Reviewerly/exHarmony huggingface dataset:
Efficiency study of VGG networks in autonomous driving tasks

In this study, we aim to evaluate the applicability of the VGG 19 architecture-based neural network for autonomousdriving tasks. The objective is to demonstrate the effectiveness of employing this neural network to solve the problemand to compare the identification quality. Experimental methodology is utilized to compare the efficiency of different neural networks. During the experimentation, we observed a disparity in the efficiency of VGG with and without theinclusion of the attention mechanism. Presently, a variety of machine learning methods are increasingly used to addressdiverse problems. Image recognition holds particular significance among these challenges. The practical problemunderinvestigation in this study pertains to the identification of roads with varying curvatures and their correspondence tothevehicle’s steering angles for the purpose of autonomous vehicle control. This paper explores multiple types of neural networks commonly used for image recognition. Previous research has demonstrated the high efficiency of these neural networks in solving various tasks, while this evaluation focuses on the applicability of neural networks in solvingtheautonomous driving problems and a performance comparison of these methods. The experimental results indicatethat VGG 19 achieves the highest accuracy of all neural networks.
Factors Affecting the Happiness Index

The happiness index is a system of measuring collective happiness as well as the well-being of a nation. The instrument measures life satisfaction and the condition of life among citizens because happiness is an indispensable human goal. A country’s happiness provides an important insight into its citizen’s well-being. For the citizens to be happy, the government must offer good governance supporting their well-being. A number of factors have a negative or positive impact on the happiness index of any given country. The factors that positively impact the happiness index include Gross Domestic Product, generosity, high life expectancy, generosity, and social support. Corruption negatively impacts the happiness index of a country. The government must ensure that they minimize the cases of corruption in the government so that citizens can have hope in the government, which increases their happiness. To ensure citizens are happy, the government ought to have a consistent gathering of happiness data on a large scale because it helps policymakers recognize deliverables that must be formed to enhance citizens’ well-being.
Research on brand differentiation strategies that can be implemented for e-commerce transformation - Using PDD as a case study

In order to promote the transformation of China’s e-commerce industry, this study proposes a feasible strategy for brand differentiation based on the current status of China’s e-commerce, which is transitioning from high-speed growth to high-quality development. Under the guidance of a brand differentiation strategy, a brand differentiation strategy design system is constructed through case summaries and development analysis of PDD. Based on the theory of brand differentiation, this enables PDD to emerge as a leading company in a saturated and competitive market. When the market is highly homogenized, showcasing unique brand characteristics and differentiated features is the key to success. Through the theory of brand differentiation, combined with PDD’s brand development journey and differentiation strategy, we can identify the unique characteristics of the brand. This provides innovative ideas and serves as a reference for the transformation of China’s e-commerce.
Evolutionary constraints and imperfect ant mimics

Adaptive evolution relies on both heritable variation and selection. Variation is the raw material upon which selection acts, so any mechanism that limits or prevents the generation of heritable variation reduces the power of selection to lead to adaptation. Such limitations are termed evolutionary constraints. While it is widely accepted that constraints play an important role in shaping evolutionary outcomes, their relative importance, as opposed to adaptation, in determining evolutionary outcomes remains a subject of debate. Evolutionary constraints are often evoked as the reason behind the persistence of inaccurate mimicry. Here, we compared the variation and accuracy of body-shape mimicry in ant-mimicking spiders with that of ant-mimicking insects, predicting greater constraints, and hence inaccuracy, in spiders mimicking ants, due to their evolutionary distance from the ant model. We found high inter-species variation in mimetic accuracy, but dorsally, no overall difference in mimetic accuracy between spider and insect mimics, which is inconsistent with a constraint causing inaccurate mimicry. Our study provides empirical evidence suggesting that imperfect mimicry in spiders and insects is predominantly shaped by adaptive processes rather than constraints or chance. Our findings contribute to our understanding of the mechanisms underlying evolutionary diversity and the processes that shape phenotypic outcomes.
Hybrid PSO-Jaya Algorithm for Solving Various Optimization Problems

