Reviewerly/exHarmony huggingface dataset:
A Feedforward Interactive Network to Explain Complex Information Processing Phenomena Including the Halo Effect and Its Generalization

Abstract Parallel interactive processing (PIP) is a parallel distributed processing approach in which a representation of an object involves interaction of its components in a way that influences information processing of the components. Prior research is summarized in the areas of categorization and probability judgment. It was found that an explanation of the halo effect and its generalization as well as phenomena in other information processing situations could be explained by PIP. The explanation of the halo effect is largely in terms of reduction of variance in judgments of components of an object produced by a holistic impression of the object. There was a focus on processes common to various subject areas.
Semantic Resume Evaluation: Leveraging XLnet for Efficient Candidate Screening

In the dynamic landscape of the contemporary job market, the demand for adept candidate screening tools is more critical than ever. Traditional manual screening processes are both time-consuming and prone to errors, making it challenging to ensure a fair evaluation of all qualified candidates. This paper introduces an innovative approach to automated resume screening, utilizing the XLnet language model. XLnet's proficiency in discerning semantic relationships between words positions it as an ideal solution for tasks such as natural language understanding and text generation.The proposed system commences with a meticulous preprocessing of resumes, involving the removal of stop words, lemmatization, and word stemming, thereby enhancing the accuracy of the XLnet model. Resumes are then encoded using XLnet, and the resulting encodings are employed to calculate the Euclidean distance between the resumes and the job description. The candidates with the minimum Euclidean distance are identified as the most qualified.Evaluation of the system on a dataset of 250 resumes showcased its ability to accurately identify the most qualified candidates in 90% of cases, marking a substantial improvement over the typical 70% accuracy achieved through manual screening.This system represents a valuable asset for employers seeking efficient and precise resume screening. Its user-friendly interface and minimal technical requirements facilitate easy implementation. Furthermore, its scalability makes it an adaptable solution for addressing various hiring demands, positioning it as a noteworthy contribution to the field of human resources technology.
Representation Enhancement-Based Proximal Policy Optimization for UAV Path Planning and Obstacle Avoidance

Path planning and obstacle avoidance are pivotal for intelligent unmanned aerial vehicle (UAV) systems in various domains, such as postdisaster rescue, target detection, and wildlife conservation. Currently, reinforcement learning (RL) has become increasingly popular in UAV decision-making. However, the RL approaches confront the challenges of partial observation and large state space when searching for random targets through continuous actions. This paper proposes a representation enhancement-based proximal policy optimization (RE-PPO) framework to address these issues. The representation enhancement (RE) module consists of observation memory improvement (OMI) and dynamic relative position-attitude reshaping (DRPAR). OMI reduces collision under partially observable conditions by separately extracting perception features and state features through an embedding network and feeding the extracted features to a gated recurrent unit (GRU) to enhance observation memory. DRPAR compresses the state space when modeling continuous actions by transforming movement trajectories of different episodes from an absolute coordinate system into different local coordinate systems to utilize similarity. In addition, three step-wise reward functions are formulated to avoid sparsity and facilitate model convergence. We evaluate the proposed method in three 3D scenarios to demonstrate its effectiveness. Compared to other methods, our method achieves a faster convergence during training and demonstrates a higher success rate and a lower rate of timeout and collision during inference. Our method can significantly enhance the autonomy and intelligence of UAV systems under partially observable conditions and provide a reasonable solution for UAV decision-making under uncertainties.
Joint graph convolution networks and transformer for human pose estimation in sports technique analysis

Human pose estimation has various applications in domains such as sports technology analysis, virtual reality, and education. However, most previous studies focused on the respective feature representations of keypoints, but disregarded the topological relationship among keypoints. To address this challenge, we propose GTPose, a network structure that integrates graph convolutional networks and Transform. First of all, a set of multi-scale convolution operations are applied to extract local feature maps of images. Secondly, the positions of keypoints are roughly estimated by using Transform to process the sequential relations between feature maps. Finally, GCN is adopted to model the topological structure between keypoints to accurately locate the location of keypoints and learn feature representations. The performance of GTPose is evaluated on two real datasets: MS COCO and MPII. Experimental results demonstrate that GTPose outperforms other methods in human pose estimation tasks. In addition, experimental results also show that the spatial relationship between keypoints is effective for accurately characterizing keypoints.
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter

ABSTRACT Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback–Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties and successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation’s distribution.
Split-Second Cryptocurrency Forecast Using Prognostic Deep Learning Algorithms: Data Curation by Deephaven

Cryptocurrency is a popular digital currency due to its security and peer-to-peer transferability. Predicting cryptocurrency prices is crucial for investors and traders to make informed decisions on buying, selling, or holding cryptocurrencies based on their expected value, potential risks, and returns. This study aims to identify the optimal model for predicting the prices of cryptocurrencies, such as Bitcoin (BTC) and Ethereum (ETH), using Deephaven for Data curation. The study involves extracting data from both cryptocurrencies by Deephaven and selecting the most correlating parameters through time lag adjustment. We use correlating cryptocurrency data to train models, such as Artificial Neural Networks (ANN), Long-Short Term Memories (LSTM), and Gated Recurrent Units (GRU). Where the trial-and-error technique was applied for selecting optimized hyper-parameters for each model. The models are then evaluated by statistical evaluators, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), separately for training and testing datasets. For Bitcoin, the results showed that the LSTM model outperform ANN and GRU models in both training and testing data with MAE, RMSE, and MAPE average values of 0.079, 1.16, and 0.0006, respectively. While for Ethereum, the results also revealed that LSTM model performance is superior with MAE, RMSE, and MAPE average values of 0.0025, 0.124 and 0.0002, respectively. While GRU (MAE 0.012, RMSE 0.117, MAPE 0.002) performs robustly against ANN (MAE 0.035, RMSE 0.149, MAPE 0.003) model.
Block-Invariant Symmetry Shift: Preprocessing Technique for Second-Quantized Hamiltonians to Improve Their Decompositions to Linear Combination of Unitaries

Computational cost of energy estimation for molecular electronic Hamiltonians via quantum phase estimation (QPE) grows with the difference between the largest and smallest eigenvalues of the Hamiltonian. In this work, we propose a preprocessing procedure that reduces the norm of the Hamiltonian without changing its eigenspectrum for the target states of a particular symmetry. The new procedure, block-invariant symmetry shift (BLISS), builds an operator T̂ such that the cost of implementing H^-T^ is reduced compared to that of Ĥ, yet H^-T^ acts on the subspaces of interest the same way as Ĥ does. BLISS performance is demonstrated for a linear combination of unitaries (LCU)-based QPE approaches on a set of small molecules. Using the number of electrons as the symmetry specifying the target set of states, BLISS provided a factor of 2 reduction of 1-norm for several LCU decompositions compared to their unshifted versions.
Real-Time Event Detection Using Self-Evolving Contextual Analysis (SECA) Approach

Dynamically monitoring and analyzing evolving real-world events (riots, earthquakes, and football matches) using publicly available short texts (social media posts) is becoming increasingly important. This content can hold critical information about various events, which can help decision-makers to make better decisions. Significant research efforts have been made in this regard. However, most of these provide solutions based on black-box engines, in which technical capabilities are required to understand their internal mechanics. Also, they offer very little information about the detected events and generally tend to answer very high-level questions, such as: “what are the main topic clusters?” “what are the main words (e.g. top ten words) of these topics?”. These challenges can limit their usage in some critical domains, where the need for transparency, and more information, to analyze a particular situation is crucial. Thus, to complement and fill the gap in the direction of existing studies, in which the effectiveness and success of the proposed approaches are insufficiently determined by their performance scores, this paper presents datasets that can be used for dynamic topic detection of different frequencies over time based on real Tweets and a new transparent method for the dynamic event detection problem called Self-Evolving Contextual Analysis (SECA). It helps to answer, for any given time frame, other fundamental questions, such as: “what are the sub-topics of, and their relationship to, a topic (or a sub-topic?)”, “what are the changing topics and sub-topics?”, “what are the new trends?”, “what are the topics no longer being discussed?” and most importantly, “why and how have these topics and changes been identified and generated?”. Moreover, Performance and Carbon Footprint assessments reveal the comparative effectiveness of the proposed approach. In addition, this paper presents a practical implementation of SECA to dynamically analyze tweets collected during the FIFA World Cup 2022 Final Match.
Augmented Reality and its Applications in Education: A Systematic Survey

