Reviewerly/exHarmony huggingface dataset:
The Effect of Marketing Effectiveness, and Consumer Behaviour on Consumer Purchase Preference for Unilever Products in Jordan

This study explores the relationship between consumer behaviour and marketing effectiveness and preferences for Unilever products in Jordan.The study employed an online survey to collect data from a sample of 384 Jordanian customers of Unilever products.The study uses PLS-SEM to analyse the relationships between the variables and test the proposed hypotheses.To evaluate the success of marketing campaigns, three metrics-customer acquisition cost, return on investment, and customer lifetime value-are employed.Consumer behaviour is evaluated using social, personal factors, and psychological, the study helps local producers improve their marketing strategies to compete with global companies by providing perspectives into the factors influencing Jordanian customers' preferences for Unilever Products.The study's findings contribute to our understanding of Jordanian consumers' preferences for Unilever products, consumer behaviour, and marketing efficiency.The study's conclusions have implications for Jordanian marketing executives and politicians on a practical level, particularly in terms of developing strategies to boost domestic producers' competitiveness and promote the growth of the regional economy.These results emphasize the value of customer behavior analysis and effective marketing tactics for businesses in the Jordanian market.
IOTA Data Preservation Implementation for Industrial Automation and Control Systems

Blockchain 3.0, an advanced iteration of blockchain technology, has emerged with diverse applications encompassing various sectors such as identity authentication, logistics, medical care, and Industry 4.0/5.0. Notably, the integration of blockchain with industrial automation and control systems (IACS) holds immense potential in this evolving landscape. As industrial automation and control systems gain popularity alongside the widespread adoption of 5G networks, Internet of Things (IoT) devices are transforming into integral nodes within the blockchain network. This facilitates decentralized communication and verification, paving the way for a fully decentralized network. This paper focuses on showcasing the implementation and execution results of data preservation from industrial automation and control systems to IOTA, a prominent distributed ledger technology. The findings demonstrate the practical application of IOTA in securely preserving data within the context of industrial automation and control systems. The presented numerical results validate the effectiveness and feasibility of leveraging IOTA for seamless data preservation, ensuring data integrity, confidentiality, and transparency. By adopting IOTA’s innovative approach based on Directed Acyclic Graph (DAG), the paper contributes to the advancement of blockchain technology in the domain of Industry 4.0/5.0.
Decoding destiny: Harnessing machine learning for breast cancer survival prediction

Breast cancer is a prevalent form of cancer that primarily affects women, although men can also be diagnosed with this disease.It ranks as the second leading cause of mortality among women worldwide.With the increasing prevalence of data-driven approaches in healthcare, the application of machine learning techniques offers a promising avenue for predicting the survival outcomes of patients with breast cancer.This article aims to provide a comprehensive overview of utilizing machine learning algorithms, implemented in Python, to predict the survival probabilities of breast cancer patients.By exploring various data pre-processing steps, feature engineering techniques and model selection strategies, this article presents a step-by-step guide for predicting breast cancer patient survival.The ultimate goal is to empower healthcare practitioners and researchers with the necessary knowledge and tools to leverage machine learning algorithms for improved prognosis and personalized treatment decisions in the context of breast cancer.
Application of Tunnel Mathematics for Solving the Steady Lamé and Navier-Stokes Equations

The theory of functions of a complex variable is a hybrid of vector algebra and ordinary algebra. It makes it possible to work with vector quantities as with algebraic ones. The modern theory of functions of a complex variable was created by the French mathematician Augustin Cauchy (1789-1857), later it was rapidly developed and found its application in solving various problems of physics—in aerodynamics, hydrodynamics, elasticity theory, etc. However, this theory is used exclusively for solving problems on the plane. Therefore, the next natural step is to extend this theory into space, so that we could obtain the solution of physical problems directly in space. The creation of such a theory is associated with some serious problems, for example, the appearance of so-called zero divisors, spatial complex numbers that are not equal to zero but when multiplying for some reason give zero. There is also a Frobenius theorem which prohibits the propagation of complex numbers into a space without abandoning some ordinary algebraic operations (for example, commutative multiplication). In this article an attempt is made to construct a theory of spatial complex functions (shortly, tunnel mathematics) in which zero divisors do not appear and all the usual algebraic operations are preserved. The possibility of applying this theory to some problems of the theory of elasticity and hydrodynamics (the steady Lamé and Navier-Stokes equations) is also considered.
Multi-scale network via progressive multi-granularity attention for fine-grained visual classification