This article proposes a hybrid algorithm PSO-Jaya, based on two heuristic algorithms — PSO (Particle swarm optimization) and Jaya. The main idea of the PSO-Jaya hybrid algorithm is to use PSO for global research of the solutions space and when PSO ceases to improve the results, which indicates a possible hit in the local optimum, Jaya is connected for the best solution throughout space, taking into account the best solutions already found. This allows the hybrid algorithm to combine efficient exploration of large areas of the solution space with the ability to minimize the probability of hitting local optimum. Two problems are used to evaluate the effectiveness of the PSO-Jaya hybrid algorithm: functions optimization and training artificial neural network for classification task Glass Identification. In calculation tests, the PSO, Jaya, PSO-Jaya algorithms are compared in view of their mean, median, standard deviation and "best" minimum error. In this connection, 50 simulations-based test functions and 30 network simulations were fulfilled. The performance analysis the PSO and Jaya algorithms was carried out as well as the hybrid algorithm for test-based problems. In all test cases, the PSO-Jaya algorithm achieved the best performance in terms of convergence speed and ability to avoid local optimum.
A CRISPR collaboration compilation

Major biopharma companies are partnering with pioneers of genome-editing technologies to realize the potential of this emerging precision medicine modality. Major biopharma companies are partnering with pioneers of genome-editing technologies to realize the potential of this emerging precision medicine modality.
Exploring Effective Models of Leadership in the Age of AI within Private Higher Education Institutions in England

Artificial intelligence (AI) has gained significant interest and application in various fields and the impacts are evident in teaching, learning, and assessment. On one hand, AI can bring the much-needed revolution in higher education by assisting leaders and management to offer personalized teaching approaches that meet specific student needs, it can also enhance efficient feedback and the automation of administrative functions including assessment and grading. These capabilities make AI an important instrument in promoting efficiency in higher education and creating room for institutions to redirect their focus on other equally important aspects such as curriculum development. However, despite these advantages, AI comes with significant setbacks that adversely impact the quality of education. Particularly, various platforms allow students to do their assignments automatically thus negating their responsibility to research and learn. As such, institutions must put in place effective models of leadership and approaches that will ensure the use of AI in an ethical manner without compromising the quality of education.
Uncovering Media Bias in Eviction Reporting: A Comprehensive Analysis Utilising Sentiment Analysis Framework and Social Media Data

This study investigates the prevalence of evictionsin South Africa and examines potential disparitiesbetween traditional media reporting and socialmedia discourse. Employing a sentiment analysisframework, we extend its application to comparethe reporting of evictions in newspaper articles(i.e. conventional media) and Twitter data (i.e.social media). Statistical machine-learningmethods are utilized to predict sentiment scoresfor both types of content, and a chi-square test isemployed to evaluate bias between news articlesand tweets. The test results reveal a significant biasin the sentiment distribution, suggesting that thedissimilarities observed between articles andtweets are not merely coincidental.
Pengembangan Literacy Digital Anak Usia Dini Melalui Pembelajaran Computer Kids

The purpose of this study is to determine the developing digital literacy in early childhood through Computer Kids learning. This research uses descriptive qualitative methods. Data collection techniques through interviews, documentation, and observation. Data validity techniques use source triangulation and triangulation methods. Data analysis uses interactive data analysis, presentation of data, data reduction, and drawing conclusions. The results showed that Computer Kids learning at PAUD Inclusion Saymara was carried out once a week in the multimedia room. Before carrying out learning, educators make plans related to the material to be delivered. The learning materials for Computer kids include getting to know the hardware, the parts of the computer, turning the computer on and off. In addition, software that is suitable for early childhood is also introduced such as interactive games, Microsoft Paint, Microsoft Word where children can recognize shapes, letters, and numbers. Through this Computer Kids learning, children have the knowledge and skills to utilize digital media wisely, intelligently, meticulously, and precisely according to their age development.
A Novel Training Approach in Deep Spiking Neural Network Based on Fuzzy Weighting and Meta-heuristic Algorithm

Abstract The challenge of supervised learning in spiking neural networks (SNNs) for digit classification from speech signals is examined in this study. Meta-heuristic algorithms and a fuzzy logic framework are used to train SNNs. Using gray wolf optimization (GWO), the features obtained from audio signals are reduced depending on the dispersion of each feature. Then, it combines fuzzy weighting system (FWS) and spike time-dependent flexibility (STDP) approach to implement the learning rule in SNN. The FWS rule produces a uniformly distributed random weight in the STDP flexibility window, so that the system requires fewer training parameters. Finally, these neurons are fed data to estimate the training weights and threshold values of the neurons using wild horse algorithm (WHO). With the parameters given, these rule weights are applied to appropriately display the class's share in extracting the relevant feature. The suggested network can classify speech signals into categories with 97.17% accuracy. The dataset was obtained using neurons operating at sparse biological rates below 600 Hz in the TIDIGITS test database. The suggested method has been evaluated on the IRIS and Trip Data datasets, where the classification results showed a 98.93% and 97.36% efficiency, respectively. Compared to earlier efforts, this study's results demonstrate that the strategy is both computationally simpler and more accurate. The accuracy of classification of digits, IRIS and Trip Data has increased by 4.9, 3.46 and 1.24%, respectively. The principal goal of this research is to improve the accuracy of SNN by developing a new high-precision training method.
Revolutionizing Saudi Healthcare: A Delphi Study on Identifying Competencies in Virtual Healthcare for Healthcare Professionals

