Reviewerly/exHarmony huggingface dataset:
Why Moments (and Generalized Moments) Are Used in Statistics and Why Expected Utility Is Used in Decision Making: A Possible Explanation

Among the most efficient characteristics of a probability distribution are its moments and, more generally, generalized moments. One of the most adequate numerical characteristics describing human behavior is expected utility. In both cases, the corresponding characteristic is the sum of results of applying appropriate nonlinear functions applied to individual inputs. In this paper, we provide a possible theoretical explanation of why such functions are efficient.
Is Our World Becoming Less Quantum?

According to the general idea of quantization, all physical dependencies are only approximately deterministic, and all physical “constants” are actually varying. A natural conclusion—that some physicists made—is that Planck’s constant (that determines the magnitude of quantum effects) can also vary. In this paper, we use another general physics idea—the second law of thermodynamics—to conclude that with time, this constant can only decrease. Thus, with time (we are talking cosmological scales, of course), our world is becoming less quantum.
Extension to Multidimensional Problems of a Fuzzy-Based Explainable and Noise-Resilient Algorithm

While Deep Neural Networks (DNNs) have shown incredible performance in a variety of data, they are brittle and opaque: easily fooled by the presence of noise, and difficult to understand the underlying reasoning for their predictions or choices. This focus on accuracy at the expense of interpretability and robustness caused little concern since, until recently, DNNs were employed primarily for scientific and limited commercial work. An increasing, widespread use of artificial intelligence and growing emphasis on user data protections, however, motivates the need for robust solutions with explainable methods and results. In this work, we extend a novel fuzzy based algorithm for regression to multidimensional problems. Previous research demonstrated that this approach outperforms neural network benchmarks while using only 5% of the number of the parameters.
Why, in Deep Learning, Non-smooth Activation Function Works Better Than Smooth Ones

Since in the physical world, most dependencies are smooth (differentiable), traditionally, smooth functions were used to approximate these dependencies. In particular, neural networks used smooth activation functions such as the sigmoid function. However, the successes of deep learning showed that in many cases, non-smooth activation functions like $$\max (0,z)$$ work much better. In this paper, we explain why in many cases, non-smooth approximating functions often work better—even when the approximated dependence is smooth.
Why Residual Neural Networks

In the traditional neural networks, the outputs of each layer serve as inputs to the next layer. It is known that in many cases, it is beneficial to also allow outputs from pre-previous etc. layers as inputs. Such networks are known as residual. In this paper, we provide a possible theoretical explanation for the empirical success of residual neural networks.
How to Best Write Research Papers: Basic English? Sophisticated English?

Instructors from English department praise our students when they use the most sophisticated grammatical constructions and the most appropriate (often rarely used) words—as long as this helps better convey all the subtleties of the meaning. On the other hand, we usually teach the students to use the most primitive Basic English when writing our papers—this way, the resulting paper will be most accessible to the international audience. Who is right? In this paper, we analyze this question by using a natural model—inspired by Zipf’s law—and we conclude that to achieve the largest possible effect, the paper should be written on an intermediate level—not too primitive, not too sophisticated (actually, on the level of the middle school).
Why Semi-supervised Learning Makes Sense: A Pedagogical Note

The main idea behind semi-supervised learning is that when we do not have enough human-generated labels, we train a machine learning system based on what we have, and we add the resulting labels (called pseudo-labels) to the training sample. Interesting, this idea works well, but why is somewhat a mystery: we did not add any new information so why is this working? There exist explanations for this empirical phenomenon, but most of these explanations are based on complicated math. In this paper, we provide a simple intuitive explanation.
PENERAPAN MODEL LIBRARY BASED LEARNING UNTUK MENINGKATKAN LITERASI MEMBACA SISWA PADA MATA PELAJARAN BAHASA INDONESIA KELAS 3

This research was structured with the aim of improving students' reading skills using the library-based learning model for students in class III B MI Nu Nurus Shofa, Bae, Kudus Academic Year 2022/2023. Learning activeness referred to in this study, namely physical activity and mental activity. This type of research is Classroom Action Research (CAR), in which the teacher is the executor of learning while the researcher is the observer. The design of this study uses the Kurt Lewin model. This research was carried out in an odd semester of the 2022/2023 academic year, namely in November 2022. The subjects of this study were students of class III B MI Nu Nurus Shofa Karangnegara Bae Kudus with a total of 37 students where there were 23 male students and 14 students. female student. The object of research is students' reading ability. Data collection techniques using observation and interviews. Data were analyzed descriptively and presented in tabular form. The results showed that there was an increase in students' reading ability, namely in the first cycle of 64.8% (good category), then in the second cycle it increased to 89.1% (very good category). This means an increase of 24.3%. Thus the use of the library-based learning model in Indonesian subjects can improve the reading ability of class III B MI Nu Nurus Shofa Karangnegara Bae Kudus.
Preferensi Sumber Informasi dalam Persiapan Olimpiade Sains Nasional Pada Kalangan Siswa SMA di Kota Surabaya

