Reviewerly/exHarmony huggingface dataset:
Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices

Crude oil price fluctuations affect the business cycle due to affecting the ups and downs of the growth of the economy, which one of the indicators of the economic business cycle phenomenon. The importance of oil price prediction requires a model that can predict future oil prices quickly, easily, and accurately so that it can be used as a reference in determining future policies. Machine learning is an accurate method that can be used in predicting and makes it easier to predict because there is no need to program computers manually. ARIMA is a machine learning algorithm while ARIMA that uses a seasonal component is called SARIMA. Based on background, research purpose is modeling crude oil price forecasting by ARIMA and SARIMA. Forecasting is done on daily crude oil price data taken from Yahoo Finance from January 27, 2020 to January 25, 2023. The evaluation results show the RMSE value of ARIMA and SARIMA is 1.905. The forecast result of 7 days ahead with ARIMA is 86.230003 while SARIMA is 86.260002. The research results are expected to be helpful for policy makers to adopt policies and make the right decisions in the use of crude oil.&#x0D;
Toward density functional theory on quantum computers?

Quantum Chemistry and Physics have been pinpointed as killer applications for quantum computers, and quantum algorithms have been designed to solve the Schrödinger equation with the wavefunction formalism. It is yet limited to small systems, as their size is dictated by the number of qubits available. Computations on large systems rely mainly on mean-field-type approaches such as density functional theory, for which no quantum advantage has been envisioned so far. In this work, we question this a priori by proposing a counter-intuitive mapping from the non-interacting to an auxiliary interacting Hamiltonian that may provide the desired advantage.
Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm

Imitating the visual characteristics of human eyes is one of the important tasks of digital image processing and computer vision. Feature correspondence of humanoid-eye binocular images is a prerequisite for obtaining the fused image. Human eyes are more sensitive to edge, because it contains much information. However, existing matching methods usually fail in producing enough edge corresponding pairs for humanoid-eye images because of viewpoint and view direction differences. To this end, we propose a novel and effective feature matching algorithm based on edge points. The proposed method consists of four steps. First, the SUSAN operator is employed to detect features, for its outstanding edge feature extraction capability. Second, the input image is constructed into a multi-scale structure based on image pyramid theory, which is then used to compute simplified SIFT descriptors for all feature points. Third, a novel multi-scale descriptor is constructed, by stitching the simplified SIFT descriptor of each layer. Finally, the similarity of multi-scale descriptors is measured by bidirectional matching, and the obtained preliminary matches are refined by subsequent procedures, to achieve accurate matching results. We respectively conduct qualitative and quantitative experiments, which demonstrate that our method can robustly match feature points in humanoid-eye binocular image pairs, and achieve favorable performance under illumination changes compared to the state-of-the-art.
Determinants of bank profitability in Georgian banking sector

This study examines the impact of bank-specific and macroeconomic determinants on the profitability of commercial banks in Georgia, where banks tend to be the largest part of the financial system, with free market systems and liberalization policies similar to those in other transition economies, using panel data analysis and random forest method (RVI) is implemented as a robustness check. The profitability indicators return on asset (ROA), return on equity (ROE), and net interest margin (NIM), all of which have been extensively utilized as indicators of profitability in previous research, were employed. The research indicates that the three most significant bank-specific factors are net loans, nonperforming loans, and capital adequacy ratios. Other bank-specific factors such as asset size and liquidity ratio have statistically minor effects on bank profitability. The other macroeconomic determinant, inflation rate, has no effect on the profitability performance levels of the banks studied.
SR-FEINR: Continuous Remote Sensing Image Super-Resolution Using Feature-Enhanced Implicit Neural Representation

Remote sensing images often have limited resolution, which can hinder their effectiveness in various applications. Super-resolution techniques can enhance the resolution of remote sensing images, and arbitrary resolution super-resolution techniques provide additional flexibility in choosing appropriate image resolutions for different tasks. However, for subsequent processing, such as detection and classification, the resolution of the input image may vary greatly for different methods. In this paper, we propose a method for continuous remote sensing image super-resolution using feature-enhanced implicit neural representation (SR-FEINR). Continuous remote sensing image super-resolution means users can scale a low-resolution image into an image with arbitrary resolution. Our algorithm is composed of three main components: a low-resolution image feature extraction module, a positional encoding module, and a feature-enhanced multi-layer perceptron module. We are the first to apply implicit neural representation in a continuous remote sensing image super-resolution task. Through extensive experiments on two popular remote sensing image datasets, we have shown that our SR-FEINR outperforms the state-of-the-art algorithms in terms of accuracy. Our algorithm showed an average improvement of 0.05 dB over the existing method on ×30 across three datasets.
Individual Differences in Leveraging Regularity in Emergent L2 Readers in Rural Côte d’Ivoire