Fine-grained visual classification (FGVC) is challenging due to the subtle inter-class variations. Key region location and discriminative feature extraction are the crucial aspects of FGVC. In this paper, we carry out research from the perspective of multi-granularity and multi-scale, explore the information in different granularities and scales, and propose a novel method called Multi-Scale Network via Progressive Multi-Granularity Attention (MSMGA-Net), which locates the key discriminative regions while avoiding recognition confusion caused by subtle inter-class variations. In order to achieve discriminative region locating, we adopt a progressive training strategy to make the network work in steps during training process, and the jigsaw puzzle generator (JPGen) is constructed to generate images with different granularity for different training steps. The focus of each step of training is to locate the key discriminative regions of specific granularity image at the corresponding stage of the network through the multi-granularity attention (MGA) module. Aiming to obtain the discriminative features, we also construct a multi-scale convolution (MSC) module for feature extraction corresponding to each step respectively, enabling the capture of different scales of details. We validated our MSMGA-Net through extensive experiments on CUB-200-2011, FGVC Aircraft and Stanford Cars. The experiments demonstrate that MSMGA-Net can get promising results. In particular, the accuracy on two publicly competitive benchmark datasets, CUB-200-2011 and FGVC Aircraft, reached 90.30% and 93.84%, respectively.
Noise-induced dynamics and photon statistics in bimodal quantum-dot micropillar lasers

Emission characteristics of quantum-dot micropillar lasers (QDMLs) are located at the intersection of nanophotonics and nonlinear dynamics, which provides an ideal platform for studying the optical interface between classical and quantum systems. In this work, a noise-induced bimodal QDML with orthogonal dual-mode outputs is modeled, and nonlinear dynamics, stochastic mode jumping and quantum statistics with the variation of stochastic noise intensity are investigated. Noise-induced effects lead to the emergence of two intensity bifurcation points for the strong and the weak mode, and the maximum output power of the strong mode becomes larger as the noise intensity increases. The anti-correlation of the two modes reaches the maximum at the second intensity bifurcation point. The dual-mode stochastic jumping frequency and effective bandwidth can exceed 100 GHz and 30 GHz under the noise-induced effect. Moreover, the noise-induced photon correlations of both modes simultaneously exhibit super-thermal bunching effects ($g^{(2)}(0)>2$) in the low injection current region. The $g^{(2)}(0)$-value of the strong mode can reach over 6 in the high injection current region. Photon bunching ($g^{(2)}(0)>1$) of both modes is observed over a wide range of noise intensities and injection currents. In the presence of the noise-induced effect, the photon number distribution of the strong or the weak mode is a mixture of Bose-Einstein and Poisson distributions. As the noise intensity increases, the photon number distribution of the strong mode is dominated by the Bose-Einstein distribution, and the proportion of the Poisson distribution is increased in the high injection current region, while that of the weak mode is reduced. Our results contribute to the development preparation of super-bunching quantum integrated light sources for improving the spatiotemporal resolution of quantum sensing measurements.
Correction to: Humans predict the forest, not the trees: statistical learning of spatiotemporal structure in visual scenes

as 'Familiar Expected > Unexpected', instead of 'Familiar Unexpected > Expected'.These errors have been corrected online.
PENGARUH KEBIASAAN BELAJAR TERHADAP HASIL BELAJAR MATA PELAJARAN ADMINISTRASI PAJAK KELAS XI MELALUI MOTIVASI BELAJAR SISWA SMK NEGERI JAKARTA TIMUR

This study aims to examine the effect of study habits on the learning outcomes of 11th-grade Tax Administration subjects among public vocational high school students in East Jakarta. The method used is a quantitative approach, employing proportional random sampling, resulting in a research sample of 122 students. Path analysis and hypothesis testing, including the T test, were utilized for data analysis. The study's findings reveal several significant relationships: there is a positive and significant influence between learning habits and learning outcomes, as well as between learning habits and learning motivation. However, no significant influence was found between learning motivation and learning outcomes, nor between learning habits and learning outcomes through learning motivation. These results indicate that higher levels of learning habits lead to better learning outcomes, and vice versa. Similarly, stronger study habits correlate with increased learning motivation, and vice versa. The research also reveals that student learning outcomes fall into the high category, while learning habits and learning motivation are in the medium category.
PublicCheck: Public Integrity Verification for Services of Run-time Deep Models

