Reviewerly/exHarmony huggingface dataset:
MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes

Neural radiance fields enable state-of-the-art photorealistic view synthesis. However, existing radiance field representations are either too compute-intensive for real-time rendering or require too much memory to scale to large scenes. We present a Memory-Efficient Radiance Field (MERF) representation that achieves real-time rendering of large-scale scenes in a browser. MERF reduces the memory consumption of prior sparse volumetric radiance fields using a combination of a sparse feature grid and high-resolution 2D feature planes. To support large-scale unbounded scenes, we introduce a novel contraction function that maps scene coordinates into a bounded volume while still allowing for efficient ray-box intersection. We design a lossless procedure for baking the parameterization used during training into a model that achieves real-time rendering while still preserving the photorealistic view synthesis quality of a volumetric radiance field.
Psychological and Social Factors Determining Investment Decisions in Cryptocurrency: Exploring the Mediating Role of Cognitive Biases

This empirical research investigates whether investment decisions of cryptocurrency investors in Pakistan are influenced by a set of cognitive biases, as propagated by the theories in behavioural finance. To fill a dearth in the existing literature, this study evaluates the psychological and social sources of the cognitive biases that in turn affect investment decisions in cryptocurrency, therefore, this research essentially examines the mediating role of cognitive biases (Herding, Overconfidence, Representativeness, and Self-serving biases) between the linkages of socio-psychological factors (Money Anxiety, Social Interactions, Stress and Internal Locus of Control) and investment decisions in cryptocurrencies. Sample size of 313 respondents has been used, employing snowball sampling method, to analyze the data using Partial Least Square Structural Equation Modeling. The results reveal that herding bias, overconfidence bias and representativeness bias partially mediate the relationship between money anxiety, social interactions, stress and investment decisions in cryptocurrencies whereas self-serving bias fails to exert any mediation effect between internal locus of control and investment decisions. The results reveal that money anxiety causes herding bias which, in turn, affects the investment decisions in cryptocurrency positively; Stress leads to representativeness bias which, in turn, undermines investment decisions in cryptocurrency and social interactions generate overconfidence bias which, in turn, affects the investment decisions negatively.
Deep Learning-Based Rate Prediction Model for Recommender System Using Clustering Techniques

Many deep learning-based recommender systems have been proposed recently.Where they involve all the users in datasets to build the latent representation of input data to be used later for predicting the missing rates.Despite the fact that, users have different interests, and these differences reduce the model prediction accuracy.This paper proposed a novel cluster-based denoising autoencoder model (cluster-based DAE) for rate prediction recommender systems.Instead of building a single model, it builds K models by using k-means algorithm to divide the users into groups based on their preferences.Each group trains a DAE model to generate recommendations for its members.The proposed method was trained and tested with MovieLens (100K, 1M, and 10M) datasets where 80% of the data are used for training and 20% for testing.The performance of the proposed method compared against other state-of-the-art methods that use deep learning to build rate prediction models.It outperformed the other compared methods in term of mean absolute error (with 12.9%, 14.7%, and 22.3%) and root mean squared error (with 24.2%, 18%, and 21.1%) using MovieLens 100K, 1M, and 10M datasets respectively.
AI-Supported Segmentation of Industrial CT Data

The segmentation of datasets has always been a vital part of many image or volume processing applications, especially regarding tomography data. Common approaches nowadays use either manually tuned methods or rely on techniques like Deep Learning. However, such methods often are useful for segmenting only components of a certainly well-defined type which mostly suffices for clinical data but not for industrial applications anymore. In order to overcome these limitations we propose an interactive approach to volumetric segmentation encompassing robust classifiers and localized volume processing. The resulting algorithm is flexible enough to be used for a broad variety of different segmentation tasks while still generating high quality results. The local processing further enables the segmentation of larger volumes which cannot be handled by existing applications.
Do consumers always believe humans create better boxes than AI? The context-dependent role of recommender creativity