Purpose: Statistical learning (SL) approaches to reading maintain that proficient reading requires assimilation of the rich statistical regularities in the writing system. Reading skills in developing first- and second-language readers in English have been shown to be predicted by individual differences in sensitivity to statistical regularities in orthography and semantics, with good readers relying more on orthographic consistency, and less on semantic associations. However, the study of SL and its relation to reading has been primarily studied in English readers in WEIRD countries, limiting the universality of our theories. Method: We examine individual differences in sensitivity to regularities utilising a word naming task in emergent French readers in rural communities in Côte d’Ivoire (N=134). Results: We show that, in contrast to previous studies, in our cohort better readers leverage semantic associations more strongly, while individual differences in sensitivity to orthographic consistency did not predict reading skill. Relatively little variance in reading skill was explained by sensitivity to these regularities. Conclusion: This showcases the importance of cross-linguistic and cross-cultural research to back up universal theories of literacy, and suggests that current SL accounts of reading must be updated to account for this variance in reading skills.
A Step Toward an Automatic Handwritten Homework Grading System for Mathematics

An automatic system that helps teachers and students verify the correctness of handwritten derivation in mathematics homework is proposed. The system acquires input image containing handwritten mathematical derivation. In our preliminary study, the system that comprises only mathematical expression recognition (MER) and computer algebra system (CAS) did not perform well due to high misrecognition rate. Therefore, our study focuses on fixing the misrecognized symbols by using symbols replacement and the surrounding information. If all the original mathematical expressions (MEs) in the derivation sequence are already equivalent, the derivation is marked as “correct”. Otherwise, the symbols with low recognition confidence will be replaced by other possible candidates to maximize the number of equivalent MEs in that derivation. If there is none of symbols replacement that makes every line equivalent, the derivation is marked as “incorrect”. The recursive expression tree comparison was applied to report the types of mistake for those problems marked as incorrect. Finally, the performance of the system was evaluated by the digitally generated dataset of 6,000 handwritten mathematical derivations. The results showed that the symbols replacement improve the F1-score of derivation step marking from 69.41 to 95.95 % for the addition/ subtraction dataset and from 61.45 to 89.95 % for the multiplication dataset when compared to the case of using raw recognized string without symbols replacement.
Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

Mining of High Utility Itemset (HUI) is an area of high importance in data mining that involves numerous methodologies for addressing it effectively. When the diversity of items and size of an item is quite vast in the given dataset, then the problem search space that needs to be solved by conventional exact approaches to High Utility Itemset Mining (HUIM) also increases in terms of exponential. This factual issue has made the researchers to choose alternate yet efficient approaches based on Evolutionary Computation (EC) to solve the HUIM problem. Particle Swarm Optimization (PSO) is an EC-based approach that has drawn the attention of many researchers to unravel different NP-Hard problems in real-time. Variants of PSO techniques have been established in recent years to increase the efficiency of the HUIs mining process. In PSO, the Minimization of execution time and generation of reasonable decent solutions were greatly influenced by the PSO control parameters namely Acceleration Coefficient and and Inertia Weight. The proposed approach is called Adaptive Particle Swarm Optimization using Reinforcement Learning with Off Policy (APSO-RLOFF), which employs the Reinforcement Learning (RL) concept to achieve the adaptive online calibration of PSO control and, in turn, to increase the performance of PSO. The state-of-the-art RL approach called the Q-Learning algorithm is employed in the APSO-RLOFF approach. In RL, state-action utility values are estimated during each episode using Q-Learning. Extensive tests are carried out on four benchmark datasets to evaluate the performance of the suggested technique. An exact approach called HUP-Miner and three EC-based approaches, namely HUPEUMU-GRAM, HUIM-BPSO, and AGA_RLOFF, are used to relate the performance of the anticipated approach. From the outcome, it is inferred that the performance metrics of APSO-RLOFF, namely no of discovered HUIs and execution time, outstrip the previously considered EC computations.
Improved Feature Representation Using Collaborative Network for Cross-Domain Sentiment Analysis