The preference of information sources in preparation for the National Science Olympiad among high school students in the city of Surabaya, has the aim of explaining and knowing how the preferences or selection of information sources among high school students are to prepare themselves to confront OSN. The number of sources of information widespread among high school students will lead to a choice of information sources that can later be used as references by students to meet academic needs and for the needs of the National Science Olympiad (OSN). The ability of students to access and obtain information sources effectively can be a provision that must be mastered by students in selecting information sources. That way, students will get sources of information according to their needs in preparing for OSN. This research uses descriptive quantitative research methods and purposive sampling. The data collection method was taken from questionnaires and secondary data. This study analyzes the selection of printed and non-printed sources of information and the factors behind respondents choosing information sources. The results obtained from this research are that students tend to have multiple preferences, it can be proven by as many as 49% of respondents choosing to use both sources of information (printed and non-printed), this shows different results from previous research and uses as the theory of this research proposed by Jhon Steppen B. Arenas, et al. .al. (2018) The factors behind the choice of information sources available by students are due to the availability of collections in locations where students make choices, and also the convenience provided when selecting information sources, both printed and non-printed sources of information to prepare for the National Science Olympiad ( OSN). The selection of information sources is also inseparable from the important role of the library, as a location to find sources of information that can support OSN preparation.
<scp>GHBS</scp>‐13 as an enhanced <scp>EAN</scp>‐13 barcode module for monitoring processed consumable products in Ghana

Abstract When it comes to tracking goods from manufacturers to consumers, barcodes are useful instruments for monitoring and certifying their legitimacy. Because of its capacity to self‐validate all generated codes, the EAN‐13 is the most extensively used barcode for processed consumable products. Further investigations into the construction of the EAN‐13 barcode revealed that several additional components were required to make the EAN‐13 more credible for consumer use. Country code, production code, product code, and check digit make up the present EAN‐13 format. Meanwhile, the type of product and the number of times it has been manufactured are key components that must be included in the EAN‐13 barcode structure for barcode analysts to interpret. The study presented the GHBS‐13, an upgraded barcode structure that captured the two new components, namely, the product type and production count. The paper proposed a universal method called Tabiri Check Digit as a mathematical means of easily computing the check digit of the two barcodes. The formula was validated using EAN‐13 and GHBS‐13 barcodes, and the results were correct. The study also established a central point platform for customers to use to validate processed consumable products they buy in Ghana.
A Wavefront Integration Algorithm Based on Radial Basis Function for Off-Axis PMD

Sampled points on a measured surface are not evenly spaced on rectangular grids but, rather, have a more general quadrilateral geometry due to the perspective effect of the off-axis configuration, which is extensively utilized in phase measuring deflectometry (PMD). The current procedures for slope-to-wavefront integration results in reconstruction errors, which impact PMD to achieve a higher measurement accuracy. Due to the advantageous properties of the radial basis function (RBF) for irregular sampling and incomplete data, the quadrilateral geometry acquired by a CCD is first realized in this paper using the ray tracing method, which is used to obtain the actual measurement area and sampling interval in off-axis PMD instead of the area defined by the mathematical formula in the past when RBF was used. The RBF integration method is researched in this paper. The simulation and experiment show that RBF is a robust and higher accuracy algorithm for off-axis PMD. We further implement the RBF integration method to measure a crystal transparent element where an off-axis configuration is required.
Designing “Landing Page” for Websites Based on the User Experience: Review, Analysis, and Interpretation

The primary web design method is user experience-based “landing page” web design. It enables you to expand the reach of a particular company, institution, or application. It aims to present́ the most relevant information on the owner’s website. A simple approach, such as split A/B testing, can provide reliable data because it includes an appropriate set of alternatives. This article provides an overview, analysis, and interpretation of the results obtained. It presents good and bad practices of existing landing pages. The approach showed optimal solutions in aggregate, segmented, and individual site users.
Sequential Mini-Batch Noise Covariance Estimator