The use of technology, especially mobile phones, has significantly increased over the past few decades and has become an essential element in our daily lives. Mobile and desktop applications based on Augmented Reality (AR) proved to be a revolutionary step in different areas, especially in the educational sector. AR provides an improved and extended version of reality with the superimposition of a virtual object in the real-world environment. These objects can be interacted with and visualized in many ways to provide full AR experiences. By interacting with augmented virtual objects, students can visualize and completely understand various difficult concepts during their studies rather than traditional learning, allowing collaborative learning and hence improving learnability, motivation, and focus. This survey paper discusses the concept of AR and its types, the need for AR applications in education, analysis of various state-of-the-art AR applications in terms of platform, augmented virtual content, interactions, usability, usefulness, performance, effectiveness, and ease-of-use under a single taxonomy. Although applications of AR in education for learning, teaching, and simulating have proven quite effective in delivering the concepts via interactive study, there are also several limitations in terms of complexity and availability of hardware, platforms, internet connections, portability, computational, and rendering speed. This study offers insights into potential trends and directions that AR technology might take in education, furnishing readers with a glimpse of forthcoming developments. This comprehensive perspective equips educators, researchers, and practitioners with valuable insights into the pedagogical implications, technological aspects, and practical considerations related to AR.
Audio and Text Sentiment Analysis of Radio Broadcasts

The rapid growth of radio broadcast services has created a vast amount of audio data that can provide insights into public opinion and emotions. This research extends the boundaries of sentiment analysis to audio data and aims to propose a computational approach which is termed as bifurcate and mix, for sentiment analysis and emotion detection that leverages advanced natural language processing techniques from audio sentiment analysis tools like Vokaturi, transcription services like AssemblyAI and text sentiment analysis lexicons like VADER to extract and categorize sentiments from the audio data to generate a more efficient real-time sentiment analysis model. The results of the analysis reveal patterns and trends in the sentiments expressed in radio broadcasts by the News Service Division: All India Radio - ‘Akashwani’. This research’s methodology will contribute to the development of novel applications for sentiment analysis in the media industry and provide valuable insights into public opinion and emotions.
Local Entropy-Based Coupled Anisotropic Diffusion for Detail-And Edge-Preserving Smoothing

It is important in image restoration to remove noise while preserving sharp edges and fine details such as blurred thin edges and low-contrast fine feature. The Perona–Malik (P-M) model is a well-known anisotropic diffusion denoising model, which can effectively remove noise while preserving edges. However, its diffusion coefficient only associates with the gradient of each pixel but not with the local region information; thus, the P-M model is not able to effectively preserve the important details of image. To address this problem, this paper proposes an anisotropic diffusion denoising model based on local entropy. The diffusion coefficient of the new model not only depends on the gradient of image but also on the local region information described by local entropy. On this basis, a coupled anisotropic diffusion scheme is proposed for detail-and edge-preserving smoothing. Experimental results show that the proposed model not only can effectively remove noise while preserving the boundaries better but also can maintain important details in an image very well.
Generalized black hole clustering algorithm

The Black Hole Clustering (BHC) algorithm is a density-based partitional clustering method inspired by the Density-based Spatial Clustering of Applications with Noise (DBSCAN). It does not require the number of clusters nor the computation of the pair-wise distance matrix between the data points, making it faster than DBSCAN. Also, it only needs one parameter that is intuitively easier to set than the epsilon parameter of DBSCAN. However, BHC needs the allocation of the so-called black holes that have to be linearly independent, making the algorithm in its current version suitable only for two or three-dimensional data sets. In this paper, we propose a generalized version of the black hole clustering algorithm (GBHC) by introducing a novel black hole allocation procedure for higher-dimensional data spaces. Furthermore, the proposed method is data-independent, so we have to run it once to obtain the black hole positions for all finite-dimensional metric spaces. We performed extensive computational experiments to compare GBHC with DBSCAN. The results show that both algorithms obtain comparable clustering solutions. GBHC, however, outperforms DBSCAN in computational complexity and explainability.
Progressive dense feature fusion network for single image deraining