Sentiment Analysis task helps us to estimate the opinion of a person from his reviews or comments about a product, person, politics, etc., Cross-Domain Sentiment Analysis (CDSA) empowers the Sentiment models with the ability to forecast the opinion of a review coming from a different domain other than the domain where the model is trained. The challenge of the CDSA model relies on bridging the relationship between words in the source and target domain. Several types of research in CDSA focus on determining the domain invariant features to adapt the model to the target domain, such model shows less focus on aspect terms of the sentence. We propose CWAN (Collaborative Word Attention Network), which integrates aspects and domain invariant features of the sentences to calculate the sentiment. CWAN uses attention networks to capture the domain-independent features and aspects of the sentences. The sentence and aspect attention models are executed collaboratively to determine the sentiment of the sentence. Amazon product review dataset is used in this experiment. The performance of the CWAN model is compared with other baseline CDSA models. The results show that CWAN outperforms other baseline models.
The Impact of Fantasy on Young Children’s Recall: A Virtual Reality Approach

Educational materials for preschool children are often embedded with fantastical elements. Nevertheless, there is still little empirical evidence on their effectiveness, especially as concerns long-term retention. Virtual reality offers new ecological possibilities for investigating this type of learning, especially through the impact of immersion. In a between-design study, 168 children aged four to six years followed a virtual reality presentation on China presented by either a realistic young girl or an anthropomorphic animal. The level of immersion was manipulated, with half of the children following the presentation in immersive virtual reality (IVR) and the other half in desktop virtual reality (D-VR). Participants were asked to complete a new/old recognition task and a quiz task immediately after the presentation and once again one week later, with an additional transfer task being added for the second series. One week later, children performed significantly better on the new/old recognition task in the realistic condition when compared to the anthropomorphic condition. However, there were no differences observed in the quiz task and in the transfer task. It therefore seems that, under certain conditions, children remember a cultural presentation better when it is presented by a realistic avatar. The results further showed that the children performed significantly worse in the IVR conditions on all tasks. A possible explanation for this result is that IVR demands excessive cognitive resources from preschool children. Further studies should explore this unexpected result, as well as what could be done to make IVR effective for learning in preschool children.
The Broad Reach of Multivariable Thinking

Simple explanations are very often inadequate and can encourage faulty inferences. We examined college students’ explanations regarding illegal immigration to determine the prevalence of single-factor explanations. The form of students’ explanations was predicted by their responses on a simple three-item forced-choice multivariable causal reasoning task in which they selected the strongest evidence against a causal claim. In a further qualitative investigation of explanations by a sample of community adults, we identified positive features among those who scored high on this multivariable causal reasoning task. We consider limitations of single-factor reasoning and means of encouraging more comprehensive explanations to support claims.
Real-time human activity recognition from smart phone using linear support vector machines

The recognition of human activity (HAR) the use of cell devices embedded in its exten sively disbursed sensors affords guidance, instructions, and take care of citizens of smart cities. Consequently, it became essential to analyze human every day sports. To examine statistical models of human conduct, synthetic intelligence strategies such as machine studying can be used. Many studies have not studied type overall performance in real-time due to statistics series. To remedy this trouble, this paper proposes a structure primarily based on open supply technology and platforms consisting of Apache Kafka, for messages to flow over the internet, method them and provide shape for existing facts in real-time and formulates the trouble of identifying human pastime by using a smartphone tool as a type hassle using statistics collection by telephone sensors. The proposed version is skilled by some machine learning algorithms. The algorithm that has proven superior and quality results helps a linear vector machines.
How do Students Regulate Their Use of Multiple Choice Practice Tests?

