Reviewerly/exHarmony huggingface dataset:
Impact of social media marketing on brand loyalty in Saudi Arabia

The aim of the study is to explore the impact of Social Media Marketing on Brand Loyalty. It focused on customers of small and medium enterprises (SMEs) specializing in the fashion products retail in Saudi Arabia. An online instrument was created through Google Forms and published on social media of SMEs that trade in fashion. In total, (1692) responses were received. Structural equation modeling (SEM) was used to test hypotheses. The results of the research showed that all dimensions of social media marketing had a positive impact on brand loyalty. The highest impact was for social media word of mouth. The study recommends the issuance of more legal legislation to protect user privacy and prevent its penetration, in addition to enacting laws that punish bad use of networks, which increases customers' trust in these sites and motivates them to carry out many marketing campaigns.
EREC: Enhanced Language Representations with Event Chains

The natural language model BERT uses a large-scale unsupervised corpus to accumulate rich linguistic knowledge during its pretraining stage, and then, the information is fine-tuned for specific downstream tasks, which greatly improves the understanding capability of various natural language tasks. For some specific tasks, the capability of the model can be enhanced by introducing external knowledge. In fact, these methods, such as ERNIE, have been proposed for integrating knowledge graphs into BERT models, which significantly enhanced its capabilities in related tasks such as entity recognition. However, for two types of tasks, commonsense causal reasoning and predicting the ending of stories, few previous studies have combined model modification and process optimization to integrate external knowledge. Therefore, referring to ERNIE, in this paper, we propose enhanced language representation with event chains (EREC), which focuses on keywords in the text corpus and their implied relations. Event chains are integrated into EREC as external knowledge. Furthermore, various graph networks are used to generate embeddings and to associate keywords in the corpus. Finally, via multi-task training, external knowledge is integrated into the model generated in the pretraining stage so as to enhance the effect of the model in downstream tasks. The experimental process of the EREC model is carried out with a three-stage design, and the experimental results show that EREC has a deeper understanding of the causal relationship and event relationship contained in the text by integrating the event chains, and it achieved significant improvements on two specific tasks.
A Fast Specular Highlight Removal Method for Smooth Liquor Bottle Surface Combined with U2-Net and LaMa Model

Highlight removal is a critical and challenging problem. In view of the complex highlight phenomenon on the surface of smooth liquor bottles in natural scenes, the traditional highlight removal algorithms cannot semantically disambiguate between all-white or near-white materials and highlights, and the recent highlight removal algorithms based on deep learning lack flexibility in network architecture, have network training difficulties and have insufficient object applicability. As a result, they cannot accurately locate and remove highlights in the face of some small sample highlight datasets with strong pertinence, which reduces the performance of some tasks. Therefore, this paper proposes a fast highlight removal method combining U2-Net and LaMa. The method consists of two stages. In the first stage, the U2-Net network is used to detect the specular reflection component in the liquor bottle input image and generate the mask map for the highlight area in batches. In the second stage, the liquor bottle input image and the mask map generated by the U2-Net are input to the LaMa network, and the surface highlights of the smooth liquor bottle are removed by relying on the powerful image inpainting performance of LaMa. Experiments on our self-made liquor bottle surface highlight dataset showed that this method outperformed other advanced methods in highlight detection and removal.
pyndl: Naïve Discriminative Learning in Python

Lately, NDL has become a popular tool in language research to examine large corpora and vocabularies, with 750,000 spoken word tokens (Shafaei-Bajestan et al., 2022) and a vocabulary size of 52,402 word types (Sering et al., 2018).In contrast to previous implementations, pyndl allows for a broader range of analysis, including non-English languages, adds further learning rules and provides better maintainability while having the same fast processing speed.As of today, it supports multiple research groups in their work and led to several scientific publications.
An improved deep neural network method for an athlete's human motion posture recognition

Aiming at improving the accuracy of motion gesture recognition and reducing the time-consuming recognition, this paper proposes an athlete's body motion gesture recognition method based on improved deep neural network. According to the movement characteristics and basic structural characteristics of the human body, the movement posture recognition standard is designed and set in the model. Collect athletes' human motion images, and perform motion image extraction, residual compensation, filtering, normalisation, and morphological processing. On this basis, the features of human motion images are extracted and fused. The recognition classifier is constructed based on the improved deep neural network, the fused feature vector is input into the classifier, and the recognition result is output. The results of the comparative experiment show that the proposed method has a high recognition rate and short overhead time only at 1.2 s.
Elephas: Distributed Deep Learning with Keras &amp; Spark