Purpose The recent surge of subscription box services calls for research to understand how consumers respond to curation services. This study aims to develop and test a theoretical model to predict consumer response to AI (vs human). Particularly, the authors tested the role of stereotyping in shaping consumer perception of creativity in this context while considering the contextual moderators, shopping goals (hedonic vs utilitarian) and product category (fashion vs meal). Design/methodology/approach Two preliminary studies and the main study (total n = 761) tested the assumptions and hypotheses of the study. Preliminary study 1 ( n = 511 Amazon mTurk, online survey) confirmed consumer stereotypes of humans and machines. Preliminary study 2, a single-factor between-subjects online experiment (recommender: human vs AI), was conducted at a large Midwestern university in the US ( n = 56). The main study was conducted as a 2(recommender: human vs AI) × 2(product: fashion vs meal) × 2(goal: utilitarian vs hedonic) between-subjects online experiment ( n = 194, Amazon mTurk). Findings The results confirmed that consumers are more likely to follow recommendations made by a human more than recommendations made by AI and the perceived creativity of the recommender explained the effect. Significant differences across product categories and shopping goals of the consumers were observed, calling for attention to the context of consumption. Originality/value This study extends the understanding of consumers' responses to recommendations in curation subscription services by highlighting the role of perceived creativity of humans versus AI.
The Promise of Explainable Deep Learning for Omics Data Analysis: Adding New Discovery Tools to AI

Deep learning has already revolutionised the way we process a wide range of data, in many areas of our daily life. The ability to learn abstractions and relationships from heterogeneous data, has provided impressively accurate prediction and classification tools to handle increasingly big datasets. This has a significant impact on the growing wealth of omics datasets, with the unprecedented opportunity for a better understanding of the complexity of living organisms. While this revolution is transforming the way we analyse these data, explainable deep learning is emerging as an additional tool with the potential to change the way we interpret biological data. Explainability addresses critical issues such as transparency, so important when computational tools are introduced especially in clinical environments. Moreover, it empowers artificial intelligence with the capability to provide new insights in the input data, thus adding an element of discovery to these already powerful resources. In this review we provide an overview of the transformative effects explainable deep learning is having on multiple sectors, ranging from genome engineering and genomics, from radiomics to drug design and clinical trials. We offer a perspective to life scientists, to better understand the potential of these tools, and a motivation to implement them in their research, by suggesting learning resources they can use to move their first steps in this field.
An Implementation of Head Up Position to Trauma Brain Injury Patients on the Level of Consciousness

Traumatic brain injury is a leading cause of death among motorized vehicle users and is caused by a failure to maintain road safety. Traumatic brain injury can result in physical, psychological, and even fatal changes. Patients with head injuries may experience impaired consciousness due to bleeding in the head, which causes an increase in intracranial pressure. Nursing interventions such as head positioning at 30° may be given to patients with traumatic brain injuries. To examine the implementation of giving a 30° head-up position to the level of consciousness in patients with traumatic brain injury. The results obtained from giving the patient a 30° head-up position are that the patient has an increased level of consciousness. The patient's level of consciousness at the initial assessment was GCS E2 VX M5, and nursing intervention was carried out by giving the head-up position 30° for three days, namely E4 VX M5. Giving a head-up position of 30° affects the level of consciousness in patients with traumatic brain injury.
On Artificial Intelligence-based emulators of physical models to forecast the evolution of lava flows

Timely forecasting of the evolution of lava flows is one of the key elements for assessing volcanic hazards. Lava flows are among the main hazardous phenomena during an effusive eruption, due to the possibility to reach urban areas and cause damage to infrastructure. Physical-mathematical models can be used to estimate the dynamics of a fluid or the fluid-solid interactions, in particular for the case of lava flows. However, high fidelity models require long execution times and large computational resources. Recently, artificial intelligence (AI) has been adopted to emulate physics-based models and deliver similar results, speeding up the simulations. We will discuss the possibility to use AI-based approaches to emulate highly complex numerical models used to simulate the spatio-temporal evolution of lava flows. Analyzing and treating the formal mathematical aspects of the models under analysis, we will verify and validate the models using test cases associated with the main features of lavas, discussing the accuracy and the performance offered by the two approaches.
Digital Library in Education Administration Management