Multiple-choice practice tests are beneficial for learning, and students encounter multiple-choice questions regularly. How do students regulate their use of multiple-choice practice testing? And, how effective is students' use of multiple-choice practice testing? In the current experiments, undergraduate participants practiced German-English word pairs. Students started with an initial study trial for each pair. Then, they had the options to restudy an item, take a practice test, or remove it from further practice. For comparison to students' use of multiple-choice practice questions, we included a second self-regulated group that had access to cued-recall practice questions. Participants chose to complete multiple-choice questions until they correctly answered each item about one time during practice, similar to students' use of cued-recall questions. We also included experimenter-controlled groups in which participants completed practice tests until they reached a higher number of correct answers during practice. As compared to the experimenter-controlled groups, participants who regulated their use of multiple-choice questions scored lower on final tests but also spent less time practicing items. Thus, when considering final test performance in relation to time spent practicing, students' choices to use multiple-choice practice questions to about one correct answer per item was comparatively effective.The online version contains supplementary material available at 10.1007/s10648-023-09761-1.
Toward improved quantum simulations and sensing with trapped two-dimensional ion crystals via parametric amplification

Improving coherence is a fundamental challenge in quantum simulation and sensing experiments with trapped ions. Here we discuss, experimentally demonstrate, and estimate the potential impacts of two different protocols that enhance, through motional parametric excitation, the coherent spin-motion coupling of ions obtained with a spin-dependent force. The experiments are performed on two-dimensional crystal arrays of approximately 100 ${}^{9}{\mathrm{Be}}^{+}$ ions confined in a Penning trap. By modulating the trapping potential at close to twice the center-of-mass mode frequency, we squeeze the motional mode and enhance the spin-motion coupling while maintaining spin coherence. With a stroboscopic protocol, we measure $5.4\ifmmode\pm\else\textpm\fi{}0.9\phantom{\rule{0.28em}{0ex}}\mathrm{dB}$ of motional squeezing below the ground-state motion, from which theory predicts a 10-dB enhancement in the sensitivity for measuring small displacements using a recently demonstrated protocol [K. A. Gilmore et al., Science 373, 673 (2021)]. With a continuous squeezing protocol, we measure and accurately calibrate the parametric coupling strength. Theory suggests this protocol can be used to improve quantum spin squeezing, limited in our system by off-resonant light scatter. We illustrate numerically the trade-offs between strong parametric amplification and motional dephasing in the form of center-of-mass frequency fluctuations for improving quantum spin squeezing in our setup.
Distinguishing and Recovering Generalized Linearized Reed–Solomon Codes

We study the distinguishability of linearized Reed–Solomon (LRS) codes by defining and analyzing analogs of the square-code and the Overbeck distinguisher for classical Reed–Solomon and Gabidulin codes, respectively. Our main results show that the square-code distinguisher works for generalized linearized Reed–Solomon (GLRS) codes defined with the trivial automorphism, whereas the Overbeck-type distinguisher can handle LRS codes in the general setting. We further show how to recover defining code parameters from any generator matrix of such codes in the zero-derivation case. For other choices of automorphisms and derivations simulations indicate that these distinguishers and recovery algorithms do not work. The corresponding LRS and GLRS codes might hence be of interest for code-based cryptography.
Cooperative Retriever and Ranker in Deep Recommenders

Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency; while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker.
Sarcasm Detection in Tweets with BERT and GloVe Embeddings

Sarcasm is a form of communication in whichthe person states opposite of what he actually means. It is ambiguous in nature. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses the context in which the user is reacting to along with his actual response.
Detection, Inspection, Return: A functional classification of fixations in complex scenes

Salience models for complex scenes typically aim to explain heatmaps or scan-paths. While heatmaps potentially conflate fixations serving different functions, scan-paths aim for a level of detail which is hard to attain. We introduce a novel approach, based on the premise that most fixations target objects and belong to three functionally meaningful classes: Detection fixations (D), foveating an object for the first time; Inspection fixations (I), successively targeting object details; and Return fixations (R), revisiting an object, e.g., to refresh its representation in memory. To test the hypothesis that these classes capture distinct fixation profiles, we reanalysed a large dataset of scene fixations. We computed separate heatmaps for D, I, and R and found significantly higher inter-observer consistency within than between classes. Across fixations landing on different semantic features, the proportion of D, I, and R fixations varied consistently, and a semantic salience model trained to predict each class independently learned diverging distributions of feature weights. Further, we found a shift from D to I and R across viewing time, in line with their functional interpretation and previous findings on ambient and focal viewing modes. We tested and confirmed that the dynamics of this shift varied as a function of trial duration. Finally, we found that the D, I, R classification could be approximated with a simple spatial heuristic, extending its applicability to images without object metadata. We propose the D, I, and R classification as a simple but powerful tool to functionally classify scene fixations and provide well documented code to implement it.