Elephas is an extension of Keras, which allows you to run distributed deep learning models at scale with Apache Spark.It was built to allow researchers and developers to distribute their deep learning experiments as easily as possible on a Spark computer cluster.With elephas, researchers can currently run data-parallel training of deep learning models with distribution modes as suggested in (Dean et al., 2012), (Recht et al., 2011) and (Noel & Osindero, 2014).Additionally, elephas supports distributed training of ensemble models.Until version 2.1.,elephas also supported distributed hyper-parameter optimization of Keras models.Elephas keeps the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models.When ready, researchers can then scale out their experiments on massive data sets.Elephas comes with full API documentation and examples to get you started.Initiated in late 2015, elephas has been actively maintained since then and has reached maturity for distributed deep learning on Spark.
Monocular depth estimation network with single-pixel depth guidance

Due to the scale ambiguity problem, the performance of monocular depth estimation (MDE) is inherently restricted. Multi-camera systems, especially those equipped with active depth cameras, have addressed this problem at the expense of increased hardware costs and space. In this Letter, we adopt a similar but cost-effective solution using only single-pixel depth guidance with a single-photon avalanche diode. To this end, we design a single-pixel guidance module (SPGM) that combines the global information from the single-pixel depth guidance with the spatial information from the image at the feature level. By integrating SPGMs into an MDE network, we introduce PhoMoNet, the first, to the best of our knowledge, end-to-end MDE network with single-pixel depth guidance. Experimental results show the effectiveness and superiority of PhoMoNet over state-of-the-art MDE networks on synthetic and real-world datasets.
A New Lifecycle Model Enabling Optimal Digital Curation

This study recognizes the international need for a broadly applicable lifecycle model to facilitate efficient and systematic digital curation. Consequently, it has developed a generic digital curation lifecycle model, titled the d-KISTI model. This model was developed by applying content analysis and thematic coding to data collected through a two-year review of relevant literature, existing conceptual lifecycle models, and empirical investigations of KISTI’s digital curation practice. It was then refined further through consultations with many international digital curation experts. The d-KISTI model presents actions and their relationships with one another that have gone previously unacknowledged in the DCC curation lifecycle model and other existing curation models. These actions and relationships, which are articulated at length within the study, reflect the rapidly changing nature of the global digital curation landscape and offer more representative curation activities to information organizations. Moreover, through its investigation and analysis of KISTI’s digital curation practices, this study contributes to existing literature on digital curation in Korea. Ultimately, the d-KISTI model seeks to optimize digital curation strategies and practices, both within Korea and internationally, and, moreover, hopes to serve as a foundational touchstone for future studies on digital curation.
Constant Time Median Filter Using 2D Wavelet Matrix

The median filter is a simple yet powerful noise reduction technique that is extensively applied in image, signal, and speech processing. It can effectively remove impulsive noise while preserving the content of the image by taking the median of neighboring pixels; thus, it has various applications, such as restoration of a damaged image and facial beautification. The median filter is typically implemented in one of two major approaches: the histogram-based method, which requires O (1) computation time per pixel when focusing on the kernel radius r , and the sorting-based method, which requires approximately O ( r 2 ) computation time per pixel but has a light constant factor. These are used differently depending on the kernel radius and the number of bits in the image. However, the computation time is still slow, particularly when the kernel radius is in the mid to large range. This paper introduces novel and efficient median filter with constant complexity O (1) for kernel size using the wavelet matrix data structure, which has been applied to query-based searches on one-dimensional data. We extended the original wavelet matrix to two-dimensional data for application to computer graphics problems. The objective of this study was to achieve high-speed median filter computation in parallel computing environment with many threads (i.e., GPUs). Our implementation for the GPU is an order of magnitude faster than the histogram method for 8-bit images. Unlike traditional histogram methods, which suffer from significant computational overhead, the proposed method can handle images with high pixel depth (e.g., 16- and 32-bit high dynamic range images). When the kernel radius is greater than 12 for 8-bit images, the proposed method outperforms the other median filter computation methods.
A Simple Algorithm for Prime Factorization and Primality Testing