Existing integrity verification approaches for deep models are designed for private verification (i.e., assuming the service provider is honest, with white-box access to model parameters). However, private verification approaches do not allow model users to verify the model at run-time. Instead, they must trust the service provider, who may tamper with the verification results. In contrast, a public verification approach that considers the possibility of dishonest service providers can benefit a wider range of users. In this paper, we propose PublicCheck, a practical public integrity verification solution for services of run-time deep models. PublicCheck considers dishonest service providers, and overcomes public verification challenges of being lightweight, providing anti-counterfeiting protection, and having fingerprinting samples that appear smooth. To capture and fingerprint the inherent prediction behaviors of a run-time model, PublicCheck generates smoothly transformed and augmented encysted samples that are enclosed around the model's decision boundary while ensuring that the verification queries are indistinguishable from normal queries. PublicCheck is also applicable when knowledge of the target model is limited (e.g., with no knowledge of gradients or model parameters). A thorough evaluation of PublicCheck demonstrates the strong capability for model integrity breach detection (100% detection accuracy with less than 10 black-box API queries) against various model integrity attacks and model compression attacks. PublicCheck also demonstrates the smooth appearance, feasibility, and efficiency of generating a plethora of encysted samples for fingerprinting.
&lt;em&gt;LiverQuant:&lt;/em&gt; An Improved Method for Quantitative Analysis of Liver Pathology

Current means to quantify cells, gene expression, and fibrosis of liver histological slides are not standardized in the research community and typically rely upon data acquired from a selection of random regions identified in each slide. As such, analyses are subject to selection bias as well as limited subsets of available data elements throughout the slide. A whole-slide analysis of cells and fibrosis would provide for a more accurate and complete quantitative analysis, along with minimization of intra- and inter-experimental variables. Herein, we present LiverQuant, a method for quantifying whole-slide scans of digitized histologic images to render a more comprehensive analysis of presented data elements. After loading images and preparing the project in the QuPath program, researchers are provided with one to two scripts per analysis that generate an average intensity threshold for their staining, automated tissue annotation, and downstream detection of their anticipated cellular matrices. When compared with two standard methodologies for histological quantification, LiverQuant had two significant advantages: increased speed and a 50-fold greater tissue area coverage. Using publicly available open-source code (GitHub), LiverQuant improves the reliability and reproducibility of experimental results while reducing the time scientists require to perform bulk analysis of liver histology. This analytical process is readily adaptable by most laboratories, requires minimal optimization, and its principles and code can be optimized for use in other organs. Graphical overview.
Design and Analysis of Fire Prevention Strategy in Public Entertainment Places

The fire prevention work in public entertainment places involves a lot of content, including a large number of unstructured, semi-structured and non-linear data information. In the current intelligent fire management activities, relevant departments and institutions should combine the grid management mode, divide the grid management units of public entertainment places, and define the corresponding grid management responsible person, in order to improve the fire prevention management level, focus on the systematic fire prevention management and control from the five perspectives of human, machine, material, law, and environment, establish an intelligent fire prevention management system, and improve the fire prevention management level of public entertainment places. This paper briefly analyzes and discusses the fire prevention strategies in public entertainment places.
Empirical Comparison of Classical and Cognitive Image Forensics

Due to the proliferation of image-processing programmes, digital photos are increasingly at risk.There is a growing field of study called Digital Image Forensics (DIF) that aims to solve the open challenge of verifying the origin and information content of digital photographs in the multimedia environment.Without a digital watermark or signature, the passive DIF can uncover an image's veracity and track its history.In order to make a fake seem authentic, it is standard practise to use an enhancement technique like contrast enhancement.Discovering the reality behind a digital picture sometimes involves exposing the effects of contrast augmentation.The goal of this study is to identify global contrast enhancement in both original and JPEG-compressed photos, whether parametric or nonparametric.To identify various kind of global contrast improvements, we propose four feature sets that use block statistics (block variance, block mean, AC discrete cosine transform (DCT) coefficients, and DC DCT coefficients).Machine learning methods that can accurately distinguish between original and global contrast-enhanced images are trained using the suggested characteristics.Both uncompressed and JPEG compressed images may be analysed for the existence of global contrast enhancement using any of the aforementioned machine learning technologies.Post-processing applications such as resampling, picture rotation, noise addition, and histogram-based anti-forensics assaults have no effect on any of the suggested feature sets.
The Generative AI Revolution, Cognitive Mediation Networks Theory and the Emergence of a New Mode of Mental Functioning: Introducing the Sophotechnic Mediation Scale