Background: Virtual care adoption accelerated during the COVID-19 pandemic, highlighting the need for healthcare professionals to develop relevant competencies. However, limited evidence exists on the core competencies required for quality virtual care delivery. Objective: This study aimed to identify the critical competencies physicians, nurses, and other health professionals need for adequate virtual care provision in Saudi Arabia using a Delphi method. Methods: A 3-round Delphi technique was applied with a panel of 42 experts, including policymakers, healthcare professionals, academicians, and telehealth specialists. In round 1, an open-ended questionnaire elicited competencies needed for virtual care. Competencies were distilled and rated for importance in rounds 2 and 3 until consensus was achieved. Results: Consensus emerged on 151 competencies across 33 domains. The most prominent domains were communication (15 competencies), professionalism (13), leadership (12), health informatics (5), digital literacy (5) and clinical expertise (11).
A First Approach to Quantum Logical Shape Classification Framework

Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to device suitable applications. This article aims to propose for the first time an approach to use quantum logic in image processing at hand of a process for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables to initiate the principal component analysis. At hand of experiments, we explore the possibility to distinguish between different geometrical objects and discuss the results in terms of quantum logical interpretation.
PRODIGY: Probing Resilience Of Domain-wall Interactions at Gauge Theory

We delve into the axion-dilaton model within the supergravity framework, with a specific focus on the intricacies of domain wall construction and stability. Employing holographic vitrification, we unravel the dynamics of domain wall formation in gauge theories featuring periodic vacuum structures. Our model, incorporating a QCD-like axion term and a stabilizing dilaton, undergoes scrutiny for conductivity variations under weak disorder. The investigation reveals the model's resilience, manifesting near-perfect conductivity under mild disorder conditions. However, the rigorous mathematical motivation for the holographic setup demands further elucidation. The scattered nature of our results prompts the necessity for a more systematic interpretation of QCD phenomena and conductivity transitions. This study contributes to the mathematical understanding of the axion-dilaton model's behavior, highlighting the imperative for a refined holographic framework and a more coherent interpretation of observed phenomena.
Image alignment-based patch distribution framework for anomaly detection

Surface quality inspection is a crucial step in industrial manufacturing. However, it is challenging to collect an adequate quantity of abnormal samples in practice. Supervised methods require sample annotation, which is costly, so unsupervised methods that are high-speed and low-cost are more suitable for industrial applications. Among the current unsupervised methods, embedding-similarity methods haven shown excellent performance, but most of them do not preprocess the images and directly use convolutional neural networks to extract image features. While in actual scenarios, image can have some degree of offset or rotation due to machine variations. Therefore, this paper proposes a new patch distribution framework for anomaly detection, specifically a novel image alignment module is proposed to enhance the utility of the model. Image alignment reduces the dense distance between pixels during training, enabling more precise learning of the feature distribution of normal samples and reduce false positives during testing. In addition, in the feature extraction stage, the middle layer of the network is selected to extract features and establish embedding connections. This not only enhances the model’s precision but also reduces its memory requirements. Experiments on the publicly available datasets MVTec AD and BeanTech AD show that our proposed new framework achieves better performance than other baseline models.
Molecular components in the $J/\psi$ and the $\rho$-$\pi$ puzzle

Abstract Motivated by the large branching fractions of $J/\psi \to f_0 (1710) \omega/f_0(1710) \phi$ and the light exotic candidates, we find that there may exist molecular states composed of $f_0(1710) \omega$ and $f_0 (1710) \phi$, which correspond to $X(2440)$ and $X(2680)$ observed in a few decades before. The branching fraction of $X(2440)$ and $X(2680)$ to various $PV$ channels and $KK\omega(\phi)$ channels are estimated in the molecular scenario. In addition, the large branching fractions of $J/\psi \to f_0 (1710) \omega/f_0(1710) \phi$ indicate the sizable molecular components in the $J/\psi$ state. Thus, we consider the $J/\psi$ as the supperposition of $c\bar{c}(1S)$, $f_0(1710) \omega$ and $f_0 (1710) \phi$ molecular states, and these molecular components have significant impact on the light hadron decays of $J/\psi$, which may shield light on the long standing $\rho-\pi$ puzzle. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
Attention-guided hybrid transformer-convolutional neural network for underwater image super-resolution