Digital libraries are a solution to the weaknesses of conventional libraries, because the characteristics of their collections are in digital form so as to provide convenience for users in terms of access that can be done anywhere and anytime. Digital libraries are currently experiencing a fairly good development. Its application is also found in various libraries, one of which is school libraries, both in junior and senior high schools. This study aims to analyze the effectiveness of digital libraries in the management of education administration in high school in terms of context, input, process, and product aspects. This research is an evaluation research with a quantitative approach. The population in this study is education staff. The sampling technique is done randomly. Data were collected using questionnaires, documentation and interviews. The steps for data analysis are as follows; 1) Change the raw score into a Z score (z-score), 2) Change the score of each CIPP variable into a T-Score, 3) Determine the direction of the T-Score for the Cotext, Input, Process and Product variables, 4) determine the direction of the T-Score score to the Glickman quadrant. The results obtained from the overall evaluation of the context, input, process, and product variables on the use of digital libraries, it was found that the effectiveness of using digital libraries was quite effective. The obstacles faced by digital libraries in managing education administration in high schools are the absence of a clear legal basis, vision, mission, goals, and also that digital library managers do not have academic qualifications according to their fields.
Progress and Trends of Affective Prosody Research over 25 Years: A Bibliometrics-Based Visualization Analysis (1997–2021)

Affective prosody is an indispensable cognitive cue that moderates social activities, and has become a prevailing research topic in psychology-related disciplines. The present study conducts the first bibliometrics-based visualization analysis concerning affective prosody to evaluate the influential cases, including countries/regions, institutions, publication venues, academic articles, and disciplinary contributions, and the diachronic changes of publication trends and research hotspots. With the combination of statistical results and a qualitative literature inspection, limitations of extant studies and promising research directions were also proposed. The present study extracted the bibliographic data of 1,624 articles retrieved from the Web of Science Core Collection, which were published over the past 25 years (1997-2021). Statistical results revealed four leading powers (the U.S., Germany, England, and Canada) and four emerging fronts (China, France, Netherlands, and Switzerland), and identified three primary research themes in this field, including clinical implication, measurable index, and modality-specific issues. Literature inspection demonstrated current limitations in individual characteristics control and experiment-related influential factors, and proposed two prosperous research directions. Findings of the present study could facilitate academic retrieval of affective prosody research, help concerned researchers identify thematic hotspots and seek appropriate collaboration, and provide convenience for research policy and management in this field.
Shares Price Forecasting Using Simple Moving Average Method and Web Scrapping

The fluctuation of share prices in a secondary market allows investors/traders to gain profits through the difference in share prices (capital gain). In order to obtain these benefits, it is necessary to analyze before buying shares through fundamental and technical analysis. One of several methods in Technical Analysis is Simple Moving Average Method. This method can predict (forecast) share prices by calculating the moving average of the share price history. Historical share prices can be obtained in real-time using the Web Scrapper technique, so the results are more quickly and accurate. Using the MAPE (Mean Absolute Percent Error) method, the level of accuracy of forecasting can be calculated. As a result, the program could run successfully and display the value of forecasting and the level of accuracy for the entire data tested in LQ45. Besides, forecasting with a value of N = 5 has the highest level of accuracy, reaching 97,6 %, while the lowest one uses the value of N = 30, which is 95,0 %.
Basel Bank Limiting Policies as Foucauldian Power/Knowledge: Risk as Assemblages, The Case of Basel Bank Limiting Policies as Discourses

I intend to write about Basel bank's limiting technologies from the point of view of trust, coming from the fact that banks have to adjust their risk understanding tools and test them constantly. It is not always known what exactly causes a risk. I am interested in risk-understanding technologies which do not govern this in advance but only require passing certain risk tests. Therefore, I will research the idea of Basel’s bank limiting technologies as assemblages that want connections only with other specified assemblages. For example, certain risk levels must be adjusted to form a harmonious whole that passes these tests. I will argue that by understanding Basel’s bank limiting technologies through the theory of assemblages, it is possible to spell out the notion of biopower in the assemblage that has only a purely economic or statistical status. This biopower means the ability of a certain bank to control its assessments by estimating those same forms in different diagrams. I will work with the condition that there are no rules as to how to control the risks, only of what the abstract risk levels must be. This is based on the standpoint of the assemblage theory's idea that certain particles can be removed because the particles are tied to the whole. The point of this analysis is to show that trust does not lie in mutual understanding, but rather in the abstract risk levels and certain tests that the bank has to go through. Trust is not created by doing something that would restrict the bank’s ability to act by itself, but by making sure that the governor knows that the bank is in trouble if it does not keep risk at a certain level compared to rules that would govern how the bank has to act. It does this by using a technique that ties the bank’s risks to the tools with which it normally functions. Therefore, it cannot cheat the system by using different tools to profit from excessive risks. This system of certain risk technologies tied to both sides of the bank’s infrastructure is called M.A.D. (Mutually Assured Destruction) because it does not show one side without the other. Therefore, biopower is applied to the bank’s risk technologies by making sure that bank knows how to work for its well-being and at the same time also ensures that it works for the general good. This sounds a lot like an invisible hand that makes individuals’ actions beneficial to the whole community. This idea seems to develop more instrumentally in this argument, but there is the same “clever lie” behind it.
Deterministic and stochastic sampling of two coupled Kerr parametric oscillators