This paper examines the recent emergence of AI-powered chatbots such as ChatGPT through the lens of the Cognitive Mediation Networks Theory (CMNT), deducing that the introduction of this radically new technology will likely create a new stage of collective cognitive functioning, called "Sophotechnic Mediation", with characteristics that can be extrapolated from the way these new tools work and the dynamics of the sociocultural structures being created around them. From that description, the Sophotechnic Mediation Scale is proposed as a means to assess the extent of an individual's internalization of the new form of thinking. A preliminary empirical investigation with 132 higher education professors and students found the instrument to be statistically consistent, yielding a unidimensional and Gaussian score that behaves as a developmental trait emerging from the interaction with generative AIs, mediated by age and mastery of previous digital technologies and their cultural elements. It is concluded that the results are suggestive of the validity of the new scale and warrant further research.
Mutual attention mechanism-driven lightweight semantic segmentation network

Abstract： ObjectiveThe intrinsic and semantic feature-based image fusion is a challenging issue in relevance to image semantic segmentation.Image semantic segmentation has been developing for such domains like medical image analysis， remote sensing mapping， automatic driving and other related contexts.Lightweight semantic segmentation network is pos• sible to meet the requirements of real applications via alleviating the constraints of speed and accuracy.The image semantic segmentation network is focused on convolutional neural network (CNN) to extract image-related texture， contour， color， gray and other related features of the objects， and it can be melted into semantic features as well.An efficient feature fusion mechanism is still to be tackled and resolved in terms of the intrinsic and semantic features extraction.For light•
Analyzing Customer Experience and Willingness to Use Towards Virtual Human Products: Real Person Generated vs. Computer Program Generated

Virtual human is the research focus in the field of human-computer interaction under the trend of metaverse. The current research on virtual human focuses on the influence of virtual human driven by computer program on the user's attitude and behavior, and the virtual human generated by real person through wearable sensing devices presents us a new form of virtual human. However, there are no studies to examine the different effects of these two virtual human concepts on customer experience and willingness to use preference. We study the differences on customer experience and willingness to use for these two kinds of virtual human products through an online experiment. Our results show that customers have higher willingness to use real person generated virtual human products than computer program generated virtual human products. The intrinsic reason can be explained by flow theory. Compared with computer program generated virtual human, real person generated virtual human has a higher "Explicit Feedback" experience and a higher "Ability vs. Challenge Balance'' experience, which affects their willingness to use the products.
Application of Finite State Machine in the 3D Game “Virus Hunter”

During the pandemic, people's space for movement is limited. The government implements the Work from Home (WFH) rule which requires everyone to work with gadgets in their respective homes. This situation makes most people frustrated with the existing limitations. Games are one of the forms of entertainment that can be done during a pandemic. In addition to entertainment, games can also be used as learning media in order to increase the level of vigilance against viruses or diseases. The Virus Hunter game is intended so that people can entertain themselves by playing and gaining insight into various kinds of viruses. This research was conducted during the stages of research and development which was then tested functionally using white box testing methods. Usability testing was carried out using the SUS method. In game design, the Non-Player Character (NPC) uses the Finite State Machine method to approach player behavior. The virus hunter game test results state that the functional application can run well and get a usability score of 75,87% in the Acceptable and Good categories in the Adjective Range.
A Blockchain Prototype for Improving Electronic Seals on Container Shipping Operations

With the widespread use of electronic seals (e-seals), their traceability and security have attracted more and more attention. Moreover, the complexity of shipping operations and container handling justifies the use of technologies to ensure information security in the face of attacks. This work contributes a blockchain-based solution with a simulated prototype for improving electronic seals for containers on terminals in ports. An electronic seal was designed, and a blockchain prototype was constructed for a container data flow. The obtained results from the prototype were evaluated using performance tests. The security issues in the blockchain were also discussed from a strategic perspective based on game theory. Finally, the simulation concluded that the blockchain improves transaction efficiency. No studies were found that integrated blockchain technology with electronic seals. Therefore, this work intends to combine blockchain technology with e-seal to improve the security of transferred data due to its immutable nature.
Instructional Design of the Concept of Definite Integral

The concept of definite integral is a core content in the teaching materials of advanced mathematics, and it is also a concept with a long history, wide application, rich mathematical and humanistic thoughts, and strong abstraction. The concept covers the limit thought, differential thought, and integral thought, which has significant mathematical abstraction and refined mathematical language expression. It contains rich ideological and political elements. Under the concept of ideological and political education of the curriculum, this paper will imperceptibly incorporate ideological and political elements into the teaching design of the concept of definite integral, in order to improve students' understanding of the concept of definite integral.