Noise covariance estimation in an adaptive Kalman filter is a problem of significant practical interest in a wide array of industrial applications. Reliable algorithms for their estimation are scarce, and the necessary and sufficient conditions for identifiability of the covariances were in dispute until very recently. This chapter presents the necessary and sufficient conditions for the identifiability of noise covariances, and then develops sequential mini-batch stochastic optimization algorithms for estimating them. The optimization criterion involves the minimization of the sum of the normalized temporal cross-correlations of the innovations; this is based on the property that the innovations of an optimal Kalman filter are uncorrelated over time. Our approach enforces the structural constraints on noise covariances and ensures the symmetry and positive definiteness of the estimated covariance matrices. Our approach is applicable to non-stationary and multiple model systems, where the noise covariances can occasionally jump up or down by an unknown level. The validation of the proposed method on several test cases demonstrates its computational efficiency and accuracy.
A morphological comparison between a death mask of the American Prophet Joseph Smith and a photograph likely to depict him

Application of forensic identification methods to establish authenticity of a historical photograph is made. Joseph Smith Junior was the Prophet and founder of The Church of Jesus Christ of Latter-day Saints, often referred to as Mormons. In 1844 Joseph and his brother Hyrum were shot and killed by a mob of angry men who opposed his church and its followers. Shortly after death, Joseph’s face was moulded, and a death mask was made. Photography was invented during the life of Joseph Smith Jnr and there are reports that he had a daguerreotype (photograph) taken, but no image has been verified to be of him. A photographic image of an Illinois man from the 1840s is linked by circumstantial evidence, such as similar clothing, to Joseph Smith Jnr and the photographer’s studio being close to where Joseph Smith III was at the time the photograph has been produced. A morphological comparison is made between the death mask and the photograph in order to establish the likelihood that the man in the photograph is the prophet. Sixteen points of anatomical similarity were found between the death mask and the photograph, the most compelling of which is asymmetry of the face and a possible scar in the area of the left eyebrow. Superim­position confirmed morphological similarity. Finding of close morphological similarity is not an ultimate proof of identification, but increases the probability that the photograph depicts Joseph Smith Junior. This is the first case of an anatomical comparison between a death mask and a photograph.
A provable M-of-N signature scheme based on the BDHI-type assumption in the random oracle model

We describe a new group-based M-of-N multisignature scheme (i.e., a protocol which allows a group of signers to produce joint signature on a common message), based on an asymmetric pairing of Type 3. The idea of the scheme is such that there are arbitrary number of signing parties with independent keys that sign the same message.Unlike the regular digital signature schemes, the signing algorithm is split into two separated stages, namely making pre-signatures and generating final aggregate signature.The security analysis is conducted in the euf-cma model, where the security of the scheme is reduced to the computational hardness of solving the bilinear Diffie-Hellman inversion problem.The reduction is made in the random oracle model.
Language vs. Nonlanguage Cognition

A basic distinction in agent-based data-driven DBS is between language and nonlanguage cognition. Language cognition transfers content between agents by means of raw data. Nonlanguage cognition maps between content and raw data inside the focus agent. In language cognition, the speaker’s action precedes the hearer’s recognition, while in nonlanguage cognition, recognition - including the output of inferencing - precedes action.
Review on Agriculture Products from Farmer to Consumer and Process Food to NGO

E-Farming is a stage that helps farmers with advancing their things. This will assist all farmers who with requiring an unequivocal motivator for their plant things, as well as end clients who require a precise rate for each thing. This will assist with the update of their regular daily existence, as well as supporting unfortunate people by giving food to those in a tight spot. Different government-based NGO's work with them to contact those people who have extra food (which they as of late wasted) and can grant consumable food to the NGO to meet their fundamental prerequisites while moreover thwarting food waste. The target of the system/application is to make a neighborhood which all experts are cleared out and the evaluated worth of green things sold directly to farmers. Finally, we use squandered food to clearly accommodate troubled people through a non-benefit affiliation. Therefore, this system can help end-client thing sureness while similarly spreading out a trust association among purchasers and producers. The remainder of the food is given to the persecuted, NGOs, and wastage/extra food is disposed of in various abilities.
Decomposing intrinsic images from a single fabric image using an unsupervised method