Deep Learning (DL) has achieved significant progress in single image deraining methods. Most of current DL methods, however, are still weak in image detail recovery and feature inherent correlation learning. In this work, we explore the detail recovery mechanisms in both network architecture and loss function for image deraining. A U-net like architecture named progressive dense feature fusion network (PDFFN) is proposed to encode rain images and decode them as clean ones. Specifically, a residual dense connection unit (ReDCU) is designed to handle rain streaks of various blurring degrees and resolutions by enriching features. Moreover, a progressive feature fusion module (PFFM), which fuses the features in different stages of U-net progressively, is devised to not only capture the inherent correlations of features across encoder and decoder but also intensify the fine-gained details. To better evaluate the perceptual similarity between the ground-truth and derained image, we propose detail perceptual loss by focusing on low-level unactivated features. Apart from the global rain removal strategy, this paper applies contextual loss on target regions to conduct joint deraining and detection. Comprehensive experiments substantiate the superiority of the proposed method, especially its detail recovery capability, as compared with state-of-the-art methods both qualitatively and quantitatively.
Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search

Abstract Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming approach that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming. At each generation, the idea is to capture promising properties of the parent population in a probabilistic model and to use corruption to transfer variations of these properties to the offspring. This work studies the influence of corruption and sampling steps on search. Corruption partially mutates candidate solutions that are used as input to the model, whereas the number of sampling steps defines how often we re-use the output during model sampling as input to the model. We study the generalization of the royal tree problem, the Airfoil problem, and the Pagie-1 problem, and find that both corruption strength and the number of sampling steps influence exploration and exploitation in search and affect performance: exploration increases with stronger corruption and lower number of sampling steps. The results indicate that both corruption and sampling steps are key to the success of the DAE-GP: it permits us to balance the exploration and exploitation behavior in search, resulting in an improved search quality. However, also selection is important for exploration and exploitation and should be chosen wisely.
3D digital image correlation analysis of medium velocity soft impacts on laminated composite

In aerospace acamedic and industrial world, soft impacts are commonly used to replace bird strike tests for the validation of materials and structures as well as the calibration of numerical models. However in general, the analysis reported show only a few part of the experimental information available. In this paper, three laminate composites made of epoxy resin reinforced by glass or carbon fibres are tested under gelatin impact at several velocities up to complete failure. A detailed analysis based on 3D Digital Image Correlation (3D DIC) and visual inspection of the three laminates is provided for a total of 21 tests with impact velocities in the range 60-112 m/s. DIC extraction provides accurate quantitative displacement fields of the rear face in both time and space. Moreover, specific failure scenarios are identified for each laminate. The results obtained provide a suitable database for the development of numerical models. In addition, all experimental data from DIC extractions are opened to the readers on the Recherche Data Gouv website for comparisons with their own tests or numerical models.
PRIME: Posterior Reconstruction of the Input for Model Explanations

Artificial intelligence (AI) has developed rapidly in the past few decades, but the corresponding explaining techniques do not keep up with the pace of development. This state of low interpretability leads developers to improve model performance by continuously increasing the use of features when training machine learning models, but it is difficult for them to judge the contribution of features to the prediction. Therefore, it is particularly important to evaluate features. Most of the existing research on model explaining focuses on characterizing the output space and ignored the research on the input space. In this paper, we propose a model-agnostic method to evaluate the feature importance by analyzing the change in the input space. The motivation is to come up with a flexible, general, and computationally efficient method of model explaining. The experiments on the synthetic datasets, standard datasets, and industrial dataset verify the effectiveness of our algorithm. At the same time, we compare our methods to the classic and late model explaining methods, such as SHAP, LIME, and Marginal Contribution Feature Importance (MCI) to further demonstrate the feasibility of our method.
Application of a Network Teaching Platform in English Classroom Teaching

The online teaching platform based on the online teaching system on the internet has been widely used in the English education industry. Language education researchers, English teachers, and educational technology professionals are interested in exploring the potential of online teaching platforms in language education. This article explores the impact of the teaching platform based on MOOC on English classroom teaching. By comparing the teaching effects of using MOOC teaching platform and traditional teaching methods, this article analyzes the impact of online teaching platform on English classroom teaching and provides theoretical data support to promote the application of online teaching platform in English classroom teaching. The research results show that compared with traditional teaching methods, the flipped classroom teaching model using the MOOC teaching platform achieves optimization of the learning process and teaching effectiveness and improves attitudes, interests, strategies, and cooperation in English learning.