Researchers experimentally test various protocols for finding the most stable state of two coupled Kerr parametric oscillators. The result can help in the endeavor to use such networks as simulation hardware for solving complex optimization problems.
Surveillance Image Super Resolution Using SR - Generative Adversarial Network

A single-image-super-resolution (SISR) is the process of converting a single low-quality (LR) image to a high-quality (HR) image. This technology is utilised in a variety of industries, including medical and satellite imaging, to retrieve quality and required information from blurred or overexposed photos. Because of the lack of ability to extract important data and images due to poor quality surveillance photographs, this method can be utilised in the field of surveillance to produce high-quality images. We'd like to use General Adversarial Networks to handle low-quality photos because existing methods have resulted in slightly fuzzy and greasy images that look like oil paintings (GAN). We'd like to introduce Super Resolution General Adversarial Networks in particular (SRGAN). This method employs perceptual losses. In this case, PSNR, MSE, and SSIM values are shown to be superior to those obtained by standard approaches in this case. The SRGAN-processed photos are of excellent quality, allowing the images to be seen through hazy and misty areas.
The Catastrophe of Corruption in the Sustainability of Foreign aid: A Prediction of Artificial Neural Network Method in Indonesia

Abstract The rising corruption levels in Indonesia are becoming a cause for concern and raise doubts about their impact on the stability of foreign aid in the country. Therefore, this study aims to predict the long-term viability of foreign aid in Indonesia based on international perceptions of corruption and corruption cases in the country. Data were obtained from World Governance Indicators, the Indonesian Ministry of Finance, and the World Bank, and the study used a backpropagation artificial neural network (ANN) for prediction. The results from ANN are compared to linear models and vector autoregression (VAR). The finding shows that ANN outperforms the other models based on the coefficient of determination and MSE values. Furthermore, it highlights the strong relationship between corruption perception and foreign aid sustainability with an R-value of 0.991. According to the ANN estimation, gratification has a significant impact on foreign aid. In response to this finding, the study recommends the Indonesian government take action to combat corruption in maintaining the international trust and ensuring the stability of foreign aid.
Korea’s Lifelong Education System and Its Enlightenment for the In-service Development of Young Teachers in Colleges and Universities in China

For a long time, South Korea has taken the construction of lifelong education system and learning-oriented social development as an important work in its educational development, so as to provide the general public with the foundation and way for lifelong learning, and provide protection for lifelong learning through legislation, which has brought good enlightenment and reference for the construction of China's lifelong education system and the in-service development of young teachers in colleges and universities.Young teachers account for most of the proportion of college teachers, and their teaching ability has a direct impact on the quality of talent training, the teaching level of colleges and universities and the healthy development of colleges and universities.Therefore, strengthening the on-the-job development of young teachers in colleges and universities is an important task in the construction of college teachers and the professional growth and development of young people themselves.The development of Korea's lifelong education system not only provides reference and reference for the construction of China's lifelong education system, but also provides good inspiration for the on-the-job development of young teachers in colleges and universities in China.Based on this, this paper first briefly introduces the lifelong education system in Korea, and then accelerates the legislation related to lifelong education to promote the improvement of self-discipline of young teachers in colleges and universities.Based on the growth of young teachers in colleges and universities, establish the concept of "people-oriented" lifelong development; Drawing on the credit bank system, this paper puts forward the Korean lifelong education system and enlightenment for the in-service development of young teachers in colleges and universities in China.
Effect of Gender and Experience on Shade Matching-A Comparative Evaluation

Shade selection critically dictates clinical success of fixed prosthesis.There are a few factors that can influence the shade selection like the illuminating light sources, gazing at colored cards before shade selection, clinical experience of the operator who selects the shade and the gender of the operator.In the Indian context, there are very few studies that critically evaluates the influence of clinical experience of the professional who does the shade selection and the role of gender.While comparing Professors, PG students and interns for their shade matching ability, professors performed better than the other two groups.Female operators performed better than the male operators but the difference was not statistically significant.