We propose a new simple and faster algorithm to factor numbers based on the nature of the prime numbers contained in such composite numbers. It is well known that every composite number has a unique representation as a product of prime numbers. In this study, we focus mainly on composite numbers that contain a product of prime numbers that are greater than or equal to 5 which are of the form <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>6</mn> <mi>k</mi> <mo>+</mo> <mn>1</mn> </math> or <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>6</mn> <mi>k</mi> <mo>+</mo> <mn>5</mn> </math> . Therefore, we use the condition that every prime or composite <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>P</mi> </math> of primes greater than or equal to 5 satisfies <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>≡</mo> <mn>1</mn> <mi class="cond"> </mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi mathvariant="normal">mod</mi> <mn>24</mn> </mrow> </mfenced> </math> . This algorithm is very fast especially when the difference in the prime components of a composite number (prime gap) is not so large. When the difference between the factors (prime gap) is not so large, it often requires just a single iteration to obtain the factors.
Hierarchical Layout Blending with Recursive Optimal Correspondence

We present a novel method for blending hierarchical layouts with semantic labels. The core of our method is a hierarchical structure correspondence algorithm, which recursively finds optimal substructure correspondences, achieving a globally optimal correspondence between a pair of hierarchical layouts. This correspondence is consistent with the structures of both layouts, allowing us to define the union of the layouts' structures. The resulting compound structure helps extract intermediate layout structures, from which blended layouts can be generated via an optimization approach. The correspondence also defines a similarity measure between layouts in a hierarchically structured view. Our method provides a new way for novel layout creation. The introduced structural similarity measure regularizes the layouts in a hyperspace. We demonstrate two applications in this paper, i.e., exploratory design of novel layouts and sketch-based layout retrieval, and test them on a magazine layout dataset. The effectiveness and feasibility of these two applications are confirmed by the user feedback and the extensive results. The code is available at https://github.com/lyf7115/LayoutBlending.
Evolutionary Game Analysis of Standardized Production of Agricultural Products Embedded in Blockchain

By embedding blockchain technology into the process of standardized production of agricultural products, we use the characteristics of blockchain such as tamper-proof, decentralization, and consensus mechanism to solve the trust problem, responsibility problem, and quality problem in the process of standardized production of agricultural products. These three subjects in the process of standardized production of agricultural products are analysed, and the three-party game model among agricultural producers, government, and consumers is constructed with the help of the evolutionary game method, and the influence of each subject in different strategies is analysed, and the inference results are simulated and experimented with MATLAB 2016 software. The results show that the standardized production of agricultural products embedded in blockchain needs to meet three basic conditions: agricultural producers need to ensure that the sum of production costs and penalties for noncompliance following technological innovation using blockchain is less than the sum of production costs and penalties for noncompliance for traditional agricultural products; the difference value between strict and loose government regulation needs to be smaller than the amount of penalty for violation of standardized production of agricultural products embedded in blockchain; the cost of whistleblowing for consumers needs to be less than the number of penalties incurred for production violations when agricultural producers choose standardized production of agricultural products embedded in blockchain. It is also found that the violation ratio of agricultural producers, the social welfare of strict government regulation, and the whistleblowing benefit of consumers are the key factors affecting the standardized production of agricultural products, and the analysis of the influence of the key factors points to the direction for promoting the standardized production of agricultural products.
A comparative study of frequentist vs Bayesian A/B testing in the detection of E-commerce fraud

Purpose This paper tests whether Bayesian A/B testing yields better decisions that traditional Neyman-Pearson hypothesis testing. It proposes a model and tests it using a large, multiyear Google Analytics (GA) dataset. Design/methodology/approach This paper is an empirical study. Competing A/B testing models were used to analyze a large, multiyear dataset of GA dataset for a firm that relies entirely on their website and online transactions for customer engagement and sales. Findings Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the intellectual property fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Frequentist A/B testing identified fraud in bounce rate at 5% significance, and bounces at 10% significance, but was unable to ascertain fraud at the standard significance cutoffs for scientific studies. Research limitations/implications None within the scope of the research plan. Practical implications Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the IP fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Social implications Bayesian A/B testing can derive economically meaningful statistics, whereas frequentist A/B testing only provide p -value’s whose meaning may be hard to grasp, and where misuse is widespread and has been a major topic in metascience. While misuse of p -values in scholarly articles may simply be grist for academic debate, the uncertainty surrounding the meaning of p -values in business analytics actually can cost firms money. Originality/value There is very little empirical research in e-commerce that uses Bayesian A/B testing. Almost all corporate testing is done via frequentist Neyman-Pearson methods.
Improving multimedia information security by enriching face antispoofing dataset with a facial forgery method

