable bahaviour of employees from a theoretical perspective. The literature evaluation served as the foundation for the predicted relationship between transformational leadership, transactional leadership, and e employee sustainable behaviour. The study contributes to the existing literature review on adding information to the theories of leadership. More study is needed to offer a more complete picture of these interactions. Much of the future research that has to be done has been determined because of the study's limitations. Further research is needed to understand the link between transactional leadership style, psychological empowerment, job satisfaction, and employee sustainable behaviour.