Single-view intrinsic decomposition of fabric images is a meaningful and challenging task for fabric analysis. However, it is costly to obtain enough ground truths for supervised training of intrinsic images. In this article, we explore a novel method to decompose the reflectance and shading from fabric images. With the introduction of wavelet transform into the proposed CNN model, this method enables us to exploit the information arising from inherent constraints during training and, by doing so, eliminate the need for ground truth labels. Based on three assumptions, we describe a new training framework for the network, including three types of loss function: prior loss, relative total variation loss, and a loss of constrained shared consistency. The trained model proved to be very efficient, requiring only 0.007 s to process one image. The results show that our method can separate the color and fine texture of fabric images to a certain extent. Finally, we propose several applications of the trained model.
Research on the Promotion of Integration Impetus Management to Integration Performance in Project of Integration Between Industry and Education

China has made fruitful achievements in the theoretical research on the integration of industry and education, but many problems have been exposed in practice.Taking universities and cooperative enterprises as research samples, this paper analyses how to effectively manage the integration impetus of industry education integration to improve its performance.Through the research on the "power-performance" of the integration of industry and education in universities and enterprises, this paper reveals the operation mechanism of the integration of industry and education, and enriches the research results of the integration of industry and education.It also provides theoretical basis and practical reference for the government, enterprises and universities to formulate scientific and reasonable support policies and operational guarantees for the integration of production and education.
Hyperspectral image super-resolution reconstruction based on image partition and detail enhancement

The hyperspectral image (HSI) super-resolution reconstruction has attracted much attention and been used widely in various study fields due to its low requirements on hardware in practice. However, most of the hyperspectral image super-resolution reconstruction studies apply one strategy for images with varying complexity of spatial information. This is not conducive to the improvement of Image processing efficiency and the extraction of complex details. Given the above, a new method named MSDESR (multilevel streams and detail enhancement) is proposed to reconstruct HSI by using partition reconstruction and detail enhancement. The MSDESR consists of a sub-map shunt block, a high-low-frequency information extraction with detail enhancement block, and a partition image reconstruction block. Firstly, the sub-map shunt block is designed to pre-classify hyperspectral images. The images are divided into complex and simple parts according to the spatial information distribution of the reconstructed sub-map. Secondly, the multiscale Retinex with detail enhancement algorithm is constructed to purify high-frequency noise-contaminated and enhance the image details by separating the samples into high- and low-frequency information. Finally, branching networks of different complexities are designed to reconstruct the images with high credibility and clear content. In this paper, datasets of QUST-1, Pavia University, Chikusei, the Washington DC Mal and XiongAn are applied in the experiments. The results show that MSDESR outperforms state-of-the-art CNN-based methods in terms of quantitative metrics, visual quality, and computational effort, with a 4.18% and 9.35% improvement in SRE and MPSNR metrics, and a 37% saving in FLOPs. Overall, the MSDESR performs well in hyperspectral image super-resolution reconstruction, which is time saving and preserves the details of spatial information.
Business Management Reference on AI Product Marketing Strategies

From the perspective of AI product interaction with users, this study provides scientific guidance for enterprise managers to understand user psychology and formulate effective marketing strategies.This study establishes a research conceptual model to explain the user's willingness to use AI products, as well as the factors that explain this process.By collecting user data, using AMOS software, we employ path-analysis methods to test how the characteristics of AI products (accuracy, autonomy, anthropomorphism, and affinity) affect consumers' perception of task attractiveness and interpersonal attraction, and explore how their willingness to AI products is influenced by interpersonal attraction perception.
Summarizing User-item Matrix By Group Utility Maximization

A user-item utility matrix represents the utility (or preference) associated with each (user, item) pair, such as citation counts, rating/vote on items or locations, and clicks on items. A high utility value indicates a strong association of the pair. In this work, we consider the problem of summarizing strong association for a large user-item matrix using a small summary size. Traditional techniques fail to distinguish user groups associated with different items (such as top- l item selection) or fail to focus on high utility (such as similarity- based subspace clustering and biclustering). We formulate a new problem, called Group Utility Maximization (GUM), to summarize the entire user population through k user groups and l items for each group; the goal is to maximize the total utility of selected items over all groups collectively. We show this problem is NP-hard even for l =1. We present two algorithms. One greedily finds the next group, called Greedy algorithm, and the other iteratively refines existing k groups, called k -max algorithm. Greedy algorithm provides the \((1-\frac{1}{e})\) approximation guarantee for a nonnegative utility matrix, whereas k -max algorithm is more efficient for large datasets. We evaluate these algorithms on real-life datasets.