The development of artificial intelligence, especially the application and development of deep learning, brings not only great convenience to people but also some challenges. Some face forgery techniques can confuse the false with the true in deep learning. With the widespread popularity of social applications and streaming media, people pay more and more attention to portrait privacy and security. To solve the great threat to personal privacy caused by deep face forgery technology, we propose an effective face forgery method to enrich the face antispoofing datasets. We first explore the representation of identity information in latent space. Based on this foundation, we use the identity feature replacement module to edit the latent codes in the latent space and finally generate the images with photo-realistic results with the help of the powerful generation ability of StyleGAN. The face forgery method can further expand the face antispoofing dataset, provide rich data for the learning-based forgery detection methods to improve their generalization and to ensure the security in preserving portrait privacy.
Information- and Coding-Theoretic Analysis of the RLWE/MLWE Channel

Several cryptosystems based on the Ring Learning with Errors (RLWE) problem have been proposed within the NIST post-quantum cryptography standardization process, e.g., NewHope. Furthermore, there are systems like Kyber which are based on the closely related MLWE assumption. Both previously mentioned schemes result in a non-zero decryption failure rate (DFR). The combination of encryption and decryption for these kinds of algorithms can be interpreted as data transmission over a noisy channel. To the best of our knowledge this paper is the first work that analyzes the capacity of this channel. We show how to modify the encryption schemes such that the input alphabets of the corresponding channels are increased. In particular, we present lower bounds on their capacities which show that the transmission rate can be significantly increased compared to standard proposals in the literature. Furthermore, under the common assumption of stochastically independent coefficient failures, we give lower bounds on achievable rates based on both the Gilbert-Varshamov bound and concrete code constructions using BCH codes. By means of our constructions, we can either increase the total bitrate (by a factor of 1.84 for Kyber and by factor of 7 for NewHope) while guaranteeing the same DFR or for the same bitrate, we can significantly reduce the DFR for all schemes considered in this work (e.g., for NewHope from 2−216 to 2−12769).
Determining approximate solution of partial differential equations using radial basis function model trained with parallel symbiotic organisms search algorithm

Summary Partial differential equations (PDEs) find extensive applications in geophysics (weather and climate modeling), astrophysics, and quantum mechanics. Solving PDEs by the conventional methods at times becomes difficult due to the complexity involved in these real‐world problems. Thus instead of an exact solution determining an approximate solution is also helpful for solving the problems. Here a model based on radial basis function (RBF) trained with parallel symbiotic organism search (PSOS) optimization is proposed to find out an approximate solution. The PSOS is a nature‐inspired optimization algorithm that ensures better accuracy due to the potential exploration of agents in the search space. Simultaneously, the computational complexity of this algorithm is low due to inbuilt parallelism. Simulation studies are reported for five real applications of PDEs: 2D modified Helmholtz equation, Poisson problem with Dirichlet boundary condition, elliptic PDE equation, and two convection‐diffusion equations. Comparison is carried out with the same RBF model parameters trained with parallel social spider optimization, original symbiotic organisms search algorithm, real coded genetic algorithm, and particle swarm optimization. The proposed model achieves minimum root mean square errors in most of the cases and achieves it with lower run time compared to the existing algorithms.
Do financial constraints and corruption limit firms' innovation capability? Evidence from developing economies

Abstract This study examines the effects of financial constraints and corruption on product and process innovation of firms in developing nations. We contribute to the existing literature by analyzing the singular and joint effects of financial constraints and corruption on firms' innovation activities. We examine firm‐level data for 74 developing nations for the period 2015–2020. Our findings show that (a) financial constraints have an adverse impact on firms' innovation, (b) corruption has a positive outcome on the innovation of firms, (c) positive effects of corruption on their innovation decisions are dampened when firms are subjected to high financial constraints, (d) larger, older, and exporting firms that are equipped with foreign technology are more prevalent in innovation schemes than smaller, younger, nonexporting, and technologically disrupted firms.