Underwater images suffer from localized distortion and blurred degradation of edge structures due to light absorption and scattering by water. However, existing super-resolution (SR) methods for underwater images cannot effectively solve the above problems and encounter model sizes that are too large. To this end, we propose an attention-guided hybrid transformer-CNN network (AHTCN) to improve the SR reconstruction of underwater images through the interaction of local and multiscale global information, as well as the long-range dependencies modeling capability. Specifically, AHTCN mainly consists of several cascaded transformer-CNN feature extraction blocks (TCFEB) and an image reconstruction module. In TCFEB, the designed attention-based channel separation mechanism can adaptively separate the weighted features while reducing the number of model parameters and then extract the local details and global structural information at different scales through the dual-stream structure. Moreover, we replace the feedforward layer in the transformer with the blueprint separable convolutional feedforward layer and propose an enhanced pyramid pooling transformer layer, which helps to strengthen the feature perception of the model. Experimental results demonstrate that AHTCN outperforms the state-of-the-art algorithms in terms of both subjective visual effects and objective quality assessment, while requiring fewer parameters.
Pengaruh Penggunaan Lensa Kontak Lunak Terhadap Kesehatan Mata Di Optik Padang

The research results showed that respondents did not fully know the correct and correct procedure for using soft contact lenses, this can be seen from 28 respondents (62.2%) knowing the correct procedure for using soft contact lenses and 17 respondents (37.8%) not knowing the procedure for using lenses correct soft contact lenses, 26 respondents (57.8%) understand the impact of using soft contact lenses and 19 respondents (42.2%) do not understand the impact of using soft contact lenses, 29 respondents (64.4%) know about how to overcome the impact of using soft contact lenses. not in accordance with standards and as many as 16 respondents (35.6%) did not know about how to overcome the impact of using soft contact lenses that do not comply with standards,
Exploring ASR fine-tuning on limited domain-specific data for low-resource languages

The majority of South Africa’s eleven languages are low-resourced, posing a major challenge to Automatic Speech Recognition (ASR) development. Modern ASR systems require an extensive amount of data that is extremely difficult to find for low-resourced languages. In addition, available speech and text corpora for these languages predominantly revolve around government, political and biblical content. Consequently, this hinders the ability of ASR systems developed for these languages to perform well especially when evaluating data outside of these domains. To alleviate this problem, the Icefall Kaldi II toolkit introduced new transformer model scripts, facilitating the adaptation of pre-trained models using limited adaptation data. In this paper, we explored the technique of using pre-trained ASR models in a domain where more data is available (government data) and adapted it to an entirely different domain with limited data (broadcast news data). The objective was to assess whether such techniques can surpass the accuracy of prior ASR models developed for these languages. Our results showed that the Conformer connectionist temporal classification (CTC) model obtained lower word error rates by a large margin in comparison to previous TDNN-F models evaluated on the same datasets. This research signifies a step forward in mitigating data scarcity challenges and enhancing ASR performance for low-resourced languages in South Africa.
Research on the Innovation and Development of Gravitational Wave Detection Technology

According to Einstein’s general theory of relativity, gravity is described as the curvature of space-time caused by gravitational sources, and to prove the existence of gravitational waves, many scientific research institutes around the world have begun to build equipment to try to detect gravitational waves in the vast background signals of the universe. At present, gravitational waves have been detected by many scientific research institutions and scientists. Now, the detection of gravitational waves has shifted to the direction of high precision and accuracy. This paper starts with the laser interferometer, the most basic instrument for gravitational wave detection. It expounds on the latest development progress and key technologies of gravitational wave detection in the current physical world, including noise suppression and gravitational wave detection spacecraft projects. In terms of laser interferometers, this paper describes their principle and key technologies and puts forward the difficult problems that still need to be tackled and studied. In terms of noise suppression, this paper describes the noise and interference that may be generated and how to suppress the noise to avoid interfering with the accuracy of the experiment. It also points out the shortcomings of current noise suppression techniques. For gravitational wave detection spacecraft projects, this paper first focuses on several key projects in the world, then describes the technologies and shortcomings of spacecraft, and puts forward the direction of improvement and in-depth research on these technologies. This paper aims to summarize the key technologies of gravitational wave detection in the world today and point out the direction of its future development.